ArticleJournal of the American Medical Informatics Association : JAMIA
Calibrating predictive model estimates to support personalized medicine.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
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- A Calibrated Deep Learning Framework Integrating Spatial Annotations and Clinical Metadata for Safe Three-Class Bone Lesion Classification on Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- An Interpretable Fuzzy Distance-Based Ensemble Framework with SHAP Analysis for Clinically Transparent Prediction of Diabetes.Diagnostics (Basel, Switzerland) · 2026Article
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- Predicting Firearm Suicide among US Army Veterans Transitioning from Active Service.Nature. Mental health · 2026Article
- Explainable AI for early developmental disability detection: a neuro-fuzzy approach.Frontiers in public health · 2026Article
- Quantifying the reliability gap in cross-domain plant disease classification: benchmarking the limited efficacy of standard mitigation techniques under controlled-to-field shift.Frontiers in plant science · 2026Article
- How well do frozen foundation models transfer? A calibration-focused benchmark for diabetic retinopathy grading.Frontiers in medicine · 2026Article
- External validation of a machine learning model to predict postpartum hemorrhage in a US northeastern healthcare system.Pregnancy (Hoboken, N.J.) · 2026Article
- Construction and Validation of a Clinical Prediction Model for Predicting Tubal Pregnancy Rupture Based on Nomogram.Journal of obstetrics and gynaecology of India · 2025Article
- A Machine-Learning-Based Approach to Informing Student Admission Decisions.Behavioral sciences (Basel, Switzerland) · 2025Article
- Aligning to the teacher: multilevel feature-aligned knowledge distillation.PeerJ. Computer science · 2025Article
- Association between cumulative changes in the Wells score and the risk of stroke-associated pneumonia in patients with acute ischemic stroke: results from the REMISE study.Frontiers in neurology · 2025Article
- Improved prediction of clinical pregnancy using artificial intelligence with enhanced inner cell mass and trophectoderm images.Scientific reports · 2024Article
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- Recalibration Methods for Improved Clinical Utility of Risk Scores.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set.BMC medical imaging · 2022Article
- Calibrated bagging deep learning for image semantic segmentation: A case study on COVID-19 chest X-ray image.PloS one · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectivePredictive models that generate individualized estimates for medically relevant outcomes are playing increasing roles in clinical care and translational research. However, current methods for calibrating these estimates lose valuable information. Our goal is to develop a new calibration method to conserve as much information as possible, and would compare favorably to existing methods in terms of important performance measures: discrimination and calibration. MATERIAL AND
methodsWe propose an adaptive technique that utilizes individualized confidence intervals (CIs) to calibrate predictions. We evaluate this new method, adaptive calibration of predictions (ACP), in artificial and real-world medical classification problems, in terms of areas under the ROC curves, the Hosmer-Lemeshow goodness-of-fit test, mean squared error, and computational complexity.
resultsACP compared favorably to other calibration methods such as binning, Platt scaling, and isotonic regression. In several experiments, binning, isotonic regression, and Platt scaling failed to improve the calibration of a logistic regression model, whereas ACP consistently improved the calibration while maintaining the same discrimination or even improving it in some experiments. In addition, the ACP algorithm is not computationally expensive. LIMITATIONS: The calculation of CIs for individual predictions may be cumbersome for certain predictive models. ACP is not completely parameter-free: the length of the CI employed may affect its results.
conclusionsACP can generate estimates that may be more suitable for individualized predictions than estimates that are calibrated using existing methods. Further studies are necessary to explore the limitations of ACP.
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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.