A context-aware approach for progression tracking of medical concepts in electronic medical records

• Published in: Journal of biomedical informatics 2015-12, Vol.58 (Suppl), p.S150-S157
• Main Authors: Chang, Nai-Wen, Dai, Hong-Jie, Jonnagaddala, Jitendra, Chen, Chih-Wei, Tsai, Richard Tzong-Han, Hsu, Wen-Lian
• Format: Article
• Language: English
• Subjects: Aged; Cardiovascular Diseases - diagnosis; Cardiovascular Diseases - epidemiology; Clinical natural language processing; Cohort Studies; Comorbidity; Computer Security; Confidentiality; Data Mining - methods; Diabetes Complications - diagnosis; Diabetes Complications - epidemiology; Disease Progression; Electronic health records; Electronic Health Records - organization & administration; Electronic medical record; Female; Humans; Incidence; Longitudinal Studies; Male; Mathematical models; Medical; Middle Aged; Narration; Natural Language Processing; Pattern Recognition, Automated - methods; Progressions; Reproduction; Risk analysis; Risk Assessment - methods; Taiwan - epidemiology; Temporal information extraction; Temporal logic; Tracking; Vocabulary, Controlled
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2015.09.013

[Display omitted] •A context-aware approach is proposed to track medical concept progression.•The context-aware approach enriches context with more reliable temporal expressions.•nttmuClinical.NET is released for processing unstructured electronic medical records. 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.