ArticleJournal of the American Medical Informatics Association : JAMIA2023
A framework for understanding label leakage in machine learning for health care.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The trial behind it
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
18 citing papers in PubMed.
- Explainable Machine Learning Predictive Models for Surgical Site Infections: Scoping Review.Journal of medical Internet research · 2026Article
- Explainable Multimodal Machine Learning Predicts 90-Day Treatment Failure in Older Patients with Fragility Fractures of the Pelvis.Journal of clinical medicine · 2026Article
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- FIRST-ICU: forecasting interventions and risk stratification in the ICU using graph neural network autoencoders.NPJ digital medicine · 2026Article
- Comment on "AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques".Global epidemiology · 2026Article
- A leakage-controlled and SHAP driven machine learning framework for paediatric respiratory disease classification using Indian hospital EHR data.BMC medical informatics and decision making · 2026Article
- Multimodal models for skin cancer classification using clinical freetext and dermatoscopic images.Communications medicine · 2026Article
- Review
- The Evolving Role of Artificial Intelligence and Machine Learning in the Wearable Electrocardiogram: A Primer on Wearable-Enabled Prediction of Cardiac Dysfunction.Bioengineering (Basel, Switzerland) · 2026Review
- Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Target leakage and the use of diagnostic variables in diabetes prediction models.Nutrition & diabetes · 2025Article
- Identifying and Predicting Cognitive Decline Using Multi-Modal Sensor Data and Machine Learning Approach.Research square · 2025Article
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- A roadmap to implementing machine learning in healthcare: from concept to practice.Frontiers in digital health · 2025Article
- Opinion on "Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare".Frontiers in immunology · 2025Article
- Why do probabilistic clinical models fail to transport between sites.NPJ digital medicine · 2024Review
- Multimodal Deep Learning for Differentiating Bacterial and Fungal Keratitis Using Prospective Representative Data.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
introductionThe pitfalls of label leakage, contamination of model input features with outcome information, are well established. Unfortunately, avoiding label leakage in clinical prediction models requires more nuance than the common advice of applying "no time machine rule." FRAMEWORK: We provide a framework for contemplating whether and when model features pose leakage concerns by considering the cadence, perspective, and applicability of predictions. To ground these concepts, we use real-world clinical models to highlight examples of appropriate and inappropriate label leakage in practice. RECOMMENDATIONS: Finally, we provide recommendations to support clinical and technical stakeholders as they evaluate the leakage tradeoffs associated with model design, development, and implementation decisions. By providing common language and dimensions to consider when designing models, we hope the clinical prediction community will be better prepared to develop statistically valid and clinically useful machine learning models.
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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.