Using recurrent neural network models for early detection of heart failure onset
Open accessAbstract
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.
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RNNs were used to predict heart failure of patients based on clinical events in their records; models trained on 12-month clinical history and tested on 6 months demonstrated an AUC of 0.883 and outperformed shallow models.
“Recently, RNNs were used to predict heart failure of patients based on clinical events in their records [44]. Models trained on 12 month period of clinical history and tested on 6 months demonstrated an Area Under the Curve (AUC) of 0.883 and outperformed shallow models.”
From Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2018) · cites this paper as [44]
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- Authors
- Choi, E., Schuetz, A., Stewart, W. F., & Sun, J.
- Affiliations
- Georgia Institute of Technology (US)
- Sutter Health (US)
- Corresponding author
- Sun, Jimeng
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-08-13
- Volume
- 24
- Issue
- 2
- Pages
- 361-370
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC5391725
- OpenAlex
- W2518582440
- Cited by
- 928
- References
- 52
- Citations by year
- 2026: 52 · 2025: 83 · 2024: 99 · 2023: 103 · 2022: 107 · 2021: 131 · 2020: 125 · 2019: 130 · 2018: 70 · 2017: 27
- FWCI
- 62.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (IIS-1418511 and CCF-1533768)
- National Heart, Lung, and Blood Institute (1R01H116832-01)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- heart failure, early detection, recurrent neural networks, gated recurrent unit, electronic health records, temporal relations, primary care patients, observation window
- MeSH terms
- Machine Learning, Heart Failure, Humans, Logistic Models, Neural Networks, Computer, Area Under Curve, Early Diagnosis, Electronic Health Records, Support Vector Machine
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