title: Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records.
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.
authors.affiliation: School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW 2052, Australia ; Asia-Pacific Ubiquitous Healthcare Research Centre, University of New South Wales, Sydney, NSW 2052, Australia ; Prince of Wales Clinical School, University of New South Wales, Sydney, NSW 2052, Australia.
authors.forename: Jitendra
authors.initials: J
authors.lastname: Jonnagaddala
authors.affiliation: School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW 2052, Australia.
authors.forename: Siaw-Teng
authors.initials: ST
authors.lastname: Liaw
authors.affiliation: Asia-Pacific Ubiquitous Healthcare Research Centre, University of New South Wales, Sydney, NSW 2052, Australia.
authors.forename: Pradeep
authors.initials: P
authors.lastname: Ray
authors.affiliation: Prince of Wales Clinical School, University of New South Wales, Sydney, NSW 2052, Australia.
authors.forename: Manish
authors.initials: M
authors.lastname: Kumar
authors.affiliation: Department of Computer Science and Information Engineering, National Taitung University, Taitung 95092, Taiwan.
authors.forename: Hong-Jie
authors.initials: HJ
authors.lastname: Dai
authors.affiliation: Master Program in Global Health and Development, College of Public Health and Nutrition, Taipei Medical University, Taipei 11042, Taiwan ; Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei 11219, Taiwan.
authors.forename: Chien-Yeh
authors.initials: CY
authors.lastname: Hsu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003199:Computer Systems; D057225:Data Mining; D048909:Diabetes Complications / Q000453:epidemiology* / Q000473:pathology; D057286:Electronic Health Records; D006331:Heart Diseases / Q000453:epidemiology* / Q000209:etiology / Q000473:pathology; D006801:Humans; D012307:Risk Factors