ArticleJournal of biomedical informatics2018
A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set.
Article in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
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Who cites it
51 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Pooled it
- Article
- Identification and Validation of an Explainable Predictive Model For Heart Failure in Patients With Hypertension.Reviews in cardiovascular medicine · 2026Article
- Evaluation of trajectory analysis for disease risk assessment: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Predicting immune reconstitution after antiretroviral therapy in HIV/AIDS using ensemble machine learning: a real-world study.Frontiers in immunology · 2026Article
- Physiological responses to short-term high-altitude acclimatization: Insights from predictive modeling approaches.Physiological reports · 2026Article
- Gated recurrent unit with decay has real-time capability for postoperative ileus surveillance and offers cross-hospital transferability.Communications medicine · 2025Article
- Adaptable graph neural networks design to support generalizability for clinical event prediction.Journal of biomedical informatics · 2025Article
- Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features.Journal of the American Statistical Association · 2025Article
- Precision cardiovascular medicine: shifting the innovation paradigm.Frontiers in science · 2025Article
- Multimodal integration strategies for clinical application in oncology.Frontiers in pharmacology · 2025Review
- Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.Journal of medical Internet research · 2024Article
- Deep learning-based prediction of one-year mortality in Finland is an accurate but unfair aging marker.Nature aging · 2024Article
- Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D's Real-Time Capabilities and Cross-Hospital Transferability.medRxiv : the preprint server for health sciences · 2024Article
- Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation.Patterns (New York, N.Y.) · 2024Review
- Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation.Journal of the American Heart Association · 2024Article
- Semisupervised transfer learning for evaluation of model classification performance.Biometrics · 2024Article
- Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies.Journal of the American Statistical Association · 2024Article
- Predicting future occlusion or stenosis of lower extremity bypass grafts using artificial intelligence to simultaneously analyze all flow velocities collected in current and previous ultrasound examinations.JVS-vascular science · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Recently, recurrent neural networks (RNNs) have been applied in predicting disease onset risks with Electronic Health Record (EHR) data. While these models demonstrated promising results on relatively small data sets, the generalizability and transferability of those models and its applicability to different patient populations across hospitals have not been evaluated. In this study, we evaluated an RNN model, RETAIN, over Cerner Health Facts® EMR data, for heart failure onset risk prediction. Our data set included over 150,000 heart failure patients and over 1,000,000 controls from nearly 400 hospitals. Convincingly, RETAIN achieved an AUC of 82% in comparison to an AUC of 79% for logistic regression, demonstrating the power of more expressive deep learning models for EHR predictive modeling. The prediction performance fluctuated across different patient groups and varied from hospital to hospital. Also, we trained RETAIN models on individual hospitals and found that the model can be applied to other hospitals with only about 3.6% of reduction of AUC. Our results demonstrated the capability of RNN for predictive modeling with large and heterogeneous EHR data, and pave the road for future improvements.
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