ISDS Annual Conference Proceedings 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution- Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ISDS 2015 Conference Abstracts Natural Language Processing and Technical Challenges of Influenza-Like Illness Surveillance Dino P. Rumoro1, Gillian S. Gibbs*1, Shital C. Shah1, Marilyn M. Hallock1, Gordon M. Trenholme1, Michael J. Waddell2 and Joseph P. Bernstein3 1Rush University, Chicago, IL, USA; 2Pangaea Information Technologies, Ltd, Chicago, IL, USA; 3H-CORE, LLC, Chicago, IL, USA Objective To review the natural language processing (NLP) and technical challenges encountered in an automated influenza-like illness (ILI) surveillance system. Introduction Processing free-text clinical information in an electronic medical record (EMR) may enhance surveillance systems for early identification of ILI outbreaks. However, processing clinical text using NLP poses a challenge in preserving the semantics of the original information recorded. In this study, we discuss several NLP and technical issues as well as potential solutions for implementation in syndromic surveillance systems. Methods This is a retrospective, cross-sectional study conducted at the EDs of a large urban academic medical center and community hospital. The study timeframe was October 1, 2014 to June 30, 2015. Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification (GUARDIAN) – a syndromic surveillance program – received and processed HL7 messages in real-time and generated ILI surveillance reports. The sophisticated GUARDIAN NLP algorithm processed each patient chart component, consistent with a physician’s manual review [1]. A random sample of 10 ILI-positive cases detected by GUARDIAN was drawn each week for manual review to confirm the positive presence of the ILI case definition terms: fever, cough, and sore throat. False ILI-positive cases, and associated causes, were documented and categorized as shown in Table 1. Results Of the 519 ILI-positive charts reviewed, 56 cases were false positive, mainly due to NLP or programming errors (e.g., incorrect concept parsing due to certain punctuation and word combinations). Temporal relationships were found to be a challenge for NLP: examples included when a clinician noted a fever in the past or documented instructions to return for a fever. Physician documentation style was also a common and difficult problem: examples include the use of italics or bold text to represent a positive or negative symptom. We identified new terms to add to the negation list and discovered a problem caused by the use of a long negation string. GUARDIAN was programmed to acknowledge negation strings up to 16 words since any more words than that decreased accuracy. We found that temperatures were occasionally corrected in the EMR, while GUARDIAN had no way of knowing the original value was an error. Lastly, on occasion, there was also reception of incorrect inpatient vitals and truncated nurse notes. With the modification of our system architecture and NLP engine, we were able to reduce the associated ILI false positives from 56 (10.8%) to 32 (6.2%). Conclusions The use of NLP can enhance the efficacy of syndromic surveillance systems. However, there are limitations to NLP processing loads. While many NLP errors can be corrected, yielding improved accuracy, some issues cannot be resolved. Sharing known technical and NLP issues, and their resolutions, can assist in minimizing errors to acceptable levels (<5%) leading to refinement of existing syndromic surveillance systems. Table 1. Review of NLP and technical challenges in ILI surveillance. Note: *Issues were resolved by modifying the system architecture and NLP engine. Keywords Influenza-like illness; Natural language processing; Lessons learned Acknowledgments GUARDIAN is funded by the US Department of Defense, Telemedicine and Advanced Technology Research Center, Award numbers W81XWH-09-1-0662 and W81XWH-11-1-0711. References Silva J, Shah S, Rumoro D, Bayram J, Hallock M, Gibbs G, Waddell M. Comparing the accuracy of syndrome surveillance systems in detecting influenza-like illness: GUARDIAN vs. RODS vs. electronic medical record reports. Artificial Intelligence in Medicine. 2013;59:169-174. *Gillian S. 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Thorpe2, Hannah Mandel1 and Michael Buck1 Monitoring and Evaluation Mechanism for Multi-Center Capacity Building Gestational Diabetes Program for Physicians in India Megha Sharma* Avian Flu, Ebola, MERS, and Other Emerging Challenges for Influenza Surveillance Practitioners Alan Siniscalchi2 and Brooke Evans*1 HIT Conformance Testing: Advancing Syndromic Surveillance System Interoperability Robert Snelick and Sheryl L. Taylor* Visualizing the Local Experience: HIV Data to Care Tool Lauren E. Snyder*1, Dean McEwen1, Mark Thrun2, 1 and Arthur Davidson1 Extending an Uncertainty Taxonomy for Suspected Pneumonia Case Review Brett R. South*1, 2, Heidi S. Kramer2, Melissa Castine1, Danielle Mowery1, 2, Barbara Jones2 and Wendy Chapman1, 2 Comparison of Exposure to Risk Factors for Giardiasis Among Endemic and Travel Cases Alexandra Swirski*1, David Pearl1, Andrew Peregrine1 and Katarina Pintar2 African One Health e-Surveillance Initiative Joy Sylvester*1, Herbert Kazoora2, Meeyoung Park1, Sheba Gitta2, Betiel H. Haile1 and Scott J. McNabb1 Strengthening Community Surveillance of Ebola Virus Disease in Sierra Leone AnhMinh A. Tran*, Adam Hoar, Alyssa J. Young, Allison Connolly, Mary-Anne Hartley, Samuel Boland, Brooke Mancuso, Guddu Kaur, John Esplana, Erin Polich and Laura Fisher Surveillance of Anthrax Foci Across Pipeline Constructions in Georgia, 2003-2014 Nikoloz Tsertsvadze, Lile Malania, Nato Abazashvili, Julieta Manvelian, Mariam Broladze and Paata Imnadze Situational Awareness of Health Events Using Social Media and the SMART Dashboard Ming-Hsiang Tsou2, 3, 1, Chin-Te Jung2, 3, 1 and Michael Peddecord*4, 3, 1 Evaluation of Legionellosis Surveillance in Michigan Focusing on Diagnostic Testing Leigh M. Tyndall Snow*1, 2, Veronica A. Fialkowski1, 2 and Mary Grace Stobierski1 Highway Emergency Response and Accident Mitigation Service (HERAM) – A Field Report Balaji Utla*, Dr. Shailendra Kumar B. Hegde, Dr. Sri Ranga Prasad Saride, Dr. Ramanuja Chary Kandaala, Mr. Sridhar Upadhya and Anukrati Saxena Enhancing Biosurveillance Specificity Using Praedico™, A Next Generation Application Alireza Vahdatpour2, Cynthia A. Lucero-Obusan1, Chris Lee2, Gina Oda1, Patricia Schirmer1, Anosh Mostaghimi1, Farshid Sedghi2, Payam Etminani2 and Mark Holodniy*1 Biosense and Violence: Progress Toward Violence Prevention Using Syndromic Surveillance Jennifer Vahora* and Stacey Hoferka Situational Awareness for Unfolding Gastrointestinal Outbreaks Using Historical Data Nileena Velappan*, Ashlynn Daughton, Esteban Abeyta, Geoffrey Fairchild, William Rosenberger and Alina Deshpande Interest of Prospective Spatio-Temporal Analysis from ED Data to Detect Unusual Health Events Pascal Vilain*, Sébastien Cossin and Laurent Filleul Using Syndromic Surveillance to Identify Synthetic Cannabinoids or Marijuana Adverse Health Events in Virginia Amanda Wahnich* Lessons Learned from the Transition to ICD-10-CM: Redefining Syndromic Surveillance Case Definitions for NC DETECT Anna E. Waller, Katherine J. Harmon* and Amy Ising Automating Ambulatory Practice Surveillance for Influenza-Like Illness Andrew Walsh* Enhancing EpiCenter Data Quality Analytics with R Andrew Walsh* Alcohol-Related ED Visits and Ohio State Football: Putting the O-H in ETOH Kristen A. Weiss* and Andrew Walsh Enhancing Syndromic Surveillance at a Local Public Health Department Jessica R. White* and Kate Goodin Using Syndromic Surveillance to Enhance Arboviral Surveillance in Arizona Jessica R. White*1, Sara Imholte2 and Krystal Collier2 Evaluating the BioSense Syndrome for Heat-Related Illness in Maricopa County, Arizona Jessica R. White*, Kate Goodin and Vjollca Berisha Sensitivity and Specificity of the Fever Syndromes in BioSense and ESSENCE Caleb Wiedeman* Analysis of ED and UCC Visits Related to Synthetic Marijuana in ESSENCE-FL, 2010-2015 Michael Wiese*1 and Charles R. Clark2 Comparison of Air Passenger Travel Volume Data Sources for Biosurveillance Diana Y. Wong*, Teresa Quitugua and Julie Waters A Suggestion to Improve Timely Feedback of Infectious Disease Surveillance Data at a Provincial Level in South Korea Seon-ju Yi*, Gichan Park, Hwami Kim, SoYoon Min, Jee soo Suh, Soojung Jo, Daeun Jeong, Young-Man Kim and Heeyoung Lee Update on the CDC National Syndromic Surveillance Program Paula Yoon and Michael Coletta* Use of Peripheral Health Units in Low-Transmission Ebola Virus Disease Surveillance Alyssa J. Young*, Allison Connolly, Adam Hoar, Brooke Mancuso, John Mark Esplana, Guddu Kaur, Laura Fisher, Mary-Anne Hartley and Anh-Minh A Tran Place Matters: Revealing Infectious Disease Disparities Using Area-Based Poverty Kimberly Yousey-Hindes*1, Sharon K. Greene2, Kelley Bemis3 and Kristen Soto4 Addressing Health Equity Through Data Collection and Linked Disease Surveillance Iris Zachary*1, Jeannette Jackson-Thompson1, 2, 3, Emily Leary1 and Eduardo Simoes1, 2, 3 Epizootology and Molecular Diagnosis of Lumpy Skin Diesease Among Livestock in Azerbaijan Shalala K. Zeynalova* Exploring Usability of School Closure Data for Influenza-Like Illness Surveillance Yenlik Zheteyeva*, Hongjiang Gao, Jianrong Shi and Amra Uzicanin Efficient Surveillance of Childhood Diabetes Using Electronic Health Record Data Victor W. Zhong*1, Jihad S. Obeid2, Jean B. Craig2, Emily R. Pfaff1, Joan Thomas1, Lindsay M. Jaacks3, Daniel P. Beavers4, Timothy S. Carey1, Jean M. Lawrence5, Dana Dabelea6, Richard F. Hamman6, Deborah A. Bowlby2, Catherine Pihoker7, Sharon H. Saydah8 a