Machine learning analysis of bleeding status in venous thromboembolism patients.

ABSTRACT:

Background

Anticoagulation therapy is the mainstay of therapy for patients with venous thromboembolism (VTE). However, continuing or stopping anticoagulants after the first 3 to 6 months is a difficult decision that requires ascertainment of the risk of bleeding and recurrent VTE. Despite the development of several statistical models to predict bleeding, the benefit of machine learning (ML) models has not been investigated in depth.

Objectives

To assess the benefits of ML algorithms in bleeding risk evaluation in VTE patients and gain insight into their baseline information.

Methods

The baseline clinical, demographic, and genotype information was collected for 2542 patients with VTE who were on extended anticoagulation therapy. Six unsupervised dimensionality reductio

SUBMITTER: Fard SS 

PROVIDER: S-EPMC11066547 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature