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Abstract: With the advent of personal computers, the Internet, portable devices, and big data analytical settings, computer-based 

medical diagnostic technologies have grown substantially since the 1950s. To address difficult issues related to health and illness, 

these technologies use the rudiments of information retrieval and representation (IRR). But, these systems have, from the beginning, 

paid little attention to TCM methods, often because these approaches have failed in randomized controlled studies. There is still a lot 

of mystery around traditional Chinese medicine (TCM), despite the fact that it is an integral aspect of healthcare systems across the 

globe, especially in a number of Asian nations. In view of current IRR techniques, it would be beneficial to compare traditional 

Chinese medicine (TCM) diagnostic and treatment methods with Western medical models in order to find out whether a new kind of 

translational medicine can be created that improves medical outcomes while lowering health care costs globally. This would be 

necessary because disease is still prevalent in society. Using bibliometric tools, multiple correspondence analysis, and data 

visualizations, this study examines author productivity, collaborations, and research trends in TCM and IRR published in SCOPUS 

from 1985 to 2020. As we embark on a new age of data-intensive scientific discovery in medicine, opportunities and difficulties have 

been identified that will help us determine the field's future courses.  

Keywords: Bibliometrics, data visualization, computer-based medical diagnostic systems, traditional Chinese medicine, data 

retrieval, herbal pharmaceutical technology, multiple correspondence analysis 

 

 

Chinese Traditional Medical Journal 
 

 

Traditional Chinese Medicine with Data Visualization: Prospects and Difficulties in 
the Age of Big Data 

Poorna Chander Rao 

Palmer iSchool of Library, Long Island University, USA 

Received on:  21 Oct 2024   Revised on: 20 Nov 2024    Accepted Date: 25 Dec 2024  
Published on: 24 Jan 2025 

 

 

 
 

Introduction  

 

Numerous resources for biomedical and health science have been 

contributed by scholars of traditional Chinese medicine (TCM) 

since its inception thousands of years ago [1]. These resources 

include published literature, medicinal materials, herbs, diagnostic 

matrices, clinical records, medical formularies, and more. True 

information science pioneers in many respects, TCM's founders 

foresaw societal issues impacting their day and set out to solve 

them.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

and aware of what was available at the time, which consisted 

mostly of classical medical texts transmitted orally from ancient 

wise men and women before the invention of written records, as 

well as botanicals and other naturally occurring compounds 

collected from the surrounding area [2]. With the goal of containing 

epidemic diseases, these pioneers in medicine went on to compile 

and index this information into seminal works (such as crude 

expert knowledge systems) that would serve as a retrieval 

mechanism for a variety of medical problems [3]. 



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These old systems mirror mathematical reasoning based on the 

existence or absence of human biological information [4] and use 

a binary system of numbers and probabilities that closely 

resembles the idea of bits and bytes that make up the foundation 

of our modern computing environment [5]. Their primary sources 

of information are patterns of natural discord, representations of 

herbal medicine, and models of treatment.  

A concept with origins in the field of artificial intelligence (AI) that 

aims to mimic the deductive process of disease diagnosis is set to 

become a common reality in the near future, thanks to 

developments in big data analytics, the Internet of Things (IoT), 

and society's persistent quest for optimal health and wellness [6]. 

According to Samsung [7], in the not-too-distant future, we may 

have ubiquitous sensors that continuously monitor our health. 

These sensors will be connected to a massive AI network, which 

will allow them to detect early warning signs of illness, encourage 

users to make healthier choices, direct medical research, rank us, 

and possibly even implement a pay-as-you-live model for health 

and life insurance premiums. Complex traditional Chinese 

medicine (TCM) algorithms, enabled by information 

representation and retrieval (IRR) technology, can now aggregate 

medical data from various sources according to suitable criteria, 

filling a gap in Western diagnostic practices. These algorithms 

mimic classical deductive and reasoning procedures to solve 

medical problems and recommend treatment protocols [8]. 

Several IRR challenges arise from the complexity of medical 

knowledge [9], such as the need for standardized data formats, 

well-formulated inputs, and efficient critical factors for 

appropriate feature representation. Because traditional Chinese 

medicine (TCM) is based on ancient practices rather than modern 

science, there is no universal system that can decipher the myriad 

medical manuscripts written in different dialects and kept in 

various places around the world. This further complicates matters 

[10]. While regional cultural differences and language variations 

do enrich and diversify the TCM terminology system, they also 

pose problems with standardization when it comes to 

modernizing this classical medical modality, particularly when 

contrasted with Western medical systems, which also face 

vocabulary issues [11]. This is because medicine has evolved and 

developed over thousands of years. The Western scientific 

community also views natural herbal medical practices and other 

uses like acupuncture as experimental, in part because of the 

subjective character of these treatments. Relying more on big data, 

information-rich experiments centered on IRR and mobile 

technologies as opposed to controlled clinical trials is the way to 

go in the future of research.  

 

a method for quantitatively gathering vast amounts of TCM data. A 

bibliometric analysis with data visualizations related to IRR and 

TCM, specifically through author, paper, and co-word analysis, will 

be conducted in this work to better understand the field's 

conceptual structure and to identify potential research 

opportunities and challenges in developing and implementing a 

TCM-based diagnostic and medical recommendation system. 

Materials and Methods 

Scopus® (http://www.scopus.com), considered by some to 

be the largest abstract and citation database of peer-reviewed 

literature, including scientific journals, books and conference 

proceedings, was initially searched on October 9, 2019 and 

again on November 16, 2019 for all citations with the Boolean 

string [(“Chinese medicine”) AND (“information retrieval”) OR 

(“information representation”)] located in the article title, abstract, 

or keywords. The results of the search revealed 192 documents 

for the period 1985-2020; a BibTeX export file was saved and read 

into R, a free software environment for statistical computing and 

graphics (http://www.r-project.org), using bibliometrix [12], a 

tool for comprehensive science mapping analysis. The function 

*readFiles* was initially used to create a single large character 

vector; this object was then converted into a data frame using the 

function *convert2df*, with cases corresponding to manuscripts 

and variables to field tags in the original export file, comprising 

all bibliographic attributes of each document based on Clarivate 

Analytics WoS Field Tag codified industry standards [13]. During 

data cleansing, four entries were removed from the data frame 

due to a lack of author and other document information: three 

represented conference proceeding introductions and a fourth 

represented an introductory chapter on semantic grid applications 

for traditional Chinese medicine. 

Results and Discussion 

Descriptive analysis 

To begin, a descriptive analysis was performed on the 

bibliographic data frame using the function *biblioAnalysis*; a 

display of the main results are included in Table 1. A total of 188 

documents from 105 sources and 899 author appearances were 

noted, including a collaboration index of 2.49. Figure 1 illustrates 

the number of publications per year for the collection period 

1985-2020; one large spike occurs in 2006 (28 citations) which 

continues into 2008, followed by a dip and then another, slower 

increase cumulating in 2017. This trend mirrors publications on 

IRR in general, which also peaks in 2006, according to Scopus, 

representing the maturation of computer browsing and the initial 

transition to mobile smart devices. 

Table 2 contains the top 10 most cited papers in the collection, 

with Kanehisa M, et al. [14] having been cited over 1,500 times for 

http://www.scopus.com/
http://www.r-project.org/


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their work in Japan on computerizing disease information using 

pathway maps, all Japanese drugs (including every TCM herbal 

formula), and gene/molecule lists. The second most-cited paper, 

Tang JL, et al. [15], represents one of the oldest papers in the current 

collection and is a summary of issues relating to randomized 

controlled trials in TCM, specifically: lack of blinding; low sample 

sizes; using another, unproven TCM treatment as the control; not 

long-term in nature; incompleteness; lack of quantitative data; 

missing intention to treat; lack of data on baseline characteristics 

or side effects; short reporting; and presence of publication bias. 

The third most-cited paper [16], was published in an American 

Heart Association journal and concludes, in similar fashion, the 

insufficiency of TCM evidence in using herbal medicinal for stroke 

patients, due to bias from poor methodology, even though the 

agents used appeared to be potentially beneficial and nontoxic in 

nature. The fourth document with the most citations [17] discusses 

newly published guidelines and technical notes by the European 

Union, in collaboration with Chinese scientists, to encourage good 

practice in the collection, assessment, and publication of TCM 

literature. The fifth most-cited document [18] reviews advances 

in automated tongue diagnosis, a key requirement for the accurate 

gathering of quantitative data, while the sixth most-cited document 

[19] discusses the development of ontology for TCM IRR. Fang YC, 

Table 1: Main information regarding the collection. 

et al. [20] and Qiao X, et al. [21] both discuss the creation of TCM 

databases, while Wojcikowski K, et al. [22] again point to difficulties 

with randomized controlled trials in TCM, particularly relating to 

the use of herbal medicinals in the treatment of kidney disorders. 

The tenth most-cited document [23] concludes that text mining 

of TCM literature and clinical data carries with it the potential to 

clarify misunderstandings, but clear operational definitions are 

first required. 

Table 3 lists total citations by country, along with average article 

citations; Japan leads this metric due to the Kanehisa M, et al. [24] 

document noted above, with China positioned strongly behind with 

865 total citations. As expected, over 50% of the top 10 countries 

are located in Asia; the United States remains far behind in this 

research area with only 10 total citations related to one published 

article. Table 4 illustrates the top author countries in the collection, 

with China strongly in the lead with 109 articles (a frequency of 

0.76224) – additionally, 89% of these articles (97) are considered 

single country publications. Given the nature of this data, TCM 

and IRR research in the East has been mainly conducted as single 

country publications (China, Hong Kong, Korea, and Japan) while 

Australia, Canada, and Germany research has been more multi- 

country in nature. 

 

Description 

Documents 188 

Period 1985 – 2020 

Sources 105 

Average citations per documents 17.9 

Authors 710 

Author Appearances 899 

Authors of single-authored documents 13 

Authors of multi-authored documents 697 

Documents per Author 0.265 

Authors per Document 3.78 

Co-Authors per Documents 4.78 

Collaboration Index 2.49 

Table 2: Top 10 most cited papers. 

Paper Total Citations (TC) TC per Year 

Nucleic Acids Res [14] 1,506 136.91 

Br Med J [15] 203 9.67 

Stroke [16] 130 10 

J Ethnopharmacol [17] 101 12.62 

IEEE Trans Med Imaging [18] 88 5.87 

Artif Intell Med [19] 80 5 

BMC Complement Altern Med [20] 79 6.58 

J Chem Inf Comput Sci [21] 76 4.22 

J Lab Clin Med [22] 69 4.93 

J Biomed Informatics [23] 59 5.9 



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Table 3: Top 10 total citations per country. 

Country TC Average Article Citations 

Japan 1,509 754.5 

China 865 7.94 

Hong Kong 286 47.67 

Australia 254 31.75 

Taiwan 79 79 

Korea 51 10.2 

United Kingdom 32 16 

Singapore 21 21 

Germany 13 6.5 

USA 10 10 

 

Table 5 lists the top 10 most productive authors in the collection, 

based on both number of published articles (full counting) and 

number of published articles fractionalized (which assigns co- 

authored publications a fraction of one to each of the co-authors); 

studies have illustrated that, oftentimes, fractional counting offers a 

more useful perspective than full counting, especially as a means to 

avoid misunderstanding or misinterpretation [25]. Fractionalized 

counting does not affect the most productive author (Zhang Y) 

but does shift the order of the others slightly and results in the 

appearance of one new author (Xiong X) in the top 10. 

Figure 2 applies the *authorProdOverTime* function on the 

collection to calculate and visualize the production of these top 

10 authors over time, in terms of number of publications and 

total citations per year, for the period 1985-2020. This illustration 

clearly depicts the top producing author (Zhang Y) as covering both 

a wide period (2005-2019) along with more recent proliferation, 

oftentimes as a co-author, as noted by the number of articles 

fractionalized (2.09). Other authors with more recent production 

Table 4: Top 10 corresponding author’s countries. 

include Yu T (8 overall publications), Li J and Wang Y (6 overall 

publications each), and Liu L (5 overall publications). 

Table 6 contains the top 10 most frequent journals, based on 

number of published articles in the collection – led by the Chinese 

Journal of Clinical Rehabilitation with 25 articles and followed by 

Evidence-Based Complementary and Alternative Medicine with 

11 publications. However, it is important to also look at this data 

from the perspective of number of documents published annually; 

this information for each of the top five sources is visualized in 

Fig. 3 using the function *sourceGrowth*, which illustrates that 

the Chinese Journal of Clinical Rehabilitation was only in existence 

from 2002-2006. Since then, four newer journals have increased 

their publication rate, particularly Evidence-Based Complementary 

and Alternative Medicine, which is second in number of articles 

but clearly the leading publication in this field, particularly as the 

journal currently holds an h-index of 72 and sits as the sixth ranked 

journal in complementary and alternative medicine [26]. 

 

Country Articles Frequency SCP MCP MCP Ratio 

China 109 0.76224 97 12 0.11 

Australia 8 0.05594 4 4 0.5 

Hong Kong 6 0.04196 5 1 0.167 

Korea 5 0.03497 5 0 0 

Canada 2 0.01399 0 2 1 

Germany 2 0.01399 1 1 0.5 

Japan 2 0.01399 2 0 0 

United Kingdom 2 0.01399 2 0 0 

Brazil 1 0.00699 1 0 0 

Hungary 1 0.00699 1 0 0 

Table 5: Top 10 Most productive authors. 

Author No. of Articles Author No. of Articles Fractionalized 

Zhang Y 10 Zhang Y 2.09 

Wu Z 9 Zhou X 2.05 



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Yu T 8 Wu Z 1.75 

Zhou X 8 Wang Y 1.45 

Chen H 7 Yu T 1.35 

Chen X 7 Li J 1.31 

Li J 6 Chen H 1.25 

Wang Y 6 Xiong X 1.25 

Cui M 5 Cui M 1.19 

Liu L 5 Chen X 1.11 

Table 6: Top 10 Most frequent journals. 

Sources No. of Articles 

Chinese Journal of Clinical Rehabilitation 25 

Evidence-Based Complementary and Alternative Medicine 11 

Journal of Ethnopharmacology 7 

Zhongguo Zhongyao Zazhi 7 

Journal of Alternative and Complementary Medicine 6 

Chinese Journal of Evidence-Based Medicine 5 

Chinese Journal of Integrative Medicine 5 

Zhongguo Zhongxiyi Jiehe Zazhi 5 

Journal of Chinese Integrative Medicine 4 

Journal of Traditional Chinese Medicine 4 

Table 7: Top 10 Most frequent keywords. 

Author Keywords No. of Articles Keywords-Plus No. of Articles 

Traditional Chinese Medicine 27 Information Retrieval 173 

Systematic Review 13 Chinese Medicine 151 

Chinese Medicine 7 Human 120 

Information Retrieval 7 Article 86 

Meta Analysis 6 Medicine 77 

Information Extraction 5 Humans 67 

Ontology 5 Review 66 

Review 5 Herbaceous Agent 54 

TCM 5 Chinese Traditional 45 

Chinese Herbal Medicine 4 Priority Journal 42 

Table 8: Historiograph legend. 

Year Reference Local Citations Global Citations 

1999 TANG JL, 1999, BR MED J 3 203 

2001 CHANG IM, 2001, ANN NEW YORK ACAD SCI 1 20 

2002 BENSOUSSAN A, 2002, J TOXICOL CLIN TOXICOL 2 36 

2002 QIAO X, 2002, J CHEM INF COMPUT SCI 2 76 

2004 KA WF, 2004, J ALTERN COMPLEMENT MED 1 7 

2004 ZHOU X, 2004, ARTIF INTELL MED 5 80 

2005 WANG JF, 2005, CLIN PHARMACOL THER - 21 

2006 LI Y, 2006, ZHONGGUO ZHONG XI YI JIE HE ZA ZHI 1 5 

2007 FLOWER A, 2007, J ALTERN COMPLEMENT MED - 31 

2007 CHEN H, 2007, BMC BIOINFORM 2 25 

2008 FANG YC, 2008, BMC COMPLEMENT ALTERN MED 5 79 



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2008 TSE HYG, 2008, J BIOMOL SCREEN - 2 

2008 CHEN H, 2008, BMC BIOINFORM - 5 

2009 MAY BH, 2009, BIOGERONTOLOGY 1 18 

2010 ZHANG X, 2010, PROC - INT CONF BIOMED ENG INF, BMEI 1 11 

2010 ZHOU X, 2010, J BIOMED INFORMATICS 3 59 

2010 BOEHM K, 2010, HEALTH INF LIBR J - 13 

2011 SAMPSON M, 2011, EVID -BASED COMPLEMENT ALTERN MED - 7 

2012 JIANG Z, 2012, IEEE INT CONF E-HEALTH NETWORKING 1 11 

2012 MAY BH, 2012, J ALTERN COMPLEMENT MED 1 16 

2012 JIANG M, 2012, EVID -BASED COMPLEMENT ALTERN MED - 43 

2014 CHEN X, 2014, COMP MATH METHODS MED - 3 

2014 CHEN H, 2014, BIOMED RES INT - 3 

2015 XIONG X, 2015, NAT REV CARDIOL - 21 

2015 YOU M, 2015, SCI WORLD J - 1 

2015 XIONG X, 2015, BMJ OPEN 1 7 

2016 MAY BH, 2016, J ALTERN COMPLEMENT MED - 9 

2016 WAN H, 2016, J AM MED INFORMATICS ASSOC - 4 

2016 YU T, 2016, PROC - INT CONF BIOMED ENG INFORMATICS, BMEI - 1 

2017 LIU YQ, 2017, CHIN J INTEGR MED 1 1 

2017 WANG L, 2017, EVID -BASED COMPLEMENT ALTERN MED - 1 

2019 YOON SH, 2019, CHIN J INTEGR MED - 3 

 

Table 7 contains the top 10 most frequent keywords using two 

keyword variations: authors’ keywords, as specifically selected 

by each author, and keywords-plus, which are those keywords 

extracted from the publication by Scopus’ database algorithms. The 

results vary, with keywords-plus identifying many more in common 

across the collection – for example, information retrieval was only 

selected by seven authors as a keyword but appears 173 times as 

a keywords-plus. Overall, while keywords-plus is as effective as 

authors’ keywords in terms of bibliometric analysis investigating 

the knowledge structure of a particular field, it is often less 

comprehensive in representing an article’s specific content [27]. 

However, within this collection, the use of keywords-plus may 

lead to a greater understanding than simply using those keywords 

identified by the authors, due to the increased volume and 

commonality of terms; this is particularly evident in Figure 4a and 

Figure 4b, which clearly illustrate greater and more prolific growth 

of keyword-plus over time, as compared to authors’ keywords. 

Network visualizations 
 

Figure 5 utilizes the *threeFieldsPlot* function to create a Sankey 

diagram. This diagram displays multiple attributes simultaneously, 

including top authors on the left, top author keywords in the center, 

and major cited references on the right. It summarizes the activity of 

the journals and keywords that are presented below. The size of the 

boxes represents total output, whereas the breadth of the bands is 

directly related to frequency.  

The picture in Figure 6 shows the web of partnerships between 

researchers in and  

 

from one country to another, using the help of the *biblioNetwork* 

and the *networkPlot* functions. The size of the sphere represents 

total production, the density of lines represents the strength of the 

cooperation, and the color represents the nature of the collaboration 

in a spherical arrangement. The bulk of studies are concentrated on 

China and its surrounding areas, especially with Western nations; 

nevertheless, six other nations—Taiwan, Brazil, Japan, Spain, Hungary, 

and Korea—are shown to be operating autonomously. Germany–

United Kingdom–China, USA–Canada–China, and Singapore–China, 

according to line density, are the primary cooperation networks, but 

they are feeble.  

