A new risk assessment model of venous thromboembolism by considering fuzzy population.

ABSTRACT:

Background

Inpatients with high risk of venous thromboembolism (VTE) usually face serious threats to their health and economic conditions. Many studies using machine learning (ML) models to predict VTE risk overlook the impact of class-imbalance problem due to the low incidence rate of VTE, resulting in inferior and unstable model performance, which hinders their ability to replace the Padua model, a widely used linear weighted model in clinic. Our study aims to develop a new VTE risk assessment model suitable for Chinese medical inpatients.

Methods

3284 inpatients in the medical department of Peking Union Medical College Hospital (PUMCH) from January 2014 to June 2016 were collected. The training and test set were divided based on the admission time and inpatients from May

SUBMITTER: Wang X 

PROVIDER: S-EPMC11686901 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature