With the increasing use of machine learning (ML)–based risk prediction models for venous thromboembolism (VTE) in patients, the prediction mechanism of ML and the number of selected factors have been research hotspots in VTE prediction. PubMed, Web of Science, MEDLINE, Embase, CINAHL, and Cochrane Library databases were searched for studies published . The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias in the included studies.
The pooled sensitivity and specificity were 0.79 (95% CI 0.78-0.80) and 0.82 (95% CI 0.81-0.82), respectively. A random-effects model was leveraged for meta-analysis of the C-index, which was 0.84 (95% CI 0.80-0.88). The most significant predictors for VTE were age, D-dimer level, and VTE history. ML has been shown to effectively predict VTE in patients.
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