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BioMed Research International
Volume 2015, Article ID 636371, 10 pages
http://dx.doi.org/10.1155/2015/636371
Research Article

Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records

1School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW 2052, Australia
2Asia-Pacific Ubiquitous Healthcare Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
3Prince of Wales Clinical School, University of New South Wales, Sydney, NSW 2052, Australia
4Department of Computer Science and Information Engineering, National Taitung University, Taitung 95092, Taiwan
5Master Program in Global Health and Development, College of Public Health and Nutrition, Taipei Medical University, Taipei 11042, Taiwan
6Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei 11219, Taiwan

Received 16 January 2015; Revised 7 July 2015; Accepted 8 July 2015

Academic Editor: Yudong Cai

Copyright © 2015 Jitendra Jonnagaddala et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302.