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

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105974

Title:	A context-aware approach for progression tracking of medical concepts in electronic medical records
Authors:	蔡宗翰;Chang, Nai-Wen;Dai, Hong-Jie;Jonnagaddala, Jitendra;Chen, Chih-Wei;Tsai, Richard Tzong-Han;Hsu, Wen-Lian
Contributors:	資訊電機學院資訊工程學系
Keywords:	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
Date:	2015-12-01
Issue Date:	2026-04-23 13:02:34 (UTC+8)
Publisher:	Academic Press Inc.;United States: Elsevier Inc
Abstract:	摘要： [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. 其他題名： J Biomed Inform 出版者： United States: Elsevier Inc 出版日期： 2015-12-01 出處： Journal of biomedical informatics, 2015-12, Vol.58 (Suppl), p.S150-S157 資源來源： ScienceDirect (Elsevier) Journals 版權： 2015 Elsevier Inc. 版權： Copyright © 2015 Elsevier Inc. All rights reserved. 識別號： ISSN: 1532-0464 識別號： ISSN: 1532-0480 識別號： EISSN: 1532-0480 識別號： DOI: 10.1016/j.jbi.2015.09.013 識別號： PMID: 26432355
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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