Machine Learning in the Prediction of Venous Thromboembolism: Systematic Review and Meta-Analysis.

ABSTRACT:

Background

With the increasing use of machine learning (ML)-based risk prediction models for venous thromboembolism (VTE) in patients, the quality and applicability of these models in practice and future research remain unknown. The prediction mechanism of ML and the number of selected factors have been research hotspots in VTE prediction.

Objective

This study aimed to systematically review the literature on the predictive value of ML for VTE.

Methods

PubMed, Web of Science, MEDLINE, Embase, CINAHL, and Cochrane Library databases were searched for studies published up to March 26, 2025. Studies that developed and validated an ML model for VTE prediction in the patient population and were published in English were eligible, and studies with duplicate data were excluded

SUBMITTER: Ma R 

PROVIDER: S-EPMC12724482 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature