Prediction of Venous Thromboembolism in Patients With Cancer Using Machine Learning Approaches: A Systematic Review and Meta-Analysis

Abstract

Purpose: Recent studies have suggested that machine learning (ML) could be used to predict venous thromboembolism (VTE) in cancer patients with high accuracy.

Methods: We aimed to evaluate the performance of ML in predicting VTE events in patients with cancer. PubMed, Web of Science, and EMBASE to identify studies were searched.

Results: Seven studies involving 12,249 patients with cancer were included. The combined results of the different ML models demonstrated good accuracy in the prediction of VTE. In the training set, the global pooled sensitivity was 0.87, the global pooled specificity was 0.87, and the AUC was 0.91, and in the test set 0.65, 0.84, and 0.80, respectively.

Conclusion: The prediction ML models showed good performance to predict VTE. External validation to determine the result's reproducibility is necessary.