Development, validation, and clinical utility of risk prediction models for cancer-associated venous thromboembolism: A retrospective and prospective cohort study.

ABSTRACT:

Objectives

This study aims to develop cancer-associated venous thromboembolism (CA-VTE) risk prediction models using survival machine learning (ML) algorithms.

Methods

This study employed a double-cohort study design (retrospective and prospective). The retrospective cohort (n ​= ​1036) was used as training set (70.0%, n ​= ​725) and internal validation set (30.0%, n ​= ​311); while the prospective cohort (n ​= ​321) was used as external validation set. Seven survival ML algorithms, including COX regression, classification, regression and survival tree, random survival forest, gradient boosting survival machine tree, extreme gradient boosting survival tree, survival support vector analysis, and survival artificial neural network, were applied to t

SUBMITTER: Jin S 

PROVIDER: S-EPMC12032184 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature