ArticlePatterns (New York, N.Y.)2023
Stable clinical risk prediction against distribution shift in electronic health records.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Bridging Data, Semantics, and Clinical Reasoning: A Knowledge Graph Framework for Pediatric Obstructive Sleep Apnea.Children (Basel, Switzerland) · 2026Article
- Fair and robust early readmission risk prediction from electronic health records via diffusion-based data augmentation and causal-invariant representation learning.Scientific reports · 2026Article
- Machine learning enhances risk stratification and treatment failure prediction in diffuse large B-cell lymphoma.HemaSphere · 2026Article
- Considering the missing science of retraining and maintenance in medical artificial intelligence, using ophthalmology as an exemplar.NPJ digital medicine · 2026Review
- Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction.NPJ digital medicine · 2026Article
- Continuous assurance for AI-driven clinical decision support systems.Frontiers in artificial intelligence · 2026Review
- Foundation Model-Guided Synthetic EHR Release: Performance Enhancement with Privacy Preservation.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Maturity Framework for Operationalizing Machine Learning Applications in Health Care: Scoping Review.Journal of medical Internet research · 2025Article
- HRP-OG: Online Learning with Generative Feature Replay for Hypertension Risk Prediction in a Nonstationary Environment.Sensors (Basel, Switzerland) · 2024Article
- Overcoming the Black Box Challenge: Building Trust in Artificial Intelligence Algorithms in Oncology.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The availability of large-scale electronic health record datasets has led to the development of artificial intelligence (AI) methods for clinical risk prediction that help improve patient care. However, existing studies have shown that AI models suffer from severe performance decay after several years of deployment, which might be caused by various temporal dataset shifts. When the shift occurs, we have access to large-scale pre-shift data and small-scale post-shift data that are not enough to train new models in the post-shift environment. In this study, we propose a new method to address the issue. We reweight patients from the pre-shift environment to mitigate the distribution shift between pre- and post-shift environments. Moreover, we adopt a Kullback-Leibler divergence loss to force the models to learn similar patient representations in pre- and post-shift environments. Our experimental results show that our model efficiently mitigates temporal shifts, improving prediction performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.