An interpretable machine learning model based on optimal feature selection for identifying CT abnormalities in patients with mild traumatic brain injury.

ABSTRACT:

Background

Minor head trauma is a frequent cause of emergency department visits, early identification and prediction of mild traumatic brain injury (mTBI) patients with abnormal brain lesions are vital for minimizing unnecessary computed tomography (CT) scans, reducing radiation exposure, and ensuring timely effective treatment and care. This study aims to develop and validate an interpretable machine learning (ML) prediction model using routine laboratory data for guiding clinical decisions on CT scan use in mTBI patients.

Methods

We conducted a multicentre study in China including data from January 2019 to July 2024. Our study included three patient cohorts: a retrospective training cohort (654 patients for training and 163 for internal testing) and two prospective validat

SUBMITTER: Pan Y 

PROVIDER: S-EPMC12002887 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature