Development and validation of a machine learning model to predict venous thromboembolism among hospitalized cancer patients.

ABSTRACT:

Objective

Hospitalized cancer patients are at high risk of venous thromboembolism (VTE). However, no predictive model has been specifically developed for this population. Machine learning (ML) is advantageous for model development. This study was aimed at developing predictive models using three different ML algorithms and logistic regression for VTE risk among hospitalized cancer patients and comparing their predictive performance.

Methods

A retrospective case-control study was conducted on hospitalized cancer patients at Hunan Cancer Hospital, China, between October 1, 2021, and February 30, 2022. Patients diagnosed with vein thrombosis before or after admission were excluded. Patient, tumor, treatment, and laboratory indicator information was obtained from the hospital in

SUBMITTER: Meng L 

PROVIDER: S-EPMC9583033 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature