Machine learning derived model for the prediction of bleeding in dual antiplatelet therapy patients.

ABSTRACT:

Objective

This study aimed to develop a predictive model for assessing bleeding risk in dual antiplatelet therapy (DAPT) patients.

Methods

A total of 18,408 DAPT patients were included. Data on patients' demographics, clinical features, underlying diseases, past history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. LASSO regression, multivariate logistic regression, and six machine learning models, including random forest (RF), k-nearest neighbor imputing (KNN), decision tree (DT), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM),

SUBMITTER: Qian Y 

PROVIDER: S-EPMC11479971 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature