ArticleJournal of the American Medical Informatics Association : JAMIA2021
Learning decision thresholds for risk stratification models from aggregate clinician behavior.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Multitask Deep Learning Model for Pediatric Echocardiography Analysis.Circulation · 2026Article
- Empty Follicle Syndrome: Current Therapeutic Approaches and the Role of Triggering Agents in Assisted Reproductive Technology.Medical sciences (Basel, Switzerland) · 2026Review
- Interpretable machine learning for predicting low-dose methylprednisolone effectiveness in long COVID.iScience · 2026Article
- Prediction models developed using artificial intelligence: similar predictive performances with highly varying predictions for individuals - an illustration in deep vein thrombosis.Diagnostic and prognostic research · 2026Article
- A Novel Model for Predicting Post-Craniotomy Meningitis Using Early Postoperative Risk Stratification: A Multi-Center Retrospective Study.Infection and drug resistance · 2026Article
- Calibrated early-warning models with fairness auditing and selective prediction for course withdrawal risk: Evidence from OULAD.PloS one · 2026Article
- Directional drift in biologically meaningful vector planes: A proposed geometric framework for early detection of subthreshold disease.PloS one · 2026Article
- Development of an explainable machine learning model for 3-year cardiovascular risk prediction in new-onset type 2 diabetes using the TyG index and ultrasound features.BMC medical informatics and decision making · 2025Article
- Performance Drift in a Nationally Deployed Population Health Risk Algorithm in the US Veterans Health Administration.JAMA health forum · 2025Article
- Decision threshold models in medical decision making: a scoping literature review.BMC medical informatics and decision making · 2024Article
- Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: A pre-implementation study.Clinical biochemistry · 2023Article
- Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning.Proceedings of machine learning research · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Using a risk stratification model to guide clinical practice often requires the choice of a cutoff-called the decision threshold-on the model's output to trigger a subsequent action such as an electronic alert. Choosing this cutoff is not always straightforward. We propose a flexible approach that leverages the collective information in treatment decisions made in real life to learn reference decision thresholds from physician practice. Using the example of prescribing a statin for primary prevention of cardiovascular disease based on 10-year risk calculated by the 2013 pooled cohort equations, we demonstrate the feasibility of using real-world data to learn the implicit decision threshold that reflects existing physician behavior. Learning a decision threshold in this manner allows for evaluation of a proposed operating point against the threshold reflective of the community standard of care. Furthermore, this approach can be used to monitor and audit model-guided clinical decision making following model deployment.
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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.