A machine learning approach for predicting 72-hour mortality of hypothermic patients only using non-invasive parameters: A multi-center retrospective cohort study.

ABSTRACT:

Objectives

Accurately predicting the mortality risk of hypothermia patients is crucial for clinical decision-making, offering ample time for physicians to intervene. However, existing methods are invasive and difficult to implement in pre-hospital settings.

Methods

In this study, records of 2,700 hypothermia patients from 125 hospitals were extracted from the eICU Collaborative Research database, comprising 360 non-survivors and 2,340 survivors. Four machine learning methods were utilized to develop a mortality prediction model for hypothermia patients based on non-invasive physiological parameters. Data from 122 hospitals were used for model training, while the remainder were utilized for performance validation.

Results

The proposed machine learning prediction model

SUBMITTER: Jiang C 

PROVIDER: S-EPMC12543198 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature