Development and validation of web-based, interpretable predictive models for sepsis and mortality in extensive burns.

ABSTRACT:

Background

Burn injuries are a common cause of trauma globally, with extensive burns (≥ 50% total body surface area burned) associated with high rates of sepsis and mortality. This study aims to identify risk factors associated with sepsis and mortality in extensively burned patients and to develop accurate, interpretable predictive models via machine learning algorithms.

Methods

A retrospective cohort study was conducted utilizing data from two Burn Critical Care Units in Eastern China from 2012-2023. A total of 237 patients with extensive burns were included. We applied ten machine learning algorithms, including random forest, gradient boosting tree (GBT), and logistic regression, to predict sepsis and mortality. The models were evaluated via AUC, precision, recall, accura

SUBMITTER: Wang SQ 

PROVIDER: S-EPMC12399588 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature