Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury.
Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury.
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DOI：
10.1053/j.akdh.2022.10.001
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发表时间：
2022-12
期刊：
影响因子：
--
通讯作者：
K. Takkavatakarn;I. Hofer
中科院分区：
文献类型：
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作者：
K. Takkavatakarn;I. Hofer
急性肾损伤（阿基）是手术后常见的并发症，尤其是在心脏和主动脉手术中，对发病率和死亡率有显著影响。早期识别高危患者并提供有效的预防和治疗方法是降低围手术期阿基可能性的主要策略。因此，已经开发了几种风险预测模型和风险评估评分来预测围手术期阿基。然而，大多数这些风险评分仅来自术前数据，而术中时间序列监测数据（如心率和血压）未包括在内。此外，阿基的病理生理学的复杂性以及其非线性和异质性，对线性统计技术的使用施加了限制。临床医学数字化的发展、电子病历的广泛使用以及连续监测使用的增加产生了大量数据。机器学习最近显示出作为一种自动整合大量数据以预测围手术期结果风险的方法的前景。在本文中，我们讨论了现有工作的发展、局限性以及使用机器学习技术预测手术后阿基的模型的潜在未来方向。
Acute kidney injury (AKI) is a common complication after a surgery, especially in cardiac and aortic procedures, and has a significant impact on morbidity and mortality. Early identification of high-risk patients and providing effective prevention and therapeutic approach are the main strategies for reducing the possibility of perioperative AKI. Consequently, several risk-prediction models and risk assessment scores have been developed for the prediction of perioperative AKI. However, a majority of these risk scores are only derived from preoperative data while the intraoperative time-series monitoring data such as heart rate and blood pressure were not included. Moreover, the complexity of the pathophysiology of AKI, as well as its nonlinear and heterogeneous nature, imposes limitations on the use of linear statistical techniques. The development of clinical medicine's digitization, the widespread availability of electronic medical records, and the increase in the use of continuous monitoring have generated vast quantities of data. Machine learning has recently shown promise as a method for automatically integrating large amounts of data in predicting the risk of perioperative outcomes. In this article, we discussed the development, limitations of existing work, and the potential future direction of models using machine learning techniques to predict AKI after a surgery.