Development of a venous thromboembolism risk prediction model for patients with primary membranous nephropathy based on machine learning.

ABSTRACT:

Objective

This study utilizes real-world data from primary membranous nephropathy (PMN) patients to preliminarily develop a venous thromboembolism (VTE) risk prediction model with machine learning. The aim is to improve the rational use of prophylactic anticoagulant therapy by predicting VTE risk in these patients.

Methods

We collected diagnostic and treatment data for PMN patients hospitalized at Sichuan Provincial People's Hospital from 1 January 2018, to 30 September 2024. The data was divided into training and test sets at an 8:2 ratio, followed by processed using combinations of three imputation methods, three sampling methods, and three feature selection methods. After preprocessing, fourteen machine learning algorithms were employed to develop a predictive model for V

SUBMITTER: Li L 

PROVIDER: S-EPMC12631078 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature