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.

journal_name

Biomed Res Int

authors

Jonnagaddala J,Liaw ST,Ray P,Kumar M,Dai HJ,Hsu CY

doi

10.1155/2015/636371

subject

Has Abstract

pub_date

2015-01-01 00:00:00

pages

636371

eissn

2314-6133

issn

2314-6141

journal_volume

2015

pub_type

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