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

随着麻醉技术的进步，外科手术越来越适用于更广泛的疾病和患者。全世界每年进行的大型外科手术超过2.3亿例。在患者安全和医疗经济方面，如何降低术后并发症的发生率和死亡率是一个重要的问题。至少有一半的术后并发症是可以预防的，而麻醉相关因素的改善对并发症的预防有很大的帮助。麻醉信息管理系统是一种特殊类型的电子健康记录，可以在围手术期自动、可靠地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，以帮助麻醉提供者和医院在快节奏的手术期间提高患者的安全性，而且还捕获了详细的数据，使最终用户能够访问管理、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中的计算挑战，开发并验证了一个自动化麻醉风险预测和决策支持系统，以识别风险因素并检测患者术后并发症和住院死亡率的风险。本项目开发新型大规模机器学习框架，整合新兴的关键计算技术，如半监督生成对抗学习、可解释深度学习、大规模优化和无监督哈希，分析大规模电子麻醉数据，以增强麻醉风险分层，提高精准健康护理质量。具体来说，pi调查：1)自动化电子麻醉数据处理的新型计算工具，2)麻醉风险分层的新型半监督生成对抗网络，3)临床标志物发现的可解释深度学习模型，4)通过新的大规模优化算法扩展大数据计算的深度学习模型，5)用于快速准确临床病例检索的新型无监督深度生成对抗哈希网络，6)使用真实的大规模麻醉数据对所提出的方法和系统进行评估。将大规模机器学习和数据密集型计算集成到电子麻醉数据挖掘中是一项创新，它有望利用全面的术前和术中患者资料预测术后结果。所开发的方法和工具影响了其他公共卫生研究，并使从事电子健康数据工作的调查人员能够有效地测试风险预测假设。该项目促进了新型教育工具的开发，以加强匹兹堡大学目前的几门课程。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.