One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making
One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making
复制标题
人工智能算法临床应用的首批验证之一：对术中低血压预测和临床决策的影响
DOI：
10.1016/j.surg.2020.09.041
复制
发表时间：
2021-05-25
期刊：
影响因子：
3.8
通讯作者：
Geerts, Bart F.
中科院分区：
文献类型：
--
作者：
van der Ven, Ward H.;Veelo, Denise P.;Geerts, Bart F.
本综述描述了人工智能算法（低血压预测指数）的开发和验证的步骤和结论，该算法是手术室环境中使用的首批机器学习预测算法之一。在两项随机对照试验中，该算法已被证明可通过实时预测即将发生的低血压事件来减少术中低血压，从而促使麻醉师在管理即将发生的低血压时更早、更频繁和不同地采取行动。然而，该算法不需要从临床患者护理中的使用演变而来的动态学习过程，这意味着该算法是固定的，并且此外没有提供对导致术中低血压早期警告的决策过程的洞察，这使得该算法成为“黑匣子”。“许多其他人工智能机器学习算法也有同样的缺点。这种算法的临床验证相对较新，需要更多的标准化，因为缺乏指南或现在才开始起草。在适应临床实践之前，还应研究人工智能算法对临床行为、结局和经济优势的影响。(c)2020作者爱思唯尔公司出版这是一个在CC BY许可证下的开放获取文章（http：//creativecommons.org/licenses/by/4.0/）。
This review describes the steps and conclusions from the development and validation of an artificial intelligence algorithm (the Hypotension Prediction Index), one of the first machine learning predictive algorithms used in the operating room environment. The algorithm has been demonstrated to reduce intraoperative hypotension in two randomized controlled trials via real-time prediction of upcoming hypotensive events prompting anesthesiologists to act earlier, more often, and differently in managing impending hypotension. However, the algorithm entails no dynamic learning process that evolves from use in clinical patient care, meaning the algorithm is fixed, and furthermore provides no insight into the decisional process that leads to an early warning for intraoperative hypotension, which makes the algorithm a "black box." Many other artificial intelligence machine learning algorithms have these same disadvantages. Clinical validation of such algorithms is relatively new and requires more standardization, as guidelines are lacking or only now start to be drafted. Before adaptation in clinical practice, impact of artificial intelligence algorithms on clinical behavior, outcomes and economic advantages should be studied too.(c) 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).