ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis 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
37 citing papers in PubMed, 1 synthesis or guideline pooled 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
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- Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help.Journal of biomedical informatics · 2026Article
- How data drift impacts the safety and interpretability of machine learning models predicting risk from blood glucose control.BMJ digital health & AI · 2026Article
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- Emerging algorithmic bias: fairness drift as the next dimension of model maintenance and sustainability.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Declining Clinical Utility of Tools for Predicting Sentinel Lymph Node Biopsy Status: A Single Institution Experience from 2000 to 2021.Annals of surgical oncology · 2025Article
- Cause of death coding in asthma.BMC medical research methodology · 2024Article
- Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.Scientific reports · 2024Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Development and External Validation of a Prediction Model for Colorectal Cancer Among Patients Awaiting Surveillance Colonoscopy Following Polypectomy.Gastro hep advances · 2024Article
- Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores.medRxiv : the preprint server for health sciences · 2023Article
- Fixing the Leaky Pipe: How to Improve the Uptake of Patient-Reported Outcomes-Based Prognostic and Predictive Models in Cancer Clinical Practice.JCO clinical cancer informatics · 2023Article
- Unsupervised Detection and Correction of Model Calibration Shift at Test-Time.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Impact of the Covid-19 pandemic on the performance of machine learning algorithms for predicting perioperative mortality.BMC medical informatics and decision making · 2023Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Advanced regression and machine learning models can provide personalized risk predictions to support clinical decision-making. We aimed to understand whether modeling methods impact the tendency of calibration to deteriorate as patient populations shift over time, with the goal of informing model updating practices. We developed models for 30-day hospital mortality using seven common regression and machine learning methods. Models were developed on 2006 admissions to Department of Veterans Affairs hospitals and validated on admissions in 2007-2013. All models maintained discrimination. Calibration was stable for the neural network model and declined for all other models. The L-2 penalized logistic regression and random forest models experienced smaller magnitudes of calibration drift than the other regression models. Calibration drift was linked with a changing case mix rather than shifts in predictoroutcome associations or outcome rate. Model updating protocols will need to be tailored to variations in calibration drift across methods.
Indexed as
Identifiers
29854127PMC5977580What OpenQuestion holds
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.