Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study.

ABSTRACT:

Introduction

Venous thromboembolism (VTE) risk assessment at admission is of great importance for early screening and timely prophylaxis and management during hospitalization. The purpose of this study is to develop and validate novel risk assessment models at admission based on machine learning (ML) methods.

Methods

In this retrospective study, a total of 3078 individuals were included with their Caprini variables within 24 hours at admission. Then several ML models were built, including logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB). The prediction performance of ML models and the Caprini risk score (CRS) was then validated and compared through a series of evaluation metrics.

Results

The values of AUROC and AUPRC were 0.798 and 0.3

SUBMITTER: Sheng W 

PROVIDER: S-EPMC10497101 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature