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Calibration drift in regression and machine learning models for acute kidney injury
Open accessAbstract
Objective: Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population. Materials and Methods: Using 2003 admissions to Department of Veterans Affairs hospitals nationwide, we developed 7 parallel models for hospital-acquired acute kidney injury using common regression and machine learning methods, validating each over 9 subsequent years. Results: Discrimination was maintained for all models. Calibration declined as all models increasingly overpredicted risk. However, the random forest and neural network models maintained calibration across ranges of probability, capturing more admissions than did the regression models. The magnitude of overprediction increased over time for the regression models while remaining stable and small for the machine learning models. Changes in the rate of acute kidney injury were strongly linked to increasing overprediction, while changes in predictor-outcome associations corresponded with diverging patterns of calibration drift across methods. Conclusions: Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making. Model updating protocols should be tailored to account for variations in calibration drift across methods and respond to periods of rapid performance drift rather than be limited to regularly scheduled annual or biannual intervals.
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“Ongoing monitoring is essential because the performance and effect(s) of AI models can change after deployment 27,28 .”
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“This extends prior reports of temporal miscalibration [32–34] by showing calibration failure is structural and progressive rather than sporadic.”
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Metadata
- Authors
- Davis, S. E., Lasko, T. A., Chen, G., Siew, E. D., & Matheny, M. E.
- Affiliations
- Vanderbilt University (US)
- Corresponding author
- Matheny, Michael E
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-03-31
- Volume
- 24
- Issue
- 6
- Pages
- 1052-1061
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC6080675
- OpenAlex
- W2604834158
- Cited by
- 312
- References
- 72
- Citations by year
- 2026: 58 · 2025: 36 · 2024: 39 · 2023: 31 · 2022: 45 · 2021: 41 · 2020: 29 · 2019: 20 · 2018: 10 · 2017: 3
- FWCI
- 15.17
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Library of Medicine (5T15LM007450-15, 1R21LM011664-01)
- Veterans Health Administration (VA HSR&D CDA-08-020, VA HSR&D IIR 11-292, VA HSR&D IIR 13-052, VA HSR&D IIR 13-073)
- Topics
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- acute kidney injury, performance drift, hospital-acquired acute kidney injury, risk prediction, random forest, artificial neural networks, regression models, clinical decision support, veterans, distribution shift
- MeSH terms
- Clinical Decision-Making, Machine Learning, Aged, Bayes Theorem, Decision Support Techniques, Female, Hospitals, Veterans, Humans, Male, Middle Aged, United States, Logistic Models, Acute Kidney Injury
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