Synthesizing Intraoperative Multivariate Time Series with Conditional Generative Adversarial Networks

项目摘要/摘要 在麻醉中，病人的安全是最重要的。术中并发症和血流动力学不稳定 与降低长期存活率相关，并可能导致心肌损伤、中风、肾脏等风险 受伤，甚至死亡。因此，预测和预防术中血流动力学不稳定是非常重要的。 在麻醉提供者的决策过程中非常重要。一种理想的术前评估系统 将根据患者信息预测所有术中并发症和手术前的生理变化 手术开始了。术中血流动力学不稳定性的预测需要分析 在不良事件发生之前，大量的生理数据和这些数据中的斑点模式。 然而，这样做需要大量的高分辨率术中数据，直接从 手术室里的生理监测器来训练机器学习模型，这些数据目前是 不可用。因此，这项拟议的培训计划的研究目标是产生一个连续的 显示麻醉管理效果的多变量术中生理时间序列 最先进的数学工具。生成的数据可以提供无限和真实的术中数据 确认术中并发症，并随后建立实时的术中临床决策支持系统。 拟议的培训计划有两个目标。目标1将使申请者能够创建数据驱动的目标 术中并发症预测和风险评估的方法。麻醉前的关键信息 OP评估将用于生成人工合成的低分辨率术中生理数据。此数据 将告知麻醉提供者特定患者术中血流动力学的类型、时间和范围 术前不稳定及并发症。目标2将使申请人能够建立一个虚拟数据库，该数据库将 提供无限高分辨率的术中数据，以训练机器学习算法，为未来的实时 术中临床决策支持系统。术中记录的低分辨率数据和关键字 麻醉术前评估的信息将被输入到第二个工具中，以升级现有的分钟- 将术中数据分辨率提高到第二分辨率水平，以增加可用数据的数量 外科病例。这项K08研究计划将使申请者能够填补应用数据的关键知识空白 科学术中现有的低分辨率病历数据和未记录的高分辨率数据 麻醉装置显示的术中数据。这一结果将为麻醉提供者和 研究人员在设计和实施数据驱动的围手术期预测系统方面优于传统的 麻醉风险评估。最终，这一K08奖项将为申请者提供高级导师， 经过培训后成为独立护士调查员的技能、研究经验和数据资源。

Project Summary/Abstract Patient safety is paramount in anesthesia. Intraoperative complications and hemodynamic instability are associated with reduced long-term survival and can lead to risks such as myocardial injury, stroke, kidney injury, and even death. Therefore, predicting and preventing intraoperative hemodynamic instability is very important in the decision-making process of anesthesia providers. An ideal pre-operative assessment system would predict, from patient information, all intraoperative complications and physiological changes before a surgical procedure begins. Predicting intraoperative hemodynamic instability during surgery requires analyzing an enormous amount of physiological data and spotting patterns in that data before adverse events occur. However, doing this requires a large volume of high-resolution intraoperative data taken directly from the physiological monitors in the operating room to train machine learning models, and these data currently are unavailable. Therefore, the research goal of this proposed training program is to generate a continuous multivariate intraoperative physiological time series that display the effects of anesthesia management using state-of-the-art mathematic tools. The generated data can provide unlimited and realistic intraoperative data to identify intraoperative complications and later build a real-time intraoperative clinical decision support system. The proposed training program has two aims. Aim 1 will enable the applicant to create a data-driven objective approach for intraoperative complication prediction and risk assessment. Key information from anesthesia pre- op assessment will be used to generate synthetic low-resolution intraoperative physiological data. This data will inform anesthesia providers of the type, timing, and range of a given patient’s intraoperative hemodynamic instability and complications before surgery. Aim 2 will enable the applicant to build a virtual database that will provide unlimited high-resolution intraoperative data to train machine learning algorithms for a future real-time intraoperative clinical decision support system. The recorded low-resolution intraoperative data and the key information from anesthesia pre-op assessment will be inputted into the second tool to upscale existing minute- resolution intraoperative data to second-resolution level for data augmentation to boost the number of available surgical cases. This K08 research program will enable the applicant to fill key knowledge gaps in applying data science in the existing low-resolution intraoperative data in medical records and non-recorded high-resolution intraoperative data displayed by anesthesia devices. The results will orient anesthesia providers and researchers in the design and implementation of data-driven perioperative prediction systems over traditional anesthesia risk assessment. Ultimately, this K08 award will provide the applicant with the senior mentorship, skills, research experience and data resources to become an independent nurse investigator after training.