From data to decisions: Predicting inpatient burn mortality with advanced classification models.

ABSTRACT:

Introduction

Burn injuries are a major global health challenge. Predicting mortality in burn patients using data mining can enhance therapeutic decision-making, but the long-term reliability of such models is a critical concern for safe clinical deployment. This study aimed to develop a high-performing mortality prediction model and rigorously evaluate its temporal stability.

Methods

This retrospective cohort study utilized data from 651 patients, comprising 93 predictive features, from the burn registry of the Injuries and Burn Subspecialized Teaching Hospital, affiliated with Isfahan University of Medical Sciences. After data preprocessing, including robust imputation using a Generalized Linear Model (GLM), five tree-based models were developed. Model performance was evalu

SUBMITTER: Sabet Kouhanjani Y 

PROVIDER: S-EPMC12758681 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature