ArticleTrauma surgery & acute care open2024
Practical guide to building machine learning-based clinical prediction models using imbalanced datasets.
Article in Trauma surgery & acute care open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence-Driven Differentiation Between Uveal Melanoma and Nevus Based on Fundus Photographs: A Systematic Review and Meta-Analysis.Translational vision science & technology · 2026Pooled it
- Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- Development and Validation of an Interpretable Machine Learning Model for Predicting ICU-Acquired Weakness in Postoperative Patients.Nursing in critical care · 2026Article
- AI-Enabled Digital Phenotyping for Personalized Risk Stratification in Internet Gaming Disorder: A Privacy-Preserving Simulation Study.Journal of personalized medicine · 2026Article
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- Real-time prediction of trauma-induced coagulopathy using an inverted transformer (trauma-former): a methodological feasibility and simulation study based on the ADEMP framework.BMC medical research methodology · 2026Article
- Evaluating the role of cost and fairness in machine learning models of suicide risk.Scientific reports · 2026Article
- Development of a machine learning model using structured and unstructured features for predicting surgery among patients with carpal tunnel syndrome: development and validation.Scientific reports · 2026Article
- Deployment of a machine learning-based predictive system for childhood diarrhea in Sub-Saharan Africa.Scientific reports · 2026Article
- Temporal machine learning framework for diabetic foot ulcer healing trajectory prediction.Biomedical engineering online · 2026Article
- Considerations for evaluating the practical utility of machine learning in suicide risk estimation: the role of cost and equity.Research square · 2025Article
- IntegratingCancers · 2025Article
- Evaluating the sample size requirements of tree-based ensemble machine learning techniques for clinical risk prediction.Statistical methods in medical research · 2025Article
- PREDICTING UNPLANNED TRAUMA INTENSIVE CARE UNIT ADMISSIONS FOR INITIAL NONOPERATIVE, NON-ICU PATIENTS.Shock (Augusta, Ga.) · 2025Article
- Navigating extreme class imbalance in suicide risk prediction.Frontiers in psychiatry · 2025Article
- MIASurviveMTP: Machine learning for immediate assessment and survival prediction after massive transfusion protocol.PloS one · 2025Article
- Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review.Children (Basel, Switzerland) · 2024Review
- It is time for some deep learning: a statistical commentary on machine learning for clinical prediction models using imbalanced datasets.Trauma surgery & acute care open · 2024Article
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical prediction models often aim to predict rare, high-risk events, but building such models requires robust understanding of imbalance datasets and their unique study design considerations. This practical guide highlights foundational prediction model principles for surgeon-data scientists and readers who encounter clinical prediction models, from feature engineering and algorithm selection strategies to model evaluation and design techniques specific to imbalanced datasets. We walk through a clinical example using readable code to highlight important considerations and common pitfalls in developing machine learning-based prediction models. We hope this practical guide facilitates developing and critically appraising robust clinical prediction models for the surgical community.
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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.