Machine learning for identification of short-term all-cause and cardiovascular deaths among patients undergoing peritoneal dialysis.

ABSTRACT: Although more and more cardiovascular risk factors have been verified in peritoneal dialysis (PD) populations in different countries and regions, it is still difficult for clinicians to accurately and individually predict death in the near future. We aimed to develop and validate machine learning-based models to predict near-term all-cause and cardiovascular death. Machine learning models were developed among 7539 PD patients, which were randomly divided into a training set and an internal test set by five random shuffles of 5-fold cross-validation, to predict the cardiovascular death and all-cause death in 3 months. We chose objectively collected markers such as patient demographics, clinical characteristics, laboratory data, and dialysis-related variables to inform the models and assesse

SUBMITTER: Xu X 

PROVIDER: S-EPMC11879343 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature