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

Edward Choi, Andy Schuetz, Walter F. Stewart, Jimeng Sun

Research output: Contribution to journalArticlepeer-review


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. Materials and Methods: Data were from a health system's EHR on 3884 incident HF cases and 28 903 controls, identified as primary care patients, between May 16, 2000, and May 23, 2013. Recurrent neural network (RNN) models using gated recurrent units (GRUs) were adapted to detect relations among time-stamped events (eg, disease diagnosis, medication orders, procedure orders, etc.) with a 12- to 18-month observation window of cases and controls. Model performance metrics were compared to regularized logistic regression, neural network, support vector machine, and K-nearest neighbor classifier approaches. Results: Using a 12-month observation window, the area under the curve (AUC) for the RNN model was 0.777, compared to AUCs for logistic regression (0.747), multilayer perceptron (MLP) with 1 hidden layer (0.765), support vector machine (SVM) (0.743), and K-nearest neighbor (KNN) (0.730). When using an 18-month observation window, the AUC for the RNN model increased to 0.883 and was significantly higher than the 0.834 AUC for the best of the baseline methods (MLP). Conclusion: Deep learning models adapted to leverage temporal relations appear to improve performance of models for detection of incident heart failure with a short observation window of 12-18 months.

Original languageEnglish (US)
Pages (from-to)361-370
Number of pages10
JournalJournal of the American Medical Informatics Association
Issue number2
StatePublished - Mar 1 2017
Externally publishedYes


  • Deep learning
  • Electronic health records
  • Heart failure prediction
  • Patient progression model
  • Recurrent neural network

ASJC Scopus subject areas

  • Health Informatics


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