Using machine learning to predict perfusionists' critical decision-making during cardiac surgery.
Using machine learning to predict perfusionists' critical decision-making during cardiac surgery.
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DOI：
10.1080/21681163.2021.2002724
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发表时间：
2022
影响因子：
1.6
通讯作者：
Gombolay, M.
中科院分区：
文献类型：
--
作者：
Dias, R. D.;Zenati, M. A.;Rance, G.;Srey, Rithy;Arney, D.;Chen, L.;Paleja, R.;Kennedy-Metz, L. R.;Gombolay, M.
心脏外科手术室是一个高风险和复杂的环境，多名专家作为一个团队工作，为患者提供安全和优质的护理。在心脏手术的心肺转流阶段，需要做出关键决策，灌注师在评估可用信息和采取特定行动过程中起着至关重要的作用。在本文中，我们报告了一项基于模拟的研究的结果，该研究使用机器学习来建立手术室（OR）关键情况下灌注师决策的预测模型。通过对30个随机种子进行30倍交叉验证，我们的机器学习方法在预测灌注师的行为方面能够达到78.2%的准确率（95%置信区间：77.8%至78.6%），仅可访问148个模拟。这项研究的结果可能会为未来开发嵌入手术室的计算机化临床决策支持工具提供信息，从而改善患者安全性和手术结局。
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.
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影响因子：
--
作者：
Dias RD;Shah JA;Zenati MA
通讯作者：
Zenati MA
影响因子：
9
作者：
Dias, Roger D.;Zenati, Marco A.;Yule, Steven J.
通讯作者：
Yule, Steven J.
影响因子：
2.5
作者：
Melly, Ludovic;Torregrossa, Gianluca;Puskas, John D.
通讯作者：
Puskas, John D.
DOI：
10.1016/j.jtcvs.2018.04.045
发表时间：
2018-11-01
影响因子：
6
作者：
Ranucci, Marco;Johnson, Ian;Pistuddi, Valeria
通讯作者：
Pistuddi, Valeria
DOI：
10.1053/j.jvca.2008.08.004
发表时间：
2008-12-01
影响因子：
2.8
作者：
Salis, Stefano;Mazzanti, Valeria V.;Sisillo, Erminio
通讯作者：
Sisillo, Erminio