In Figure 7, we can see an example of a word co-occurrence network 

that groups and maps phrases taken from abstracts of authors. This 

information was first retrieved from each manuscript's textual 

abstract field using the *termExtraction* function in conjunction with 

word stemming. The bigger clusters of information science (red), 

herbology (green), and biomedicine (blue) create broader categories 

in this picture, with TCM standing alone at the bottom left. Figure 8 

shows the same thing for author keyword co-occurrences; it follows 

the same pattern as the extracted words from the author abstracts, 

but because each author knew the work inside and out when they 

chose their keywords, this visualization is more organized and shows 

a logical progression from traditional Chinese medicine (TCM), 

through an information retrieval main section, herbology, and finally 

Western biomedicine. 



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Figure 2: Top 10 author productivity for the period 1985-2020. 

Figure 3: Source Growth, Top Five Journals, 1985-2020. 

 
 

 

 
 

 

Figure 1: Publications per year 1985-2020 (Scopus). 



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Figure 5: Sankey Diagram of Main Authors, Keywords, and Journals. 

Figure 6: Country collaboration network. 

Figure 7: Author abstract co-occurrence network. 

 
 

 

 
 

 

 

 
 
 
 
 
 
 
 
 
 
 

 
Figure 4a: Author keyword growth per year, 1985-2020. Figure 4b: Keyword-plus growth per year, 1985-2020. 



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Figure 9: Historiograph of TCM and IRR (minimum one citation). 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Figure 10: Conceptual map and keyword plus clusters with axis definitions. 

 

 

Visualizing influential papers 

In each and every scientific field, a number of publications play 

influential roles in its evolution; these articles and their impacts can 

be accelerating factors in research development [28]. It is therefore 

important to identify and visualize the most influential articles on 

TCM and IRR published between 1985 and 2020, in order to better 

understand the nature and chronology of the field through its key 

authors, papers, and subjects. Using the *histNetwork* function, 

we created a historical citation network from the collection, using 

a minimum number of one global citation for the documents 

included in the analysis (see Table 8 for legend). We then employed 

the *histPlot* function to plot the historical co-citation network 

in the style of Garfield [29], using both local and total citation 

distributions; nodes displayed in Fig. 9 identify the thirty two 

specific articles identified in the collection and sort the main 

bibliography in ascending order by year. Each circle represents a 

paper, and arrows, pointing from one node to the next (usually to 

an older paper), indicates the citation relationship between these 

key works. 
 

 
 

 

Figure 8: Keywords plus co-occurrence network. 



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In the historiograph for TCM and IRR, the paper by Tang JL, et 

al. [30] serves as the first work noted and surveyed the efficacy 

of randomized controlled trials in TCM literature; this spurned a 

line of research (highlighted in blue) extending to Flower A, et al. 

[31] who advocated for the Delphi method, Sampson M, et al. [32] 

who searched for additional databases to identify more successful 

controlled trials, and Jiang M, et al. [33] who evaluated evidence- 

based literature for TCM diagnosis and knowledge discovery. 

The second and most dominant area of research in Figure 9 

(highlighted in red) focuses on TCM information databases and 

begins with Chang IM [34] who investigated anti-aging and health- 

promoting elements derived from traditional herbal remedies found 

in the Traditional Oriental Medicine Database, leading to future 

research by Boehm K, et al. [35], who provided an overview of 45 

published database resources for complementary and alternative 

medicine. Bensoussan A, et al. [36] established research goals for 

the search and retrieval of scientific evidence regarding the toxicity 

of Chinese herbal medicine, which contributed to Wang JF, et al. 

[37] and the construction of a TCM information database. Qiao X, 

et al. [38] created a structured database of components extracted 

from TCM herbs, while Zhou X, et al. [39] wrote an influential paper 

(5 local citations) that used ontology to construct a unified TCM 

language system for information retrieval and integration. This led 

to Zhou X, et al. [40], which investigated research issues regarding 

TCM text mining, You M, et al. [41], who developed an intelligent 

system for customized clinical TCM case management and analysis, 

Wan H, et al. [42], who constructed a heterogeneous factor graph 

model for extracting relations from TCM literature, and Yu T, et al. 

[43], who utilized semantic web technologies to build cross-cultural 

communication between TCM and Western medicine. 

Chen H, et al. [44] used semantic and knowledge-based 

techniques to build e-toolkits that facilitate TCM information 

sharing; this contributed to Chen H, et al. [45], which introduced 

state-of-the-art semantic web technologies for biomedicine as a 

whole, including applications for TCM and translational research. 

Tse HYG, et al. [46] developed an online TCM herbal medical 

database built again from the earlier herbal works of Bensoussan A, 

et al. [47] and Qiao X, et al. [48]. Li Y, et al. [49] focused on utilizing 

data mining techniques that compared clinical characteristics of 

TCM and Western medicine in the diagnosis of rheumatoid arthritis, 

which led to Fang YC, et al. [50], another influential paper, who 

developed a database to provide information about TCM, genes, 

diseases, effects, and ingredients from a wide variety of biomedical 

literature. This was further studied by Zhang X, et al. [51], who 

created a hierarchical symptom-herb topic model for TCM research 

in the treatment of diabetes, and Jiang Z, et al. [52], who used link 

topic models to analyze TCM symptom-herb regularities. Chen X, 

et al. [53], also used this research to develop a semantic search 

engine for IRR in modern biology and TCM, along with Chen H, et 

al. [54], who presented a general web ontology language reasoning 

framework to study biological entities across TCM and Western 

medicine. These works ultimately influenced Wang L, et al. [55], 

who used topic model and multi-label classifiers to predict the 

function of TCM herbal prescriptions. 

A third, smaller research group in Fig. 9 (highlighted in green) 

begins with Ka WF [56], who introduced journals and other TCM 

research materials available online and May BH, et al. [57], who 

searched English and Chinese databases to review the effectiveness 

and safety of Chinese herbal medicines for use in the treatment 

of cognitive and memory impairment. These two works led to 

additional research by May BH, et al. [58], comparing and evaluating 

published TCM collections for research and drug discovery 

searches, and May BH, et al. [59], who searched a database of over 

1,000 classical and pre-modern TCM texts for the treatment of 

memory impairment. A fourth research group was also mapped on 

the historiograph (highlighted in purple) relating to difficulties in 

drawing clinical conclusions in the treatment of specific Western 

medicine disorders with TCM: Xiong X [60] reviewed an article on 

randomized controlled trials for the treatment of cardiovascular 

disease with TCM, while Xiong X, et al. [61] researched the clinical 

effects of a TCM herbal decoction in the treatment of hypertension. 

A fifth and final research group (highlighted in yellow), albeit 

small, begins late and relates to the standardization of TCM: Liu 

YQ, et al. [62] focused on standards and proposals established by 

the International Organization for Standardization (ISO), which 

Yoon SH, et al. [63] built from this to investigate the pros and cons 

of proposing standard terminology for acupotomy, a treatment 

modality which involves the use of both an acupuncture needle 

and a surgical scalpel. These five pathways of research in TCM 

and IRR help illustrate both the research difficulties in the field as 

well as opportunities in the treatment of specific diseases and the 

construction of modern databases and ontologies for the future use 

of this medical modality. 

Visualizing the conceptual structure of the field 

An exploratory multivariate approach, multiple correspondence 

analysis enables numerical and graphical examination of patterns in 

connections of categorical dependent variables, including keywords 

[64]. To create a field-specific conceptual map, we utilized keywords-

plus with no stemming and up to five clusters, all generated using the 

*conceptualStructure* function. The distribution of the dots and their 

relative positions across the dimensions are used to interpret the 

results; words with similar distributions are shown closer together in 

Figure 10. The following is the translation of map data that is 

proposed by Cuccurullo C. et al. [65]: The size of each point is directly 

related to the keyword's overall contribution; the closeness of 

adjacent points indicates the presence or absence of shared 

substance; and the map's dimensions mirror the topical orientation's 

characteristic poles within TCM and IRR; the center of the map 

represents the average position of all the articles, thus the research 



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field's center.  

In the upper right corner, you can see Cluster 1 (highlighted in red). 

This cluster comprises terms from publications discussing 

traditional Chinese medicine (TCM) herbs and natural substances as 

potential remedies for both short-term and long-term health 

problems. In the middle and lower center of the map, you can see 

the biggest cluster (highlighted in blue). It comprises terms from 

publications about the creation of databases and retrieval systems 

for scientific TCM medical literature, including Zhou X, et al. [66]. In 

the third cluster, which is centered to the left and is highlighted in 

green, you may find terms related to traditional Chinese medicine 

(TCM) diagnosis, ontology, and data mining in publications. Cluster 

four (the purple one) is in the bottom right corner and has terms 

from publications about the effectiveness (or lack thereof) of 

evidence-based research and traditional Chinese medicine (TCM), 

especially in relation to clinical trials. In the upper left-center 

quadrant, you can see Cluster 5, which is orange-highlighted, and 

which includes terms from publications about how TCM 

information is represented in IRR systems.  

The first dimension of published research stretches horizontally 

from theoretical to experimental, as shown in this conceptual 

structure analysis. Vertically expanding as the second dimension, 

this one specifies published publications and the keywords 

associated with them throughout a range from clinical to highly 

technical. Since TCM is an individualized medicine that treats each 

patient according to their specific pattern of disharmony, based on 

information mainly obtained through quantitatively-based 

examinations conducted by humans, there are obvious research 

gaps in the upper and lower left quadrants. This indicates that more 

scientific research is needed to support both clinical and technical 

IRR work in TCM theory. Western biomedicine views much of the 

research on the right side of the map—namely, Chinese herbal 

pharmaceutical medicine and its integration with biomedicine—as 

experimental because there has been no success in controlled trials 

measuring the efficacy of drugs or the results of TCM treatments. So, 

this map shows the way forward for TCM and IRR research: using 

new research paradigms and tools, like big data analytics and 

internet of things technologies, to combine Eastern and Western 

medicine in a translational approach, to learn more about how 

diseases develop and what impacts they have on the body. 

Conclusion 

In the future of both Samsung [67] and Sinclair DA, et al. [68], a huge, all-

encompassing network of medical IRR systems will be behind and 

underneath our increasingly monitored, emphasized, and refined lives as a 

way to attain a new state of existence. It may be time for the broad 

discipline of information studies to emerge from its proverbial shell. Such 

frameworks have started to materialize in different parts of the universe: 

Automakers have started to program themselves, and algorithms that 

understand our tastes, habits, goals, and bioactivities are being fine-tuned.  

 

in order to get where they're going; drivers are finding out where their 

passengers are going even before they meet them; and music is continuously 

being indexed and streamed globally, regardless of language or culture. For 

the user, device, network, and provider to engage in a digital, behind-the-

scenes dance of mobile technology driven by capitalism, all of these systems 

need underlying IRR architecture. One can only speculate as to whether, in 

the future, we might be able to solve half of our medical problems by 

combining TCM and IRR, by applying these conceptual frameworks to 

problems similar to those ancient TCM practitioners encountered, but with a 

focus on health and wellbeing rather than profit and market share. This is an 

admirable goal to strive toward, especially since that the prevalence of many 

diseases—including chronic ones like obesity and mental illness—seems to 

be rising. Therefore, further study into TCM is necessary, especially 

concerning IRR and how it connects to the need to anchor contemporary 

clinical and technological research in classical theory. Perhaps TCM's future 

lies in a big data design and deployment methodology with a new medical 

IRR at its core. This methodology would be translational in nature, reducing 

health care costs and improving medical outcomes worldwide, much like Jim 

Gray's vision of scientific discovery, which is driven by the collection, 

analysis, and comprehension of digital data by an ever-increasing 

interdisciplinary community of both professional and citizen-like scientists 

[69]. 

Acknowledgements 

This paper and the research components related to information 

representation and retrieval would not have been possible without 

the support of the author’s doctoral advisor, Dr. Heting Chu, 

professor in the Palmer iSchool of Library and Information Science 

at Long Island University. 

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