A proof-of-concept study on mortality prediction with machine learning algorithms using burn intensive care data.

ABSTRACT:

Introduction

Burn injuries are a common traumatic injury. Large burns have high mortality requiring intensive care and accurate mortality predictions. To assess if machine learning (ML) could improve predictions, ML algorithms were tested and compared with the original and revised Baux score.

Methods

Admission data and mortality outcomes were collected from patients at Uppsala University Hospital Burn Centre from 2002 to 2019. Prognostic variables were selected, ML algorithms trained and predictions assessed by analysis of the area under the receiver operating characteristic curve (AUC). Comparison was made with Baux scores using DeLong test.

Results

A total of 17 prognostic variables were selected from 92 patients. AUCs in leave-one-out cross-validation for a decisio

SUBMITTER: Fransen J 

PROVIDER: S-EPMC8859689 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature