id	sid	tid	token	lemma	pos
fcis-29274	1	1	frontiers	frontier	NOUN
fcis-29274	1	2	in	in	ADP
fcis-29274	1	3	computing	computing	NOUN
fcis-29274	1	4	and	and	CCONJ
fcis-29274	1	5	intelligent	intelligent	ADJ
fcis-29274	1	6	systems	system	NOUN
fcis-29274	1	7	issn	issn	VERB
fcis-29274	1	8	:	:	PUNCT
fcis-29274	1	9	2832	2832	NUM
fcis-29274	1	10	-	-	SYM
fcis-29274	1	11	6024	6024	NUM
fcis-29274	1	12	|	|	NOUN
fcis-29274	1	13	vol	vol	NOUN
fcis-29274	1	14	.	.	PROPN
fcis-29274	2	1	11	11	NUM
fcis-29274	2	2	,	,	PUNCT
fcis-29274	2	3	no	no	INTJ
fcis-29274	2	4	.	.	NOUN
fcis-29274	2	5	1	1	NUM
fcis-29274	2	6	,	,	PUNCT
fcis-29274	2	7	2025	2025	NUM
fcis-29274	2	8	64	64	NUM
fcis-29274	2	9	identifying	identify	VERB
fcis-29274	2	10	depression	depression	NOUN
fcis-29274	2	11	using	use	VERB
fcis-29274	2	12	machine	machine	NOUN
fcis-29274	2	13	learning	learn	VERB
fcis-29274	2	14	jiang	jiang	PROPN
fcis-29274	2	15	lu	lu	PROPN
fcis-29274	2	16	*	*	PUNCT
fcis-29274	2	17	department	department	PROPN
fcis-29274	2	18	of	of	ADP
fcis-29274	2	19	tianjin	tianjin	PROPN
fcis-29274	2	20	university	university	PROPN
fcis-29274	2	21	of	of	ADP
fcis-29274	2	22	commerce	commerce	PROPN
fcis-29274	2	23	,	,	PUNCT
fcis-29274	2	24	tianjin	tianjin	PROPN
fcis-29274	2	25	,	,	PUNCT
fcis-29274	2	26	china	china	PROPN
fcis-29274	2	27	*	*	PUNCT
fcis-29274	2	28	corresponding	correspond	VERB
fcis-29274	2	29	author	author	NOUN
fcis-29274	2	30	email	email	NOUN
fcis-29274	2	31	:	:	PUNCT
fcis-29274	2	32	2524493299@qq.com	2524493299@qq.com	NUM
fcis-29274	2	33	abstract	abstract	NOUN
fcis-29274	2	34	:	:	PUNCT
fcis-29274	2	35	depression	depression	NOUN
fcis-29274	2	36	is	be	AUX
fcis-29274	2	37	the	the	DET
fcis-29274	2	38	leading	lead	VERB
fcis-29274	2	39	cause	cause	NOUN
fcis-29274	2	40	of	of	ADP
fcis-29274	2	41	disability	disability	NOUN
fcis-29274	2	42	worldwide	worldwide	ADV
fcis-29274	2	43	.	.	PUNCT
fcis-29274	3	1	however	however	ADV
fcis-29274	3	2	,	,	PUNCT
fcis-29274	3	3	accurately	accurately	ADV
fcis-29274	3	4	estimating	estimate	VERB
fcis-29274	3	5	the	the	DET
fcis-29274	3	6	epidemiological	epidemiological	ADJ
fcis-29274	3	7	factors	factor	NOUN
fcis-29274	3	8	that	that	PRON
fcis-29274	3	9	contribute	contribute	VERB
fcis-29274	3	10	to	to	ADP
fcis-29274	3	11	depression	depression	NOUN
fcis-29274	3	12	remains	remain	VERB
fcis-29274	3	13	challenging	challenge	VERB
fcis-29274	3	14	.	.	PUNCT
fcis-29274	4	1	deep	deep	ADJ
fcis-29274	4	2	learning	learn	VERB
fcis-29274	4	3	algorithms	algorithm	NOUN
fcis-29274	4	4	can	can	AUX
fcis-29274	4	5	be	be	AUX
fcis-29274	4	6	used	use	VERB
fcis-29274	4	7	to	to	PART
fcis-29274	4	8	assess	assess	VERB
fcis-29274	4	9	the	the	DET
fcis-29274	4	10	factors	factor	NOUN
fcis-29274	4	11	that	that	PRON
fcis-29274	4	12	contribute	contribute	VERB
fcis-29274	4	13	to	to	ADP
fcis-29274	4	14	the	the	DET
fcis-29274	4	15	prevalence	prevalence	NOUN
fcis-29274	4	16	and	and	CCONJ
fcis-29274	4	17	clinical	clinical	ADJ
fcis-29274	4	18	manifestations	manifestation	NOUN
fcis-29274	4	19	of	of	ADP
fcis-29274	4	20	depression	depression	NOUN
fcis-29274	4	21	.	.	PUNCT
fcis-29274	5	1	in	in	ADP
fcis-29274	5	2	this	this	DET
fcis-29274	5	3	paper	paper	NOUN
fcis-29274	5	4	,	,	PUNCT
fcis-29274	5	5	five	five	NUM
fcis-29274	5	6	machine	machine	NOUN
fcis-29274	5	7	learning	learning	NOUN
fcis-29274	5	8	models	model	NOUN
fcis-29274	5	9	,	,	PUNCT
fcis-29274	5	10	logistic	logistic	ADJ
fcis-29274	5	11	regression	regression	NOUN
fcis-29274	5	12	,	,	PUNCT
fcis-29274	5	13	decision	decision	NOUN
fcis-29274	5	14	tree	tree	NOUN
fcis-29274	5	15	,	,	PUNCT
fcis-29274	5	16	random	random	ADJ
fcis-29274	5	17	forest	forest	NOUN
fcis-29274	5	18	,	,	PUNCT
fcis-29274	5	19	svm	svm	PROPN
fcis-29274	5	20	,	,	PUNCT
fcis-29274	5	21	and	and	CCONJ
fcis-29274	5	22	catboost	catboost	NOUN
fcis-29274	5	23	,	,	PUNCT
fcis-29274	5	24	were	be	AUX
fcis-29274	5	25	used	use	VERB
fcis-29274	5	26	to	to	PART
fcis-29274	5	27	assess	assess	VERB
fcis-29274	5	28	depression	depression	NOUN
fcis-29274	5	29	in	in	ADP
fcis-29274	5	30	5533	5533	NUM
fcis-29274	5	31	adult	adult	NOUN
fcis-29274	5	32	participants	participant	NOUN
fcis-29274	5	33	from	from	ADP
fcis-29274	5	34	the	the	DET
fcis-29274	5	35	2018	2018	NUM
fcis-29274	5	36	nhanes	nhane	NOUN
fcis-29274	5	37	database	database	NOUN
fcis-29274	5	38	.	.	PUNCT
fcis-29274	6	1	the	the	DET
fcis-29274	6	2	results	result	NOUN
fcis-29274	6	3	show	show	VERB
fcis-29274	6	4	that	that	SCONJ
fcis-29274	6	5	random	random	ADJ
fcis-29274	6	6	forest	forest	NOUN
fcis-29274	6	7	is	be	AUX
fcis-29274	6	8	the	the	DET
fcis-29274	6	9	best	good	ADJ
fcis-29274	6	10	model	model	NOUN
fcis-29274	6	11	for	for	ADP
fcis-29274	6	12	identifying	identify	VERB
fcis-29274	6	13	depression	depression	NOUN
fcis-29274	6	14	,	,	PUNCT
fcis-29274	6	15	with	with	ADP
fcis-29274	6	16	the	the	DET
fcis-29274	6	17	highest	high	ADJ
fcis-29274	6	18	area	area	NOUN
fcis-29274	6	19	under	under	ADP
fcis-29274	6	20	the	the	DET
fcis-29274	6	21	working	work	VERB
fcis-29274	6	22	characteristic	characteristic	ADJ
fcis-29274	6	23	curve	curve	NOUN
fcis-29274	6	24	(	(	PUNCT
fcis-29274	6	25	auc	auc	NOUN
fcis-29274	6	26	)	)	PUNCT
fcis-29274	6	27	,	,	PUNCT
fcis-29274	6	28	followed	follow	VERB
fcis-29274	6	29	by	by	ADP
fcis-29274	6	30	catboost	catboost	NOUN
fcis-29274	6	31	and	and	CCONJ
fcis-29274	6	32	decision	decision	NOUN
fcis-29274	6	33	tree	tree	NOUN
fcis-29274	6	34	models	model	NOUN
fcis-29274	6	35	.	.	PUNCT
fcis-29274	7	1	this	this	PRON
fcis-29274	7	2	suggests	suggest	VERB
fcis-29274	7	3	that	that	SCONJ
fcis-29274	7	4	using	use	VERB
fcis-29274	7	5	machine	machine	NOUN
fcis-29274	7	6	learning	learning	NOUN
fcis-29274	7	7	can	can	AUX
fcis-29274	7	8	accurately	accurately	ADV
fcis-29274	7	9	predict	predict	VERB
fcis-29274	7	10	depression	depression	NOUN
fcis-29274	7	11	risk	risk	NOUN
fcis-29274	7	12	.	.	PUNCT
fcis-29274	8	1	keywords	keyword	NOUN
fcis-29274	8	2	:	:	PUNCT
fcis-29274	8	3	depression	depression	NOUN
fcis-29274	8	4	;	;	PUNCT
fcis-29274	8	5	machine	machine	NOUN
fcis-29274	8	6	learning	learning	NOUN
fcis-29274	8	7	;	;	PUNCT
fcis-29274	8	8	catboost	catboost	ADJ
fcis-29274	8	9	;	;	PUNCT
fcis-29274	8	10	random	random	ADJ
fcis-29274	8	11	forest	forest	NOUN
fcis-29274	8	12	;	;	PUNCT
fcis-29274	8	13	decision	decision	NOUN
fcis-29274	8	14	tree	tree	NOUN
fcis-29274	8	15	.	.	PUNCT
fcis-29274	9	1	1	1	X
fcis-29274	9	2	.	.	X
fcis-29274	9	3	introduction	introduction	NOUN
fcis-29274	9	4	depression	depression	NOUN
fcis-29274	9	5	is	be	AUX
fcis-29274	9	6	a	a	DET
fcis-29274	9	7	common	common	ADJ
fcis-29274	9	8	mental	mental	ADJ
fcis-29274	9	9	disorder	disorder	NOUN
fcis-29274	9	10	.	.	PUNCT
fcis-29274	10	1	according	accord	VERB
fcis-29274	10	2	to	to	ADP
fcis-29274	10	3	the	the	DET
fcis-29274	10	4	world	world	PROPN
fcis-29274	10	5	health	health	PROPN
fcis-29274	10	6	organization	organization	NOUN
fcis-29274	10	7	,	,	PUNCT
fcis-29274	10	8	approximately	approximately	ADV
fcis-29274	10	9	35,020.14	35,020.14	NUM
fcis-29274	10	10	million	million	NUM
fcis-29274	10	11	people	people	NOUN
fcis-29274	10	12	suffer	suffer	VERB
fcis-29274	10	13	from	from	ADP
fcis-29274	10	14	this	this	DET
fcis-29274	10	15	disease	disease	NOUN
fcis-29274	10	16	each	each	DET
fcis-29274	10	17	year	year	NOUN
fcis-29274	10	18	,	,	PUNCT
fcis-29274	10	19	and	and	CCONJ
fcis-29274	10	20	this	this	DET
fcis-29274	10	21	situation	situation	NOUN
fcis-29274	10	22	has	have	AUX
fcis-29274	10	23	persisted	persist	VERB
fcis-29274	10	24	for	for	ADP
fcis-29274	10	25	many	many	ADJ
fcis-29274	10	26	years	year	NOUN
fcis-29274	11	1	[	[	X
fcis-29274	11	2	1].it	1].it	NUM
fcis-29274	11	3	is	be	AUX
fcis-29274	11	4	estimated	estimate	VERB
fcis-29274	11	5	that	that	SCONJ
fcis-29274	11	6	5	5	NUM
fcis-29274	11	7	%	%	NOUN
fcis-29274	11	8	of	of	ADP
fcis-29274	11	9	adults	adult	NOUN
fcis-29274	11	10	suffer	suffer	VERB
fcis-29274	11	11	from	from	ADP
fcis-29274	11	12	depression	depression	NOUN
fcis-29274	11	13	,	,	PUNCT
fcis-29274	11	14	and	and	CCONJ
fcis-29274	11	15	the	the	DET
fcis-29274	11	16	global	global	ADJ
fcis-29274	11	17	prevalence	prevalence	NOUN
fcis-29274	11	18	of	of	ADP
fcis-29274	11	19	depression	depression	NOUN
fcis-29274	11	20	has	have	AUX
fcis-29274	11	21	been	be	AUX
fcis-29274	11	22	increasing	increase	VERB
fcis-29274	11	23	in	in	ADP
fcis-29274	11	24	recent	recent	ADJ
fcis-29274	11	25	decades	decade	NOUN
fcis-29274	11	26	due	due	ADP
fcis-29274	11	27	to	to	ADP
fcis-29274	11	28	urbanization	urbanization	NOUN
fcis-29274	11	29	,	,	PUNCT
fcis-29274	11	30	overall	overall	ADJ
fcis-29274	11	31	population	population	NOUN
fcis-29274	11	32	growth	growth	NOUN
fcis-29274	11	33	and	and	CCONJ
fcis-29274	11	34	changes	change	NOUN
fcis-29274	11	35	in	in	ADP
fcis-29274	11	36	its	its	PRON
fcis-29274	11	37	age	age	NOUN
fcis-29274	11	38	structure	structure	NOUN
fcis-29274	11	39	[	[	X
fcis-29274	11	40	2].depression	2].depression	PROPN
fcis-29274	11	41	is	be	AUX
fcis-29274	11	42	the	the	DET
fcis-29274	11	43	leading	lead	VERB
fcis-29274	11	44	cause	cause	NOUN
fcis-29274	11	45	of	of	ADP
fcis-29274	11	46	disability	disability	NOUN
fcis-29274	11	47	worldwide	worldwide	ADV
fcis-29274	11	48	and	and	CCONJ
fcis-29274	11	49	a	a	DET
fcis-29274	11	50	major	major	ADJ
fcis-29274	11	51	contributor	contributor	NOUN
fcis-29274	11	52	to	to	ADP
fcis-29274	11	53	the	the	DET
fcis-29274	11	54	global	global	ADJ
fcis-29274	11	55	burden	burden	NOUN
fcis-29274	11	56	of	of	ADP
fcis-29274	11	57	disease	disease	NOUN
fcis-29274	11	58	[	[	X
fcis-29274	11	59	3].therefore	3].therefore	NUM
fcis-29274	11	60	,	,	PUNCT
fcis-29274	11	61	large	large	ADJ
fcis-29274	11	62	-	-	PUNCT
fcis-29274	11	63	scale	scale	NOUN
fcis-29274	11	64	national	national	ADJ
fcis-29274	11	65	surveys	survey	NOUN
fcis-29274	11	66	have	have	AUX
fcis-29274	11	67	been	be	AUX
fcis-29274	11	68	conducted	conduct	VERB
fcis-29274	11	69	to	to	PART
fcis-29274	11	70	determine	determine	VERB
fcis-29274	11	71	the	the	DET
fcis-29274	11	72	prevalence	prevalence	NOUN
fcis-29274	11	73	and	and	CCONJ
fcis-29274	11	74	risk	risk	NOUN
fcis-29274	11	75	factors	factor	NOUN
fcis-29274	11	76	of	of	ADP
fcis-29274	11	77	depression	depression	NOUN
fcis-29274	11	78	.	.	PUNCT
fcis-29274	12	1	in	in	ADP
fcis-29274	12	2	the	the	DET
fcis-29274	12	3	united	united	PROPN
fcis-29274	12	4	states	states	PROPN
fcis-29274	12	5	,	,	PUNCT
fcis-29274	12	6	several	several	ADJ
fcis-29274	12	7	national	national	ADJ
fcis-29274	12	8	surveys	survey	NOUN
fcis-29274	12	9	have	have	AUX
fcis-29274	12	10	measured	measure	VERB
fcis-29274	12	11	the	the	DET
fcis-29274	12	12	incidence	incidence	NOUN
fcis-29274	12	13	of	of	ADP
fcis-29274	12	14	depression	depression	NOUN
fcis-29274	12	15	in	in	ADP
fcis-29274	12	16	adolescents	adolescent	NOUN
fcis-29274	12	17	[	[	X
fcis-29274	12	18	4	4	X
fcis-29274	12	19	]	]	PUNCT
fcis-29274	12	20	and	and	CCONJ
fcis-29274	12	21	the	the	DET
fcis-29274	12	22	general	general	ADJ
fcis-29274	12	23	population	population	NOUN
fcis-29274	13	1	[	[	X
fcis-29274	13	2	5	5	NUM
fcis-29274	13	3	]	]	PUNCT
fcis-29274	13	4	.	.	PUNCT
fcis-29274	14	1	the	the	DET
fcis-29274	14	2	results	result	NOUN
fcis-29274	14	3	show	show	VERB
fcis-29274	14	4	that	that	SCONJ
fcis-29274	14	5	depression	depression	NOUN
fcis-29274	14	6	has	have	AUX
fcis-29274	14	7	seriously	seriously	ADV
fcis-29274	14	8	affected	affect	VERB
fcis-29274	14	9	people	people	NOUN
fcis-29274	14	10	's	's	PART
fcis-29274	14	11	daily	daily	ADJ
fcis-29274	14	12	life	life	NOUN
fcis-29274	14	13	and	and	CCONJ
fcis-29274	14	14	physical	physical	ADJ
fcis-29274	14	15	and	and	CCONJ
fcis-29274	14	16	mental	mental	ADJ
fcis-29274	14	17	health	health	NOUN
fcis-29274	14	18	,	,	PUNCT
fcis-29274	14	19	and	and	CCONJ
fcis-29274	14	20	even	even	ADV
fcis-29274	14	21	further	far	ADV
fcis-29274	14	22	affected	affected	ADJ
fcis-29274	14	23	family	family	NOUN
fcis-29274	14	24	harmony	harmony	NOUN
fcis-29274	14	25	and	and	CCONJ
fcis-29274	14	26	social	social	ADJ
fcis-29274	14	27	relationships	relationship	NOUN
fcis-29274	14	28	.	.	PUNCT
fcis-29274	15	1	depression	depression	NOUN
fcis-29274	15	2	has	have	AUX
fcis-29274	15	3	placed	place	VERB
fcis-29274	15	4	a	a	DET
fcis-29274	15	5	heavy	heavy	ADJ
fcis-29274	15	6	burden	burden	NOUN
fcis-29274	15	7	on	on	ADP
fcis-29274	15	8	the	the	DET
fcis-29274	15	9	patient	patient	NOUN
fcis-29274	15	10	's	's	PART
fcis-29274	15	11	family	family	NOUN
fcis-29274	15	12	and	and	CCONJ
fcis-29274	15	13	society	society	NOUN
fcis-29274	15	14	,	,	PUNCT
fcis-29274	15	15	and	and	CCONJ
fcis-29274	15	16	therefore	therefore	ADV
fcis-29274	15	17	depression	depression	NOUN
fcis-29274	15	18	has	have	AUX
fcis-29274	15	19	become	become	VERB
fcis-29274	15	20	an	an	DET
fcis-29274	15	21	important	important	ADJ
fcis-29274	15	22	public	public	ADJ
fcis-29274	15	23	health	health	NOUN
fcis-29274	15	24	issue	issue	NOUN
fcis-29274	15	25	.	.	PUNCT
fcis-29274	16	1	adults	adult	NOUN
fcis-29274	16	2	show	show	VERB
fcis-29274	16	3	complex	complex	ADJ
fcis-29274	16	4	pathogenesis	pathogenesis	NOUN
fcis-29274	16	5	of	of	ADP
fcis-29274	16	6	depression	depression	NOUN
fcis-29274	16	7	,	,	PUNCT
fcis-29274	16	8	which	which	PRON
fcis-29274	16	9	is	be	AUX
fcis-29274	16	10	affected	affect	VERB
fcis-29274	16	11	by	by	ADP
fcis-29274	16	12	multiple	multiple	ADJ
fcis-29274	16	13	risk	risk	NOUN
fcis-29274	16	14	factors	factor	NOUN
fcis-29274	16	15	such	such	ADJ
fcis-29274	16	16	as	as	ADP
fcis-29274	16	17	gender	gender	NOUN
fcis-29274	16	18	,	,	PUNCT
fcis-29274	16	19	age	age	NOUN
fcis-29274	16	20	,	,	PUNCT
fcis-29274	16	21	education	education	NOUN
fcis-29274	16	22	level	level	NOUN
fcis-29274	16	23	,	,	PUNCT
fcis-29274	16	24	chronic	chronic	ADJ
fcis-29274	16	25	diseases	disease	NOUN
fcis-29274	16	26	,	,	PUNCT
fcis-29274	16	27	sleep	sleep	NOUN
fcis-29274	16	28	and	and	CCONJ
fcis-29274	16	29	physical	physical	ADJ
fcis-29274	16	30	health	health	NOUN
fcis-29274	17	1	[	[	X
fcis-29274	17	2	6	6	NUM
fcis-29274	17	3	]	]	PUNCT
fcis-29274	17	4	.	.	PUNCT
fcis-29274	18	1	some	some	DET
fcis-29274	18	2	previous	previous	ADJ
fcis-29274	18	3	studies	study	NOUN
fcis-29274	18	4	have	have	AUX
fcis-29274	18	5	also	also	ADV
fcis-29274	18	6	identified	identify	VERB
fcis-29274	18	7	the	the	DET
fcis-29274	18	8	relationship	relationship	NOUN
fcis-29274	18	9	between	between	ADP
fcis-29274	18	10	demographics	demographic	NOUN
fcis-29274	18	11	[	[	X
fcis-29274	18	12	7	7	NUM
fcis-29274	18	13	]	]	PUNCT
fcis-29274	18	14	,	,	PUNCT
fcis-29274	18	15	lifestyle	lifestyle	NOUN
fcis-29274	18	16	behavior	behavior	NOUN
fcis-29274	18	17	conditions	condition	NOUN
fcis-29274	18	18	[	[	X
fcis-29274	18	19	8	8	NUM
fcis-29274	18	20	]	]	PUNCT
fcis-29274	18	21	and	and	CCONJ
fcis-29274	18	22	physical	physical	ADJ
fcis-29274	18	23	exercise	exercise	NOUN
fcis-29274	19	1	[	[	X
fcis-29274	19	2	9	9	NUM
fcis-29274	19	3	]	]	PUNCT
fcis-29274	19	4	and	and	CCONJ
fcis-29274	19	5	depression	depression	NOUN
fcis-29274	19	6	.	.	PUNCT
fcis-29274	20	1	therefore	therefore	ADV
fcis-29274	20	2	,	,	PUNCT
fcis-29274	20	3	establishing	establish	VERB
fcis-29274	20	4	a	a	DET
fcis-29274	20	5	depression	depression	NOUN
fcis-29274	20	6	risk	risk	NOUN
fcis-29274	20	7	assessment	assessment	NOUN
fcis-29274	20	8	model	model	NOUN
fcis-29274	20	9	based	base	VERB
fcis-29274	20	10	on	on	ADP
fcis-29274	20	11	risk	risk	NOUN
fcis-29274	20	12	factors	factor	NOUN
fcis-29274	20	13	is	be	AUX
fcis-29274	20	14	conducive	conducive	ADJ
fcis-29274	20	15	to	to	ADP
fcis-29274	20	16	the	the	DET
fcis-29274	20	17	early	early	ADJ
fcis-29274	20	18	detection	detection	NOUN
fcis-29274	20	19	and	and	CCONJ
fcis-29274	20	20	early	early	ADJ
fcis-29274	20	21	treatment	treatment	NOUN
fcis-29274	20	22	of	of	ADP
fcis-29274	20	23	people	people	NOUN
fcis-29274	20	24	at	at	ADP
fcis-29274	20	25	high	high	ADJ
fcis-29274	20	26	risk	risk	NOUN
fcis-29274	20	27	of	of	ADP
fcis-29274	20	28	depression	depression	NOUN
fcis-29274	20	29	.	.	PUNCT
fcis-29274	21	1	traditional	traditional	ADJ
fcis-29274	21	2	machine	machine	NOUN
fcis-29274	21	3	learning	learning	NOUN
fcis-29274	21	4	methods	method	NOUN
fcis-29274	21	5	,	,	PUNCT
fcis-29274	21	6	such	such	ADJ
fcis-29274	21	7	as	as	ADP
fcis-29274	21	8	multivariate	multivariate	NOUN
fcis-29274	21	9	logistic	logistic	ADJ
fcis-29274	21	10	regression	regression	NOUN
fcis-29274	21	11	,	,	PUNCT
fcis-29274	21	12	can	can	AUX
fcis-29274	21	13	help	help	VERB
fcis-29274	21	14	locate	locate	VERB
fcis-29274	21	15	clinical	clinical	ADJ
fcis-29274	21	16	manifestations	manifestation	NOUN
fcis-29274	21	17	of	of	ADP
fcis-29274	21	18	depression	depression	NOUN
fcis-29274	21	19	in	in	ADP
fcis-29274	21	20	these	these	DET
fcis-29274	21	21	survey	survey	NOUN
fcis-29274	21	22	data	datum	NOUN
fcis-29274	21	23	.	.	PUNCT
fcis-29274	22	1	dipnall	dipnall	PROPN
fcis-29274	22	2	used	use	VERB
fcis-29274	22	3	regression	regression	NOUN
fcis-29274	22	4	analysis	analysis	NOUN
fcis-29274	22	5	in	in	ADP
fcis-29274	22	6	machine	machine	NOUN
fcis-29274	22	7	learning	learn	VERB
fcis-29274	22	8	to	to	PART
fcis-29274	22	9	find	find	VERB
fcis-29274	22	10	many	many	ADJ
fcis-29274	22	11	biomarkers	biomarker	NOUN
fcis-29274	22	12	associated	associate	VERB
fcis-29274	22	13	with	with	ADP
fcis-29274	22	14	depression	depression	NOUN
fcis-29274	22	15	in	in	ADP
fcis-29274	22	16	the	the	DET
fcis-29274	22	17	national	national	ADJ
fcis-29274	22	18	health	health	PROPN
fcis-29274	22	19	and	and	CCONJ
fcis-29274	22	20	nutrition	nutrition	NOUN
fcis-29274	22	21	examination	examination	NOUN
fcis-29274	22	22	survey	survey	NOUN
fcis-29274	22	23	dataset	dataset	VERB
fcis-29274	22	24	[	[	X
fcis-29274	22	25	10	10	NUM
fcis-29274	22	26	]	]	PUNCT
fcis-29274	22	27	.	.	PUNCT
fcis-29274	23	1	sohrab	sohrab	PROPN
fcis-29274	23	2	used	use	VERB
fcis-29274	23	3	the	the	DET
fcis-29274	23	4	national	national	ADJ
fcis-29274	23	5	health	health	PROPN
fcis-29274	23	6	and	and	CCONJ
fcis-29274	23	7	nutrition	nutrition	NOUN
fcis-29274	23	8	examination	examination	NOUN
fcis-29274	23	9	survey	survey	NOUN
fcis-29274	23	10	data	datum	NOUN
fcis-29274	23	11	from	from	ADP
fcis-29274	23	12	2005	2005	NUM
fcis-29274	23	13	to	to	ADP
fcis-29274	23	14	2016	2016	NUM
fcis-29274	23	15	and	and	CCONJ
fcis-29274	23	16	used	use	VERB
fcis-29274	23	17	logistic	logistic	ADJ
fcis-29274	23	18	regression	regression	NOUN
fcis-29274	23	19	to	to	PART
fcis-29274	23	20	assess	assess	VERB
fcis-29274	23	21	the	the	DET
fcis-29274	23	22	temporal	temporal	ADJ
fcis-29274	23	23	trend	trend	NOUN
fcis-29274	23	24	of	of	ADP
fcis-29274	23	25	depression	depression	NOUN
fcis-29274	23	26	prevalence	prevalence	NOUN
fcis-29274	24	1	[	[	X
fcis-29274	24	2	11	11	NUM
fcis-29274	24	3	]	]	PUNCT
fcis-29274	24	4	.	.	PUNCT
fcis-29274	25	1	however	however	ADV
fcis-29274	25	2	,	,	PUNCT
fcis-29274	25	3	traditional	traditional	ADJ
fcis-29274	25	4	machine	machine	NOUN
fcis-29274	25	5	learning	learning	NOUN
fcis-29274	25	6	models	model	NOUN
fcis-29274	25	7	have	have	VERB
fcis-29274	25	8	some	some	DET
fcis-29274	25	9	limitations	limitation	NOUN
fcis-29274	25	10	.	.	PUNCT
fcis-29274	26	1	for	for	ADP
fcis-29274	26	2	example	example	NOUN
fcis-29274	26	3	,	,	PUNCT
fcis-29274	26	4	before	before	ADP
fcis-29274	26	5	building	build	VERB
fcis-29274	26	6	a	a	DET
fcis-29274	26	7	regression	regression	NOUN
fcis-29274	26	8	model	model	NOUN
fcis-29274	26	9	,	,	PUNCT
fcis-29274	26	10	we	we	PRON
fcis-29274	26	11	should	should	AUX
fcis-29274	26	12	fully	fully	ADV
fcis-29274	26	13	understand	understand	VERB
fcis-29274	26	14	our	our	PRON
fcis-29274	26	15	data	datum	NOUN
fcis-29274	26	16	attributes	attribute	NOUN
fcis-29274	26	17	and	and	CCONJ
fcis-29274	26	18	model	model	NOUN
fcis-29274	26	19	functions	function	NOUN
fcis-29274	26	20	in	in	ADP
fcis-29274	26	21	order	order	NOUN
fcis-29274	26	22	to	to	PART
fcis-29274	26	23	successfully	successfully	ADV
fcis-29274	26	24	apply	apply	VERB
fcis-29274	26	25	the	the	DET
fcis-29274	26	26	model	model	NOUN
fcis-29274	26	27	.	.	PUNCT
fcis-29274	27	1	therefore	therefore	ADV
fcis-29274	27	2	,	,	PUNCT
fcis-29274	27	3	some	some	DET
fcis-29274	27	4	other	other	ADJ
fcis-29274	27	5	machine	machine	NOUN
fcis-29274	27	6	learning	learning	NOUN
fcis-29274	27	7	models	model	NOUN
fcis-29274	27	8	can	can	AUX
fcis-29274	27	9	be	be	AUX
fcis-29274	27	10	combined	combine	VERB
fcis-29274	27	11	for	for	ADP
fcis-29274	27	12	prediction	prediction	NOUN
fcis-29274	27	13	.	.	PUNCT
fcis-29274	28	1	for	for	ADP
fcis-29274	28	2	example	example	NOUN
fcis-29274	28	3	,	,	PUNCT
fcis-29274	28	4	mudasir	mudasir	PROPN
fcis-29274	28	5	used	use	VERB
fcis-29274	28	6	a	a	DET
fcis-29274	28	7	dataset	dataset	NOUN
fcis-29274	28	8	of	of	ADP
fcis-29274	28	9	various	various	ADJ
fcis-29274	28	10	depression	depression	NOUN
fcis-29274	28	11	signals	signal	NOUN
fcis-29274	28	12	from	from	ADP
fcis-29274	28	13	online	online	ADJ
fcis-29274	28	14	social	social	ADJ
fcis-29274	28	15	network	network	NOUN
fcis-29274	28	16	(	(	PUNCT
fcis-29274	28	17	osn	osn	NOUN
fcis-29274	28	18	)	)	PUNCT
fcis-29274	28	19	platforms	platform	NOUN
fcis-29274	28	20	(	(	PUNCT
fcis-29274	28	21	including	include	VERB
fcis-29274	28	22	facebook	facebook	NOUN
fcis-29274	28	23	,	,	PUNCT
fcis-29274	28	24	twitter	twitter	NOUN
fcis-29274	28	25	,	,	PUNCT
fcis-29274	28	26	and	and	CCONJ
fcis-29274	28	27	youtube	youtube	NOUN
fcis-29274	28	28	)	)	PUNCT
fcis-29274	28	29	to	to	PART
fcis-29274	28	30	propose	propose	VERB
fcis-29274	28	31	an	an	DET
fcis-29274	28	32	efficient	efficient	ADJ
fcis-29274	28	33	model	model	NOUN
fcis-29274	28	34	based	base	VERB
fcis-29274	28	35	on	on	ADP
fcis-29274	28	36	artificial	artificial	ADJ
fcis-29274	28	37	intelligence	intelligence	NOUN
fcis-29274	28	38	(	(	PUNCT
fcis-29274	28	39	ai	ai	NOUN
fcis-29274	28	40	)	)	PUNCT
fcis-29274	28	41	and	and	CCONJ
fcis-29274	28	42	deep	deep	ADJ
fcis-29274	28	43	learning	learning	NOUN
fcis-29274	28	44	(	(	PUNCT
fcis-29274	28	45	dl	dl	INTJ
fcis-29274	28	46	)	)	PUNCT
fcis-29274	28	47	to	to	PART
fcis-29274	28	48	identify	identify	VERB
fcis-29274	28	49	patients	patient	NOUN
fcis-29274	28	50	with	with	ADP
fcis-29274	28	51	depression	depression	NOUN
fcis-29274	28	52	on	on	ADP
fcis-29274	28	53	social	social	ADJ
fcis-29274	28	54	media	medium	NOUN
fcis-29274	28	55	platforms	platform	NOUN
fcis-29274	28	56	.	.	PUNCT
fcis-29274	29	1	experiments	experiment	NOUN
fcis-29274	29	2	showed	show	VERB
fcis-29274	29	3	that	that	SCONJ
fcis-29274	29	4	the	the	DET
fcis-29274	29	5	deep	deep	ADJ
fcis-29274	29	6	learning	learning	NOUN
fcis-29274	29	7	models	model	NOUN
fcis-29274	29	8	lstm	lstm	PROPN
fcis-29274	29	9	and	and	CCONJ
fcis-29274	29	10	cnn	cnn	PROPN
fcis-29274	29	11	as	as	ADV
fcis-29274	29	12	well	well	ADV
fcis-29274	29	13	as	as	ADP
fcis-29274	29	14	the	the	DET
fcis-29274	29	15	hybrid	hybrid	NOUN
fcis-29274	29	16	(	(	PUNCT
fcis-29274	29	17	cnn+lstm	cnn+lstm	NOUN
fcis-29274	29	18	)	)	PUNCT
fcis-29274	29	19	model	model	NOUN
fcis-29274	29	20	achieved	achieve	VERB
fcis-29274	29	21	high	high	ADJ
fcis-29274	29	22	accuracy	accuracy	NOUN
fcis-29274	29	23	on	on	ADP
fcis-29274	29	24	all	all	DET
fcis-29274	29	25	single	single	ADJ
fcis-29274	29	26	and	and	CCONJ
fcis-29274	29	27	combined	combined	ADJ
fcis-29274	29	28	datasets	dataset	NOUN
fcis-29274	29	29	[	[	X
fcis-29274	29	30	12	12	NUM
fcis-29274	29	31	]	]	PUNCT
fcis-29274	29	32	.	.	PUNCT
fcis-29274	30	1	zhang	zhang	PROPN
fcis-29274	30	2	chenyang	chenyang	PROPN
fcis-29274	30	3	used	use	VERB
fcis-29274	30	4	five	five	NUM
fcis-29274	30	5	machine	machine	NOUN
fcis-29274	30	6	learning	learning	NOUN
fcis-29274	30	7	models	model	NOUN
fcis-29274	30	8	to	to	PART
fcis-29274	30	9	identify	identify	VERB
fcis-29274	30	10	depression	depression	NOUN
fcis-29274	30	11	in	in	ADP
fcis-29274	30	12	middleaged	middleaged	ADJ
fcis-29274	30	13	and	and	CCONJ
fcis-29274	30	14	elderly	elderly	ADJ
fcis-29274	30	15	people	people	NOUN
fcis-29274	30	16	using	use	VERB
fcis-29274	30	17	the	the	DET
fcis-29274	30	18	national	national	ADJ
fcis-29274	30	19	health	health	PROPN
fcis-29274	30	20	and	and	CCONJ
fcis-29274	30	21	nutrition	nutrition	NOUN
fcis-29274	30	22	examination	examination	NOUN
fcis-29274	30	23	survey	survey	NOUN
fcis-29274	30	24	data	datum	NOUN
fcis-29274	30	25	from	from	ADP
fcis-29274	30	26	2011	2011	NUM
fcis-29274	30	27	to	to	ADP
fcis-29274	30	28	2018	2018	NUM
fcis-29274	30	29	.	.	PUNCT
fcis-29274	31	1	the	the	DET
fcis-29274	31	2	results	result	NOUN
fcis-29274	31	3	showed	show	VERB
fcis-29274	31	4	that	that	SCONJ
fcis-29274	31	5	catboost	catboost	NOUN
fcis-29274	31	6	was	be	AUX
fcis-29274	31	7	the	the	DET
fcis-29274	31	8	best	good	ADJ
fcis-29274	31	9	model	model	NOUN
fcis-29274	31	10	for	for	ADP
fcis-29274	31	11	identifying	identify	VERB
fcis-29274	31	12	depression	depression	NOUN
fcis-29274	31	13	,	,	PUNCT
fcis-29274	31	14	with	with	ADP
fcis-29274	31	15	the	the	DET
fcis-29274	31	16	highest	high	ADJ
fcis-29274	31	17	area	area	NOUN
fcis-29274	31	18	under	under	ADP
fcis-29274	31	19	the	the	DET
fcis-29274	31	20	operating	operate	VERB
fcis-29274	31	21	characteristic	characteristic	ADJ
fcis-29274	31	22	curve	curve	NOUN
fcis-29274	31	23	(	(	PUNCT
fcis-29274	31	24	auc	auc	NOUN
fcis-29274	31	25	)	)	PUNCT
fcis-29274	32	1	[	[	X
fcis-29274	32	2	13	13	NUM
fcis-29274	32	3	]	]	PUNCT
fcis-29274	32	4	.	.	PUNCT
fcis-29274	33	1	in	in	ADP
fcis-29274	33	2	this	this	DET
fcis-29274	33	3	paper	paper	NOUN
fcis-29274	33	4	,	,	PUNCT
fcis-29274	33	5	we	we	PRON
fcis-29274	33	6	focus	focus	VERB
fcis-29274	33	7	on	on	ADP
fcis-29274	33	8	investigating	investigate	VERB
fcis-29274	33	9	how	how	SCONJ
fcis-29274	33	10	machine	machine	NOUN
fcis-29274	33	11	learning	learn	VERB
fcis-29274	33	12	algorithms	algorithm	NOUN
fcis-29274	33	13	can	can	AUX
fcis-29274	33	14	assess	assess	VERB
fcis-29274	33	15	epidemiological	epidemiological	ADJ
fcis-29274	33	16	,	,	PUNCT
fcis-29274	33	17	demographic	demographic	ADJ
fcis-29274	33	18	,	,	PUNCT
fcis-29274	33	19	lifestyle	lifestyle	NOUN
fcis-29274	33	20	,	,	PUNCT
fcis-29274	33	21	and	and	CCONJ
fcis-29274	33	22	other	other	ADJ
fcis-29274	33	23	factors	factor	NOUN
fcis-29274	33	24	that	that	PRON
fcis-29274	33	25	contribute	contribute	VERB
fcis-29274	33	26	to	to	ADP
fcis-29274	33	27	depression	depression	NOUN
fcis-29274	33	28	in	in	ADP
fcis-29274	33	29	large	large	ADJ
fcis-29274	33	30	survey	survey	NOUN
fcis-29274	33	31	datasets	dataset	NOUN
fcis-29274	33	32	.	.	PUNCT
fcis-29274	34	1	we	we	PRON
fcis-29274	34	2	further	far	ADV
fcis-29274	34	3	compare	compare	VERB
fcis-29274	34	4	the	the	DET
fcis-29274	34	5	performance	performance	NOUN
fcis-29274	34	6	of	of	ADP
fcis-29274	34	7	the	the	DET
fcis-29274	34	8	catboost	catboost	ADJ
fcis-29274	34	9	algorithm	algorithm	NOUN
fcis-29274	34	10	with	with	ADP
fcis-29274	34	11	several	several	ADJ
fcis-29274	34	12	traditional	traditional	ADJ
fcis-29274	34	13	machine	machine	NOUN
fcis-29274	34	14	learning	learning	NOUN
fcis-29274	34	15	algorithms	algorithm	NOUN
fcis-29274	34	16	,	,	PUNCT
fcis-29274	34	17	such	such	ADJ
fcis-29274	34	18	as	as	ADP
fcis-29274	34	19	decision	decision	NOUN
fcis-29274	34	20	trees	tree	NOUN
fcis-29274	34	21	and	and	CCONJ
fcis-29274	34	22	logistic	logistic	ADJ
fcis-29274	34	23	regression	regression	NOUN
fcis-29274	34	24	.	.	PUNCT
fcis-29274	35	1	our	our	PRON
fcis-29274	35	2	goal	goal	NOUN
fcis-29274	35	3	is	be	AUX
fcis-29274	35	4	to	to	PART
fcis-29274	35	5	evaluate	evaluate	VERB
fcis-29274	35	6	the	the	DET
fcis-29274	35	7	utility	utility	NOUN
fcis-29274	35	8	of	of	ADP
fcis-29274	35	9	machine	machine	NOUN
fcis-29274	35	10	learning	learn	VERB
fcis-29274	35	11	in	in	ADP
fcis-29274	35	12	identifying	identify	VERB
fcis-29274	35	13	risk	risk	NOUN
fcis-29274	35	14	factors	factor	NOUN
fcis-29274	35	15	associated	associate	VERB
fcis-29274	35	16	with	with	ADP
fcis-29274	35	17	depression	depression	NOUN
fcis-29274	35	18	in	in	ADP
fcis-29274	35	19	adults	adult	NOUN
fcis-29274	35	20	in	in	ADP
fcis-29274	35	21	large	large	ADJ
fcis-29274	35	22	survey	survey	NOUN
fcis-29274	35	23	datasets	dataset	NOUN
fcis-29274	35	24	,	,	PUNCT
fcis-29274	35	25	and	and	CCONJ
fcis-29274	35	26	to	to	PART
fcis-29274	35	27	provide	provide	VERB
fcis-29274	35	28	a	a	DET
fcis-29274	35	29	scientific	scientific	ADJ
fcis-29274	35	30	basis	basis	NOUN
fcis-29274	35	31	for	for	ADP
fcis-29274	35	32	early	early	ADJ
fcis-29274	35	33	detection	detection	NOUN
fcis-29274	35	34	and	and	CCONJ
fcis-29274	35	35	early	early	ADJ
fcis-29274	35	36	treatment	treatment	NOUN
fcis-29274	35	37	of	of	ADP
fcis-29274	35	38	people	people	NOUN
fcis-29274	35	39	at	at	ADP
fcis-29274	35	40	high	high	ADJ
fcis-29274	35	41	risk	risk	NOUN
fcis-29274	35	42	of	of	ADP
fcis-29274	35	43	depression	depression	NOUN
fcis-29274	35	44	,	,	PUNCT
fcis-29274	35	45	making	make	VERB
fcis-29274	35	46	machine	machine	NOUN
fcis-29274	35	47	learning	learn	VERB
fcis-29274	35	48	a	a	DET
fcis-29274	35	49	valuable	valuable	ADJ
fcis-29274	35	50	tool	tool	NOUN
fcis-29274	35	51	for	for	ADP
fcis-29274	35	52	clinicians	clinician	NOUN
fcis-29274	35	53	to	to	PART
fcis-29274	35	54	decide	decide	VERB
fcis-29274	35	55	to	to	PART
fcis-29274	35	56	care	care	VERB
fcis-29274	35	57	for	for	ADP
fcis-29274	35	58	their	their	PRON
fcis-29274	35	59	patients	patient	NOUN
fcis-29274	35	60	.	.	PUNCT
fcis-29274	36	1	2	2	X
fcis-29274	36	2	.	.	X
fcis-29274	36	3	method	method	PROPN
fcis-29274	36	4	2.1	2.1	NUM
fcis-29274	36	5	.	.	PUNCT
fcis-29274	37	1	dataset	dataset	NOUN
fcis-29274	37	2	and	and	CCONJ
fcis-29274	37	3	study	study	NOUN
fcis-29274	37	4	population	population	NOUN
fcis-29274	37	5	this	this	DET
fcis-29274	37	6	paper	paper	NOUN
fcis-29274	37	7	uses	use	VERB
fcis-29274	37	8	data	datum	NOUN
fcis-29274	37	9	from	from	ADP
fcis-29274	37	10	the	the	DET
fcis-29274	37	11	2017	2017	NUM
fcis-29274	37	12	-	-	PUNCT
fcis-29274	37	13	2018	2018	NUM
fcis-29274	37	14	national	national	ADJ
fcis-29274	37	15	health	health	NOUN
fcis-29274	37	16	and	and	CCONJ
fcis-29274	37	17	nutrition	nutrition	NOUN
fcis-29274	37	18	examination	examination	NOUN
fcis-29274	37	19	survey	survey	NOUN
fcis-29274	37	20	(	(	PUNCT
fcis-29274	37	21	nhanes	nhane	NOUN
fcis-29274	37	22	)	)	PUNCT
fcis-29274	37	23	to	to	PART
fcis-29274	37	24	train	train	VERB
fcis-29274	37	25	machine	machine	NOUN
fcis-29274	37	26	learning	learn	VERB
fcis-29274	37	27	algorithms	algorithm	NOUN
fcis-29274	37	28	and	and	CCONJ
fcis-29274	37	29	other	other	ADJ
fcis-29274	37	30	machine	machine	NOUN
fcis-29274	37	31	learning	learn	VERB
fcis-29274	37	32	classifiers	classifier	NOUN
fcis-29274	37	33	.	.	PUNCT
fcis-29274	38	1	nhanes	nhane	NOUN
fcis-29274	38	2	is	be	AUX
fcis-29274	38	3	a	a	DET
fcis-29274	38	4	cross	cross	ADJ
fcis-29274	38	5	-	-	ADJ
fcis-29274	38	6	sectional	sectional	ADJ
fcis-29274	38	7	,	,	PUNCT
fcis-29274	38	8	population	population	NOUN
fcis-29274	38	9	-	-	PUNCT
fcis-29274	38	10	based	base	VERB
fcis-29274	38	11	study	study	NOUN
fcis-29274	38	12	of	of	ADP
fcis-29274	38	13	the	the	DET
fcis-29274	38	14	non	non	ADJ
fcis-29274	38	15	-	-	ADJ
fcis-29274	38	16	institutionalized	institutionalized	ADJ
fcis-29274	38	17	civilian	civilian	ADJ
fcis-29274	38	18	population	population	NOUN
fcis-29274	38	19	in	in	ADP
fcis-29274	38	20	the	the	DET
fcis-29274	38	21	united	united	PROPN
fcis-29274	38	22	states	states	PROPN
fcis-29274	38	23	that	that	PRON
fcis-29274	38	24	assesses	assess	VERB
fcis-29274	38	25	the	the	DET
fcis-29274	38	26	health	health	NOUN
fcis-29274	38	27	and	and	CCONJ
fcis-29274	38	28	nutritional	nutritional	ADJ
fcis-29274	38	29	status	status	NOUN
fcis-29274	38	30	of	of	ADP
fcis-29274	38	31	adults	adult	NOUN
fcis-29274	38	32	and	and	CCONJ
fcis-29274	38	33	children	child	NOUN
fcis-29274	38	34	[	[	X
fcis-29274	38	35	14	14	NUM
fcis-29274	38	36	]	]	PUNCT
fcis-29274	38	37	.	.	PUNCT
fcis-29274	39	1	its	its	PRON
fcis-29274	39	2	data	data	NOUN
fcis-29274	39	3	collection	collection	NOUN
fcis-29274	39	4	includes	include	VERB
fcis-29274	39	5	an	an	DET
fcis-29274	39	6	inhome	inhome	NOUN
fcis-29274	39	7	interview	interview	NOUN
fcis-29274	39	8	component	component	NOUN
fcis-29274	39	9	and	and	CCONJ
fcis-29274	39	10	a	a	DET
fcis-29274	39	11	physical	physical	ADJ
fcis-29274	39	12	examination	examination	NOUN
fcis-29274	39	13	component	component	NOUN
fcis-29274	39	14	conducted	conduct	VERB
fcis-29274	39	15	in	in	ADP
fcis-29274	39	16	a	a	DET
fcis-29274	39	17	mobile	mobile	ADJ
fcis-29274	39	18	examination	examination	NOUN
fcis-29274	39	19	center	center	NOUN
fcis-29274	39	20	.	.	PUNCT
fcis-29274	40	1	the	the	DET
fcis-29274	40	2	interview	interview	NOUN
fcis-29274	40	3	includes	include	VERB
fcis-29274	40	4	demographic	demographic	ADJ
fcis-29274	40	5	,	,	PUNCT
fcis-29274	40	6	socioeconomic	socioeconomic	ADJ
fcis-29274	40	7	,	,	PUNCT
fcis-29274	40	8	dietary	dietary	ADJ
fcis-29274	40	9	,	,	PUNCT
fcis-29274	40	10	and	and	CCONJ
fcis-29274	40	11	health	health	NOUN
fcis-29274	40	12	-	-	PUNCT
fcis-29274	40	13	related	relate	VERB
fcis-29274	40	14	questions	question	NOUN
fcis-29274	40	15	,	,	PUNCT
fcis-29274	40	16	and	and	CCONJ
fcis-29274	40	17	the	the	DET
fcis-29274	40	18	physical	physical	ADJ
fcis-29274	40	19	examination	examination	NOUN
fcis-29274	40	20	65	65	NUM
fcis-29274	40	21	component	component	NOUN
fcis-29274	40	22	includes	include	VERB
fcis-29274	40	23	medical	medical	ADJ
fcis-29274	40	24	,	,	PUNCT
fcis-29274	40	25	dental	dental	ADJ
fcis-29274	40	26	,	,	PUNCT
fcis-29274	40	27	physiological	physiological	ADJ
fcis-29274	40	28	,	,	PUNCT
fcis-29274	40	29	medical	medical	ADJ
fcis-29274	40	30	examinations	examination	NOUN
fcis-29274	40	31	,	,	PUNCT
fcis-29274	40	32	and	and	CCONJ
fcis-29274	40	33	laboratory	laboratory	NOUN
fcis-29274	40	34	measurements	measurement	NOUN
fcis-29274	40	35	[	[	X
fcis-29274	40	36	15	15	NUM
fcis-29274	40	37	]	]	PUNCT
fcis-29274	40	38	.	.	PUNCT
fcis-29274	41	1	all	all	PRON
fcis-29274	41	2	nhanes	nhane	NOUN
fcis-29274	41	3	data	datum	NOUN
fcis-29274	41	4	are	be	AUX
fcis-29274	41	5	in	in	ADP
fcis-29274	41	6	the	the	DET
fcis-29274	41	7	public	public	ADJ
fcis-29274	41	8	domain	domain	NOUN
fcis-29274	41	9	and	and	CCONJ
fcis-29274	41	10	can	can	AUX
fcis-29274	41	11	be	be	AUX
fcis-29274	41	12	accessed	access	VERB
fcis-29274	41	13	on	on	ADP
fcis-29274	41	14	the	the	DET
fcis-29274	41	15	website	website	NOUN
fcis-29274	41	16	of	of	ADP
fcis-29274	41	17	the	the	DET
fcis-29274	41	18	national	national	ADJ
fcis-29274	41	19	center	center	NOUN
fcis-29274	41	20	for	for	ADP
fcis-29274	41	21	health	health	NOUN
fcis-29274	41	22	statistics	statistic	NOUN
fcis-29274	41	23	(	(	PUNCT
fcis-29274	41	24	https://www.cdc.gov/nchs/nhanes	https://www.cdc.gov/nchs/nhanes	PROPN
fcis-29274	41	25	)	)	PUNCT
fcis-29274	41	26	.	.	PUNCT
fcis-29274	42	1	figure	figure	NOUN
fcis-29274	42	2	1	1	NUM
fcis-29274	42	3	.	.	PUNCT
fcis-29274	43	1	flowchart	flowchart	NOUN
fcis-29274	43	2	of	of	ADP
fcis-29274	43	3	inclusion	inclusion	NOUN
fcis-29274	43	4	and	and	CCONJ
fcis-29274	43	5	exclusion	exclusion	NOUN
fcis-29274	43	6	criteria	criterion	NOUN
fcis-29274	43	7	for	for	ADP
fcis-29274	43	8	study	study	NOUN
fcis-29274	43	9	samples	sample	NOUN
fcis-29274	43	10	the	the	DET
fcis-29274	43	11	dataset	dataset	NOUN
fcis-29274	43	12	used	use	VERB
fcis-29274	43	13	in	in	ADP
fcis-29274	43	14	this	this	DET
fcis-29274	43	15	article	article	NOUN
fcis-29274	43	16	initially	initially	ADV
fcis-29274	43	17	included	include	VERB
fcis-29274	43	18	9254	9254	NUM
fcis-29274	43	19	participants	participant	NOUN
fcis-29274	43	20	,	,	PUNCT
fcis-29274	43	21	and	and	CCONJ
fcis-29274	43	22	the	the	DET
fcis-29274	43	23	inclusion	inclusion	NOUN
fcis-29274	43	24	criteria	criterion	NOUN
fcis-29274	43	25	for	for	ADP
fcis-29274	43	26	the	the	DET
fcis-29274	43	27	analysis	analysis	NOUN
fcis-29274	43	28	sample	sample	NOUN
fcis-29274	43	29	were	be	AUX
fcis-29274	43	30	adults	adult	NOUN
fcis-29274	43	31	aged	aged	ADJ
fcis-29274	43	32	18	18	NUM
fcis-29274	43	33	+	+	CCONJ
fcis-29274	43	34	who	who	PRON
fcis-29274	43	35	answered	answer	VERB
fcis-29274	43	36	questions	question	NOUN
fcis-29274	43	37	related	relate	VERB
fcis-29274	43	38	to	to	ADP
fcis-29274	43	39	depression	depression	NOUN
fcis-29274	43	40	.	.	PUNCT
fcis-29274	44	1	therefore	therefore	ADV
fcis-29274	44	2	,	,	PUNCT
fcis-29274	44	3	we	we	PRON
fcis-29274	44	4	ultimately	ultimately	ADV
fcis-29274	44	5	used	use	VERB
fcis-29274	44	6	5533	5533	NUM
fcis-29274	44	7	participants	participant	NOUN
fcis-29274	44	8	as	as	ADP
fcis-29274	44	9	research	research	NOUN
fcis-29274	44	10	subjects	subject	NOUN
fcis-29274	44	11	.	.	PUNCT
fcis-29274	45	1	the	the	DET
fcis-29274	45	2	values	value	NOUN
fcis-29274	45	3	"	"	PUNCT
fcis-29274	45	4	9	9	NUM
fcis-29274	45	5	"	"	PUNCT
fcis-29274	45	6	,	,	PUNCT
fcis-29274	45	7	"	"	PUNCT
fcis-29274	45	8	99	99	NUM
fcis-29274	45	9	"	"	PUNCT
fcis-29274	45	10	,	,	PUNCT
fcis-29274	45	11	"	"	PUNCT
fcis-29274	45	12	777	777	NUM
fcis-29274	45	13	"	"	PUNCT
fcis-29274	45	14	,	,	PUNCT
fcis-29274	45	15	and	and	CCONJ
fcis-29274	45	16	"	"	PUNCT
fcis-29274	45	17	999	999	NUM
fcis-29274	45	18	"	"	PUNCT
fcis-29274	45	19	represent	represent	VERB
fcis-29274	45	20	"	"	PUNCT
fcis-29274	45	21	do	do	AUX
fcis-29274	45	22	n't	not	PART
fcis-29274	45	23	know	know	VERB
fcis-29274	45	24	"	"	PUNCT
fcis-29274	45	25	answers	answer	NOUN
fcis-29274	45	26	to	to	ADP
fcis-29274	45	27	the	the	DET
fcis-29274	45	28	variables	variable	NOUN
fcis-29274	45	29	and	and	CCONJ
fcis-29274	45	30	are	be	AUX
fcis-29274	45	31	therefore	therefore	ADV
fcis-29274	45	32	considered	consider	VERB
fcis-29274	45	33	missing	missing	ADJ
fcis-29274	45	34	values	value	NOUN
fcis-29274	45	35	.	.	PUNCT
fcis-29274	46	1	the	the	DET
fcis-29274	46	2	specific	specific	ADJ
fcis-29274	46	3	inclusion	inclusion	NOUN
fcis-29274	46	4	and	and	CCONJ
fcis-29274	46	5	exclusion	exclusion	NOUN
fcis-29274	46	6	criteria	criterion	NOUN
fcis-29274	46	7	are	be	AUX
fcis-29274	46	8	shown	show	VERB
fcis-29274	46	9	in	in	ADP
fcis-29274	46	10	figure	figure	NOUN
fcis-29274	46	11	1	1	NUM
fcis-29274	46	12	.	.	X
fcis-29274	46	13	2.2	2.2	NUM
fcis-29274	46	14	.	.	PUNCT
fcis-29274	47	1	disease	disease	NOUN
fcis-29274	47	2	definition	definition	NOUN
fcis-29274	47	3	depression	depression	NOUN
fcis-29274	47	4	severity	severity	NOUN
fcis-29274	47	5	was	be	AUX
fcis-29274	47	6	determined	determine	VERB
fcis-29274	47	7	by	by	ADP
fcis-29274	47	8	nine	nine	NUM
fcis-29274	47	9	questions	question	NOUN
fcis-29274	47	10	from	from	ADP
fcis-29274	47	11	the	the	DET
fcis-29274	47	12	patient	patient	ADJ
fcis-29274	47	13	health	health	NOUN
fcis-29274	47	14	questionnaire	questionnaire	NOUN
fcis-29274	47	15	(	(	PUNCT
fcis-29274	47	16	phq	phq	NOUN
fcis-29274	47	17	)	)	PUNCT
fcis-29274	47	18	,	,	PUNCT
fcis-29274	47	19	a	a	DET
fcis-29274	47	20	version	version	NOUN
fcis-29274	47	21	of	of	ADP
fcis-29274	47	22	the	the	DET
fcis-29274	47	23	primemd	primemd	NOUN
fcis-29274	47	24	diagnostic	diagnostic	ADJ
fcis-29274	47	25	tool	tool	NOUN
fcis-29274	47	26	that	that	PRON
fcis-29274	47	27	is	be	AUX
fcis-29274	47	28	a	a	DET
fcis-29274	47	29	brief	brief	ADJ
fcis-29274	47	30	,	,	PUNCT
fcis-29274	47	31	reliable	reliable	ADJ
fcis-29274	47	32	,	,	PUNCT
fcis-29274	47	33	and	and	CCONJ
fcis-29274	47	34	valid	valid	ADJ
fcis-29274	47	35	measure	measure	NOUN
fcis-29274	47	36	.	.	PUNCT
fcis-29274	48	1	the	the	DET
fcis-29274	48	2	phq-9	phq-9	PUNCT
fcis-29274	48	3	assesses	assesse	NOUN
fcis-29274	48	4	symptoms	symptom	NOUN
fcis-29274	48	5	of	of	ADP
fcis-29274	48	6	a	a	DET
fcis-29274	48	7	major	major	ADJ
fcis-29274	48	8	depressive	depressive	ADJ
fcis-29274	48	9	episode	episode	NOUN
fcis-29274	48	10	in	in	ADP
fcis-29274	48	11	the	the	DET
fcis-29274	48	12	previous	previous	ADJ
fcis-29274	48	13	2	2	NUM
fcis-29274	48	14	weeks	week	NOUN
fcis-29274	48	15	and	and	CCONJ
fcis-29274	48	16	has	have	VERB
fcis-29274	48	17	a	a	DET
fcis-29274	48	18	score	score	NOUN
fcis-29274	48	19	range	range	NOUN
fcis-29274	48	20	of	of	ADP
fcis-29274	48	21	0	0	NUM
fcis-29274	48	22	-	-	SYM
fcis-29274	48	23	27	27	NUM
fcis-29274	48	24	.	.	PUNCT
fcis-29274	49	1	we	we	PRON
fcis-29274	49	2	defined	define	VERB
fcis-29274	49	3	a	a	DET
fcis-29274	49	4	score	score	NOUN
fcis-29274	49	5	greater	great	ADJ
fcis-29274	49	6	than	than	ADP
fcis-29274	49	7	or	or	CCONJ
fcis-29274	49	8	equal	equal	ADJ
fcis-29274	49	9	to	to	ADP
fcis-29274	49	10	10	10	NUM
fcis-29274	49	11	as	as	ADP
fcis-29274	49	12	clinically	clinically	ADV
fcis-29274	49	13	relevant	relevant	ADJ
fcis-29274	49	14	depression	depression	NOUN
fcis-29274	49	15	because	because	SCONJ
fcis-29274	49	16	it	it	PRON
fcis-29274	49	17	has	have	VERB
fcis-29274	49	18	reliable	reliable	ADJ
fcis-29274	49	19	sensitivity	sensitivity	NOUN
fcis-29274	49	20	and	and	CCONJ
fcis-29274	49	21	specificity	specificity	NOUN
fcis-29274	49	22	for	for	ADP
fcis-29274	49	23	detecting	detect	VERB
fcis-29274	49	24	major	major	ADJ
fcis-29274	49	25	depression	depression	NOUN
fcis-29274	49	26	at	at	ADP
fcis-29274	49	27	this	this	DET
fcis-29274	49	28	threshold	threshold	NOUN
fcis-29274	49	29	.	.	PUNCT
fcis-29274	50	1	we	we	PRON
fcis-29274	50	2	eliminated	eliminate	VERB
fcis-29274	50	3	people	people	NOUN
fcis-29274	50	4	with	with	ADP
fcis-29274	50	5	missing	miss	VERB
fcis-29274	50	6	answers	answer	NOUN
fcis-29274	50	7	to	to	ADP
fcis-29274	50	8	six	six	NUM
fcis-29274	50	9	questions	question	NOUN
fcis-29274	50	10	.	.	PUNCT
fcis-29274	51	1	during	during	ADP
fcis-29274	51	2	2017	2017	NUM
fcis-29274	51	3	-	-	SYM
fcis-29274	51	4	2018	2018	NUM
fcis-29274	51	5	,	,	PUNCT
fcis-29274	51	6	5533	5533	NUM
fcis-29274	51	7	participants	participant	NOUN
fcis-29274	51	8	were	be	AUX
fcis-29274	51	9	assessed	assess	VERB
fcis-29274	51	10	for	for	ADP
fcis-29274	51	11	depression	depression	NOUN
fcis-29274	51	12	and	and	CCONJ
fcis-29274	51	13	472	472	NUM
fcis-29274	51	14	were	be	AUX
fcis-29274	51	15	diagnosed	diagnose	VERB
fcis-29274	51	16	with	with	ADP
fcis-29274	51	17	depression	depression	NOUN
fcis-29274	51	18	(	(	PUNCT
fcis-29274	51	19	8.53	8.53	NUM
fcis-29274	51	20	%	%	NOUN
fcis-29274	51	21	)	)	PUNCT
fcis-29274	51	22	.	.	PUNCT
fcis-29274	52	1	2.3	2.3	NUM
fcis-29274	52	2	.	.	PUNCT
fcis-29274	53	1	variable	variable	ADJ
fcis-29274	53	2	selection	selection	NOUN
fcis-29274	53	3	table	table	NOUN
fcis-29274	53	4	1	1	NUM
fcis-29274	53	5	.	.	PUNCT
fcis-29274	54	1	depression	depression	NOUN
fcis-29274	54	2	baseline	baseline	NOUN
fcis-29274	54	3	table	table	NOUN
fcis-29274	54	4	variable	variable	ADJ
fcis-29274	54	5	healthy(n=5055	healthy(n=5055	NOUN
fcis-29274	54	6	)	)	PUNCT
fcis-29274	54	7	depression(n=472	depression(n=472	PROPN
fcis-29274	54	8	)	)	PUNCT
fcis-29274	54	9	p	p	NOUN
fcis-29274	54	10	test	test	NOUN
fcis-29274	54	11	age	age	NOUN
fcis-29274	54	12	49.78	49.78	NUM
fcis-29274	54	13	(	(	PUNCT
fcis-29274	54	14	18.70	18.70	NUM
fcis-29274	54	15	)	)	PUNCT
fcis-29274	54	16	49.93	49.93	NUM
fcis-29274	54	17	(	(	PUNCT
fcis-29274	54	18	18.03	18.03	NUM
fcis-29274	54	19	)	)	PUNCT
fcis-29274	54	20	0.87	0.87	NUM
fcis-29274	54	21	gender	gender	NOUN
fcis-29274	54	22	(	(	PUNCT
fcis-29274	54	23	%	%	INTJ
fcis-29274	54	24	)	)	PUNCT
fcis-29274	55	1	<	<	X
fcis-29274	55	2	0.001	0.001	NUM
fcis-29274	55	3	male	male	ADJ
fcis-29274	55	4	2478	2478	NUM
fcis-29274	55	5	(	(	PUNCT
fcis-29274	55	6	49.0	49.0	NUM
fcis-29274	55	7	)	)	PUNCT
fcis-29274	55	8	188	188	NUM
fcis-29274	55	9	(	(	PUNCT
fcis-29274	55	10	39.8	39.8	NUM
fcis-29274	55	11	)	)	PUNCT
fcis-29274	55	12	female	female	NOUN
fcis-29274	55	13	2577	2577	NUM
fcis-29274	55	14	(	(	PUNCT
fcis-29274	55	15	51.0	51.0	NUM
fcis-29274	55	16	)	)	PUNCT
fcis-29274	55	17	284	284	NUM
fcis-29274	55	18	(	(	PUNCT
fcis-29274	55	19	60.2	60.2	NUM
fcis-29274	55	20	)	)	PUNCT
fcis-29274	55	21	race	race	NOUN
fcis-29274	55	22	(	(	PUNCT
fcis-29274	55	23	%	%	INTJ
fcis-29274	55	24	)	)	PUNCT
fcis-29274	55	25	<	<	X
fcis-29274	55	26	0.001	0.001	NUM
fcis-29274	55	27	mexican	mexican	PROPN
fcis-29274	55	28	american	american	PROPN
fcis-29274	55	29	688	688	NUM
fcis-29274	55	30	(	(	PUNCT
fcis-29274	55	31	13.6	13.6	NUM
fcis-29274	55	32	)	)	PUNCT
fcis-29274	55	33	63	63	NUM
fcis-29274	55	34	(	(	PUNCT
fcis-29274	55	35	13.3	13.3	NUM
fcis-29274	55	36	)	)	PUNCT
fcis-29274	55	37	other	other	ADJ
fcis-29274	55	38	hispanic	hispanic	NOUN
fcis-29274	55	39	718	718	NUM
fcis-29274	55	40	(	(	PUNCT
fcis-29274	55	41	14.2	14.2	NUM
fcis-29274	55	42	)	)	PUNCT
fcis-29274	55	43	89	89	NUM
fcis-29274	55	44	(	(	PUNCT
fcis-29274	55	45	18.9	18.9	NUM
fcis-29274	55	46	)	)	PUNCT
fcis-29274	55	47	non	non	ADJ
fcis-29274	55	48	-	-	ADJ
fcis-29274	55	49	hispanic	hispanic	ADJ
fcis-29274	55	50	white	white	ADJ
fcis-29274	55	51	1701	1701	NUM
fcis-29274	55	52	(	(	PUNCT
fcis-29274	55	53	33.6	33.6	NUM
fcis-29274	55	54	)	)	PUNCT
fcis-29274	55	55	193	193	NUM
fcis-29274	55	56	(	(	PUNCT
fcis-29274	55	57	40.9	40.9	NUM
fcis-29274	55	58	)	)	PUNCT
fcis-29274	55	59	non	non	ADJ
fcis-29274	55	60	-	-	ADJ
fcis-29274	55	61	hispanic	hispanic	ADJ
fcis-29274	55	62	black	black	ADJ
fcis-29274	55	63	1183	1183	NUM
fcis-29274	55	64	(	(	PUNCT
fcis-29274	55	65	23.4	23.4	NUM
fcis-29274	55	66	)	)	SYM
fcis-29274	55	67	99	99	NUM
fcis-29274	55	68	(	(	PUNCT
fcis-29274	55	69	21.0	21.0	NUM
fcis-29274	55	70	)	)	PUNCT
fcis-29274	55	71	non	non	ADJ
fcis-29274	55	72	-	-	ADJ
fcis-29274	55	73	hispanic	hispanic	ADJ
fcis-29274	55	74	asian	asian	ADJ
fcis-29274	55	75	765	765	NUM
fcis-29274	55	76	(	(	PUNCT
fcis-29274	55	77	15.1	15.1	NUM
fcis-29274	55	78	)	)	SYM
fcis-29274	55	79	28	28	NUM
fcis-29274	55	80	(	(	PUNCT
fcis-29274	55	81	5.9	5.9	NUM
fcis-29274	55	82	)	)	PUNCT
fcis-29274	55	83	education	education	NOUN
fcis-29274	55	84	(	(	PUNCT
fcis-29274	55	85	%	%	INTJ
fcis-29274	55	86	)	)	PUNCT
fcis-29274	55	87	<	<	X
fcis-29274	55	88	0.001	0.001	NUM
fcis-29274	55	89	<	<	NOUN
fcis-29274	55	90	high	high	ADJ
fcis-29274	55	91	school	school	NOUN
fcis-29274	55	92	994	994	NUM
fcis-29274	55	93	(	(	PUNCT
fcis-29274	55	94	19.7	19.7	NUM
fcis-29274	55	95	)	)	PUNCT
fcis-29274	55	96	122	122	NUM
fcis-29274	55	97	(	(	PUNCT
fcis-29274	55	98	26.0	26.0	NUM
fcis-29274	55	99	)	)	PUNCT
fcis-29274	55	100	=	=	NOUN
fcis-29274	55	101	high	high	ADJ
fcis-29274	55	102	school	school	NOUN
fcis-29274	55	103	1252	1252	NUM
fcis-29274	55	104	(	(	PUNCT
fcis-29274	55	105	24.8	24.8	NUM
fcis-29274	55	106	)	)	PUNCT
fcis-29274	55	107	131	131	NUM
fcis-29274	55	108	(	(	PUNCT
fcis-29274	55	109	27.9	27.9	NUM
fcis-29274	55	110	)	)	PUNCT
fcis-29274	55	111	>	>	PUNCT
fcis-29274	55	112	high	high	ADJ
fcis-29274	55	113	school	school	NOUN
fcis-29274	55	114	2799	2799	NUM
fcis-29274	55	115	(	(	PUNCT
fcis-29274	55	116	55.5	55.5	NUM
fcis-29274	55	117	)	)	PUNCT
fcis-29274	55	118	217	217	NUM
fcis-29274	55	119	(	(	PUNCT
fcis-29274	55	120	46.2	46.2	NUM
fcis-29274	55	121	)	)	PUNCT
fcis-29274	55	122	marital	marital	ADJ
fcis-29274	55	123	(	(	PUNCT
fcis-29274	55	124	%	%	INTJ
fcis-29274	55	125	)	)	PUNCT
fcis-29274	56	1	<	<	X
fcis-29274	56	2	0.001	0.001	NUM
fcis-29274	56	3	single	single	ADJ
fcis-29274	56	4	2898	2898	NUM
fcis-29274	56	5	(	(	PUNCT
fcis-29274	56	6	60.3	60.3	NUM
fcis-29274	56	7	)	)	SYM
fcis-29274	56	8	193	193	NUM
fcis-29274	56	9	(	(	PUNCT
fcis-29274	56	10	43.0	43.0	NUM
fcis-29274	56	11	)	)	PUNCT
fcis-29274	56	12	married	married	ADJ
fcis-29274	56	13	1054	1054	NUM
fcis-29274	56	14	(	(	PUNCT
fcis-29274	56	15	21.9	21.9	NUM
fcis-29274	56	16	)	)	PUNCT
fcis-29274	56	17	162	162	NUM
fcis-29274	56	18	(	(	PUNCT
fcis-29274	56	19	36.1	36.1	NUM
fcis-29274	56	20	)	)	PUNCT
fcis-29274	56	21	divorced	divorce	VERB
fcis-29274	56	22	853	853	NUM
fcis-29274	56	23	(	(	PUNCT
fcis-29274	56	24	17.8	17.8	NUM
fcis-29274	56	25	)	)	PUNCT
fcis-29274	56	26	94	94	NUM
fcis-29274	56	27	(	(	PUNCT
fcis-29274	56	28	20.9	20.9	NUM
fcis-29274	56	29	)	)	PUNCT
fcis-29274	56	30	poverty	poverty	NOUN
fcis-29274	56	31	index	index	NOUN
fcis-29274	56	32	ratio	ratio	NOUN
fcis-29274	56	33	2.57	2.57	NUM
fcis-29274	56	34	(	(	PUNCT
fcis-29274	56	35	1.61	1.61	NUM
fcis-29274	56	36	)	)	PUNCT
fcis-29274	56	37	1.96	1.96	NUM
fcis-29274	56	38	(	(	PUNCT
fcis-29274	56	39	1.47	1.47	NUM
fcis-29274	56	40	)	)	PUNCT
fcis-29274	56	41	<	<	X
fcis-29274	56	42	0.001	0.001	NUM
fcis-29274	56	43	bmi	bmi	NOUN
fcis-29274	56	44	(	(	PUNCT
fcis-29274	56	45	kg	kg	NOUN
fcis-29274	56	46	/	/	SYM
fcis-29274	56	47	m2	m2	PROPN
fcis-29274	56	48	)	)	PUNCT
fcis-29274	56	49	28.69	28.69	NUM
fcis-29274	56	50	(	(	PUNCT
fcis-29274	56	51	6.85	6.85	NUM
fcis-29274	56	52	)	)	PUNCT
fcis-29274	56	53	30.41	30.41	NUM
fcis-29274	56	54	(	(	PUNCT
fcis-29274	56	55	7.72	7.72	NUM
fcis-29274	56	56	)	)	PUNCT
fcis-29274	56	57	<	<	X
fcis-29274	56	58	0.001	0.001	NUM
fcis-29274	56	59	smoke	smoke	NOUN
fcis-29274	56	60	(	(	PUNCT
fcis-29274	56	61	%	%	INTJ
fcis-29274	56	62	)	)	PUNCT
fcis-29274	56	63	0.029	0.029	NUM
fcis-29274	56	64	no	no	DET
fcis-29274	56	65	69	69	NUM
fcis-29274	56	66	(	(	PUNCT
fcis-29274	56	67	3.5	3.5	NUM
fcis-29274	56	68	)	)	PUNCT
fcis-29274	56	69	2	2	NUM
fcis-29274	56	70	(	(	PUNCT
fcis-29274	56	71	0.8	0.8	NUM
fcis-29274	56	72	)	)	PUNCT
fcis-29274	57	1	yes	yes	INTJ
fcis-29274	57	2	1897	1897	NUM
fcis-29274	57	3	(	(	PUNCT
fcis-29274	57	4	96.5	96.5	NUM
fcis-29274	57	5	)	)	PUNCT
fcis-29274	57	6	260	260	NUM
fcis-29274	57	7	(	(	PUNCT
fcis-29274	57	8	99.2	99.2	NUM
fcis-29274	57	9	)	)	PUNCT
fcis-29274	57	10	never	never	ADV
fcis-29274	57	11	548	548	NUM
fcis-29274	57	12	(	(	PUNCT
fcis-29274	57	13	11.8	11.8	NUM
fcis-29274	57	14	)	)	PUNCT
fcis-29274	57	15	34	34	NUM
fcis-29274	57	16	(	(	PUNCT
fcis-29274	57	17	7.2	7.2	NUM
fcis-29274	57	18	)	)	PUNCT
fcis-29274	57	19	drinke	drinke	NOUN
fcis-29274	57	20	(	(	PUNCT
fcis-29274	57	21	%	%	INTJ
fcis-29274	57	22	)	)	PUNCT
fcis-29274	57	23	0.002	0.002	NUM
fcis-29274	57	24	former	former	ADJ
fcis-29274	57	25	drinker	drinker	NOUN
fcis-29274	57	26	921	921	NUM
fcis-29274	57	27	(	(	PUNCT
fcis-29274	57	28	19.8	19.8	NUM
fcis-29274	57	29	)	)	PUNCT
fcis-29274	57	30	124	124	NUM
fcis-29274	57	31	(	(	PUNCT
fcis-29274	57	32	26.3	26.3	NUM
fcis-29274	57	33	)	)	PUNCT
fcis-29274	57	34	light	light	ADJ
fcis-29274	57	35	drinker	drinker	NOUN
fcis-29274	57	36	2057	2057	NUM
fcis-29274	57	37	(	(	PUNCT
fcis-29274	57	38	44.2	44.2	NUM
fcis-29274	57	39	)	)	PUNCT
fcis-29274	57	40	200	200	NUM
fcis-29274	57	41	(	(	PUNCT
fcis-29274	57	42	42.5	42.5	NUM
fcis-29274	57	43	)	)	PUNCT
fcis-29274	57	44	moderate	moderate	ADJ
fcis-29274	57	45	drinker	drinker	NOUN
fcis-29274	57	46	856	856	NUM
fcis-29274	57	47	(	(	PUNCT
fcis-29274	57	48	18.4	18.4	NUM
fcis-29274	57	49	)	)	PUNCT
fcis-29274	57	50	84	84	NUM
fcis-29274	57	51	(	(	PUNCT
fcis-29274	57	52	17.8	17.8	NUM
fcis-29274	57	53	)	)	PUNCT
fcis-29274	57	54	heavy	heavy	ADJ
fcis-29274	57	55	drinker	drinker	NOUN
fcis-29274	57	56	268	268	NUM
fcis-29274	57	57	(	(	PUNCT
fcis-29274	57	58	5.8	5.8	NUM
fcis-29274	57	59	)	)	PUNCT
fcis-29274	57	60	29	29	NUM
fcis-29274	57	61	(	(	PUNCT
fcis-29274	57	62	6.2	6.2	NUM
fcis-29274	57	63	)	)	PUNCT
fcis-29274	57	64	albumin	albumin	NOUN
fcis-29274	57	65	-	-	PUNCT
fcis-29274	57	66	to	to	ADP
fcis-29274	57	67	-	-	PUNCT
fcis-29274	57	68	creatinine	creatinine	NOUN
fcis-29274	57	69	ratio	ratio	NOUN
fcis-29274	57	70	(	(	PUNCT
fcis-29274	57	71	mg	mg	PROPN
fcis-29274	57	72	/	/	SYM
fcis-29274	57	73	g	g	NOUN
fcis-29274	57	74	)	)	PUNCT
fcis-29274	57	75	47.18	47.18	NUM
fcis-29274	57	76	(	(	PUNCT
fcis-29274	57	77	353.07	353.07	NUM
fcis-29274	57	78	)	)	PUNCT
fcis-29274	57	79	71.42	71.42	NUM
fcis-29274	57	80	(	(	PUNCT
fcis-29274	57	81	304.89	304.89	NUM
fcis-29274	57	82	)	)	PUNCT
fcis-29274	57	83	0.154	0.154	NUM
fcis-29274	57	84	glycohemoglobin	glycohemoglobin	PROPN
fcis-29274	57	85	(	(	PUNCT
fcis-29274	57	86	%	%	INTJ
fcis-29274	57	87	)	)	PUNCT
fcis-29274	57	88	5.82	5.82	NUM
fcis-29274	57	89	(	(	PUNCT
fcis-29274	57	90	1.05	1.05	NUM
fcis-29274	57	91	)	)	PUNCT
fcis-29274	57	92	6.02	6.02	NUM
fcis-29274	57	93	(	(	PUNCT
fcis-29274	57	94	1.40	1.40	NUM
fcis-29274	57	95	)	)	PUNCT
fcis-29274	57	96	<	<	NOUN
fcis-29274	57	97	0.001	0.001	NUM
fcis-29274	57	98	triglycerides	triglyceride	NOUN
fcis-29274	57	99	(	(	PUNCT
fcis-29274	57	100	mmol	mmol	NOUN
fcis-29274	57	101	/	/	SYM
fcis-29274	57	102	l	l	NOUN
fcis-29274	57	103	)	)	PUNCT
fcis-29274	57	104	1.25	1.25	NUM
fcis-29274	57	105	(	(	PUNCT
fcis-29274	57	106	1.13	1.13	NUM
fcis-29274	57	107	)	)	PUNCT
fcis-29274	57	108	1.44	1.44	NUM
fcis-29274	57	109	(	(	PUNCT
fcis-29274	57	110	1.43	1.43	NUM
fcis-29274	57	111	)	)	PUNCT
fcis-29274	57	112	0.019	0.019	NUM
fcis-29274	57	113	ldl	ldl	PROPN
fcis-29274	57	114	-	-	PUNCT
fcis-29274	57	115	c	c	PROPN
fcis-29274	57	116	(	(	PUNCT
fcis-29274	57	117	mmol	mmol	NOUN
fcis-29274	57	118	/	/	SYM
fcis-29274	57	119	l	l	NOUN
fcis-29274	57	120	)	)	PUNCT
fcis-29274	57	121	2.88	2.88	NUM
fcis-29274	57	122	(	(	PUNCT
fcis-29274	57	123	0.94	0.94	NUM
fcis-29274	57	124	)	)	PUNCT
fcis-29274	57	125	2.93	2.93	NUM
fcis-29274	57	126	(	(	PUNCT
fcis-29274	57	127	1.05	1.05	NUM
fcis-29274	57	128	)	)	PUNCT
fcis-29274	57	129	0.48	0.48	NUM
fcis-29274	57	130	all	all	DET
fcis-29274	57	131	participants	participant	NOUN
fcis-29274	57	132	in	in	ADP
fcis-29274	57	133	2017	2017	NUM
fcis-29274	57	134	-	-	SYM
fcis-29274	57	135	2018	2018	NUM
fcis-29274	57	136	(	(	PUNCT
fcis-29274	57	137	n=9254	n=9254	PROPN
fcis-29274	57	138	)	)	PUNCT
fcis-29274	57	139	adults	adult	NOUN
fcis-29274	57	140	(	(	PUNCT
fcis-29274	57	141	n=5856	n=5856	PROPN
fcis-29274	57	142	)	)	PUNCT
fcis-29274	57	143	included	include	VERB
fcis-29274	57	144	participants	participant	NOUN
fcis-29274	57	145	(	(	PUNCT
fcis-29274	57	146	n=5533	n=5533	PROPN
fcis-29274	57	147	)	)	PUNCT
fcis-29274	57	148	excluded	exclude	VERB
fcis-29274	57	149	participants	participant	NOUN
fcis-29274	57	150	aged	age	VERB
fcis-29274	57	151	<	<	X
fcis-29274	57	152	18	18	NUM
fcis-29274	57	153	(	(	PUNCT
fcis-29274	57	154	n=3398	n=3398	NOUN
fcis-29274	57	155	)	)	PUNCT
fcis-29274	57	156	exclude	exclude	VERB
fcis-29274	57	157	not	not	PART
fcis-29274	57	158	participating	participate	VERB
fcis-29274	57	159	in	in	ADP
fcis-29274	57	160	the	the	DET
fcis-29274	57	161	depression	depression	NOUN
fcis-29274	57	162	(	(	PUNCT
fcis-29274	57	163	n=323	n=323	NOUN
fcis-29274	57	164	)	)	PUNCT
fcis-29274	57	165	66	66	NUM
fcis-29274	57	166	this	this	DET
fcis-29274	57	167	paper	paper	NOUN
fcis-29274	57	168	selects	select	NOUN
fcis-29274	57	169	indicators	indicator	NOUN
fcis-29274	57	170	from	from	ADP
fcis-29274	57	171	four	four	NUM
fcis-29274	57	172	aspects	aspect	NOUN
fcis-29274	57	173	:	:	PUNCT
fcis-29274	57	174	demographics	demographic	NOUN
fcis-29274	57	175	,	,	PUNCT
fcis-29274	57	176	socioeconomic	socioeconomic	ADJ
fcis-29274	57	177	status	status	NOUN
fcis-29274	57	178	,	,	PUNCT
fcis-29274	57	179	lifestyle	lifestyle	NOUN
fcis-29274	57	180	,	,	PUNCT
fcis-29274	57	181	and	and	CCONJ
fcis-29274	57	182	some	some	DET
fcis-29274	57	183	disease	disease	NOUN
fcis-29274	57	184	indicators	indicator	NOUN
fcis-29274	57	185	.	.	PUNCT
fcis-29274	58	1	among	among	ADP
fcis-29274	58	2	them	they	PRON
fcis-29274	58	3	,	,	PUNCT
fcis-29274	58	4	demographic	demographic	ADJ
fcis-29274	58	5	variables	variable	NOUN
fcis-29274	58	6	are	be	AUX
fcis-29274	58	7	self	self	NOUN
fcis-29274	58	8	-	-	PUNCT
fcis-29274	58	9	reported	report	VERB
fcis-29274	58	10	,	,	PUNCT
fcis-29274	58	11	and	and	CCONJ
fcis-29274	58	12	the	the	DET
fcis-29274	58	13	body	body	NOUN
fcis-29274	58	14	mass	mass	NOUN
fcis-29274	58	15	index	index	NOUN
fcis-29274	58	16	(	(	PUNCT
fcis-29274	58	17	bmi	bmi	PROPN
fcis-29274	58	18	)	)	PUNCT
fcis-29274	58	19	is	be	AUX
fcis-29274	58	20	calculated	calculate	VERB
fcis-29274	58	21	by	by	ADP
fcis-29274	58	22	measuring	measure	VERB
fcis-29274	58	23	weight	weight	NOUN
fcis-29274	58	24	(	(	PUNCT
fcis-29274	58	25	kg	kg	NOUN
fcis-29274	58	26	)	)	PUNCT
fcis-29274	58	27	divided	divide	VERB
fcis-29274	58	28	by	by	ADP
fcis-29274	58	29	the	the	DET
fcis-29274	58	30	square	square	NOUN
fcis-29274	58	31	of	of	ADP
fcis-29274	58	32	height	height	NOUN
fcis-29274	58	33	(	(	PUNCT
fcis-29274	58	34	m	m	NOUN
fcis-29274	58	35	)	)	PUNCT
fcis-29274	58	36	;	;	PUNCT
fcis-29274	58	37	some	some	DET
fcis-29274	58	38	disease	disease	NOUN
fcis-29274	58	39	indicators	indicator	NOUN
fcis-29274	58	40	are	be	AUX
fcis-29274	58	41	selected	select	VERB
fcis-29274	58	42	from	from	ADP
fcis-29274	58	43	three	three	NUM
fcis-29274	58	44	aspects	aspect	NOUN
fcis-29274	58	45	:	:	PUNCT
fcis-29274	58	46	liver	liver	NOUN
fcis-29274	58	47	and	and	CCONJ
fcis-29274	58	48	kidney	kidney	NOUN
fcis-29274	58	49	,	,	PUNCT
fcis-29274	58	50	diabetes	diabetes	NOUN
fcis-29274	58	51	,	,	PUNCT
fcis-29274	58	52	and	and	CCONJ
fcis-29274	58	53	heart	heart	NOUN
fcis-29274	58	54	disease	disease	NOUN
fcis-29274	58	55	.	.	PUNCT
fcis-29274	59	1	the	the	DET
fcis-29274	59	2	specific	specific	ADJ
fcis-29274	59	3	variables	variable	NOUN
fcis-29274	59	4	and	and	CCONJ
fcis-29274	59	5	variable	variable	ADJ
fcis-29274	59	6	levels	level	NOUN
fcis-29274	59	7	are	be	AUX
fcis-29274	59	8	shown	show	VERB
fcis-29274	59	9	in	in	ADP
fcis-29274	59	10	table	table	NOUN
fcis-29274	59	11	1	1	NUM
fcis-29274	59	12	.	.	SYM
fcis-29274	60	1	3	3	X
fcis-29274	60	2	.	.	X
fcis-29274	60	3	data	datum	NOUN
fcis-29274	60	4	processing	processing	NOUN
fcis-29274	60	5	in	in	ADP
fcis-29274	60	6	order	order	NOUN
fcis-29274	60	7	to	to	PART
fcis-29274	60	8	solve	solve	VERB
fcis-29274	60	9	the	the	DET
fcis-29274	60	10	problem	problem	NOUN
fcis-29274	60	11	of	of	ADP
fcis-29274	60	12	poor	poor	ADJ
fcis-29274	60	13	quality	quality	NOUN
fcis-29274	60	14	of	of	ADP
fcis-29274	60	15	the	the	DET
fcis-29274	60	16	data	datum	NOUN
fcis-29274	60	17	set	set	NOUN
fcis-29274	60	18	,	,	PUNCT
fcis-29274	60	19	it	it	PRON
fcis-29274	60	20	needs	need	VERB
fcis-29274	60	21	to	to	PART
fcis-29274	60	22	be	be	AUX
fcis-29274	60	23	preprocessed	preprocesse	VERB
fcis-29274	60	24	accordingly	accordingly	ADV
fcis-29274	60	25	.	.	PUNCT
fcis-29274	61	1	in	in	ADP
fcis-29274	61	2	this	this	DET
fcis-29274	61	3	paper	paper	NOUN
fcis-29274	61	4	,	,	PUNCT
fcis-29274	61	5	the	the	DET
fcis-29274	61	6	data	datum	NOUN
fcis-29274	61	7	set	set	VERB
fcis-29274	61	8	is	be	AUX
fcis-29274	61	9	processed	process	VERB
fcis-29274	61	10	step	step	NOUN
fcis-29274	61	11	by	by	ADP
fcis-29274	61	12	step	step	NOUN
fcis-29274	61	13	as	as	SCONJ
fcis-29274	61	14	follows	follow	VERB
fcis-29274	61	15	:	:	PUNCT
fcis-29274	61	16	missing	miss	VERB
fcis-29274	61	17	value	value	NOUN
fcis-29274	61	18	processing	processing	NOUN
fcis-29274	61	19	,	,	PUNCT
fcis-29274	61	20	outlier	outlier	NOUN
fcis-29274	61	21	processing	processing	NOUN
fcis-29274	61	22	,	,	PUNCT
fcis-29274	61	23	data	datum	NOUN
fcis-29274	61	24	imbalance	imbalance	NOUN
fcis-29274	61	25	processing	processing	NOUN
fcis-29274	61	26	,	,	PUNCT
fcis-29274	61	27	feature	feature	NOUN
fcis-29274	61	28	selection	selection	NOUN
fcis-29274	61	29	,	,	PUNCT
fcis-29274	61	30	and	and	CCONJ
fcis-29274	61	31	finally	finally	ADV
fcis-29274	61	32	the	the	DET
fcis-29274	61	33	data	datum	NOUN
fcis-29274	61	34	that	that	PRON
fcis-29274	61	35	can	can	AUX
fcis-29274	61	36	be	be	AUX
fcis-29274	61	37	used	use	VERB
fcis-29274	61	38	by	by	ADP
fcis-29274	61	39	the	the	DET
fcis-29274	61	40	machine	machine	NOUN
fcis-29274	61	41	learning	learn	VERB
fcis-29274	61	42	algorithm	algorithm	NOUN
fcis-29274	61	43	is	be	AUX
fcis-29274	61	44	formed	form	VERB
fcis-29274	61	45	.	.	PUNCT
fcis-29274	62	1	the	the	DET
fcis-29274	62	2	processing	processing	NOUN
fcis-29274	62	3	of	of	ADP
fcis-29274	62	4	missing	miss	VERB
fcis-29274	62	5	values	value	NOUN
fcis-29274	62	6	in	in	ADP
fcis-29274	62	7	this	this	DET
fcis-29274	62	8	paper	paper	NOUN
fcis-29274	62	9	uses	use	VERB
fcis-29274	62	10	r	r	NOUN
fcis-29274	62	11	studio	studio	NOUN
fcis-29274	62	12	,	,	PUNCT
fcis-29274	62	13	and	and	CCONJ
fcis-29274	62	14	the	the	DET
fcis-29274	62	15	model	model	NOUN
fcis-29274	62	16	construction	construction	NOUN
fcis-29274	62	17	is	be	AUX
fcis-29274	62	18	analyzed	analyze	VERB
fcis-29274	62	19	and	and	CCONJ
fcis-29274	62	20	processed	process	VERB
fcis-29274	62	21	using	use	VERB
fcis-29274	62	22	scikit	scikit	NOUN
fcis-29274	62	23	-	-	PUNCT
fcis-29274	62	24	learn	learn	VERB
fcis-29274	62	25	(	(	PUNCT
fcis-29274	62	26	a	a	DET
fcis-29274	62	27	third	third	ADJ
fcis-29274	62	28	-	-	PUNCT
fcis-29274	62	29	party	party	NOUN
fcis-29274	62	30	python	python	NOUN
fcis-29274	62	31	package	package	NOUN
fcis-29274	62	32	)	)	PUNCT
fcis-29274	62	33	in	in	ADP
fcis-29274	62	34	jupyter	jupyter	NOUN
fcis-29274	62	35	-	-	PUNCT
fcis-29274	62	36	notebook	notebook	NOUN
fcis-29274	62	37	.	.	PUNCT
fcis-29274	63	1	3.1	3.1	NUM
fcis-29274	63	2	.	.	PUNCT
fcis-29274	63	3	feature	feature	NOUN
fcis-29274	63	4	selection	selection	NOUN
fcis-29274	63	5	recursive	recursive	ADJ
fcis-29274	63	6	feature	feature	NOUN
fcis-29274	63	7	elimination	elimination	NOUN
fcis-29274	63	8	(	(	PUNCT
fcis-29274	63	9	rfe	rfe	PROPN
fcis-29274	63	10	)	)	PUNCT
fcis-29274	63	11	is	be	AUX
fcis-29274	63	12	a	a	DET
fcis-29274	63	13	feature	feature	NOUN
fcis-29274	63	14	selection	selection	NOUN
fcis-29274	63	15	method	method	NOUN
fcis-29274	63	16	that	that	PRON
fcis-29274	63	17	aims	aim	VERB
fcis-29274	63	18	to	to	PART
fcis-29274	63	19	find	find	VERB
fcis-29274	63	20	the	the	DET
fcis-29274	63	21	most	most	ADV
fcis-29274	63	22	influential	influential	ADJ
fcis-29274	63	23	features	feature	NOUN
fcis-29274	63	24	by	by	ADP
fcis-29274	63	25	recursively	recursively	ADV
fcis-29274	63	26	reducing	reduce	VERB
fcis-29274	63	27	the	the	DET
fcis-29274	63	28	size	size	NOUN
fcis-29274	63	29	of	of	ADP
fcis-29274	63	30	the	the	DET
fcis-29274	63	31	feature	feature	NOUN
fcis-29274	63	32	set	set	NOUN
fcis-29274	63	33	.	.	PUNCT
fcis-29274	64	1	it	it	PRON
fcis-29274	64	2	is	be	AUX
fcis-29274	64	3	done	do	VERB
fcis-29274	64	4	by	by	ADP
fcis-29274	64	5	recursively	recursively	ADV
fcis-29274	64	6	building	build	VERB
fcis-29274	64	7	the	the	DET
fcis-29274	64	8	model	model	NOUN
fcis-29274	64	9	and	and	CCONJ
fcis-29274	64	10	selecting	select	VERB
fcis-29274	64	11	the	the	DET
fcis-29274	64	12	most	most	ADV
fcis-29274	64	13	important	important	ADJ
fcis-29274	64	14	features	feature	NOUN
fcis-29274	64	15	(	(	PUNCT
fcis-29274	64	16	based	base	VERB
fcis-29274	64	17	on	on	ADP
fcis-29274	64	18	weights	weight	NOUN
fcis-29274	64	19	)	)	PUNCT
fcis-29274	64	20	,	,	PUNCT
fcis-29274	64	21	removing	remove	VERB
fcis-29274	64	22	the	the	DET
fcis-29274	64	23	least	least	ADV
fcis-29274	64	24	important	important	ADJ
fcis-29274	64	25	features	feature	NOUN
fcis-29274	64	26	,	,	PUNCT
fcis-29274	64	27	and	and	CCONJ
fcis-29274	64	28	then	then	ADV
fcis-29274	64	29	repeating	repeat	VERB
fcis-29274	64	30	the	the	DET
fcis-29274	64	31	process	process	NOUN
fcis-29274	64	32	on	on	ADP
fcis-29274	64	33	the	the	DET
fcis-29274	64	34	remaining	remain	VERB
fcis-29274	64	35	features	feature	NOUN
fcis-29274	64	36	until	until	SCONJ
fcis-29274	64	37	a	a	DET
fcis-29274	64	38	preset	preset	ADJ
fcis-29274	64	39	number	number	NOUN
fcis-29274	64	40	of	of	ADP
fcis-29274	64	41	features	feature	NOUN
fcis-29274	64	42	is	be	AUX
fcis-29274	64	43	reached	reach	VERB
fcis-29274	64	44	or	or	CCONJ
fcis-29274	64	45	the	the	DET
fcis-29274	64	46	model	model	NOUN
fcis-29274	64	47	performance	performance	NOUN
fcis-29274	64	48	becomes	become	VERB
fcis-29274	64	49	stable	stable	ADJ
fcis-29274	64	50	.	.	PUNCT
fcis-29274	65	1	3.2	3.2	NUM
fcis-29274	65	2	.	.	PUNCT
fcis-29274	66	1	missing	miss	VERB
fcis-29274	66	2	value	value	NOUN
fcis-29274	66	3	handling	handle	VERB
fcis-29274	66	4	missing	miss	VERB
fcis-29274	66	5	data	datum	NOUN
fcis-29274	66	6	are	be	AUX
fcis-29274	66	7	present	present	ADJ
fcis-29274	66	8	in	in	ADP
fcis-29274	66	9	almost	almost	ADV
fcis-29274	66	10	every	every	PRON
fcis-29274	66	11	study	study	NOUN
fcis-29274	66	12	,	,	PUNCT
fcis-29274	66	13	and	and	CCONJ
fcis-29274	66	14	the	the	DET
fcis-29274	66	15	choice	choice	NOUN
fcis-29274	66	16	of	of	ADP
fcis-29274	66	17	method	method	NOUN
fcis-29274	66	18	to	to	PART
fcis-29274	66	19	handle	handle	VERB
fcis-29274	66	20	missing	missing	ADJ
fcis-29274	66	21	data	datum	NOUN
fcis-29274	66	22	is	be	AUX
fcis-29274	66	23	usually	usually	ADV
fcis-29274	66	24	not	not	PART
fcis-29274	66	25	important	important	ADJ
fcis-29274	66	26	when	when	SCONJ
fcis-29274	66	27	the	the	DET
fcis-29274	66	28	proportion	proportion	NOUN
fcis-29274	66	29	of	of	ADP
fcis-29274	66	30	missing	miss	VERB
fcis-29274	66	31	data	datum	NOUN
fcis-29274	66	32	is	be	AUX
fcis-29274	66	33	less	less	ADJ
fcis-29274	66	34	than	than	ADP
fcis-29274	66	35	5	5	NUM
fcis-29274	66	36	%	%	NOUN
fcis-29274	66	37	.	.	PUNCT
fcis-29274	67	1	however	however	ADV
fcis-29274	67	2	,	,	PUNCT
fcis-29274	67	3	in	in	ADP
fcis-29274	67	4	large	large	ADJ
fcis-29274	67	5	epidemiological	epidemiological	ADJ
fcis-29274	67	6	studies	study	NOUN
fcis-29274	67	7	,	,	PUNCT
fcis-29274	67	8	it	it	PRON
fcis-29274	67	9	is	be	AUX
fcis-29274	67	10	not	not	PART
fcis-29274	67	11	uncommon	uncommon	ADJ
fcis-29274	67	12	for	for	SCONJ
fcis-29274	67	13	the	the	DET
fcis-29274	67	14	proportion	proportion	NOUN
fcis-29274	67	15	of	of	ADP
fcis-29274	67	16	missing	miss	VERB
fcis-29274	67	17	data	datum	NOUN
fcis-29274	67	18	to	to	PART
fcis-29274	67	19	exceed	exceed	VERB
fcis-29274	67	20	this	this	DET
fcis-29274	67	21	percentage	percentage	NOUN
fcis-29274	67	22	,	,	PUNCT
fcis-29274	67	23	which	which	PRON
fcis-29274	67	24	may	may	AUX
fcis-29274	67	25	reduce	reduce	VERB
fcis-29274	67	26	statistical	statistical	ADJ
fcis-29274	67	27	power	power	NOUN
fcis-29274	67	28	,	,	PUNCT
fcis-29274	67	29	produce	produce	VERB
fcis-29274	67	30	biased	biased	ADJ
fcis-29274	67	31	parameters	parameter	NOUN
fcis-29274	67	32	,	,	PUNCT
fcis-29274	67	33	and	and	CCONJ
fcis-29274	67	34	increase	increase	VERB
fcis-29274	67	35	the	the	DET
fcis-29274	67	36	risk	risk	NOUN
fcis-29274	67	37	of	of	ADP
fcis-29274	67	38	type	type	NOUN
fcis-29274	67	39	i	i	PRON
fcis-29274	67	40	errors	error	NOUN
fcis-29274	67	41	.	.	PUNCT
fcis-29274	68	1	therefore	therefore	ADV
fcis-29274	68	2	,	,	PUNCT
fcis-29274	68	3	it	it	PRON
fcis-29274	68	4	is	be	AUX
fcis-29274	68	5	very	very	ADV
fcis-29274	68	6	important	important	ADJ
fcis-29274	68	7	to	to	PART
fcis-29274	68	8	handle	handle	VERB
fcis-29274	68	9	missing	miss	VERB
fcis-29274	68	10	data	datum	NOUN
fcis-29274	68	11	correctly	correctly	ADV
fcis-29274	68	12	[	[	X
fcis-29274	68	13	16	16	NUM
fcis-29274	68	14	]	]	PUNCT
fcis-29274	68	15	.	.	PUNCT
fcis-29274	69	1	multiple	multiple	ADJ
fcis-29274	69	2	imputation	imputation	NOUN
fcis-29274	69	3	is	be	AUX
fcis-29274	69	4	a	a	DET
fcis-29274	69	5	useful	useful	ADJ
fcis-29274	69	6	and	and	CCONJ
fcis-29274	69	7	flexible	flexible	ADJ
fcis-29274	69	8	strategy	strategy	NOUN
fcis-29274	69	9	to	to	PART
fcis-29274	69	10	address	address	VERB
fcis-29274	69	11	the	the	DET
fcis-29274	69	12	problem	problem	NOUN
fcis-29274	69	13	of	of	ADP
fcis-29274	69	14	missing	miss	VERB
fcis-29274	69	15	values	value	NOUN
fcis-29274	69	16	.	.	PUNCT
fcis-29274	70	1	multiple	multiple	ADJ
fcis-29274	70	2	imputation	imputation	NOUN
fcis-29274	70	3	is	be	AUX
fcis-29274	70	4	considered	consider	VERB
fcis-29274	70	5	when	when	SCONJ
fcis-29274	70	6	missingness	missingness	NOUN
fcis-29274	70	7	is	be	AUX
fcis-29274	70	8	not	not	PART
fcis-29274	70	9	completely	completely	ADV
fcis-29274	70	10	random	random	ADJ
fcis-29274	70	11	,	,	PUNCT
fcis-29274	70	12	depending	depend	VERB
fcis-29274	70	13	on	on	ADP
fcis-29274	70	14	the	the	DET
fcis-29274	70	15	observed	observed	ADJ
fcis-29274	70	16	or	or	CCONJ
fcis-29274	70	17	unobserved	unobserved	ADJ
fcis-29274	70	18	values	value	NOUN
fcis-29274	70	19	.	.	PUNCT
fcis-29274	71	1	however	however	ADV
fcis-29274	71	2	,	,	PUNCT
fcis-29274	71	3	this	this	DET
fcis-29274	71	4	method	method	NOUN
fcis-29274	71	5	is	be	AUX
fcis-29274	71	6	applicable	applicable	ADJ
fcis-29274	71	7	even	even	ADV
fcis-29274	71	8	if	if	SCONJ
fcis-29274	71	9	the	the	DET
fcis-29274	71	10	pattern	pattern	NOUN
fcis-29274	71	11	of	of	ADP
fcis-29274	71	12	missing	miss	VERB
fcis-29274	71	13	data	datum	NOUN
fcis-29274	71	14	is	be	AUX
fcis-29274	71	15	not	not	PART
fcis-29274	71	16	random	random	ADJ
fcis-29274	71	17	.	.	PUNCT
fcis-29274	72	1	first	first	ADV
fcis-29274	72	2	,	,	PUNCT
fcis-29274	72	3	we	we	PRON
fcis-29274	72	4	perform	perform	VERB
fcis-29274	72	5	missing	miss	VERB
fcis-29274	72	6	value	value	NOUN
fcis-29274	72	7	processing	processing	NOUN
fcis-29274	72	8	on	on	ADP
fcis-29274	72	9	the	the	DET
fcis-29274	72	10	dataset	dataset	NOUN
fcis-29274	72	11	to	to	PART
fcis-29274	72	12	detect	detect	VERB
fcis-29274	72	13	whether	whether	SCONJ
fcis-29274	72	14	there	there	PRON
fcis-29274	72	15	are	be	VERB
fcis-29274	72	16	missing	miss	VERB
fcis-29274	72	17	values	value	NOUN
fcis-29274	72	18	in	in	ADP
fcis-29274	72	19	the	the	DET
fcis-29274	72	20	data	datum	NOUN
fcis-29274	72	21	.	.	PUNCT
fcis-29274	73	1	the	the	DET
fcis-29274	73	2	missing	missing	ADJ
fcis-29274	73	3	type	type	NOUN
fcis-29274	73	4	analysis	analysis	NOUN
fcis-29274	73	5	showed	show	VERB
fcis-29274	73	6	that	that	SCONJ
fcis-29274	73	7	there	there	PRON
fcis-29274	73	8	were	be	VERB
fcis-29274	73	9	11,288	11,288	NUM
fcis-29274	73	10	missing	miss	VERB
fcis-29274	73	11	values	value	NOUN
fcis-29274	73	12	,	,	PUNCT
fcis-29274	73	13	of	of	ADP
fcis-29274	73	14	which	which	PRON
fcis-29274	73	15	12	12	NUM
fcis-29274	73	16	cases	case	NOUN
fcis-29274	73	17	were	be	AUX
fcis-29274	73	18	missing	miss	VERB
fcis-29274	73	19	for	for	ADP
fcis-29274	73	20	education	education	NOUN
fcis-29274	73	21	(	(	PUNCT
fcis-29274	73	22	dmdeduc	dmdeduc	NOUN
fcis-29274	73	23	)	)	PUNCT
fcis-29274	73	24	,	,	PUNCT
fcis-29274	73	25	with	with	ADP
fcis-29274	73	26	a	a	DET
fcis-29274	73	27	missing	missing	ADJ
fcis-29274	73	28	rate	rate	NOUN
fcis-29274	73	29	of	of	ADP
fcis-29274	73	30	0.2	0.2	NUM
fcis-29274	73	31	%	%	NOUN
fcis-29274	73	32	;	;	PUNCT
fcis-29274	73	33	273	273	NUM
fcis-29274	73	34	cases	case	NOUN
fcis-29274	73	35	were	be	AUX
fcis-29274	73	36	missing	miss	VERB
fcis-29274	73	37	for	for	ADP
fcis-29274	73	38	marital	marital	ADJ
fcis-29274	73	39	status	status	NOUN
fcis-29274	73	40	(	(	PUNCT
fcis-29274	73	41	dmdmartl	dmdmartl	NOUN
fcis-29274	73	42	)	)	PUNCT
fcis-29274	73	43	,	,	PUNCT
fcis-29274	73	44	with	with	ADP
fcis-29274	73	45	a	a	DET
fcis-29274	73	46	missing	missing	ADJ
fcis-29274	73	47	rate	rate	NOUN
fcis-29274	73	48	of	of	ADP
fcis-29274	73	49	4.9	4.9	NUM
fcis-29274	73	50	%	%	NOUN
fcis-29274	73	51	;	;	PUNCT
fcis-29274	73	52	734	734	NUM
fcis-29274	73	53	cases	case	NOUN
fcis-29274	73	54	were	be	AUX
fcis-29274	73	55	missing	miss	VERB
fcis-29274	73	56	for	for	ADP
fcis-29274	73	57	poverty	poverty	NOUN
fcis-29274	73	58	index	index	NOUN
fcis-29274	73	59	ratio	ratio	NOUN
fcis-29274	73	60	(	(	PUNCT
fcis-29274	73	61	indfmpir	indfmpir	PROPN
fcis-29274	73	62	)	)	PUNCT
fcis-29274	73	63	,	,	PUNCT
fcis-29274	73	64	with	with	ADP
fcis-29274	73	65	a	a	DET
fcis-29274	73	66	missing	missing	ADJ
fcis-29274	73	67	rate	rate	NOUN
fcis-29274	73	68	of	of	ADP
fcis-29274	73	69	13.3	13.3	NUM
fcis-29274	73	70	%	%	NOUN
fcis-29274	73	71	;	;	PUNCT
fcis-29274	73	72	52	52	NUM
fcis-29274	73	73	cases	case	NOUN
fcis-29274	73	74	were	be	AUX
fcis-29274	73	75	missing	miss	VERB
fcis-29274	73	76	for	for	ADP
fcis-29274	73	77	body	body	NOUN
fcis-29274	73	78	mass	mass	NOUN
fcis-29274	73	79	index	index	NOUN
fcis-29274	73	80	(	(	PUNCT
fcis-29274	73	81	bmi	bmi	PROPN
fcis-29274	73	82	)	)	PUNCT
fcis-29274	73	83	,	,	PUNCT
fcis-29274	73	84	with	with	ADP
fcis-29274	73	85	a	a	DET
fcis-29274	73	86	missing	missing	ADJ
fcis-29274	73	87	rate	rate	NOUN
fcis-29274	73	88	of	of	ADP
fcis-29274	73	89	4.1	4.1	NUM
fcis-29274	73	90	%	%	NOUN
fcis-29274	73	91	;	;	PUNCT
fcis-29274	73	92	3,300	3,300	NUM
fcis-29274	73	93	cases	case	NOUN
fcis-29274	73	94	were	be	AUX
fcis-29274	73	95	missing	miss	VERB
fcis-29274	73	96	for	for	ADP
fcis-29274	73	97	smoking	smoking	NOUN
fcis-29274	73	98	status	status	NOUN
fcis-29274	73	99	(	(	PUNCT
fcis-29274	73	100	issmoke	issmoke	NOUN
fcis-29274	73	101	)	)	PUNCT
fcis-29274	73	102	,	,	PUNCT
fcis-29274	73	103	with	with	ADP
fcis-29274	73	104	a	a	DET
fcis-29274	73	105	missing	missing	ADJ
fcis-29274	73	106	rate	rate	NOUN
fcis-29274	73	107	of	of	ADP
fcis-29274	73	108	59.6	59.6	NUM
fcis-29274	73	109	%	%	NOUN
fcis-29274	73	110	;	;	PUNCT
fcis-29274	73	111	406	406	NUM
fcis-29274	73	112	cases	case	NOUN
fcis-29274	73	113	were	be	AUX
fcis-29274	73	114	missing	miss	VERB
fcis-29274	73	115	for	for	ADP
fcis-29274	73	116	drinking	drinking	NOUN
fcis-29274	73	117	status	status	NOUN
fcis-29274	73	118	(	(	PUNCT
fcis-29274	73	119	alcohol	alcohol	NOUN
fcis-29274	73	120	)	)	PUNCT
fcis-29274	73	121	,	,	PUNCT
fcis-29274	73	122	with	with	ADP
fcis-29274	73	123	a	a	DET
fcis-29274	73	124	missing	missing	ADJ
fcis-29274	73	125	rate	rate	NOUN
fcis-29274	73	126	of	of	ADP
fcis-29274	73	127	7.3	7.3	NUM
fcis-29274	73	128	%	%	NOUN
fcis-29274	73	129	;	;	PUNCT
fcis-29274	73	130	146	146	NUM
fcis-29274	73	131	cases	case	NOUN
fcis-29274	73	132	were	be	AUX
fcis-29274	73	133	missing	miss	VERB
fcis-29274	73	134	for	for	ADP
fcis-29274	73	135	albumin	albumin	ADJ
fcis-29274	73	136	-	-	PUNCT
fcis-29274	73	137	creatinine	creatinine	NOUN
fcis-29274	73	138	ratio	ratio	NOUN
fcis-29274	73	139	(	(	PUNCT
fcis-29274	73	140	urdact	urdact	NOUN
fcis-29274	73	141	)	)	PUNCT
fcis-29274	73	142	,	,	PUNCT
fcis-29274	73	143	with	with	ADP
fcis-29274	73	144	a	a	DET
fcis-29274	73	145	missing	missing	ADJ
fcis-29274	73	146	rate	rate	NOUN
fcis-29274	73	147	of	of	ADP
fcis-29274	73	148	2.6	2.6	NUM
fcis-29274	73	149	%	%	NOUN
fcis-29274	73	150	;	;	PUNCT
fcis-29274	73	151	272	272	NUM
fcis-29274	73	152	cases	case	NOUN
fcis-29274	73	153	were	be	AUX
fcis-29274	73	154	missing	miss	VERB
fcis-29274	73	155	for	for	ADP
fcis-29274	73	156	glycohemoglobin	glycohemoglobin	PROPN
fcis-29274	73	157	(	(	PUNCT
fcis-29274	73	158	lbxgh	lbxgh	NOUN
fcis-29274	73	159	)	)	PUNCT
fcis-29274	73	160	,	,	PUNCT
fcis-29274	73	161	with	with	ADP
fcis-29274	73	162	a	a	DET
fcis-29274	73	163	missing	missing	ADJ
fcis-29274	73	164	rate	rate	NOUN
fcis-29274	73	165	of	of	ADP
fcis-29274	73	166	4.9	4.9	NUM
fcis-29274	73	167	%	%	NOUN
fcis-29274	73	168	;	;	PUNCT
fcis-29274	73	169	3040	3040	NUM
fcis-29274	73	170	cases	case	NOUN
fcis-29274	73	171	were	be	AUX
fcis-29274	73	172	missing	miss	VERB
fcis-29274	73	173	for	for	ADP
fcis-29274	73	174	triglycerides	triglyceride	NOUN
fcis-29274	73	175	(	(	PUNCT
fcis-29274	73	176	lbdtrsi	lbdtrsi	NOUN
fcis-29274	73	177	)	)	PUNCT
fcis-29274	73	178	,	,	PUNCT
fcis-29274	73	179	with	with	ADP
fcis-29274	73	180	a	a	DET
fcis-29274	73	181	missing	missing	ADJ
fcis-29274	73	182	rate	rate	NOUN
fcis-29274	73	183	of	of	ADP
fcis-29274	73	184	54.9	54.9	NUM
fcis-29274	73	185	%	%	NOUN
fcis-29274	73	186	;	;	PUNCT
fcis-29274	73	187	3047	3047	NUM
fcis-29274	73	188	cases	case	NOUN
fcis-29274	73	189	were	be	AUX
fcis-29274	73	190	missing	miss	VERB
fcis-29274	73	191	for	for	ADP
fcis-29274	73	192	low	low	ADJ
fcis-29274	73	193	-	-	PUNCT
fcis-29274	73	194	density	density	NOUN
fcis-29274	73	195	lipoprotein	lipoprotein	ADJ
fcis-29274	73	196	cholesterol	cholesterol	NOUN
fcis-29274	73	197	(	(	PUNCT
fcis-29274	73	198	lbdldnsi	lbdldnsi	PROPN
fcis-29274	73	199	)	)	PUNCT
fcis-29274	73	200	,	,	PUNCT
fcis-29274	73	201	with	with	ADP
fcis-29274	73	202	a	a	DET
fcis-29274	73	203	missing	missing	ADJ
fcis-29274	73	204	rate	rate	NOUN
fcis-29274	73	205	of	of	ADP
fcis-29274	73	206	55.1	55.1	NUM
fcis-29274	73	207	%	%	NOUN
fcis-29274	73	208	.	.	PUNCT
fcis-29274	74	1	in	in	ADP
fcis-29274	74	2	order	order	NOUN
fcis-29274	74	3	to	to	PART
fcis-29274	74	4	observe	observe	VERB
fcis-29274	74	5	the	the	DET
fcis-29274	74	6	missing	miss	VERB
fcis-29274	74	7	data	datum	NOUN
fcis-29274	74	8	of	of	ADP
fcis-29274	74	9	each	each	DET
fcis-29274	74	10	variable	variable	NOUN
fcis-29274	74	11	more	more	ADV
fcis-29274	74	12	intuitively	intuitively	ADV
fcis-29274	74	13	,	,	PUNCT
fcis-29274	74	14	we	we	PRON
fcis-29274	74	15	used	use	VERB
fcis-29274	74	16	the	the	DET
fcis-29274	74	17	vim	vim	PROPN
fcis-29274	74	18	package	package	NOUN
fcis-29274	74	19	in	in	ADP
fcis-29274	74	20	r	r	NOUN
fcis-29274	74	21	studio	studio	NOUN
fcis-29274	74	22	to	to	PART
fcis-29274	74	23	draw	draw	VERB
fcis-29274	74	24	a	a	DET
fcis-29274	74	25	visualization	visualization	NOUN
fcis-29274	74	26	of	of	ADP
fcis-29274	74	27	missing	miss	VERB
fcis-29274	74	28	data	datum	NOUN
fcis-29274	74	29	.	.	PUNCT
fcis-29274	75	1	the	the	DET
fcis-29274	75	2	left	left	ADJ
fcis-29274	75	3	side	side	NOUN
fcis-29274	75	4	is	be	AUX
fcis-29274	75	5	a	a	DET
fcis-29274	75	6	histogram	histogram	NOUN
fcis-29274	75	7	of	of	ADP
fcis-29274	75	8	the	the	DET
fcis-29274	75	9	missing	miss	VERB
fcis-29274	75	10	data	data	NOUN
fcis-29274	75	11	ratio	ratio	NOUN
fcis-29274	75	12	.	.	PUNCT
fcis-29274	76	1	it	it	PRON
fcis-29274	76	2	can	can	AUX
fcis-29274	76	3	be	be	AUX
fcis-29274	76	4	seen	see	VERB
fcis-29274	76	5	that	that	SCONJ
fcis-29274	76	6	the	the	DET
fcis-29274	76	7	most	most	ADV
fcis-29274	76	8	missing	missing	ADJ
fcis-29274	76	9	values	value	NOUN
fcis-29274	76	10	are	be	AUX
fcis-29274	76	11	in	in	ADP
fcis-29274	76	12	the	the	DET
fcis-29274	76	13	attraction	attraction	NOUN
fcis-29274	76	14	condition	condition	NOUN
fcis-29274	76	15	,	,	PUNCT
fcis-29274	76	16	followed	follow	VERB
fcis-29274	76	17	by	by	ADP
fcis-29274	76	18	triglycerides	triglyceride	NOUN
fcis-29274	76	19	and	and	CCONJ
fcis-29274	76	20	low	low	ADJ
fcis-29274	76	21	-	-	PUNCT
fcis-29274	76	22	density	density	NOUN
fcis-29274	76	23	lipoprotein	lipoprotein	ADJ
fcis-29274	76	24	cholesterol	cholesterol	NOUN
fcis-29274	76	25	;	;	PUNCT
fcis-29274	76	26	the	the	DET
fcis-29274	76	27	right	right	ADJ
fcis-29274	76	28	side	side	NOUN
fcis-29274	76	29	is	be	AUX
fcis-29274	76	30	a	a	DET
fcis-29274	76	31	missing	missing	ADJ
fcis-29274	76	32	data	data	NOUN
fcis-29274	76	33	pattern	pattern	NOUN
fcis-29274	76	34	diagram	diagram	NOUN
fcis-29274	76	35	,	,	PUNCT
fcis-29274	76	36	blue	blue	NOUN
fcis-29274	76	37	represents	represent	VERB
fcis-29274	76	38	missing	miss	VERB
fcis-29274	76	39	values	value	NOUN
fcis-29274	76	40	,	,	PUNCT
fcis-29274	76	41	and	and	CCONJ
fcis-29274	76	42	red	red	NOUN
fcis-29274	76	43	represents	represent	VERB
fcis-29274	76	44	non	non	ADJ
fcis-29274	76	45	-	-	ADJ
fcis-29274	76	46	missing	missing	ADJ
fcis-29274	76	47	values	value	NOUN
fcis-29274	76	48	.	.	PUNCT
fcis-29274	77	1	the	the	DET
fcis-29274	77	2	specific	specific	ADJ
fcis-29274	77	3	visualization	visualization	NOUN
fcis-29274	77	4	is	be	AUX
fcis-29274	77	5	shown	show	VERB
fcis-29274	77	6	in	in	ADP
fcis-29274	77	7	figure	figure	NOUN
fcis-29274	77	8	2	2	NUM
fcis-29274	77	9	:	:	PUNCT
fcis-29274	77	10	figure	figure	NOUN
fcis-29274	77	11	2	2	NUM
fcis-29274	77	12	.	.	PUNCT
fcis-29274	77	13	missing	miss	VERB
fcis-29274	77	14	data	data	NOUN
fcis-29274	77	15	visualization	visualization	NOUN
fcis-29274	77	16	for	for	ADP
fcis-29274	77	17	missing	miss	VERB
fcis-29274	77	18	data	datum	NOUN
fcis-29274	77	19	,	,	PUNCT
fcis-29274	77	20	this	this	DET
fcis-29274	77	21	paper	paper	NOUN
fcis-29274	77	22	adopts	adopt	VERB
fcis-29274	77	23	the	the	DET
fcis-29274	77	24	random	random	ADJ
fcis-29274	77	25	forest	forest	NOUN
fcis-29274	77	26	method	method	NOUN
fcis-29274	77	27	in	in	ADP
fcis-29274	77	28	the	the	DET
fcis-29274	77	29	multiple	multiple	ADJ
fcis-29274	77	30	imputation	imputation	NOUN
fcis-29274	77	31	method	method	NOUN
fcis-29274	77	32	to	to	PART
fcis-29274	77	33	interpolate	interpolate	VERB
fcis-29274	77	34	the	the	DET
fcis-29274	77	35	missing	miss	VERB
fcis-29274	77	36	data	datum	NOUN
fcis-29274	77	37	5	5	NUM
fcis-29274	77	38	times	time	NOUN
fcis-29274	77	39	,	,	PUNCT
fcis-29274	77	40	and	and	CCONJ
fcis-29274	77	41	finally	finally	ADV
fcis-29274	77	42	selects	select	VERB
fcis-29274	77	43	the	the	DET
fcis-29274	77	44	set	set	NOUN
fcis-29274	77	45	of	of	ADP
fcis-29274	77	46	data	datum	NOUN
fcis-29274	77	47	with	with	ADP
fcis-29274	77	48	the	the	DET
fcis-29274	77	49	smallest	small	ADJ
fcis-29274	77	50	aic	aic	PROPN
fcis-29274	77	51	.	.	PUNCT
fcis-29274	78	1	next	next	ADV
fcis-29274	78	2	,	,	PUNCT
fcis-29274	78	3	we	we	PRON
fcis-29274	78	4	check	check	VERB
fcis-29274	78	5	the	the	DET
fcis-29274	78	6	results	result	NOUN
fcis-29274	78	7	after	after	ADP
fcis-29274	78	8	interpolation	interpolation	NOUN
fcis-29274	78	9	.	.	PUNCT
fcis-29274	79	1	we	we	PRON
fcis-29274	79	2	use	use	VERB
fcis-29274	79	3	r	r	NOUN
fcis-29274	79	4	studio	studio	NOUN
fcis-29274	79	5	to	to	PART
fcis-29274	79	6	draw	draw	VERB
fcis-29274	79	7	a	a	DET
fcis-29274	79	8	density	density	NOUN
fcis-29274	79	9	map	map	NOUN
fcis-29274	79	10	to	to	PART
fcis-29274	79	11	view	view	VERB
fcis-29274	79	12	the	the	DET
fcis-29274	79	13	distribution	distribution	NOUN
fcis-29274	79	14	of	of	ADP
fcis-29274	79	15	the	the	DET
fcis-29274	79	16	interpolated	interpolate	VERB
fcis-29274	79	17	data	datum	NOUN
fcis-29274	79	18	set	set	VERB
fcis-29274	79	19	and	and	CCONJ
fcis-29274	79	20	the	the	DET
fcis-29274	79	21	observed	observed	ADJ
fcis-29274	79	22	data	datum	NOUN
fcis-29274	79	23	.	.	PUNCT
fcis-29274	80	1	as	as	SCONJ
fcis-29274	80	2	shown	show	VERB
fcis-29274	80	3	in	in	ADP
fcis-29274	80	4	figure	figure	NOUN
fcis-29274	80	5	3	3	NUM
fcis-29274	80	6	,	,	PUNCT
fcis-29274	80	7	the	the	DET
fcis-29274	80	8	interpolation	interpolation	NOUN
fcis-29274	80	9	result	result	NOUN
fcis-29274	80	10	is	be	AUX
fcis-29274	80	11	similar	similar	ADJ
fcis-29274	80	12	to	to	ADP
fcis-29274	80	13	the	the	DET
fcis-29274	80	14	distribution	distribution	NOUN
fcis-29274	80	15	type	type	NOUN
fcis-29274	80	16	of	of	ADP
fcis-29274	80	17	the	the	DET
fcis-29274	80	18	original	original	ADJ
fcis-29274	80	19	data	datum	NOUN
fcis-29274	80	20	,	,	PUNCT
fcis-29274	80	21	so	so	CCONJ
fcis-29274	80	22	the	the	DET
fcis-29274	80	23	interpolated	interpolate	VERB
fcis-29274	80	24	data	data	NOUN
fcis-29274	80	25	is	be	AUX
fcis-29274	80	26	of	of	ADP
fcis-29274	80	27	good	good	ADJ
fcis-29274	80	28	quality	quality	NOUN
fcis-29274	80	29	and	and	CCONJ
fcis-29274	80	30	can	can	AUX
fcis-29274	80	31	be	be	AUX
fcis-29274	80	32	used	use	VERB
fcis-29274	80	33	for	for	ADP
fcis-29274	80	34	subsequent	subsequent	ADJ
fcis-29274	80	35	analysis	analysis	NOUN
fcis-29274	80	36	.	.	PUNCT
fcis-29274	81	1	67	67	NUM
fcis-29274	81	2	figure	figure	NOUN
fcis-29274	81	3	3	3	NUM
fcis-29274	81	4	.	.	PUNCT
fcis-29274	81	5	density	density	NOUN
fcis-29274	81	6	plot	plot	NOUN
fcis-29274	81	7	of	of	ADP
fcis-29274	81	8	the	the	DET
fcis-29274	81	9	dataset	dataset	NOUN
fcis-29274	81	10	after	after	ADP
fcis-29274	81	11	interpolation	interpolation	NOUN
fcis-29274	81	12	3.3	3.3	NUM
fcis-29274	81	13	.	.	PUNCT
fcis-29274	82	1	data	datum	NOUN
fcis-29274	82	2	imbalance	imbalance	NOUN
fcis-29274	82	3	handling	handling	NOUN
fcis-29274	82	4	next	next	ADV
fcis-29274	82	5	,	,	PUNCT
fcis-29274	82	6	we	we	PRON
fcis-29274	82	7	will	will	AUX
fcis-29274	82	8	deal	deal	VERB
fcis-29274	82	9	with	with	ADP
fcis-29274	82	10	the	the	DET
fcis-29274	82	11	problem	problem	NOUN
fcis-29274	82	12	of	of	ADP
fcis-29274	82	13	data	datum	NOUN
fcis-29274	82	14	imbalance	imbalance	NOUN
fcis-29274	82	15	.	.	PUNCT
fcis-29274	83	1	the	the	DET
fcis-29274	83	2	problem	problem	NOUN
fcis-29274	83	3	of	of	ADP
fcis-29274	83	4	data	datum	NOUN
fcis-29274	83	5	imbalance	imbalance	NOUN
fcis-29274	83	6	is	be	AUX
fcis-29274	83	7	very	very	ADV
fcis-29274	83	8	common	common	ADJ
fcis-29274	83	9	in	in	ADP
fcis-29274	83	10	data	datum	NOUN
fcis-29274	83	11	mining	mining	NOUN
fcis-29274	83	12	,	,	PUNCT
fcis-29274	83	13	and	and	CCONJ
fcis-29274	83	14	is	be	AUX
fcis-29274	83	15	often	often	ADV
fcis-29274	83	16	seen	see	VERB
fcis-29274	83	17	in	in	ADP
fcis-29274	83	18	anti	anti	ADJ
fcis-29274	83	19	-	-	ADJ
fcis-29274	83	20	fraud	fraud	ADJ
fcis-29274	83	21	detection	detection	NOUN
fcis-29274	83	22	,	,	PUNCT
fcis-29274	83	23	medical	medical	ADJ
fcis-29274	83	24	diagnosis	diagnosis	NOUN
fcis-29274	83	25	,	,	PUNCT
fcis-29274	83	26	oil	oil	NOUN
fcis-29274	83	27	spill	spill	NOUN
fcis-29274	83	28	detection	detection	NOUN
fcis-29274	83	29	,	,	PUNCT
fcis-29274	83	30	facial	facial	ADJ
fcis-29274	83	31	recognition	recognition	NOUN
fcis-29274	83	32	,	,	PUNCT
fcis-29274	83	33	outlier	outlier	NOUN
fcis-29274	83	34	detection	detection	NOUN
fcis-29274	83	35	,	,	PUNCT
fcis-29274	83	36	etc	etc	X
fcis-29274	83	37	.	.	X
fcis-29274	84	1	this	this	PRON
fcis-29274	84	2	is	be	AUX
fcis-29274	84	3	caused	cause	VERB
fcis-29274	84	4	by	by	ADP
fcis-29274	84	5	the	the	DET
fcis-29274	84	6	skewed	skewed	ADJ
fcis-29274	84	7	nature	nature	NOUN
fcis-29274	84	8	of	of	ADP
fcis-29274	84	9	the	the	DET
fcis-29274	84	10	data	datum	NOUN
fcis-29274	84	11	,	,	PUNCT
fcis-29274	84	12	and	and	CCONJ
fcis-29274	84	13	these	these	DET
fcis-29274	84	14	problems	problem	NOUN
fcis-29274	84	15	will	will	AUX
fcis-29274	84	16	affect	affect	VERB
fcis-29274	84	17	the	the	DET
fcis-29274	84	18	process	process	NOUN
fcis-29274	84	19	of	of	ADP
fcis-29274	84	20	machine	machine	NOUN
fcis-29274	84	21	learning	learning	NOUN
fcis-29274	84	22	in	in	ADP
fcis-29274	84	23	the	the	DET
fcis-29274	84	24	classification	classification	NOUN
fcis-29274	84	25	process	process	NOUN
fcis-29274	84	26	.	.	PUNCT
fcis-29274	85	1	in	in	ADP
fcis-29274	85	2	such	such	DET
fcis-29274	85	3	a	a	DET
fcis-29274	85	4	problem	problem	NOUN
fcis-29274	85	5	,	,	PUNCT
fcis-29274	85	6	the	the	DET
fcis-29274	85	7	classes	class	NOUN
fcis-29274	85	8	have	have	VERB
fcis-29274	85	9	different	different	ADJ
fcis-29274	85	10	proportions	proportion	NOUN
fcis-29274	85	11	of	of	ADP
fcis-29274	85	12	samples	sample	NOUN
fcis-29274	85	13	,	,	PUNCT
fcis-29274	85	14	where	where	SCONJ
fcis-29274	85	15	a	a	DET
fcis-29274	85	16	large	large	ADJ
fcis-29274	85	17	number	number	NOUN
fcis-29274	85	18	of	of	ADP
fcis-29274	85	19	samples	sample	NOUN
fcis-29274	85	20	belong	belong	VERB
fcis-29274	85	21	to	to	ADP
fcis-29274	85	22	one	one	NUM
fcis-29274	85	23	class	class	NOUN
fcis-29274	85	24	and	and	CCONJ
fcis-29274	85	25	a	a	DET
fcis-29274	85	26	smaller	small	ADJ
fcis-29274	85	27	number	number	NOUN
fcis-29274	85	28	of	of	ADP
fcis-29274	85	29	samples	sample	NOUN
fcis-29274	85	30	in	in	ADP
fcis-29274	85	31	the	the	DET
fcis-29274	85	32	other	other	ADJ
fcis-29274	85	33	class	class	NOUN
fcis-29274	85	34	,	,	PUNCT
fcis-29274	85	35	which	which	PRON
fcis-29274	85	36	is	be	AUX
fcis-29274	85	37	usually	usually	ADV
fcis-29274	85	38	the	the	DET
fcis-29274	85	39	basic	basic	ADJ
fcis-29274	85	40	class	class	NOUN
fcis-29274	85	41	,	,	PUNCT
fcis-29274	85	42	but	but	CCONJ
fcis-29274	85	43	unfortunately	unfortunately	ADV
fcis-29274	85	44	is	be	AUX
fcis-29274	85	45	misclassified	misclassifie	VERB
fcis-29274	85	46	by	by	ADP
fcis-29274	85	47	many	many	ADJ
fcis-29274	85	48	classifiers	classifier	NOUN
fcis-29274	85	49	.	.	PUNCT
fcis-29274	86	1	to	to	ADP
fcis-29274	86	2	date	date	NOUN
fcis-29274	86	3	,	,	PUNCT
fcis-29274	86	4	a	a	DET
fcis-29274	86	5	large	large	ADJ
fcis-29274	86	6	number	number	NOUN
fcis-29274	86	7	of	of	ADP
fcis-29274	86	8	studies	study	NOUN
fcis-29274	86	9	have	have	AUX
fcis-29274	86	10	been	be	AUX
fcis-29274	86	11	conducted	conduct	VERB
fcis-29274	86	12	to	to	PART
fcis-29274	86	13	implement	implement	VERB
fcis-29274	86	14	different	different	ADJ
fcis-29274	86	15	technologies	technology	NOUN
fcis-29274	86	16	and	and	CCONJ
fcis-29274	86	17	methods	method	NOUN
fcis-29274	86	18	to	to	PART
fcis-29274	86	19	solve	solve	VERB
fcis-29274	86	20	the	the	DET
fcis-29274	86	21	problem	problem	NOUN
fcis-29274	86	22	of	of	ADP
fcis-29274	86	23	unbalanced	unbalanced	ADJ
fcis-29274	86	24	data	datum	NOUN
fcis-29274	87	1	[	[	X
fcis-29274	87	2	17	17	NUM
fcis-29274	87	3	]	]	PUNCT
fcis-29274	87	4	.	.	PUNCT
fcis-29274	88	1	the	the	DET
fcis-29274	88	2	most	most	ADV
fcis-29274	88	3	commonly	commonly	ADV
fcis-29274	88	4	used	use	VERB
fcis-29274	88	5	methods	method	NOUN
fcis-29274	88	6	are	be	AUX
fcis-29274	88	7	undersampling	undersample	VERB
fcis-29274	88	8	and	and	CCONJ
fcis-29274	88	9	oversampling	oversampling	ADJ
fcis-29274	88	10	.	.	PUNCT
fcis-29274	89	1	as	as	SCONJ
fcis-29274	89	2	shown	show	VERB
fcis-29274	89	3	in	in	ADP
fcis-29274	89	4	figure	figure	NOUN
fcis-29274	89	5	6	6	NUM
fcis-29274	89	6	,	,	PUNCT
fcis-29274	89	7	the	the	DET
fcis-29274	89	8	number	number	NOUN
fcis-29274	89	9	of	of	ADP
fcis-29274	89	10	non	non	ADJ
fcis-29274	89	11	-	-	ADJ
fcis-29274	89	12	depressed	depressed	ADJ
fcis-29274	89	13	patients	patient	NOUN
fcis-29274	89	14	is	be	AUX
fcis-29274	89	15	much	much	ADV
fcis-29274	89	16	less	less	ADJ
fcis-29274	89	17	than	than	ADP
fcis-29274	89	18	that	that	PRON
fcis-29274	89	19	of	of	ADP
fcis-29274	89	20	depressed	depressed	ADJ
fcis-29274	89	21	patients	patient	NOUN
fcis-29274	89	22	,	,	PUNCT
fcis-29274	89	23	and	and	CCONJ
fcis-29274	89	24	the	the	DET
fcis-29274	89	25	proportion	proportion	NOUN
fcis-29274	89	26	of	of	ADP
fcis-29274	89	27	the	the	DET
fcis-29274	89	28	minority	minority	NOUN
fcis-29274	89	29	class	class	NOUN
fcis-29274	89	30	is	be	AUX
fcis-29274	89	31	8.34	8.34	NUM
fcis-29274	89	32	%	%	NOUN
fcis-29274	89	33	,	,	PUNCT
fcis-29274	89	34	which	which	PRON
fcis-29274	89	35	is	be	AUX
fcis-29274	89	36	a	a	DET
fcis-29274	89	37	moderately	moderately	ADV
fcis-29274	89	38	unbalanced	unbalanced	ADJ
fcis-29274	89	39	sample	sample	NOUN
fcis-29274	89	40	.	.	PUNCT
fcis-29274	90	1	therefore	therefore	ADV
fcis-29274	90	2	,	,	PUNCT
fcis-29274	90	3	this	this	DET
fcis-29274	90	4	paper	paper	NOUN
fcis-29274	90	5	uses	use	VERB
fcis-29274	90	6	the	the	DET
fcis-29274	90	7	smote	smote	ADJ
fcis-29274	90	8	oversampling	oversampling	ADJ
fcis-29274	90	9	method	method	NOUN
fcis-29274	90	10	for	for	ADP
fcis-29274	90	11	processing	processing	NOUN
fcis-29274	90	12	.	.	PUNCT
fcis-29274	91	1	figure	figure	NOUN
fcis-29274	91	2	4	4	NUM
fcis-29274	91	3	.	.	PUNCT
fcis-29274	91	4	bar	bar	NOUN
fcis-29274	91	5	graph	graph	NOUN
fcis-29274	91	6	of	of	ADP
fcis-29274	91	7	people	people	NOUN
fcis-29274	91	8	suffering	suffer	VERB
fcis-29274	91	9	from	from	ADP
fcis-29274	91	10	depression	depression	NOUN
fcis-29274	91	11	3.4	3.4	NUM
fcis-29274	91	12	.	.	PUNCT
fcis-29274	92	1	data	datum	NOUN
fcis-29274	92	2	standardization	standardization	NOUN
fcis-29274	92	3	after	after	ADP
fcis-29274	92	4	exploring	explore	VERB
fcis-29274	92	5	the	the	DET
fcis-29274	92	6	dataset	dataset	NOUN
fcis-29274	92	7	,	,	PUNCT
fcis-29274	92	8	this	this	DET
fcis-29274	92	9	article	article	NOUN
fcis-29274	92	10	found	find	VERB
fcis-29274	92	11	that	that	SCONJ
fcis-29274	92	12	it	it	PRON
fcis-29274	92	13	is	be	AUX
fcis-29274	92	14	necessary	necessary	ADJ
fcis-29274	92	15	to	to	PART
fcis-29274	92	16	convert	convert	VERB
fcis-29274	92	17	some	some	DET
fcis-29274	92	18	categorical	categorical	ADJ
fcis-29274	92	19	variables	variable	NOUN
fcis-29274	92	20	into	into	ADP
fcis-29274	92	21	dummy	dummy	ADJ
fcis-29274	92	22	variables	variable	NOUN
fcis-29274	92	23	and	and	CCONJ
fcis-29274	92	24	scale	scale	VERB
fcis-29274	92	25	all	all	DET
fcis-29274	92	26	values	value	NOUN
fcis-29274	92	27	before	before	ADP
fcis-29274	92	28	training	train	VERB
fcis-29274	92	29	the	the	DET
fcis-29274	92	30	machine	machine	NOUN
fcis-29274	92	31	learning	learn	VERB
fcis-29274	92	32	model	model	NOUN
fcis-29274	92	33	.	.	PUNCT
fcis-29274	93	1	first	first	ADV
fcis-29274	93	2	,	,	PUNCT
fcis-29274	93	3	we	we	PRON
fcis-29274	93	4	used	use	VERB
fcis-29274	93	5	the	the	DET
fcis-29274	93	6	get_dummies	get_dummie	NOUN
fcis-29274	93	7	method	method	NOUN
fcis-29274	93	8	in	in	ADP
fcis-29274	93	9	python	python	NOUN
fcis-29274	93	10	to	to	PART
fcis-29274	93	11	create	create	VERB
fcis-29274	93	12	dummy	dummy	ADJ
fcis-29274	93	13	columns	column	NOUN
fcis-29274	93	14	for	for	ADP
fcis-29274	93	15	categorical	categorical	ADJ
fcis-29274	93	16	variables	variable	NOUN
fcis-29274	93	17	.	.	PUNCT
fcis-29274	94	1	then	then	ADV
fcis-29274	94	2	,	,	PUNCT
fcis-29274	94	3	in	in	ADP
fcis-29274	94	4	order	order	NOUN
fcis-29274	94	5	to	to	PART
fcis-29274	94	6	eliminate	eliminate	VERB
fcis-29274	94	7	the	the	DET
fcis-29274	94	8	differences	difference	NOUN
fcis-29274	94	9	in	in	ADP
fcis-29274	94	10	dimensions	dimension	NOUN
fcis-29274	94	11	and	and	CCONJ
fcis-29274	94	12	value	value	NOUN
fcis-29274	94	13	ranges	range	VERB
fcis-29274	94	14	between	between	ADP
fcis-29274	94	15	features	feature	NOUN
fcis-29274	94	16	,	,	PUNCT
fcis-29274	94	17	the	the	DET
fcis-29274	94	18	data	datum	NOUN
fcis-29274	94	19	needs	need	VERB
fcis-29274	94	20	to	to	PART
fcis-29274	94	21	be	be	AUX
fcis-29274	94	22	standardized	standardize	VERB
fcis-29274	94	23	.	.	PUNCT
fcis-29274	95	1	data	datum	NOUN
fcis-29274	95	2	normalization	normalization	NOUN
fcis-29274	95	3	is	be	AUX
fcis-29274	95	4	to	to	PART
fcis-29274	95	5	rescale	rescale	VERB
fcis-29274	95	6	the	the	DET
fcis-29274	95	7	sample	sample	NOUN
fcis-29274	95	8	data	datum	NOUN
fcis-29274	95	9	according	accord	VERB
fcis-29274	95	10	to	to	ADP
fcis-29274	95	11	a	a	DET
fcis-29274	95	12	certain	certain	ADJ
fcis-29274	95	13	calculation	calculation	NOUN
fcis-29274	95	14	method	method	NOUN
fcis-29274	95	15	so	so	SCONJ
fcis-29274	95	16	that	that	SCONJ
fcis-29274	95	17	the	the	DET
fcis-29274	95	18	value	value	NOUN
fcis-29274	95	19	changes	change	VERB
fcis-29274	95	20	in	in	ADP
fcis-29274	95	21	a	a	DET
fcis-29274	95	22	certain	certain	ADJ
fcis-29274	95	23	range	range	NOUN
fcis-29274	95	24	.	.	PUNCT
fcis-29274	96	1	data	datum	NOUN
fcis-29274	96	2	normalization	normalization	NOUN
fcis-29274	96	3	can	can	AUX
fcis-29274	96	4	speed	speed	VERB
fcis-29274	96	5	up	up	ADP
fcis-29274	96	6	the	the	DET
fcis-29274	96	7	algorithm	algorithm	NOUN
fcis-29274	96	8	to	to	PART
fcis-29274	96	9	find	find	VERB
fcis-29274	96	10	the	the	DET
fcis-29274	96	11	optimal	optimal	ADJ
fcis-29274	96	12	value	value	NOUN
fcis-29274	96	13	.	.	PUNCT
fcis-29274	97	1	we	we	PRON
fcis-29274	97	2	use	use	VERB
fcis-29274	97	3	the	the	DET
fcis-29274	97	4	standardscaler	standardscaler	NOUN
fcis-29274	97	5	method	method	NOUN
fcis-29274	97	6	in	in	ADP
fcis-29274	97	7	sklearn.procession	sklearn.procession	NOUN
fcis-29274	97	8	to	to	PART
fcis-29274	97	9	process	process	VERB
fcis-29274	97	10	the	the	DET
fcis-29274	97	11	data	datum	NOUN
fcis-29274	97	12	and	and	CCONJ
fcis-29274	97	13	standardize	standardize	VERB
fcis-29274	97	14	the	the	DET
fcis-29274	97	15	data	datum	NOUN
fcis-29274	97	16	to	to	PART
fcis-29274	97	17	fall	fall	VERB
fcis-29274	97	18	in	in	ADP
fcis-29274	97	19	the	the	DET
fcis-29274	97	20	[	[	X
fcis-29274	97	21	-1,1	-1,1	NOUN
fcis-29274	97	22	]	]	X
fcis-29274	97	23	interval	interval	NOUN
fcis-29274	97	24	.	.	PUNCT
fcis-29274	98	1	4	4	X
fcis-29274	98	2	.	.	X
fcis-29274	98	3	construction	construction	NOUN
fcis-29274	98	4	of	of	ADP
fcis-29274	98	5	depression	depression	NOUN
fcis-29274	98	6	prediction	prediction	NOUN
fcis-29274	98	7	model	model	NOUN
fcis-29274	98	8	according	accord	VERB
fcis-29274	98	9	to	to	ADP
fcis-29274	98	10	the	the	DET
fcis-29274	98	11	existing	exist	VERB
fcis-29274	98	12	literature	literature	NOUN
fcis-29274	98	13	,	,	PUNCT
fcis-29274	98	14	machine	machine	NOUN
fcis-29274	98	15	learning	learning	NOUN
fcis-29274	98	16	algorithms	algorithm	NOUN
fcis-29274	98	17	are	be	AUX
fcis-29274	98	18	used	use	VERB
fcis-29274	98	19	to	to	PART
fcis-29274	98	20	predict	predict	VERB
fcis-29274	98	21	the	the	DET
fcis-29274	98	22	risk	risk	NOUN
fcis-29274	98	23	of	of	ADP
fcis-29274	98	24	depression	depression	NOUN
fcis-29274	98	25	,	,	PUNCT
fcis-29274	98	26	which	which	PRON
fcis-29274	98	27	mainly	mainly	ADV
fcis-29274	98	28	include	include	VERB
fcis-29274	98	29	five	five	NUM
fcis-29274	98	30	models	model	NOUN
fcis-29274	98	31	:	:	PUNCT
fcis-29274	98	32	logistic	logistic	ADJ
fcis-29274	98	33	regression	regression	NOUN
fcis-29274	98	34	,	,	PUNCT
fcis-29274	98	35	decision	decision	NOUN
fcis-29274	98	36	tree	tree	NOUN
fcis-29274	98	37	,	,	PUNCT
fcis-29274	98	38	random	random	ADJ
fcis-29274	98	39	forest	forest	NOUN
fcis-29274	98	40	,	,	PUNCT
fcis-29274	98	41	support	support	NOUN
fcis-29274	98	42	vector	vector	NOUN
fcis-29274	98	43	machine	machine	NOUN
fcis-29274	98	44	and	and	CCONJ
fcis-29274	98	45	catboost	catboost	NOUN
fcis-29274	98	46	.	.	PUNCT
fcis-29274	99	1	for	for	ADP
fcis-29274	99	2	depression	depression	NOUN
fcis-29274	99	3	data	datum	NOUN
fcis-29274	99	4	set	set	VERB
fcis-29274	99	5	,	,	PUNCT
fcis-29274	99	6	a	a	DET
fcis-29274	99	7	machine	machine	NOUN
fcis-29274	99	8	learning	learning	NOUN
fcis-29274	99	9	model	model	NOUN
fcis-29274	99	10	is	be	AUX
fcis-29274	99	11	used	use	VERB
fcis-29274	99	12	to	to	PART
fcis-29274	99	13	predict	predict	VERB
fcis-29274	99	14	whether	whether	SCONJ
fcis-29274	99	15	people	people	NOUN
fcis-29274	99	16	suffer	suffer	VERB
fcis-29274	99	17	from	from	ADP
fcis-29274	99	18	depression	depression	NOUN
fcis-29274	99	19	.	.	PUNCT
fcis-29274	100	1	the	the	DET
fcis-29274	100	2	specific	specific	ADJ
fcis-29274	100	3	experimental	experimental	ADJ
fcis-29274	100	4	process	process	NOUN
fcis-29274	100	5	is	be	AUX
fcis-29274	100	6	shown	show	VERB
fcis-29274	100	7	in	in	ADP
fcis-29274	100	8	figure	figure	NOUN
fcis-29274	100	9	5.before	5.before	NUM
fcis-29274	100	10	building	build	VERB
fcis-29274	100	11	the	the	DET
fcis-29274	100	12	model	model	NOUN
fcis-29274	100	13	,	,	PUNCT
fcis-29274	100	14	the	the	DET
fcis-29274	100	15	data	datum	NOUN
fcis-29274	100	16	set	set	VERB
fcis-29274	100	17	needs	need	VERB
fcis-29274	100	18	to	to	PART
fcis-29274	100	19	be	be	AUX
fcis-29274	100	20	divided	divide	VERB
fcis-29274	100	21	.	.	PUNCT
fcis-29274	101	1	after	after	ADP
fcis-29274	101	2	preprocessing	preprocesse	VERB
fcis-29274	101	3	,	,	PUNCT
fcis-29274	101	4	70	70	NUM
fcis-29274	101	5	%	%	NOUN
fcis-29274	101	6	of	of	ADP
fcis-29274	101	7	the	the	DET
fcis-29274	101	8	data	datum	NOUN
fcis-29274	101	9	is	be	AUX
fcis-29274	101	10	randomly	randomly	ADV
fcis-29274	101	11	selected	select	VERB
fcis-29274	101	12	as	as	ADP
fcis-29274	101	13	the	the	DET
fcis-29274	101	14	training	training	NOUN
fcis-29274	101	15	set	set	NOUN
fcis-29274	101	16	,	,	PUNCT
fcis-29274	101	17	and	and	CCONJ
fcis-29274	101	18	30	30	NUM
fcis-29274	101	19	%	%	NOUN
fcis-29274	101	20	of	of	ADP
fcis-29274	101	21	the	the	DET
fcis-29274	101	22	modeling	modeling	NOUN
fcis-29274	101	23	data	datum	NOUN
fcis-29274	101	24	set	set	VERB
fcis-29274	101	25	is	be	AUX
fcis-29274	101	26	used	use	VERB
fcis-29274	101	27	as	as	ADP
fcis-29274	101	28	the	the	DET
fcis-29274	101	29	test	test	NOUN
fcis-29274	101	30	set	set	VERB
fcis-29274	101	31	.	.	PUNCT
fcis-29274	102	1	4.1	4.1	NUM
fcis-29274	102	2	.	.	PUNCT
fcis-29274	103	1	data	datum	NOUN
fcis-29274	103	2	modeling	modeling	NOUN
fcis-29274	103	3	and	and	CCONJ
fcis-29274	103	4	forecasting	forecasting	NOUN
fcis-29274	103	5	after	after	ADP
fcis-29274	103	6	the	the	DET
fcis-29274	103	7	previous	previous	ADJ
fcis-29274	103	8	data	datum	NOUN
fcis-29274	103	9	preprocessing	preprocessing	NOUN
fcis-29274	103	10	,	,	PUNCT
fcis-29274	103	11	our	our	PRON
fcis-29274	103	12	data	datum	NOUN
fcis-29274	103	13	already	already	ADV
fcis-29274	103	14	conforms	conform	VERB
fcis-29274	103	15	to	to	ADP
fcis-29274	103	16	the	the	DET
fcis-29274	103	17	input	input	NOUN
fcis-29274	103	18	of	of	ADP
fcis-29274	103	19	the	the	DET
fcis-29274	103	20	model	model	NOUN
fcis-29274	103	21	algorithm	algorithm	NOUN
fcis-29274	103	22	.	.	PUNCT
fcis-29274	104	1	next	next	ADV
fcis-29274	104	2	,	,	PUNCT
fcis-29274	104	3	we	we	PRON
fcis-29274	104	4	build	build	VERB
fcis-29274	104	5	models	model	NOUN
fcis-29274	104	6	and	and	CCONJ
fcis-29274	104	7	test	test	NOUN
fcis-29274	104	8	efficiencies	efficiency	NOUN
fcis-29274	104	9	on	on	ADP
fcis-29274	104	10	the	the	DET
fcis-29274	104	11	basis	basis	NOUN
fcis-29274	104	12	of	of	ADP
fcis-29274	104	13	these	these	DET
fcis-29274	104	14	data	datum	NOUN
fcis-29274	104	15	.	.	PUNCT
fcis-29274	105	1	in	in	ADP
fcis-29274	105	2	this	this	DET
fcis-29274	105	3	paper	paper	NOUN
fcis-29274	105	4	,	,	PUNCT
fcis-29274	105	5	five	five	NUM
fcis-29274	105	6	machine	machine	NOUN
fcis-29274	105	7	learning	learning	NOUN
fcis-29274	105	8	models	model	NOUN
fcis-29274	105	9	,	,	PUNCT
fcis-29274	105	10	logistic	logistic	ADJ
fcis-29274	105	11	regression	regression	NOUN
fcis-29274	105	12	,	,	PUNCT
fcis-29274	105	13	decision	decision	NOUN
fcis-29274	105	14	tree	tree	NOUN
fcis-29274	105	15	,	,	PUNCT
fcis-29274	105	16	support	support	VERB
fcis-29274	105	17	vector	vector	NOUN
fcis-29274	105	18	machine	machine	NOUN
fcis-29274	105	19	,	,	PUNCT
fcis-29274	105	20	random	random	ADJ
fcis-29274	105	21	forest	forest	NOUN
fcis-29274	105	22	and	and	CCONJ
fcis-29274	105	23	catboost	catboost	VERB
fcis-29274	105	24	classification	classification	NOUN
fcis-29274	105	25	model	model	NOUN
fcis-29274	105	26	,	,	PUNCT
fcis-29274	105	27	are	be	AUX
fcis-29274	105	28	used	use	VERB
fcis-29274	105	29	for	for	ADP
fcis-29274	105	30	prediction	prediction	NOUN
fcis-29274	105	31	.	.	PUNCT
fcis-29274	106	1	68	68	NUM
fcis-29274	106	2	figure	figure	NOUN
fcis-29274	106	3	5	5	NUM
fcis-29274	106	4	.	.	PUNCT
fcis-29274	106	5	flow	flow	VERB
fcis-29274	106	6	chart	chart	NOUN
fcis-29274	106	7	of	of	ADP
fcis-29274	106	8	depression	depression	NOUN
fcis-29274	106	9	prediction	prediction	NOUN
fcis-29274	106	10	model	model	NOUN
fcis-29274	106	11	based	base	VERB
fcis-29274	106	12	on	on	ADP
fcis-29274	106	13	machine	machine	NOUN
fcis-29274	106	14	learning	learning	NOUN
fcis-29274	106	15	after	after	SCONJ
fcis-29274	106	16	the	the	DET
fcis-29274	106	17	construction	construction	NOUN
fcis-29274	106	18	of	of	ADP
fcis-29274	106	19	the	the	DET
fcis-29274	106	20	model	model	NOUN
fcis-29274	106	21	,	,	PUNCT
fcis-29274	106	22	accuracy	accuracy	NOUN
fcis-29274	106	23	rate	rate	NOUN
fcis-29274	106	24	,	,	PUNCT
fcis-29274	106	25	accuracy	accuracy	NOUN
fcis-29274	106	26	rate	rate	NOUN
fcis-29274	106	27	,	,	PUNCT
fcis-29274	106	28	recall	recall	NOUN
fcis-29274	106	29	rate	rate	NOUN
fcis-29274	106	30	,	,	PUNCT
fcis-29274	106	31	f1	f1	NOUN
fcis-29274	106	32	-	-	PUNCT
fcis-29274	106	33	score	score	NOUN
fcis-29274	106	34	and	and	CCONJ
fcis-29274	106	35	auc	auc	NOUN
fcis-29274	106	36	indicators	indicator	NOUN
fcis-29274	106	37	are	be	AUX
fcis-29274	106	38	usually	usually	ADV
fcis-29274	106	39	used	use	VERB
fcis-29274	106	40	to	to	PART
fcis-29274	106	41	evaluate	evaluate	VERB
fcis-29274	106	42	the	the	DET
fcis-29274	106	43	prediction	prediction	NOUN
fcis-29274	106	44	results	result	NOUN
fcis-29274	106	45	of	of	ADP
fcis-29274	106	46	various	various	ADJ
fcis-29274	106	47	machine	machine	NOUN
fcis-29274	106	48	learning	learn	VERB
fcis-29274	106	49	algorithms	algorithm	NOUN
fcis-29274	106	50	for	for	ADP
fcis-29274	106	51	depression	depression	NOUN
fcis-29274	106	52	recognition	recognition	NOUN
fcis-29274	106	53	.	.	PUNCT
fcis-29274	107	1	accuracy	accuracy	NOUN
fcis-29274	107	2	is	be	AUX
fcis-29274	107	3	used	use	VERB
fcis-29274	107	4	to	to	PART
fcis-29274	107	5	measure	measure	VERB
fcis-29274	107	6	the	the	DET
fcis-29274	107	7	prediction	prediction	NOUN
fcis-29274	107	8	effect	effect	NOUN
fcis-29274	107	9	of	of	ADP
fcis-29274	107	10	a	a	DET
fcis-29274	107	11	model	model	NOUN
fcis-29274	107	12	and	and	CCONJ
fcis-29274	107	13	explain	explain	VERB
fcis-29274	107	14	whether	whether	SCONJ
fcis-29274	107	15	the	the	DET
fcis-29274	107	16	model	model	NOUN
fcis-29274	107	17	achieves	achieve	VERB
fcis-29274	107	18	the	the	DET
fcis-29274	107	19	best	good	ADJ
fcis-29274	107	20	prediction	prediction	NOUN
fcis-29274	107	21	effect	effect	NOUN
fcis-29274	107	22	.	.	PUNCT
fcis-29274	108	1	by	by	ADP
fcis-29274	108	2	comparing	compare	VERB
fcis-29274	108	3	the	the	DET
fcis-29274	108	4	calibration	calibration	NOUN
fcis-29274	108	5	accuracy	accuracy	NOUN
fcis-29274	108	6	of	of	ADP
fcis-29274	108	7	the	the	DET
fcis-29274	108	8	model	model	NOUN
fcis-29274	108	9	output	output	NOUN
fcis-29274	108	10	results	result	NOUN
fcis-29274	108	11	and	and	CCONJ
fcis-29274	108	12	combining	combine	VERB
fcis-29274	108	13	the	the	DET
fcis-29274	108	14	output	output	NOUN
fcis-29274	108	15	results	result	NOUN
fcis-29274	108	16	of	of	ADP
fcis-29274	108	17	the	the	DET
fcis-29274	108	18	model	model	NOUN
fcis-29274	108	19	algorithm	algorithm	PROPN
fcis-29274	108	20	fit	fit	PROPN
fcis-29274	108	21	,	,	PUNCT
fcis-29274	108	22	we	we	PRON
fcis-29274	108	23	found	find	VERB
fcis-29274	108	24	that	that	SCONJ
fcis-29274	108	25	among	among	ADP
fcis-29274	108	26	the	the	DET
fcis-29274	108	27	five	five	NUM
fcis-29274	108	28	models	model	NOUN
fcis-29274	108	29	,	,	PUNCT
fcis-29274	108	30	random	random	ADJ
fcis-29274	108	31	forest	forest	NOUN
fcis-29274	108	32	and	and	CCONJ
fcis-29274	108	33	decision	decision	NOUN
fcis-29274	108	34	tree	tree	NOUN
fcis-29274	108	35	had	have	VERB
fcis-29274	108	36	the	the	DET
fcis-29274	108	37	best	good	ADJ
fcis-29274	108	38	performance	performance	NOUN
fcis-29274	108	39	,	,	PUNCT
fcis-29274	108	40	and	and	CCONJ
fcis-29274	108	41	the	the	DET
fcis-29274	108	42	accuracy	accuracy	NOUN
fcis-29274	108	43	rate	rate	NOUN
fcis-29274	108	44	of	of	ADP
fcis-29274	108	45	detecting	detect	VERB
fcis-29274	108	46	depression	depression	NOUN
fcis-29274	108	47	was	be	AUX
fcis-29274	108	48	100	100	NUM
fcis-29274	108	49	%	%	NOUN
fcis-29274	108	50	.	.	PUNCT
fcis-29274	109	1	this	this	PRON
fcis-29274	109	2	was	be	AUX
fcis-29274	109	3	followed	follow	VERB
fcis-29274	109	4	by	by	ADP
fcis-29274	109	5	the	the	DET
fcis-29274	109	6	catboost	catboost	PROPN
fcis-29274	109	7	model	model	NOUN
fcis-29274	109	8	,	,	PUNCT
fcis-29274	109	9	which	which	PRON
fcis-29274	109	10	detected	detect	VERB
fcis-29274	109	11	depression	depression	NOUN
fcis-29274	109	12	with	with	ADP
fcis-29274	109	13	96.32	96.32	NUM
fcis-29274	109	14	%	%	NOUN
fcis-29274	109	15	accuracy	accuracy	NOUN
fcis-29274	109	16	.	.	PUNCT
fcis-29274	110	1	the	the	DET
fcis-29274	110	2	accuracy	accuracy	NOUN
fcis-29274	110	3	of	of	ADP
fcis-29274	110	4	catboost	catboost	ADJ
fcis-29274	110	5	model	model	NOUN
fcis-29274	110	6	is	be	AUX
fcis-29274	110	7	the	the	DET
fcis-29274	110	8	highest	high	ADJ
fcis-29274	110	9	,	,	PUNCT
fcis-29274	110	10	91.67	91.67	NUM
fcis-29274	110	11	%	%	NOUN
fcis-29274	110	12	,	,	PUNCT
fcis-29274	110	13	followed	follow	VERB
fcis-29274	110	14	by	by	ADP
fcis-29274	110	15	random	random	ADJ
fcis-29274	110	16	forest	forest	NOUN
fcis-29274	110	17	,	,	PUNCT
fcis-29274	110	18	88.84	88.84	NUM
fcis-29274	110	19	%	%	NOUN
fcis-29274	110	20	.	.	PUNCT
fcis-29274	111	1	the	the	DET
fcis-29274	111	2	predictive	predictive	ADJ
fcis-29274	111	3	roc	roc	PROPN
fcis-29274	111	4	curves	curve	NOUN
fcis-29274	111	5	of	of	ADP
fcis-29274	111	6	these	these	DET
fcis-29274	111	7	five	five	NUM
fcis-29274	111	8	algorithms	algorithm	NOUN
fcis-29274	111	9	are	be	AUX
fcis-29274	111	10	shown	show	VERB
fcis-29274	111	11	in	in	ADP
fcis-29274	111	12	figure	figure	NOUN
fcis-29274	111	13	6	6	NUM
fcis-29274	111	14	.	.	PUNCT
fcis-29274	111	15	table	table	NOUN
fcis-29274	111	16	2	2	NUM
fcis-29274	111	17	.	.	PUNCT
fcis-29274	111	18	evaluation	evaluation	NOUN
fcis-29274	111	19	of	of	ADP
fcis-29274	111	20	depression	depression	NOUN
fcis-29274	111	21	recognition	recognition	NOUN
fcis-29274	111	22	and	and	CCONJ
fcis-29274	111	23	prediction	prediction	NOUN
fcis-29274	111	24	results	result	NOUN
fcis-29274	111	25	by	by	ADP
fcis-29274	111	26	various	various	ADJ
fcis-29274	111	27	algorithms	algorithm	NOUN
fcis-29274	111	28	data	datum	NOUN
fcis-29274	111	29	index	index	NOUN
fcis-29274	111	30	model	model	NOUN
fcis-29274	111	31	logistic	logistic	ADJ
fcis-29274	111	32	regression	regression	NOUN
fcis-29274	111	33	decision	decision	NOUN
fcis-29274	111	34	tree	tree	NOUN
fcis-29274	111	35	support	support	NOUN
fcis-29274	111	36	vector	vector	NOUN
fcis-29274	111	37	machine	machine	NOUN
fcis-29274	111	38	random	random	PROPN
fcis-29274	111	39	forest	forest	NOUN
fcis-29274	111	40	catboost	catboost	PROPN
fcis-29274	111	41	training	train	VERB
fcis-29274	111	42	acc	acc	PROPN
fcis-29274	111	43	(	(	PUNCT
fcis-29274	111	44	%	%	INTJ
fcis-29274	111	45	)	)	PUNCT
fcis-29274	112	1	72.05	72.05	NUM
fcis-29274	112	2	100	100	NUM
fcis-29274	112	3	80.48	80.48	NUM
fcis-29274	112	4	100	100	NUM
fcis-29274	112	5	96.32	96.32	NUM
fcis-29274	112	6	auc	auc	NOUN
fcis-29274	112	7	0.788	0.788	NUM
fcis-29274	112	8	1.000	1.000	NUM
fcis-29274	112	9	0.885	0.885	NUM
fcis-29274	112	10	1.000	1.000	NUM
fcis-29274	112	11	0.993	0.993	NUM
fcis-29274	112	12	precision	precision	NOUN
fcis-29274	112	13	0.722	0.722	NUM
fcis-29274	112	14	1.000	1.000	NUM
fcis-29274	112	15	0.806	0.806	NUM
fcis-29274	112	16	1.000	1.000	NUM
fcis-29274	112	17	0.964	0.964	NUM
fcis-29274	112	18	recall	recall	VERB
fcis-29274	112	19	0.721	0.721	NUM
fcis-29274	112	20	1.000	1.000	NUM
fcis-29274	112	21	0.805	0.805	NUM
fcis-29274	112	22	1.000	1.000	NUM
fcis-29274	112	23	0.963	0.963	NUM
fcis-29274	112	24	f1	f1	NOUN
fcis-29274	112	25	-	-	PUNCT
fcis-29274	112	26	score	score	NOUN
fcis-29274	112	27	0.720	0.720	NUM
fcis-29274	112	28	1.000	1.000	NUM
fcis-29274	112	29	0.805	0.805	NUM
fcis-29274	112	30	1.000	1.000	NUM
fcis-29274	112	31	0.963	0.963	NUM
fcis-29274	112	32	testing	test	VERB
fcis-29274	112	33	acc	acc	PROPN
fcis-29274	112	34	(	(	PUNCT
fcis-29274	112	35	%	%	INTJ
fcis-29274	112	36	)	)	PUNCT
fcis-29274	112	37	72.21	72.21	NUM
fcis-29274	112	38	81.3	81.3	NUM
fcis-29274	112	39	78.53	78.53	NUM
fcis-29274	112	40	88.84	88.84	NUM
fcis-29274	112	41	91.67	91.67	NUM
fcis-29274	112	42	auc	auc	NOUN
fcis-29274	112	43	0.782	0.782	NUM
fcis-29274	112	44	0.813	0.813	NUM
fcis-29274	112	45	0.856	0.856	NUM
fcis-29274	112	46	0.953	0.953	NUM
fcis-29274	112	47	0.967	0.967	NUM
fcis-29274	112	48	precision	precision	NOUN
fcis-29274	112	49	0.722	0.722	NUM
fcis-29274	112	50	0.814	0.814	NUM
fcis-29274	112	51	0.786	0.786	NUM
fcis-29274	112	52	0.889	0.889	NUM
fcis-29274	112	53	0.919	0.919	NUM
fcis-29274	112	54	recall	recall	VERB
fcis-29274	112	55	0.722	0.722	NUM
fcis-29274	112	56	0.813	0.813	NUM
fcis-29274	112	57	0.785	0.785	NUM
fcis-29274	112	58	0.888	0.888	NUM
fcis-29274	112	59	0.917	0.917	NUM
fcis-29274	112	60	f1	f1	NOUN
fcis-29274	112	61	-	-	PUNCT
fcis-29274	112	62	score	score	NOUN
fcis-29274	112	63	0.722	0.722	NUM
fcis-29274	112	64	0.813	0.813	NUM
fcis-29274	112	65	0.785	0.785	NUM
fcis-29274	112	66	0.888	0.888	NUM
fcis-29274	112	67	0.917	0.917	NUM
fcis-29274	112	68	figure	figure	NOUN
fcis-29274	112	69	6	6	NUM
fcis-29274	112	70	.	.	PUNCT
fcis-29274	113	1	roc	roc	PROPN
fcis-29274	113	2	curves	curve	NOUN
fcis-29274	113	3	of	of	ADP
fcis-29274	113	4	subjects	subject	NOUN
fcis-29274	113	5	of	of	ADP
fcis-29274	113	6	five	five	NUM
fcis-29274	113	7	models	model	NOUN
fcis-29274	113	8	5	5	NUM
fcis-29274	113	9	.	.	PUNCT
fcis-29274	113	10	conclusion	conclusion	NOUN
fcis-29274	113	11	5.1	5.1	NUM
fcis-29274	113	12	.	.	PUNCT
fcis-29274	114	1	summary	summary	NOUN
fcis-29274	114	2	in	in	ADP
fcis-29274	114	3	this	this	DET
fcis-29274	114	4	study	study	NOUN
fcis-29274	114	5	,	,	PUNCT
fcis-29274	114	6	the	the	DET
fcis-29274	114	7	test	test	NOUN
fcis-29274	114	8	set	set	VERB
fcis-29274	114	9	aucs	aucs	NOUN
fcis-29274	114	10	of	of	ADP
fcis-29274	114	11	the	the	DET
fcis-29274	114	12	four	four	NUM
fcis-29274	114	13	machine	machine	NOUN
fcis-29274	114	14	learning	learning	NOUN
fcis-29274	114	15	models	model	NOUN
fcis-29274	114	16	were	be	AUX
fcis-29274	114	17	all	all	PRON
fcis-29274	114	18	above	above	ADP
fcis-29274	114	19	0.7	0.7	NUM
fcis-29274	114	20	,	,	PUNCT
fcis-29274	114	21	among	among	ADP
fcis-29274	114	22	which	which	PRON
fcis-29274	114	23	the	the	DET
fcis-29274	114	24	catboost	catboost	NOUN
fcis-29274	114	25	model	model	NOUN
fcis-29274	114	26	had	have	VERB
fcis-29274	114	27	the	the	DET
fcis-29274	114	28	highest	high	ADJ
fcis-29274	114	29	accuracy	accuracy	NOUN
fcis-29274	114	30	.	.	PUNCT
fcis-29274	115	1	this	this	DET
fcis-29274	115	2	result	result	NOUN
fcis-29274	115	3	shows	show	VERB
fcis-29274	115	4	that	that	SCONJ
fcis-29274	115	5	the	the	DET
fcis-29274	115	6	catboost	catboost	ADJ
fcis-29274	115	7	algorithm	algorithm	NOUN
fcis-29274	115	8	can	can	AUX
fcis-29274	115	9	well	well	ADV
fcis-29274	115	10	identify	identify	VERB
fcis-29274	115	11	the	the	DET
fcis-29274	115	12	occurrence	occurrence	NOUN
fcis-29274	115	13	of	of	ADP
fcis-29274	115	14	depression	depression	NOUN
fcis-29274	115	15	from	from	ADP
fcis-29274	115	16	other	other	ADJ
fcis-29274	115	17	health	health	NOUN
fcis-29274	115	18	-	-	PUNCT
fcis-29274	115	19	related	relate	VERB
fcis-29274	115	20	and	and	CCONJ
fcis-29274	115	21	demographic	demographic	ADJ
fcis-29274	115	22	factors	factor	NOUN
fcis-29274	115	23	in	in	ADP
fcis-29274	115	24	large	large	ADJ
fcis-29274	115	25	survey	survey	NOUN
fcis-29274	115	26	data	data	NOUN
fcis-29274	115	27	sets	set	NOUN
fcis-29274	115	28	.	.	PUNCT
fcis-29274	116	1	unlike	unlike	ADP
fcis-29274	116	2	traditional	traditional	ADJ
fcis-29274	116	3	statistical	statistical	ADJ
fcis-29274	116	4	methods	method	NOUN
fcis-29274	116	5	that	that	PRON
fcis-29274	116	6	rely	rely	VERB
fcis-29274	116	7	on	on	ADP
fcis-29274	116	8	researcher	researcher	NOUN
fcis-29274	116	9	input	input	NOUN
fcis-29274	116	10	to	to	PART
fcis-29274	116	11	specify	specify	VERB
fcis-29274	116	12	variables	variable	NOUN
fcis-29274	116	13	relevant	relevant	ADJ
fcis-29274	116	14	to	to	ADP
fcis-29274	116	15	a	a	DET
fcis-29274	116	16	particular	particular	ADJ
fcis-29274	116	17	analysis	analysis	NOUN
fcis-29274	116	18	,	,	PUNCT
fcis-29274	116	19	machine	machine	NOUN
fcis-29274	116	20	learning	learning	NOUN
fcis-29274	116	21	methods	method	NOUN
fcis-29274	116	22	can	can	AUX
fcis-29274	116	23	identify	identify	VERB
fcis-29274	116	24	which	which	PRON
fcis-29274	116	25	variables	variable	VERB
fcis-29274	116	26	in	in	ADP
fcis-29274	116	27	a	a	DET
fcis-29274	116	28	given	give	VERB
fcis-29274	116	29	data	datum	NOUN
fcis-29274	116	30	set	set	VERB
fcis-29274	116	31	are	be	AUX
fcis-29274	116	32	associated	associate	VERB
fcis-29274	116	33	or	or	CCONJ
fcis-29274	116	34	unrelated	unrelated	ADJ
fcis-29274	116	35	to	to	ADP
fcis-29274	116	36	the	the	DET
fcis-29274	116	37	outcome	outcome	NOUN
fcis-29274	116	38	of	of	ADP
fcis-29274	116	39	interest	interest	NOUN
fcis-29274	116	40	.	.	PUNCT
fcis-29274	117	1	this	this	PRON
fcis-29274	117	2	is	be	AUX
fcis-29274	117	3	also	also	ADV
fcis-29274	117	4	the	the	DET
fcis-29274	117	5	advantage	advantage	NOUN
fcis-29274	117	6	of	of	ADP
fcis-29274	117	7	machine	machine	NOUN
fcis-29274	117	8	learning	learning	NOUN
fcis-29274	117	9	in	in	ADP
fcis-29274	117	10	building	build	VERB
fcis-29274	117	11	clinical	clinical	ADJ
fcis-29274	117	12	prediction	prediction	NOUN
fcis-29274	117	13	models	model	NOUN
fcis-29274	117	14	.	.	PUNCT
fcis-29274	118	1	the	the	DET
fcis-29274	118	2	possible	possible	ADJ
fcis-29274	118	3	applicability	applicability	NOUN
fcis-29274	118	4	of	of	ADP
fcis-29274	118	5	machine	machine	NOUN
fcis-29274	118	6	learning	learning	NOUN
fcis-29274	118	7	models	model	NOUN
fcis-29274	118	8	in	in	ADP
fcis-29274	118	9	clinical	clinical	ADJ
fcis-29274	118	10	practice	practice	NOUN
fcis-29274	118	11	can	can	AUX
fcis-29274	118	12	be	be	AUX
fcis-29274	118	13	a	a	DET
fcis-29274	118	14	web	web	NOUN
fcis-29274	118	15	-	-	PUNCT
fcis-29274	118	16	based	base	VERB
fcis-29274	118	17	tool	tool	NOUN
fcis-29274	118	18	for	for	ADP
fcis-29274	118	19	assessing	assess	VERB
fcis-29274	118	20	participants	participant	NOUN
fcis-29274	118	21	'	'	PART
fcis-29274	118	22	risk	risk	NOUN
fcis-29274	118	23	of	of	ADP
fcis-29274	118	24	depression	depression	NOUN
fcis-29274	118	25	.	.	PUNCT
fcis-29274	119	1	although	although	SCONJ
fcis-29274	119	2	it	it	PRON
fcis-29274	119	3	can	can	AUX
fcis-29274	119	4	not	not	PART
fcis-29274	119	5	replace	replace	VERB
fcis-29274	119	6	some	some	DET
fcis-29274	119	7	traditional	traditional	ADJ
fcis-29274	119	8	screening	screening	NOUN
fcis-29274	119	9	tools	tool	NOUN
fcis-29274	119	10	(	(	PUNCT
fcis-29274	119	11	such	such	ADJ
fcis-29274	119	12	as	as	ADP
fcis-29274	119	13	phq-9	phq-9	NOUN
fcis-29274	119	14	)	)	PUNCT
fcis-29274	119	15	,	,	PUNCT
fcis-29274	119	16	we	we	PRON
fcis-29274	119	17	can	can	AUX
fcis-29274	119	18	use	use	VERB
fcis-29274	119	19	it	it	PRON
fcis-29274	119	20	to	to	PART
fcis-29274	119	21	estimate	estimate	VERB
fcis-29274	119	22	the	the	DET
fcis-29274	119	23	prevalence	prevalence	NOUN
fcis-29274	119	24	of	of	ADP
fcis-29274	119	25	depression	depression	NOUN
fcis-29274	119	26	in	in	ADP
fcis-29274	119	27	areas	area	NOUN
fcis-29274	119	28	where	where	SCONJ
fcis-29274	119	29	there	there	PRON
fcis-29274	119	30	are	be	VERB
fcis-29274	119	31	no	no	DET
fcis-29274	119	32	personal	personal	ADJ
fcis-29274	119	33	mental	mental	ADJ
fcis-29274	119	34	health	health	NOUN
fcis-29274	119	35	surveys	survey	NOUN
fcis-29274	119	36	.	.	PUNCT
fcis-29274	120	1	data	datum	NOUN
fcis-29274	120	2	set	set	VERB
fcis-29274	120	3	data	datum	NOUN
fcis-29274	120	4	preprocessing	preprocesse	VERB
fcis-29274	120	5	feature	feature	NOUN
fcis-29274	120	6	selection	selection	NOUN
fcis-29274	120	7	model	model	NOUN
fcis-29274	120	8	training	training	NOUN
fcis-29274	120	9	and	and	CCONJ
fcis-29274	120	10	evaluation	evaluation	NOUN
fcis-29274	120	11	nhanes	nhane	NOUN
fcis-29274	120	12	data	datum	NOUN
fcis-29274	120	13	set	set	VERB
fcis-29274	120	14	missing	miss	VERB
fcis-29274	120	15	value	value	NOUN
fcis-29274	120	16	outlier	outlier	NOUN
fcis-29274	121	1	data	datum	NOUN
fcis-29274	121	2	imbalance	imbalance	NOUN
fcis-29274	121	3	recursive	recursive	ADJ
fcis-29274	121	4	feature	feature	NOUN
fcis-29274	121	5	elimination	elimination	NOUN
fcis-29274	121	6	training	training	NOUN
fcis-29274	121	7	testing	testing	NOUN
fcis-29274	121	8	model	model	NOUN
fcis-29274	121	9	training	training	NOUN
fcis-29274	121	10	evaluation	evaluation	NOUN
fcis-29274	121	11	result	result	NOUN
fcis-29274	121	12	analysis	analysis	NOUN
fcis-29274	121	13	application	application	NOUN
fcis-29274	121	14	measurement	measurement	NOUN
fcis-29274	121	15	69	69	NUM
fcis-29274	121	16	5.2	5.2	NUM
fcis-29274	121	17	.	.	PUNCT
fcis-29274	122	1	strengths	strength	NOUN
fcis-29274	122	2	and	and	CCONJ
fcis-29274	122	3	limitations	limitation	NOUN
fcis-29274	122	4	the	the	DET
fcis-29274	122	5	data	datum	NOUN
fcis-29274	122	6	mining	mining	NOUN
fcis-29274	122	7	method	method	NOUN
fcis-29274	122	8	of	of	ADP
fcis-29274	122	9	splitting	split	VERB
fcis-29274	122	10	the	the	DET
fcis-29274	122	11	data	data	NOUN
fcis-29274	122	12	file	file	NOUN
fcis-29274	122	13	into	into	ADP
fcis-29274	122	14	training	training	NOUN
fcis-29274	122	15	and	and	CCONJ
fcis-29274	122	16	validation	validation	NOUN
fcis-29274	122	17	minimizes	minimize	VERB
fcis-29274	122	18	the	the	DET
fcis-29274	122	19	problem	problem	NOUN
fcis-29274	122	20	of	of	ADP
fcis-29274	122	21	overfitting	overfitting	NOUN
fcis-29274	122	22	,	,	PUNCT
fcis-29274	122	23	which	which	PRON
fcis-29274	122	24	is	be	AUX
fcis-29274	122	25	often	often	ADV
fcis-29274	122	26	problematic	problematic	ADJ
fcis-29274	122	27	in	in	ADP
fcis-29274	122	28	traditional	traditional	ADJ
fcis-29274	122	29	statistical	statistical	ADJ
fcis-29274	122	30	techniques	technique	NOUN
fcis-29274	122	31	with	with	ADP
fcis-29274	122	32	a	a	DET
fcis-29274	122	33	large	large	ADJ
fcis-29274	122	34	number	number	NOUN
fcis-29274	122	35	of	of	ADP
fcis-29274	122	36	predictors	predictor	NOUN
fcis-29274	122	37	.	.	PUNCT
fcis-29274	123	1	enhanced	enhanced	ADJ
fcis-29274	123	2	machine	machine	NOUN
fcis-29274	123	3	learning	learning	NOUN
fcis-29274	123	4	techniques	technique	NOUN
fcis-29274	123	5	can	can	AUX
fcis-29274	123	6	adapt	adapt	VERB
fcis-29274	123	7	to	to	ADP
fcis-29274	123	8	different	different	ADJ
fcis-29274	123	9	types	type	NOUN
fcis-29274	123	10	of	of	ADP
fcis-29274	123	11	variables	variable	NOUN
fcis-29274	123	12	and	and	CCONJ
fcis-29274	123	13	have	have	AUX
fcis-29274	123	14	been	be	AUX
fcis-29274	123	15	found	find	VERB
fcis-29274	123	16	to	to	PART
fcis-29274	123	17	have	have	VERB
fcis-29274	123	18	high	high	ADJ
fcis-29274	123	19	predictive	predictive	ADJ
fcis-29274	123	20	accuracy	accuracy	NOUN
fcis-29274	123	21	.	.	PUNCT
fcis-29274	124	1	shrinkage	shrinkage	NOUN
fcis-29274	124	2	is	be	AUX
fcis-29274	124	3	also	also	ADV
fcis-29274	124	4	used	use	VERB
fcis-29274	124	5	to	to	PART
fcis-29274	124	6	avoid	avoid	VERB
fcis-29274	124	7	overfitting	overfitte	VERB
fcis-29274	124	8	.	.	PUNCT
fcis-29274	125	1	at	at	ADP
fcis-29274	125	2	the	the	DET
fcis-29274	125	3	same	same	ADJ
fcis-29274	125	4	time	time	NOUN
fcis-29274	125	5	,	,	PUNCT
fcis-29274	125	6	this	this	DET
fcis-29274	125	7	study	study	NOUN
fcis-29274	125	8	also	also	ADV
fcis-29274	125	9	has	have	VERB
fcis-29274	125	10	several	several	ADJ
fcis-29274	125	11	limitations	limitation	NOUN
fcis-29274	125	12	.	.	PUNCT
fcis-29274	126	1	first	first	ADV
fcis-29274	126	2	,	,	PUNCT
fcis-29274	126	3	this	this	DET
fcis-29274	126	4	study	study	NOUN
fcis-29274	126	5	treated	treat	VERB
fcis-29274	126	6	depression	depression	NOUN
fcis-29274	126	7	as	as	ADP
fcis-29274	126	8	a	a	DET
fcis-29274	126	9	binary	binary	ADJ
fcis-29274	126	10	variable	variable	NOUN
fcis-29274	126	11	.	.	PUNCT
fcis-29274	127	1	therefore	therefore	ADV
fcis-29274	127	2	,	,	PUNCT
fcis-29274	127	3	we	we	PRON
fcis-29274	127	4	can	can	AUX
fcis-29274	127	5	not	not	PART
fcis-29274	127	6	evaluate	evaluate	VERB
fcis-29274	127	7	the	the	DET
fcis-29274	127	8	correlation	correlation	NOUN
fcis-29274	127	9	between	between	ADP
fcis-29274	127	10	various	various	ADJ
fcis-29274	127	11	factors	factor	NOUN
fcis-29274	127	12	and	and	CCONJ
fcis-29274	127	13	the	the	DET
fcis-29274	127	14	severity	severity	NOUN
fcis-29274	127	15	of	of	ADP
fcis-29274	127	16	depression	depression	NOUN
fcis-29274	127	17	.	.	PUNCT
fcis-29274	128	1	second	second	ADJ
fcis-29274	128	2	,	,	PUNCT
fcis-29274	128	3	the	the	DET
fcis-29274	128	4	prevalence	prevalence	NOUN
fcis-29274	128	5	of	of	ADP
fcis-29274	128	6	depression	depression	NOUN
fcis-29274	128	7	in	in	ADP
fcis-29274	128	8	2018	2018	NUM
fcis-29274	128	9	may	may	AUX
fcis-29274	128	10	be	be	AUX
fcis-29274	128	11	underestimated	underestimate	VERB
fcis-29274	128	12	due	due	ADJ
fcis-29274	128	13	to	to	ADP
fcis-29274	128	14	missing	miss	VERB
fcis-29274	128	15	values	value	NOUN
fcis-29274	128	16	.	.	PUNCT
fcis-29274	129	1	third	third	ADJ
fcis-29274	129	2	,	,	PUNCT
fcis-29274	129	3	because	because	SCONJ
fcis-29274	129	4	nhanes	nhane	NOUN
fcis-29274	129	5	is	be	AUX
fcis-29274	129	6	a	a	DET
fcis-29274	129	7	crosssectional	crosssectional	ADJ
fcis-29274	129	8	survey	survey	NOUN
fcis-29274	129	9	(	(	PUNCT
fcis-29274	129	10	rather	rather	ADV
fcis-29274	129	11	than	than	ADP
fcis-29274	129	12	a	a	DET
fcis-29274	129	13	longitudinal	longitudinal	ADJ
fcis-29274	129	14	survey	survey	NOUN
fcis-29274	129	15	)	)	PUNCT
fcis-29274	129	16	,	,	PUNCT
fcis-29274	129	17	we	we	PRON
fcis-29274	129	18	can	can	AUX
fcis-29274	129	19	not	not	PART
fcis-29274	129	20	measure	measure	VERB
fcis-29274	129	21	the	the	DET
fcis-29274	129	22	prognosis	prognosis	NOUN
fcis-29274	129	23	of	of	ADP
fcis-29274	129	24	the	the	DET
fcis-29274	129	25	disease	disease	NOUN
fcis-29274	129	26	or	or	CCONJ
fcis-29274	129	27	the	the	DET
fcis-29274	129	28	future	future	ADJ
fcis-29274	129	29	occurrence	occurrence	NOUN
fcis-29274	129	30	of	of	ADP
fcis-29274	129	31	depression	depression	NOUN
fcis-29274	129	32	in	in	ADP
fcis-29274	129	33	the	the	DET
fcis-29274	129	34	population	population	NOUN
fcis-29274	129	35	.	.	PUNCT
fcis-29274	130	1	5.3	5.3	NUM
fcis-29274	130	2	.	.	PUNCT
fcis-29274	130	3	directions	direction	NOUN
fcis-29274	130	4	for	for	ADP
fcis-29274	130	5	further	further	ADJ
fcis-29274	130	6	work	work	NOUN
fcis-29274	130	7	although	although	SCONJ
fcis-29274	130	8	the	the	DET
fcis-29274	130	9	research	research	NOUN
fcis-29274	130	10	of	of	ADP
fcis-29274	130	11	the	the	DET
fcis-29274	130	12	paper	paper	NOUN
fcis-29274	130	13	has	have	AUX
fcis-29274	130	14	been	be	AUX
fcis-29274	130	15	completed	complete	VERB
fcis-29274	130	16	and	and	CCONJ
fcis-29274	130	17	achieved	achieve	VERB
fcis-29274	130	18	the	the	DET
fcis-29274	130	19	expected	expect	VERB
fcis-29274	130	20	goals	goal	NOUN
fcis-29274	130	21	and	and	CCONJ
fcis-29274	130	22	initial	initial	ADJ
fcis-29274	130	23	success	success	NOUN
fcis-29274	130	24	,	,	PUNCT
fcis-29274	130	25	there	there	PRON
fcis-29274	130	26	is	be	VERB
fcis-29274	130	27	still	still	ADV
fcis-29274	130	28	room	room	NOUN
fcis-29274	130	29	for	for	ADP
fcis-29274	130	30	improvement	improvement	NOUN
fcis-29274	130	31	and	and	CCONJ
fcis-29274	130	32	further	further	ADJ
fcis-29274	130	33	improvement	improvement	NOUN
fcis-29274	130	34	.	.	PUNCT
fcis-29274	131	1	here	here	ADV
