Applications of Machine Learning in Prognostication of Mild Traumatic Brain Injury: A Systematic Review.

Abstract

Objective: The aim of the study is to review the literature regarding the current state and clinical applicability of machine learning models in prognosticating the outcomes of patients with mild traumatic brain injury in the early clinical presentation.

Design: Databases were searched for studies including machine learning and mild traumatic brain injury from inception to March 10, 2023. Included studies had a primary outcome of predicting post-mild traumatic brain injury prognosis or sequelae. The Prediction model study Risk of Bias for Predictive Models assessment tool was used for assessing the risk of bias and applicability of included studies.

Results: Out of 1235 articles, 10 met the inclusion criteria, including data from 127,929 patients. The most frequently used modeling techniques were support vector machine and artificial neural network and area under the curve ranged from 0.66 to 0.889. Despite promise, several limitations to studies exist such as low sample sizes, database restrictions, inconsistencies in patient presentation definitions, and lack of comparison to traditional clinical judgment or tools.

Conclusions: Machine learning models show potential in early stage mild traumatic brain injury prognostication, but to achieve widespread adoption, future clinical studies prognosticating mild traumatic brain injury using machine learning need to reduce bias, provide clarity and consistency in defining patient populations targeted and validate against established benchmarks.