Machine learning in surgical safety

机器学习在手术安全中的应用

• 批准号: RGPIN-2020-05910
• 负责人: Rudzicz, Frank
• 金额: $ 1.75万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717411
• 关键词: Machine; learning; surgical; safety

Post-operative complications have a significant cost, both economically and to the affected individuals. To date, most studies of surgical complications have investigated the pre- and post-operative periods, rather than focusing on the surgery itself. The operating room is a complex environment, and many factors work in tandem. Moreover, assessing surgical quality has been both subjective and time-consuming. Machine learning (ML) can potentially circumvent these identified difficulties. The objective of this program is to use machine learning to identify risk events in surgery that can be used to predict post-operative complications. This program of research focuses on using ML to predict patients at risk for a post-operative complication. This has several subcomponents, including: 1) analysis of the electronic medical record, 2) analysis of surgical video, 3) communication within the team, and and 4) explainable AI. Where possible, efforts will be taken to make inference as close to real-time as possible. This work will use data that is unique worldwide, from the OR Black Box that our group has created, including video recordings of surgery and various statistical covariates, including structured data about the operation, as in the electronic medical record. High risk events on the surgical timeline, including bleeding events, are identified. Standard ML baselines, such as convolutional neural networks, process these data to develop a predictive model for post-operative complications. We already have considerable retrospective data, and the focus is on the algorithms. This proposed study will significantly enhance our understanding of how machine learning can detect surgical factors across speech, text, video, and their combination.

术后并发症在经济上和对受影响的个人来说都有很大的代价。迄今为止，大多数关于手术并发症的研究都调查了术前和术后时期，而不是关注手术本身。手术室是一个复杂的环境，许多因素协同工作。此外，评估手术质量既主观又耗时。机器学习（ML）可以潜在地规避这些已识别的困难。该计划的目的是使用机器学习来识别手术中的风险事件，这些风险事件可用于预测术后并发症。 该研究计划的重点是使用ML来预测患者术后并发症的风险。它有几个子组件，包括：1）电子病历分析，2）手术视频分析，3）团队内部沟通，以及4）可解释的人工智能。在可能的情况下，将努力使推理尽可能接近实时。这项工作将使用来自我们团队创建的OR黑匣子的全球唯一数据，包括手术视频记录和各种统计协变量，包括有关手术的结构化数据，如电子病历。确定手术时间轴上的高风险事件，包括出血事件。标准的ML基线（如卷积神经网络）处理这些数据，以开发术后并发症的预测模型。我们已经有了相当多的回顾性数据，重点是算法。 这项拟议的研究将大大提高我们对机器学习如何在语音、文本、视频及其组合中检测手术因素的理解。