title: A context-aware approach for progression tracking of medical concepts in electronic medical records.
abstract: Electronic medical records (EMRs) for diabetic patients contain information about heart disease risk factors such as high blood pressure, cholesterol levels, and smoking status. Discovering the described risk factors and tracking their progression over time may support medical personnel in making clinical decisions, as well as facilitate data modeling and biomedical research. Such highly patient-specific knowledge is essential to driving the advancement of evidence-based practice, and can also help improve personalized medicine and care. One general approach for tracking the progression of diseases and their risk factors described in EMRs is to first recognize all temporal expressions, and then assign each of them to the nearest target medical concept. However, this method may not always provide the correct associations. In light of this, this work introduces a context-aware approach to assign the time attributes of the recognized risk factors by reconstructing contexts that contain more reliable temporal expressions. The evaluation results on the i2b2 test set demonstrate the efficacy of the proposed approach, which achieved an F-score of 0.897. To boost the approach's ability to process unstructured clinical text and to allow for the reproduction of the demonstrated results, a set of developed .NET libraries used to develop the system is available at https://sites.google.com/site/hongjiedai/projects/nttmuclinicalnet.
authors.affiliation: Institute of Information Science, Academia Sinica, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taiwan.
authors.forename: Nai-Wen
authors.initials: NW
authors.lastname: Chang
authors.affiliation: Department of Computer Science and Information Engineering, National Taitung University, Taiwan.
authors.forename: Hong-Jie
authors.initials: HJ
authors.lastname: Dai
authors.affiliation: School of Public Health and Community Medicine, University of New South Wales, Australia.
authors.forename: Jitendra
authors.initials: J
authors.lastname: Jonnagaddala
authors.affiliation: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan.
authors.forename: Chih-Wei
authors.initials: CW
authors.lastname: Chen
authors.affiliation: Department of Computer Science and Information Engineering, National Central University, Taiwan.
authors.forename: Richard Tzong-Han
authors.initials: RT
authors.lastname: Tsai
authors.affiliation: Institute of Information Science, Academia Sinica, Taiwan.
authors.forename: Wen-Lian
authors.initials: WL
authors.lastname: Hsu
mesh_terms: D000368:Aged; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000453:epidemiology*; D015331:Cohort Studies; D015897:Comorbidity; D016494:Computer Security; D003219:Confidentiality; D057225:Data Mining / Q000379:methods*; D048909:Diabetes Complications / Q000175:diagnosis / Q000453:epidemiology*; D018450:Disease Progression; D057286:Electronic Health Records / Q000458:organization & administration*; D005260:Female; D006801:Humans; D015994:Incidence; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D033262:Narration; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D018570:Risk Assessment / Q000379:methods; D013624:Taiwan / Q000453:epidemiology; D018875:Vocabulary, Controlled
keywords: Clinical natural language processing; Electronic medical record; Temporal information extraction