Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning.

ABSTRACT:

Background

Gastrointestinal bleeding (GIB) is a clinical challenge in kidney failure. INSPIRE group assessed if machine learning could determine a hemodialysis (HD) patient's 180-day GIB hospitalization risk.

Methods

An eXtreme Gradient Boosting (XGBoost) and logistic regression model were developed using an HD dataset in United States (2017-2020). Patient data was randomly split (50% training, 30% validation, and 20% testing). HD treatments ≤ 180 days before GIB hospitalization were classified as positive observations; others were negative. Models considered 1,303 exposures/covariates. Performance was measured using unseen testing data.

Results

Incidence of 180-day GIB hospitalization was 1.18% in HD population (n = 451,579), and 1.12% in testing dataset (n = 38,853)

SUBMITTER: Larkin JW 

PROVIDER: S-EPMC11490046 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature