التفاصيل البيبلوغرافية
| العنوان: |
Using machine learning to predict perfusionists' critical decision-making during cardiac surgery. |
| المؤلفون: |
Dias, R. D., Zenati, M. A., Rance, G., Srey, Rithy, Arney, D., Chen, L., Paleja, R., Kennedy-Metz, L. R., Gombolay, M. |
| المصدر: |
Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation; May2022, Vol. 10 Issue 3, p308-312, 5p |
| مصطلحات موضوعية: |
CARDIAC surgery, CARDIOPULMONARY bypass, DECISION making, OPERATING rooms, MACHINE learning, PATIENT safety |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
PDF full text from Publisher 
Full Text Finder