Development of an interpretable machine learning model for predicting venous thromboembolism in intensive care unit patients with intracerebral hemorrhage.

ABSTRACT:

Background

Venous thromboembolism (VTE) is a frequent and potentially life-threatening complication in patients with intracerebral hemorrhage (ICH) in intensive care units (ICU). However, the necessity of prophylactic anticoagulation therapy for these patients remains controversial. This study aims to develop an interpretable machine learning (ML) model to accurately predict the risk of VTE in critically ill ICH patients, thereby enabling timely and individualized preventive measures.

Methods

A retrospective analysis was performed on clinical data from the MIMIC-IV database and ICU patients diagnosed with ICH at Qinghai Provincial People's Hospital. After data preprocessing, 1,545 cases from the MIMIC-IV database were randomly divided into a training set (1,097 cases) and a

SUBMITTER: He M 

PROVIDER: S-EPMC12819677 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature