Applicability of machine learning technique in the screening of patients with mild traumatic brain injury - Miriam Leiko Terabe; Miyoko Massago; Pedro Henrique Iora; Thiago Augusto Hernandes Rocha; João Vitor Perez de Souza; Lily Huo; Mamoru Massago; Dalton Makoto Senda; Elisabete Mitiko Kobayashi; João Ricardo Vissoci; Catherine Ann Staton; Luciano de Andrade (PLoS ONE, 2023)
Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As such, this study aims to evaluate the applicability of a machine learning (ML) technique in the screening of patients with mild TBI in the Regional University Hospital of Maringá, Paraná state, Brazil. This is an observational, descriptive, cross-sectional, and retrospective study using ML technique to develop a protocol that predicts which patients with an initial diagnosis of mild TBI should be recommended for a head CT. Among the tested models, he linear extreme gradient boosting was the best algorithm, with the highest sensitivity (0.70 ± 0.06). Our predictive model can assist in the screening of mild TBI patients, assisting health professionals to manage the resource utilization, and improve the quality and safety of patient care.