Early prediction of sepsis-induced coagulopathy in the ICU using interpretable machine learning: a multi-center retrospective cohort study.

ABSTRACT:

Background

Sepsis-induced coagulopathy (SIC) is a fatal complication in ICU patients, yet early risk prediction remains challenging. This study aimed to develop an interpretable machine learning model for predicting SIC within seven days of ICU admission.

Methods

Clinical data for model development were retrieved from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Feature selection was performed using three distinct algorithms: least absolute shrinkage and selection operator (LASSO) regression, random forest recursive feature elimination (RF-RFE), and the Boruta method. Ten machine learning models underwent training employing 5-fold cross-validation on the training subset, with subsequent evaluation on the validation subset encompassing

SUBMITTER: Sha T 

PROVIDER: S-EPMC12626953 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature