Abstract

This research explores the potential of machine learning (ML) algorithms in forecasting the early course of bleeding in traumatic brain injury (TBI) patients. Leveraging diverse clinical data sources, including electronic health records and imaging data, ML models are developed and validated using a curated dataset of TBI patients. The study aims to compare the performance of ML models with traditional clinical prediction models and assess their clinical utility and feasibility in real-world TBI care settings. The findings are expected to inform the development of innovative tools for improving patient outcomes and optimizing resource allocation in TBI management protocols.