Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.

ABSTRACT:

Background

Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic inflammation and coagulopathy. This study aimed to develop and validate a machine learning (ML) model to predict SIC risk in patients with sepsis.

Methods

Patients with sepsis admitted to the intensive care unit (ICU) between March 1, 2021, and March 1, 2024, at Hebei General Hospital and Handan Central Hospital (East District) were retrospectively included. Patients were categorized into SIC and non-SIC groups. Data were split into training (70%) and testing (30%) sets. Additionally, for temporal validation, patients with sepsis admitted between March 1, 2024, and October 31, 2024, at Hebei General Hospital were included. Feature selection was performed using least absolute shrinka

SUBMITTER: Tan R 

PROVIDER: S-EPMC11911172 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature