Advancing polytrauma care: developing and validating machine learning models for early mortality prediction.

ABSTRACT:

Background

Rapid identification of high-risk polytrauma patients is crucial for early intervention and improved outcomes. This study aimed to develop and validate machine learning models for predicting 72 h mortality in adult polytrauma patients using readily available clinical parameters.

Methods

A retrospective analysis was conducted on polytrauma patients from the Dryad database and our institution. Missing values pertinent to eligible individuals within the Dryad database were compensated for through the k-nearest neighbor algorithm, subsequently randomizing them into training and internal validation factions on a 7:3 ratio. The patients of our institution functioned as external validation cohorts. The predictive efficacy of random forest (RF), neural network, and XGBoos

SUBMITTER: He W 

PROVIDER: S-EPMC10518974 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature