Using recurrent neural network models for early detection of heart failure onset.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Mar 1, 2017

Abstract

OBJECTIVE: We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality.

Authors

Edward Choi

College of Computing Georgia Institute of Technology Atlanta, GA, USA.
Andy Schuetz

Research Development & Dissemination Sutter Health Walnut Creek, CA, USA.
Walter F Stewart

Research Development & Dissemination Sutter Health Walnut Creek, CA, USA.
Jimeng Sun

College of Computing Georgia Institute of Technology Atlanta, GA, USA.

Keywords

Area Under Curve Early Diagnosis Electronic Health Records Heart Failure Humans Logistic Models Machine Learning Neural Networks, Computer Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (27521897)

Using recurrent neural network models for early detection of heart failure onset.

Abstract

Authors

Keywords

External Resources

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