Using machine learning methods to predict 28-day mortality in patients with hepatic encephalopathy.

ABSTRACT:

Background

Hepatic encephalopathy (HE) is associated with marked increases in morbidity and mortality for cirrhosis patients. This study aimed to develop and validate machine learning (ML) models to predict 28-day mortality for patients with HE.

Methods

A retrospective cohort study was conducted in the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients from MIMIC-IV were randomized into training and validation cohorts in a ratio of 7:3. Training cohort was used for establishing the model while validation cohort was used for validation. The outcome was defined as 28-day mortality. Predictors were identified by recursive feature elimination (RFE) within 24 h of intensive care unit (ICU) admission. The area under the curve (AUC) and calibration curve were

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC10077693 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature