Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage.

ABSTRACT: Patients with intracerebral hemorrhage (ICH) are highly susceptible to sepsis. This study evaluates the efficacy of machine learning (ML) models in predicting sepsis risk in intensive care units (ICUs) patients with ICH. We conducted a retrospective analysis on ICH patients using the MIMIC-IV database, randomly dividing them into training and validation cohorts. We identified sepsis prognostic factors using Least Absolute Shrinkage and Selection Operator (LASSO) and backward stepwise logistic regression. Several machine learning algorithms were developed and assessed for predictive accuracy, with external validation performed using the eICU Collaborative Research Database (eICU-CRD). We analyzed 2,214 patients, including 1,550 in the training set, 664 in the validation set, and 513 for ext

SUBMITTER: Tang L 

PROVIDER: S-EPMC12065919 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature