Clinical validation and optimization of machine learning models for early prediction of sepsis.

ABSTRACT:

Introduction

Sepsis is a global health threat that has a high incidence and mortality rate. Early prediction of sepsis onset can drive effective interventions and improve patients' outcome.

Methods

Data were collected retrospectively from a cohort of 2,329 adult patients with positive bacteria cultures from a tertiary hospital in China between October 1, 2019 and September 30, 2020. Thirty six clinical features were selected as inputs for the models. We trained models in predicting sepsis by machine learning (ML) methods, including logistic regression, decision tree, random forest (RF), multi-layer perceptron, and light gradient boosting. We evaluated the performance of the five ML models and the evaluation metrics were: area under the ROC curve (AUC), accuracy, F1-score, se

SUBMITTER: Liu X 

PROVIDER: S-EPMC11836818 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature