Use of Machine Learning on Integrated Electronic Medical Record, Genetic andWaveform Data to Predict Perioperative Cardiorespiratory Instability

项目概要/摘要 本K 01申请的目的是为霍费尔博士提供必要的培训和研究经验 将自己定位为一名独立调查员，专注于将机器学习（ML）用于各种 医疗保健数据，以预测围手术期的结果。职业发展活动包括 升级机器学习课程，从机器学习基础的在线课程开始， 加州大学洛杉矶分校的ML在医疗保健中的应用课程。补充这些课程的是应用程序教程 这些技术应用于医疗数据，一项研究计划旨在将ML用于医疗数据， 预测围手术期心肺不稳定（CRI）-特别是低血压和心律失常。 为了实现这些目标，霍费尔博士建立了一个机器学习领域的杰出领导团队， 围手术期医学和临床信息学。他的主要导师马克西姆·坎内森博士是 围手术期药物和ML在生理信号上的应用。Eran Halperin博士，这个专业的共同导师- 他是ML及其在基因组和其他医疗数据中应用的专家。霍费尔博士正在与 与坎内松博士和哈尔佩林博士就联合项目发表演讲。坎内森博士和哈尔佩林博士都有很强的 辅导个人谁已经发展到独立和富有成效的学术生涯的跟踪记录。 博士由道格拉斯贝尔博士组成的咨询委员会将协助霍费尔（他将提供以下方面的指导 Mohammed Mahbouba博士（提供有关数据安全的支持 和创建企业级分析解决方案）和Jeanine Wiener-Kronish博士（提供有关 围手术期结局预测中的相关问题）。 每年超过1500万例术后并发症涉及管理CRI的挑战， 复杂性，耗资超过1650亿美元，但没有分数存在预测CRI。这项研究将利用独特的 在加州大学洛杉矶分校的基础设施，整个EMR数据已与生理波形和基因组相结合， 超过30，000名患者的数据。该提案将在这些数据上使用各种ML技术来创建 CRI的预测模型。 总之，该提案将为霍费尔博士提供ML方面的技术培训和实践经验。 使用ML预测围手术期结局的价值。这项研究有可能建立模型， 临床医生预测并因此避免围手术期不稳定性，从而改善患者结果。而且这个 该计划将为霍费尔博士提供成功竞争专注于使用机器学习的R 01所需的工具 基于各种医疗保健数据建立模型，以预测CRI的下游影响-围手术期并发症。

Project Summary/Abstract The objective of this K01 application is to give Dr. Hofer the necessary training and research experience to establish himself as an independent investigator focused on using machine learning (ML) on a variety of healthcare data to predict outcomes during the perioperative period. The career development activities consist of escalating coursework on machine learning beginning with an online course of ML fundamentals and ending with a UCLA course on ML applications in healthcare. Augmenting these courses are tutorials on the application of these techniques to healthcare data and a research program is designed to use ML on healthcare data to predict perioperative cardio-respiratory instability (CRI) – specifically hypotension and arrhythmia. To achieve these goals, Dr. Hofer has established an outstanding team of leaders in machine learning, perioperative medicine, and clinical informatics. Dr. Maxime Cannesson, his primary mentor, is an expert in perioperative medicine and the use of ML on physiologic signals. Dr. Eran Halperin, the co-mentor for this pro- posal, is an expert in ML and its application to genomic and other healthcare data. Dr. Hofer has ongoing collab- orations with Drs. Cannesson and Halperin on joint projects. Both Drs. Cannesson and Halperin have a strong track record of mentoring individuals who have progressed to independent and productive academic careers. Dr. Hofer will be aided by an advisory committee consisting of Dr. Douglas Bell (who will provide guidance on integrating data from multiple sources), Dr. Mohammed Mahbouba (providing support regarding data security and creating enterprise level analytic solutions) and Dr. Jeanine Wiener-Kronish (providing guidance on the most relevant questions in perioperative outcome prediction). Challenges managing CRI have been implicated in the more than 15 million annual postoperative com- plications, costing more than $165 billion, however no scores exist to predict CRI. This study will leverage unique infrastructure at UCLA where whole EMR data has been combined with physiologic waveforms and genomic data on more than 30,000 patients. This proposal will use a variety of ML techniques on these data to create predictive models for CRI. In summary, this proposal will provide Dr. Hofer with both technical training in ML and hands on experi- ence in using ML to predict perioperative outcomes. This study has the potential to create models that will help clinicians predict, and thus avoid, perioperative instability, thereby improving patient outcomes. Additionally, this program will provide Dr. Hofer with the tools he needs to successfully compete for a R01 focusing on using ML models on a variety of healthcare data to predict the downstream effects of CRI – perioperative complications.