• Timothy Zhang, Anton Nikouline, David Lightfoot, and Brodie Nolan.
• Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. Electronic address: timothymed.zhang@mail.utoronto.ca.
• Ann Emerg Med. 2022 Nov 1; 80 (5): 440455440-455.

Study ObjectiveMachine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically injured patients provide a uniquely heterogeneous population with severe injuries that can be difficult to predict. Given the relative infancy of machine learning applications in medicine, this systematic review aimed to better understand the current state of machine learning development and implementation to help create a basis for future research.MethodsWe conducted a systematic review from inception to May 2021, using Embase, MEDLINE through Ovid, Web of Science, Google Scholar, and relevant gray literature, for uses of machine learning in predicting the outcomes of trauma patients. The screening and data extraction were performed by 2 independent reviewers.ResultsOf the 14,694 identified articles screened, 67 were included for data extraction. Artificial neural networks comprised the most commonly used model, and mortality was the most prevalent outcome of interest. In terms of machine learning model development, there was a lack of studies that employed external validation, feature selection methods, and performed formal calibration testing. Significant heterogeneity in reporting was also observed between the machine learning models employed, patient populations, performance metrics, and features employed.ConclusionThis review highlights the heterogeneity in the development and reporting of machine learning models for the prediction of trauma outcomes. While these models present an area of opportunity as an ancillary to clinical decision-making, we recommend more standardization and rigorous guidelines for the development of future models.Copyright © 2022 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

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