Researchml predictionxgboostjoint arthroplastyexternal validation
Machine Learning Evaluates VTE Risk After Arthroplasty
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7.2
A systematic review in JMIR Medical Informatics (2026) assessed 34 machine learning prediction models from nine studies through searches to December 15, 2024. The authors found high heterogeneity and pervasive risk of bias; several models reported AUCs >0.9 but lacked external validation and likely overfit. They call for prospective designs, robust data handling, and external validation to improve clinical applicability.
Scoring Rationale
Systematic synthesis with PROSPERO registration and peer-reviewed source, but scope limited to nine heterogeneous studies
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