SCH: INT: New Machine Learning Framework to Conduct Anesthesia Risk Stratification and Decision Support for Precision Health

SCH：INT：用于进行麻醉风险分层和精准健康决策支持的新机器学习框架

• 批准号: 2347604
• 负责人: Heng Huang
• 金额: $ 118.23万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2347604&HistoricalAwards=false
• 关键词: SCH; INT; New; Machine; Learning

With advances in anesthesia techniques, surgery has become increasingly applicable to a wider range of diseases and patients. Worldwide more than 230 million major surgical procedures are carried out each year. In terms of patient safety and medical economics, an important issue is how to reduce the incidence of postoperative complications and mortality. At least half of postoperative complications can be prevented, while improvements in anesthesia-associated factors contribute greatly to the prevention of complications. Anesthesia information management system is a specialized type of electronic health record that allow the automatic and reliable collection and storage of patient data during the perioperative period. The electronic anesthesia data not only provide a rich data set to assist both anesthesia providers and hospitals with their goals to improve patient safety during the fast-paced intra-operative period, but also capture detailed data to allow end users to access information for management, quality assurance, and research purposes. This project addresses the computational challenges in large-scale electronic anesthesia data mining, develops and validates an automated anesthesia risk prediction and decision support system to identify risk factors and detect patients at risk of postoperative complications and in-hospital mortality. This project develops novel large-scale machine learning framework to integrate the emerging key computational techniques, such as semi-supervised generative adversarial learning, interpretable deep learning, large-scale optimization, and unsupervised hashing, to analyze large-scale electronic anesthesia data for enhancing anesthesia risk stratification and improving the quality of care for precision health. Specifically, the PIs investigate: 1) new computational tools to automate electronic anesthesia data processing, 2) novel semi-supervised generative adversarial network for anesthesia risk stratification, 3) interpretable deep learning model for clinical markers discovery, 4) scale up deep learning models for big data computation via new large-scale optimization algorithms, 5) new unsupervised deep generative adversarial hashing network for fast and accurate clinical case retrieval, and 6) evaluate the proposed methods and system using real large-scale anesthesia data. It is innovative to integrate large-scale machine learning and data-intensive computing for electronic anesthesia data mining that holds great promise for predicting postoperative outcomes using the comprehensive preoperative and intra-operative patient profiles. The developed methods and tools impact other public health research and enable investigators working on electronic health data to effectively test risk prediction hypothesis. This project facilitates the development of novel educational tools to enhance several current courses at University of Pittsburgh.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着麻醉技术的进步，手术已越来越适用于更广泛的疾病和患者。每年在全球范围内进行超过2.3亿个主要手术程序。在患者安全和医学经济学方面，一个重要的问题是如何减少术后并发症和死亡率的发生率。至少可以预防术后并发症的一半，而与麻醉相关因素的改善对预防并发症有很大贡献。麻醉信息管理系统是一种专门的电子健康记录，可在围手术期间自动收集和存储患者数据。电子麻醉数据不仅提供了丰富的数据集，可以帮助麻醉提供者和医院实现其目标，以在快速节奏的术中提高患者安全性，而且还捕获详细的数据，以允许最终用户访问管理，质量保证和研究目的的信息。该项目解决了大规模电子麻醉数据挖掘的计算挑战，开发和验证了自动麻醉风险预测和决策支持系统，以识别风险因素，并检测有术后并发症和医院死亡率风险的患者。该项目开发了新型的大型机器学习框架，以整合新兴的关键计算技术，例如半手审查的生成对抗性学习，可解释的深度学习，大规模优化和无处可比的散列，以分析大规模的电子麻醉数据，以增强麻醉风险的风险分层和改善质量健康的质量健康。 Specifically, the PIs investigate: 1) new computational tools to automate electronic anesthesia data processing, 2) novel semi-supervised generative adversarial network for anesthesia risk stratification, 3) interpretable deep learning model for clinical markers discovery, 4) scale up deep learning models for big data computation via new large-scale optimization algorithms, 5) new unsupervised deep generative adversarial hashing network for fast and accurate临床病例检索，6）使用实际的大规模麻醉数据评估所提出的方法和系统。将大规模的机器学习和数据密集型计算集成到电子麻醉数据挖掘方面具有创新性，该计算具有巨大的希望，可以使用全面的术前和术中患者概况来预测术后结果。开发的方法和工具会影响其他公共卫生研究，并使研究人员能够从事电子健康数据，以有效检验风险预测假设。该项目促进了新型教育工具的开发，以增强匹兹堡大学的几个当前课程。该奖项反映了NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。