Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study.

ABSTRACT:

Purpose

We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients.

Methods

In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables

SUBMITTER: Zhu G 

PROVIDER: S-EPMC10863571 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature