Synthesizing Intraoperative Multivariate Time Series with Conditional Generative Adversarial Networks

使用条件生成对抗网络合成术中多元时间序列

• 批准号: 10188838
• 负责人: Fei Zhang
• 金额: $ 18.98万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10188838
• 关键词: Adverse event; Algorithms; Anesthesia procedures; Award; Caring; Cessation of life; Clinical Decision Support Systems; Clinical Investigator Award; Complex; Data; Data Analyses; Data Collection; Data Display; Data Management Resources; Data Science; Data Security; Databases; Decision Making; Devices; Elderly; Environment; Evaluation; Event; Future; Goals; Individual; Injury to Kidney; Intervention; Intraoperative Complications; Intraoperative Monitoring; Knowledge; Lead; Learning; Machine Learning; Management Information Systems; Mathematics; Measures; Medical Records; Mentorship; Methods; Modeling; Morbidity - disease rate; Neural Network Simulation; Nurses; Operating Rooms; Operative Surgical Procedures; Patient-Focused Outcomes; Patients; Pattern; Perioperative; Physiologic Monitoring; Physiological; Prevention; Process; Provider; Records; Regulation; Research; Research Personnel; Resolution; Risk; Risk Assessment; Science; Series; Spottings; Stroke; Supervision; System; Time; Training; Training Activity; Training Programs; base; clinical decision support; data resource; design; experience; hemodynamics; high risk; improved; large scale data; machine learning algorithm; mortality; myocardial injury; network architecture; network models; novel; older patient; patient safety; prediction algorithm; preservation; prevent; profiles in patients; programs; risk prediction; skills; success; tool; virtual

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.

项目总结/摘要 患者安全在麻醉中至关重要。术中并发症和血流动力学不稳定是 与长期生存率降低相关，并可能导致心肌损伤、中风、肾脏损害等风险。 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很重要。理想的术前评估系统 根据患者信息，可以预测所有术中并发症和手术前的生理变化。 外科手术开始。预测术中血流动力学不稳定需要分析 在不良事件发生之前，大量的生理数据和数据中的斑点模式。 然而，这样做需要大量的高分辨率术中数据直接从 生理监测器在手术室训练机器学习模型，这些数据目前是 不可用.因此，本文提出的训练方案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的和真实的术中数据， 识别术中并发症，然后建立实时术中临床决策支持系统。 拟议的培训方案有两个目标。目标1将使申请人能够创建数据驱动的目标 用于术中并发症预测和风险评估的方法。麻醉前的关键信息 手术评估将用于生成合成的低分辨率术中生理数据。该数据 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 手术前不稳定和并发症。目标2将使申请者能够建立一个虚拟数据库， 提供无限的高分辨率术中数据，以训练机器学习算法， 术中临床决策支持系统。记录的低分辨率术中数据和关键 来自麻醉术前评估的信息将被输入到第二个工具中， 分辨率术中数据到第二分辨率水平，用于数据增强，以增加可用的 外科病例。这个K 08研究计划将使申请人能够填补应用数据的关键知识空白 科学在现有的低分辨率术中数据的医疗记录和非记录的高分辨率 麻醉设备显示的术中数据。结果将指导麻醉提供者， 研究人员在设计和实施数据驱动的围手术期预测系统，而不是传统的 麻醉风险评估。最终，这个K 08奖项将为申请人提供高级导师， 技能、研究经验和数据资源，经过培训后成为一名独立的护士调查员。