Using machine learning to predict perfusionists' critical decision-making during cardiac surgery. Issue 3 (4th May 2022)

• Record Type: Journal Article
• Authors: Dias, R. D.; Zenati, M. A.; Rance, G.; Srey, Rithy; Arney, D.; Chen, L.; Paleja, R.; Kennedy-Metz, L. R.; Gombolay, M.
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 10:Issue 3(2022)
• Page Start: 308
• Page End: 312
• Publication Date: 2022-05-04
• Subjects: Decision-making -- machine learning -- cardiac surgery -- perfusionists -- decision support; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; 616.0757
• DOI: 10.1080/21681163.2021.2002724
• Languages: English
• ISSNs: 2168-1163

Abstract

ABSTRACT: The cardiac surgery operating room is a high-risk and complex environment in which multiple experts work as a team to provide safe and excellent care to patients. During the cardiopulmonary bypass phase of cardiac surgery, critical decisions need to be made and the perfusionists play a crucial role in assessing available information and taking a certain course of action. In this paper, we report the findings of a simulation-based study using machine learning to build predictive models of perfusionists' decision-making during critical situations in the operating room (OR). Performing 30-fold cross-validation across 30 random seeds, our machine learning approach was able to achieve an accuracy of 78.2% (95% confidence interval: 77.8% to 78.6%) in predicting perfusionists' actions, having access to only 148 simulations. The findings from this study may inform future development of computerised clinical decision support tools to be embedded into the OR, improving patient safety and surgical outcomes.