Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review
Vasiliki Danilatou,
Dimitrios Dimopoulos,
Theodoros Kostoulas
et al.
Abstract:Background: Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific Clinical Prediction Models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records (EHRs). We aimed to explore ML-CPMs applications in VTE for risk stratification, outcome prediction, diagnosis, and … Show more
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