Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning-Based Modeling Study.

ABSTRACT:

Background

Sepsis is a leading cause of death in patients with trauma, and the risk of mortality increases significantly for each hour of delay in treatment. A hypermetabolic baseline and explosive inflammatory immune response mask clinical signs and symptoms of sepsis in trauma patients, making early diagnosis of sepsis more challenging. Machine learning-based predictive modeling has shown great promise in evaluating and predicting sepsis risk in the general intensive care unit (ICU) setting, but there has been no sepsis prediction model specifically developed for trauma patients so far.

Objective

To develop a machine learning model to predict the risk of sepsis at an hourly scale among ICU-admitted trauma patients.

Methods

We extracted data from adult trauma patient

SUBMITTER: Li J 

PROVIDER: S-EPMC10131736 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature