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

Jitendra Jonnagaddala, Siaw Teng Liaw, Pradeep Ray, Manish Kumar, Hong Jie Dai, Chien Yeh Hsu

研究成果: 雜誌貢獻文章

12 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
文章編號636371
期刊BioMed Research International
2015
DOIs
出版狀態已發佈 - 2015

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

指紋 深入研究「Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records」主題。共同形成了獨特的指紋。

  • 引用此