ArticleJournal of the American Medical Informatics Association : JAMIA2019
A nonparametric updating method to correct clinical prediction model drift.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 2 of them syntheses that pooled it.
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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
63 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.Journal of medical Internet research · 2024Pooled it
- Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine.Applied clinical informatics · 2021Pooled it
- Sustained Improvements After Intervention to Prevent Contrast-Associated Acute Kidney Injury: A Randomized Controlled Trial.Journal of the American Heart Association · 2025Trial
- Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine.Scientific reports · 2022Trial
- Comparison of Methods for Incorporating Related Data When Developing Clinical Prediction Models: A Simulation Study.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation.IEEE transactions on bio-medical engineering · 2026Review
- Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help.Journal of biomedical informatics · 2026Article
- Article
- Performance of predictive AI-based clinical decision support systems across clinical domains: A systematic review and meta-analysis.PLOS digital health · 2026Article
- Gaps in artificial intelligence research for rural health in the United States: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Continuous assurance for AI-driven clinical decision support systems.Frontiers in artificial intelligence · 2026Review
- Development and Validation of a Machine Learning Model to Predict the Risk of Medical Decision-Making Delay in Acute Myocardial Infarction Patients From Multicenter Tertiary Hospitals in China.International journal of general medicine · 2026Article
- Maturity Framework for Operationalizing Machine Learning Applications in Health Care: Scoping Review.Journal of medical Internet research · 2025Article
- Monitoring strategies for continuous evaluation of deployed clinical prediction models.Journal of biomedical informatics · 2025Article
- Correcting for case-mix shift when developing clinical prediction models.BMC medical research methodology · 2025Article
- Emerging algorithmic bias: fairness drift as the next dimension of model maintenance and sustainability.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Hospital-wide, dynamic, individualized prediction of central line-associated bloodstream infections-development and temporal evaluation of six prediction models.BMC infectious diseases · 2025Article
- A roadmap to implementing machine learning in healthcare: from concept to practice.Frontiers in digital health · 2025Article
- Exploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit.Critical care research and practice · 2025Review
- Development, validation and recalibration of a prediction model for prediabetes: an EHR and NHANES-based study.BMC medical informatics and decision making · 2024Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveClinical prediction models require updating as performance deteriorates over time. We developed a testing procedure to select updating methods that minimizes overfitting, incorporates uncertainty associated with updating sample sizes, and is applicable to both parametric and nonparametric models. MATERIALS AND
methodsWe describe a procedure to select an updating method for dichotomous outcome models by balancing simplicity against accuracy. We illustrate the test's properties on simulated scenarios of population shift and 2 models based on Department of Veterans Affairs inpatient admissions.
resultsIn simulations, the test generally recommended no update under no population shift, no update or modest recalibration under case mix shifts, intercept correction under changing outcome rates, and refitting under shifted predictor-outcome associations. The recommended updates provided superior or similar calibration to that achieved with more complex updating. In the case study, however, small update sets lead the test to recommend simpler updates than may have been ideal based on subsequent performance. DISCUSSION: Our test's recommendations highlighted the benefits of simple updating as opposed to systematic refitting in response to performance drift. The complexity of recommended updating methods reflected sample size and magnitude of performance drift, as anticipated. The case study highlights the conservative nature of our test.
conclusionsThis new test supports data-driven updating of models developed with both biostatistical and machine learning approaches, promoting the transportability and maintenance of a wide array of clinical prediction models and, in turn, a variety of applications relying on modern prediction tools.
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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.