fcis-29274	131	2	are	be	AUX
fcis-29274	131	3	a	a	DET
fcis-29274	131	4	few	few	ADJ
fcis-29274	131	5	key	key	ADJ
fcis-29274	131	6	points	point	NOUN
fcis-29274	131	7	for	for	ADP
fcis-29274	131	8	brief	brief	ADJ
fcis-29274	131	9	discussion	discussion	NOUN
fcis-29274	131	10	:	:	PUNCT
fcis-29274	131	11	1	1	X
fcis-29274	131	12	.	.	X
fcis-29274	131	13	there	there	PRON
fcis-29274	131	14	are	be	VERB
fcis-29274	131	15	many	many	ADJ
fcis-29274	131	16	algorithms	algorithm	NOUN
fcis-29274	131	17	in	in	ADP
fcis-29274	131	18	machine	machine	NOUN
fcis-29274	131	19	learning	learning	NOUN
fcis-29274	131	20	.	.	PUNCT
fcis-29274	132	1	this	this	DET
fcis-29274	132	2	paper	paper	NOUN
fcis-29274	132	3	only	only	ADV
fcis-29274	132	4	uses	use	VERB
fcis-29274	132	5	a	a	DET
fcis-29274	132	6	few	few	ADJ
fcis-29274	132	7	of	of	ADP
fcis-29274	132	8	them	they	PRON
fcis-29274	132	9	.	.	PUNCT
fcis-29274	133	1	in	in	ADP
fcis-29274	133	2	the	the	DET
fcis-29274	133	3	future	future	ADJ
fcis-29274	133	4	research	research	NOUN
fcis-29274	133	5	process	process	NOUN
fcis-29274	133	6	,	,	PUNCT
fcis-29274	133	7	we	we	PRON
fcis-29274	133	8	will	will	AUX
fcis-29274	133	9	continue	continue	VERB
fcis-29274	133	10	to	to	PART
fcis-29274	133	11	study	study	VERB
fcis-29274	133	12	other	other	ADJ
fcis-29274	133	13	model	model	NOUN
fcis-29274	133	14	algorithms	algorithm	NOUN
fcis-29274	133	15	,	,	PUNCT
fcis-29274	133	16	summarize	summarize	VERB
fcis-29274	133	17	their	their	PRON
fcis-29274	133	18	respective	respective	ADJ
fcis-29274	133	19	characteristics	characteristic	NOUN
fcis-29274	133	20	and	and	CCONJ
fcis-29274	133	21	applicability	applicability	NOUN
fcis-29274	133	22	,	,	PUNCT
fcis-29274	133	23	and	and	CCONJ
fcis-29274	133	24	integrate	integrate	VERB
fcis-29274	133	25	them	they	PRON
fcis-29274	133	26	and	and	CCONJ
fcis-29274	133	27	apply	apply	VERB
fcis-29274	133	28	them	they	PRON
fcis-29274	133	29	to	to	ADP
fcis-29274	133	30	the	the	DET
fcis-29274	133	31	prediction	prediction	NOUN
fcis-29274	133	32	system	system	NOUN
fcis-29274	133	33	.	.	PUNCT
fcis-29274	134	1	2	2	X
fcis-29274	134	2	.	.	X
fcis-29274	134	3	the	the	DET
fcis-29274	134	4	structure	structure	NOUN
fcis-29274	134	5	of	of	ADP
fcis-29274	134	6	the	the	DET
fcis-29274	134	7	prediction	prediction	NOUN
fcis-29274	134	8	system	system	NOUN
fcis-29274	134	9	is	be	AUX
fcis-29274	134	10	still	still	ADV
fcis-29274	134	11	relatively	relatively	ADV
fcis-29274	134	12	simple	simple	ADJ
fcis-29274	134	13	.	.	PUNCT
fcis-29274	135	1	the	the	DET
fcis-29274	135	2	data	datum	NOUN
fcis-29274	135	3	set	set	VERB
fcis-29274	135	4	used	use	VERB
fcis-29274	135	5	by	by	ADP
fcis-29274	135	6	the	the	DET
fcis-29274	135	7	system	system	NOUN
fcis-29274	135	8	is	be	AUX
fcis-29274	135	9	too	too	ADV
fcis-29274	135	10	structured	structured	ADJ
fcis-29274	135	11	and	and	CCONJ
fcis-29274	135	12	has	have	VERB
fcis-29274	135	13	certain	certain	ADJ
fcis-29274	135	14	limitations	limitation	NOUN
fcis-29274	135	15	.	.	PUNCT
fcis-29274	136	1	we	we	PRON
fcis-29274	136	2	will	will	AUX
fcis-29274	136	3	continue	continue	VERB
fcis-29274	136	4	to	to	PART
fcis-29274	136	5	improve	improve	VERB
fcis-29274	136	6	and	and	CCONJ
fcis-29274	136	7	enrich	enrich	VERB
fcis-29274	136	8	the	the	DET
fcis-29274	136	9	system	system	NOUN
fcis-29274	136	10	structure	structure	NOUN
fcis-29274	136	11	in	in	ADP
fcis-29274	136	12	the	the	DET
fcis-29274	136	13	future	future	NOUN
fcis-29274	136	14	,	,	PUNCT
fcis-29274	136	15	and	and	CCONJ
fcis-29274	136	16	try	try	VERB
fcis-29274	136	17	to	to	PART
fcis-29274	136	18	use	use	VERB
fcis-29274	136	19	realtime	realtime	ADJ
fcis-29274	136	20	data	datum	NOUN
fcis-29274	136	21	for	for	ADP
fcis-29274	136	22	more	more	ADJ
fcis-29274	136	23	in	in	ADP
fcis-29274	136	24	-	-	PUNCT
fcis-29274	136	25	depth	depth	NOUN
fcis-29274	136	26	prediction	prediction	NOUN
fcis-29274	136	27	research	research	NOUN
fcis-29274	136	28	.	.	PUNCT
fcis-29274	137	1	3	3	X
fcis-29274	137	2	.	.	X
fcis-29274	137	3	the	the	DET
fcis-29274	137	4	data	datum	NOUN
fcis-29274	137	5	processing	processing	NOUN
fcis-29274	137	6	in	in	ADP
fcis-29274	137	7	this	this	DET
fcis-29274	137	8	paper	paper	NOUN
fcis-29274	137	9	uses	use	VERB
fcis-29274	137	10	the	the	DET
fcis-29274	137	11	jupyter	jupyter	ADV
fcis-29274	137	12	scientific	scientific	ADJ
fcis-29274	137	13	computing	computing	NOUN
fcis-29274	137	14	tool	tool	NOUN
fcis-29274	137	15	class	class	NOUN
fcis-29274	137	16	.	.	PUNCT
fcis-29274	138	1	the	the	DET
fcis-29274	138	2	processing	processing	NOUN
fcis-29274	138	3	process	process	NOUN
fcis-29274	138	4	is	be	AUX
fcis-29274	138	5	completed	complete	VERB
fcis-29274	138	6	by	by	ADP
fcis-29274	138	7	writing	write	VERB
fcis-29274	138	8	program	program	NOUN
fcis-29274	138	9	algorithms	algorithm	NOUN
fcis-29274	138	10	.	.	PUNCT
fcis-29274	139	1	some	some	PRON
fcis-29274	139	2	are	be	AUX
fcis-29274	139	3	too	too	ADV
fcis-29274	139	4	complicated	complicated	ADJ
fcis-29274	139	5	and	and	CCONJ
fcis-29274	139	6	take	take	VERB
fcis-29274	139	7	up	up	ADP
fcis-29274	139	8	a	a	DET
fcis-29274	139	9	lot	lot	NOUN
fcis-29274	139	10	of	of	ADP
fcis-29274	139	11	time	time	NOUN
fcis-29274	139	12	.	.	PUNCT
fcis-29274	140	1	we	we	PRON
fcis-29274	140	2	will	will	AUX
fcis-29274	140	3	continue	continue	VERB
fcis-29274	140	4	to	to	PART
fcis-29274	140	5	strengthen	strengthen	VERB
fcis-29274	140	6	the	the	DET
fcis-29274	140	7	research	research	NOUN
fcis-29274	140	8	and	and	CCONJ
fcis-29274	140	9	study	study	NOUN
fcis-29274	140	10	of	of	ADP
fcis-29274	140	11	dedicated	dedicated	ADJ
fcis-29274	140	12	data	data	NOUN
fcis-29274	140	13	mining	mining	NOUN
fcis-29274	140	14	tools	tool	NOUN
fcis-29274	140	15	in	in	ADP
fcis-29274	140	16	the	the	DET
fcis-29274	140	17	future	future	NOUN
fcis-29274	140	18	.	.	PUNCT
fcis-29274	141	1	references	reference	NOUN
fcis-29274	141	2	[	[	X
fcis-29274	141	3	1	1	NUM
fcis-29274	141	4	]	]	PUNCT
fcis-29274	141	5	k.	k.	PROPN
fcis-29274	141	6	smith	smith	PROPN
fcis-29274	141	7	.	.	PUNCT
fcis-29274	142	1	mental	mental	ADJ
fcis-29274	142	2	health	health	NOUN
fcis-29274	142	3	:	:	PUNCT
fcis-29274	142	4	a	a	DET
fcis-29274	142	5	world	world	NOUN
fcis-29274	142	6	of	of	ADP
fcis-29274	142	7	depression[j	depression[j	PROPN
fcis-29274	142	8	]	]	PUNCT
fcis-29274	142	9	.	.	PUNCT
fcis-29274	143	1	nature	nature	NOUN
fcis-29274	143	2	.	.	PUNCT
fcis-29274	144	1	2014,515(7526	2014,515(7526	NUM
fcis-29274	144	2	):	):	PUNCT
fcis-29274	144	3	181	181	NUM
fcis-29274	144	4	.	.	PUNCT
fcis-29274	145	1	[	[	X
fcis-29274	145	2	2	2	X
fcis-29274	145	3	]	]	X
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fcis-29274	145	10	,	,	PUNCT
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fcis-29274	145	13	h	h	PROPN
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fcis-29274	145	51	for	for	ADP
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fcis-29274	146	3	,	,	PUNCT
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fcis-29274	146	5	,	,	PUNCT
fcis-29274	146	6	392	392	NUM
fcis-29274	146	7	(	(	PUNCT
fcis-29274	146	8	10159	10159	NUM
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fcis-29274	146	10	17891858	17891858	NUM
fcis-29274	146	11	.	.	PUNCT
fcis-29274	147	1	[	[	X
fcis-29274	147	2	3	3	X
fcis-29274	147	3	]	]	PUNCT
fcis-29274	147	4	world	world	PROPN
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fcis-29274	147	6	organization	organization	NOUN
fcis-29274	147	7	(	(	PUNCT
fcis-29274	147	8	who	who	PRON
fcis-29274	147	9	)	)	PUNCT
fcis-29274	147	10	.	.	PUNCT
fcis-29274	148	1	depression.https://	depression.https://	PROPN
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fcis-29274	148	5	-	-	SYM
fcis-29274	148	6	01	01	NUM
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fcis-29274	149	1	[	[	X
fcis-29274	149	2	4	4	X
fcis-29274	149	3	]	]	X
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fcis-29274	149	30	correlates	correlate	VERB
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fcis-29274	150	13	,	,	PUNCT
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fcis-29274	154	9	:	:	PUNCT
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fcis-29274	154	14	follow	follow	NOUN
fcis-29274	154	15	-	-	PUNCT
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fcis-29274	156	17	al	al	PROPN
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fcis-29274	156	20	-	-	ADJ
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fcis-29274	156	29	]	]	PUNCT
fcis-29274	156	30	.	.	PUNCT
fcis-29274	157	1	jama	jama	PROPN
fcis-29274	157	2	,	,	PUNCT
fcis-29274	157	3	1996	1996	NUM
fcis-29274	157	4	,	,	PUNCT
fcis-29274	157	5	276(4	276(4	NUM
fcis-29274	157	6	):	):	PUNCT
fcis-29274	157	7	293	293	NUM
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fcis-29274	157	9	299	299	NUM
fcis-29274	157	10	.	.	PUNCT
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fcis-29274	164	10	koohi	koohi	PROPN
fcis-29274	164	11	f	f	PROPN
fcis-29274	164	12	,	,	PUNCT
fcis-29274	164	13	et	et	PROPN
fcis-29274	164	14	al	al	PROPN
fcis-29274	164	15	.	.	PUNCT
fcis-29274	165	1	the	the	DET
fcis-29274	165	2	trend	trend	NOUN
fcis-29274	165	3	and	and	CCONJ
fcis-29274	165	4	pattern	pattern	NOUN
fcis-29274	165	5	of	of	ADP
fcis-29274	165	6	depression	depression	NOUN
fcis-29274	165	7	prevalence	prevalence	NOUN
fcis-29274	165	8	in	in	ADP
fcis-29274	165	9	the	the	DET
fcis-29274	165	10	us	us	PROPN
fcis-29274	165	11	:	:	PUNCT
fcis-29274	165	12	data	datum	NOUN
fcis-29274	165	13	from	from	ADP
fcis-29274	165	14	national	national	ADJ
fcis-29274	165	15	health	health	PROPN
fcis-29274	165	16	and	and	CCONJ
fcis-29274	165	17	nutrition	nutrition	NOUN
fcis-29274	165	18	examination	examination	NOUN
fcis-29274	165	19	survey	survey	NOUN
fcis-29274	165	20	(	(	PUNCT
fcis-29274	165	21	nhanes	nhane	NOUN
fcis-29274	165	22	)	)	PUNCT
fcis-29274	165	23	2005	2005	NUM
fcis-29274	165	24	to	to	ADP
fcis-29274	165	25	2016[j	2016[j	PROPN
fcis-29274	165	26	]	]	PUNCT
fcis-29274	165	27	.	.	PUNCT
fcis-29274	166	1	journal	journal	PROPN
fcis-29274	166	2	of	of	ADP
fcis-29274	166	3	affective	affective	ADJ
fcis-29274	166	4	disorders	disorder	NOUN
fcis-29274	166	5	,	,	PUNCT
fcis-29274	166	6	2022,298	2022,298	NUM
fcis-29274	166	7	:	:	PUNCT
fcis-29274	166	8	508	508	NUM
fcis-29274	166	9	-	-	SYM
fcis-29274	166	10	515	515	NUM
fcis-29274	166	11	.	.	PUNCT
fcis-29274	167	1	[	[	X
fcis-29274	167	2	12	12	NUM
fcis-29274	167	3	]	]	X
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fcis-29274	167	5	m	m	PROPN
fcis-29274	167	6	a	a	PROPN
fcis-29274	167	7	,	,	PUNCT
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fcis-29274	167	9	m	m	PROPN
fcis-29274	167	10	a	a	DET
fcis-29274	167	11	,	,	PUNCT
fcis-29274	167	12	shakil	shakil	NOUN
fcis-29274	167	13	k	k	PROPN
fcis-29274	167	14	a	a	PROPN
fcis-29274	167	15	,	,	PUNCT
fcis-29274	167	16	et	et	PROPN
fcis-29274	167	17	al	al	PROPN
fcis-29274	167	18	.	.	PUNCT
fcis-29274	168	1	depression	depression	PROPN
fcis-29274	168	2	screening	screen	VERB
fcis-29274	168	3	in	in	ADP
fcis-29274	168	4	humans	human	NOUN
fcis-29274	168	5	with	with	ADP
fcis-29274	168	6	aiand	aiand	NOUN
fcis-29274	168	7	deep	deep	ADJ
fcis-29274	168	8	learning	learn	VERB
fcis-29274	168	9	techniques[j	techniques[j	NOUN
fcis-29274	168	10	]	]	PUNCT
fcis-29274	168	11	.	.	PUNCT
fcis-29274	169	1	ieee	ieee	NOUN
fcis-29274	169	2	transactions	transaction	NOUN
fcis-29274	169	3	on	on	ADP
fcis-29274	169	4	computational	computational	ADJ
fcis-29274	169	5	social	social	ADJ
fcis-29274	169	6	systems	system	NOUN
fcis-29274	169	7	,	,	PUNCT
fcis-29274	169	8	2022	2022	NUM
fcis-29274	169	9	.	.	PUNCT
fcis-29274	170	1	[	[	X
fcis-29274	170	2	13	13	NUM
fcis-29274	170	3	]	]	X
fcis-29274	170	4	zhang	zhang	PROPN
fcis-29274	170	5	c	c	PROPN
fcis-29274	170	6	,	,	PUNCT
fcis-29274	170	7	chen	chen	PROPN
fcis-29274	170	8	x	x	PROPN
fcis-29274	170	9	,	,	PUNCT
fcis-29274	170	10	wang	wang	PROPN
fcis-29274	170	11	s	s	PROPN
fcis-29274	170	12	,	,	PUNCT
fcis-29274	170	13	et	et	PROPN
fcis-29274	170	14	al	al	PROPN
fcis-29274	170	15	.	.	PUNCT
fcis-29274	170	16	using	use	VERB
fcis-29274	170	17	catboost	catboost	ADJ
fcis-29274	170	18	algorithm	algorithm	NOUN
fcis-29274	170	19	to	to	PART
fcis-29274	170	20	identify	identify	VERB
fcis-29274	170	21	middle	middle	NOUN
fcis-29274	170	22	-	-	PUNCT
fcis-29274	170	23	aged	aged	ADJ
fcis-29274	170	24	and	and	CCONJ
fcis-29274	170	25	elderly	elderly	ADJ
fcis-29274	170	26	depression	depression	NOUN
fcis-29274	170	27	,	,	PUNCT
fcis-29274	170	28	national	national	ADJ
fcis-29274	170	29	health	health	NOUN
fcis-29274	170	30	and	and	CCONJ
fcis-29274	170	31	nutrition	nutrition	NOUN
fcis-29274	170	32	examination	examination	NOUN
fcis-29274	170	33	survey	survey	NOUN
fcis-29274	170	34	2011–2018[j	2011–2018[j	NUM
fcis-29274	170	35	]	]	X
fcis-29274	170	36	.	.	PUNCT
fcis-29274	171	1	psychiatry	psychiatry	NOUN
fcis-29274	171	2	research	research	NOUN
fcis-29274	171	3	,	,	PUNCT
fcis-29274	171	4	2021	2021	NUM
fcis-29274	171	5	,	,	PUNCT
fcis-29274	171	6	306	306	NUM
fcis-29274	171	7	:	:	SYM
fcis-29274	171	8	114261	114261	NUM
fcis-29274	171	9	.	.	PUNCT
fcis-29274	172	1	[	[	X
fcis-29274	172	2	14	14	NUM
fcis-29274	172	3	]	]	X
fcis-29274	172	4	nhanes	nhane	NOUN
fcis-29274	172	5	--	--	PUNCT
fcis-29274	172	6	about	about	ADP
fcis-29274	172	7	the	the	DET
fcis-29274	172	8	national	national	ADJ
fcis-29274	172	9	health	health	PROPN
fcis-29274	172	10	and	and	CCONJ
fcis-29274	172	11	nutrition	nutrition	NOUN
fcis-29274	172	12	examination	examination	NOUN
fcis-29274	172	13	survey	survey	NOUN
fcis-29274	172	14	.	.	PUNCT
fcis-29274	173	1	available	available	ADJ
fcis-29274	173	2	online	online	ADV
fcis-29274	173	3	:	:	PUNCT
fcis-29274	173	4	https://	https://	PROPN
fcis-29274	173	5	www.cdc	www.cdc	NOUN
fcis-29274	173	6	.	.	PUNCT
fcis-29274	174	1	gov	gov	NOUN
fcis-29274	174	2	/	/	SYM
fcis-29274	174	3	nchs	nch	NOUN
fcis-29274	174	4	/	/	SYM
fcis-29274	174	5	nhanes	nhanes	PROPN
fcis-29274	174	6	/	/	SYM
fcis-29274	174	7	about_nhanes.htm	about_nhanes.htm	PROPN
fcis-29274	174	8	(	(	PUNCT
fcis-29274	174	9	accessed	access	VERB
fcis-29274	174	10	on	on	ADP
fcis-29274	174	11	28	28	NUM
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fcis-29274	174	13	2021	2021	NUM
fcis-29274	174	14	)	)	PUNCT
fcis-29274	174	15	.	.	PUNCT
fcis-29274	175	1	[	[	X
fcis-29274	175	2	15	15	NUM
fcis-29274	175	3	]	]	X
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fcis-29274	175	7	tolar	tolar	PROPN
fcis-29274	175	8	-	-	PUNCT
fcis-29274	175	9	peterson	peterson	PROPN
fcis-29274	175	10	t	t	PROPN
fcis-29274	175	11	,	,	PUNCT
fcis-29274	175	12	bailey	bailey	PROPN
fcis-29274	175	13	r	r	PROPN
fcis-29274	175	14	h	h	PROPN
fcis-29274	175	15	,	,	PUNCT
fcis-29274	175	16	et	et	PROPN
fcis-29274	175	17	al	al	PROPN
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fcis-29274	175	20	insecurity	insecurity	NOUN
fcis-29274	175	21	and	and	CCONJ
fcis-29274	175	22	depression	depression	NOUN
fcis-29274	175	23	among	among	ADP
fcis-29274	175	24	usadults	usadult	NOUN
fcis-29274	175	25	:	:	PUNCT
fcis-29274	175	26	nhanes	nhane	NOUN
fcis-29274	175	27	2005–2016[j	2005–2016[j	NUM
fcis-29274	175	28	]	]	X
fcis-29274	175	29	.	.	PUNCT
fcis-29274	176	1	nutrients	nutrient	NOUN
fcis-29274	176	2	,	,	PUNCT
fcis-29274	176	3	2022	2022	NUM
fcis-29274	176	4	,	,	PUNCT
fcis-29274	176	5	14(15	14(15	NUM
fcis-29274	176	6	):	):	PUNCT
fcis-29274	176	7	3081	3081	NUM
fcis-29274	176	8	.	.	PUNCT
fcis-29274	177	1	[	[	X
fcis-29274	177	2	16	16	NUM
fcis-29274	177	3	]	]	X
fcis-29274	177	4	heymans	heymans	PROPN
fcis-29274	177	5	m	m	VERB
fcis-29274	177	6	w	w	PROPN
fcis-29274	177	7	,	,	PUNCT
fcis-29274	177	8	twisk	twisk	PROPN
fcis-29274	177	9	j	j	PROPN
fcis-29274	177	10	w	w	PROPN
fcis-29274	177	11	r.	r.	PROPN
fcis-29274	177	12	handling	handle	VERB
fcis-29274	177	13	missing	miss	VERB
fcis-29274	177	14	data	datum	NOUN
fcis-29274	177	15	in	in	ADP
fcis-29274	177	16	clinical	clinical	ADJ
fcis-29274	177	17	research[j	research[j	NOUN
fcis-29274	177	18	]	]	PUNCT
fcis-29274	177	19	.	.	PUNCT
fcis-29274	178	1	journal	journal	PROPN
fcis-29274	178	2	of	of	ADP
fcis-29274	178	3	clinical	clinical	ADJ
fcis-29274	178	4	epidemiology	epidemiology	NOUN
fcis-29274	178	5	,	,	PUNCT
fcis-29274	178	6	2022	2022	NUM
fcis-29274	178	7	,	,	PUNCT
fcis-29274	178	8	151	151	NUM
fcis-29274	178	9	:	:	SYM
fcis-29274	178	10	185188	185188	NUM
fcis-29274	178	11	.	.	PUNCT
fcis-29274	179	1	[	[	X
fcis-29274	179	2	17	17	NUM
fcis-29274	179	3	]	]	X
fcis-29274	179	4	ali	ali	PROPN
fcis-29274	179	5	h	h	PROPN
fcis-29274	179	6	,	,	PUNCT
fcis-29274	179	7	salleh	salleh	PROPN
fcis-29274	179	8	m	m	PROPN
fcis-29274	179	9	n	n	PRON
fcis-29274	179	10	m	m	PRON
fcis-29274	179	11	,	,	PUNCT
fcis-29274	179	12	saedudin	saedudin	VERB
fcis-29274	179	13	r	r	NOUN
fcis-29274	179	14	,	,	PUNCT
fcis-29274	179	15	et	et	PROPN
fcis-29274	179	16	al	al	PROPN
fcis-29274	179	17	.	.	PROPN
fcis-29274	179	18	imbalance	imbalance	NOUN
fcis-29274	179	19	class	class	NOUN
fcis-29274	179	20	problems	problem	NOUN
fcis-29274	179	21	in	in	ADP
fcis-29274	179	22	data	datum	NOUN
fcis-29274	179	23	mining	mining	NOUN
fcis-29274	179	24	:	:	PUNCT
fcis-29274	179	25	a	a	DET
fcis-29274	179	26	review[j	review[j	PROPN
fcis-29274	179	27	]	]	PUNCT
fcis-29274	179	28	.	.	PUNCT
fcis-29274	180	1	indonesian	indonesian	ADJ
fcis-29274	180	2	journal	journal	PROPN
fcis-29274	180	3	of	of	ADP
fcis-29274	180	4	electrical	electrical	ADJ
fcis-29274	180	5	engineering	engineering	NOUN
fcis-29274	180	6	and	and	CCONJ
fcis-29274	180	7	computer	computer	NOUN
fcis-29274	180	8	science	science	NOUN
fcis-29274	180	9	,	,	PUNCT
fcis-29274	180	10	2019	2019	NUM
fcis-29274	180	11	,	,	PUNCT
fcis-29274	180	12	14(3	14(3	NUM
fcis-29274	180	13	):	):	PUNCT
fcis-29274	180	14	1560	1560	NUM
fcis-29274	180	15	-	-	SYM
fcis-29274	180	16	1571	1571	NUM
fcis-29274	180	17	.	.	PUNCT