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

使用机器学习集成电子病历、遗传和波形数据来预测围手术期心肺不稳定性

• 批准号: 10247089
• 负责人: Ira Hofer
• 金额: $ 17.53万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-25 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10247089
• 关键词: Acute; Adoption; Advisory Committees; Affect; African American; Algorithms; American; Arrhythmia; Atlases; California; Clinical Informatics; Collaborations; Complex; Computerized Medical Record; Data; Data Security; Databases; Disease; Ensure; Event; Functional disorder; Genetic; Genetic Databases; Genetic Risk; Genomics; Goals; Health; Healthcare; Hypotension; Individual; Infrastructure; Inpatients; Joints; Latino; Logistic Regressions; Los Angeles; Machine Learning; Manuals; Medical Genetics; Medical History; Medicine; Mentors; Modeling; Modernization; Morphologic artifacts; Operative Surgical Procedures; Outcome; Outpatients; Patient-Focused Outcomes; Patients; Performance; Perioperative; Perioperative complication; Pharmaceutical Preparations; Phenotype; Physiological; Physiology; Population Analysis; Population Heterogeneity; Postoperative Complications; Prevalence; Process; Provider; Recurrence; Research; Research Personnel; Risk; Signal Transduction; Subgroup; Techniques; Time; Training; Universities; Work; acute stress; automated algorithm; base; biobank; biological adaptation to stress; career; career development; cost; deep neural network; design; diverse data; electronic data; ethnic minority population; experience; feeding; genetic information; genomic data; hemodynamics; improved; long short term memory; multiple data sources; online course; outcome prediction; phenotyping algorithm; prediction algorithm; predictive modeling; pressure; prevent; prognostic model; programs; random forest; structured data; tool

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.

项目摘要/摘要 此K01应用程序的目标是给霍费尔博士必要的培训和研究经验 将自己确立为一名独立调查员，专注于在各种 医疗保健数据，以预测围手术期的结果。职业发展活动包括 升级机器学习的课程，从ML基础的在线课程开始，到结束 在加州大学洛杉矶分校开设了ML在医疗保健中的应用课程。对这些课程的补充是关于应用程序的教程 将这些技术应用到医疗数据中，并设计了一个研究程序来使用医疗数据上的ML 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，霍费尔博士在机器学习领域建立了一支杰出的领导者团队， 围手术期医学和临床信息学。他的主要导师马克西姆·坎尼森博士是一位 围手术期医学及ML对生理信号的应用。伊兰·哈尔佩林博士，这位专业- PoSal是ML及其在基因组和其他医疗数据中的应用方面的专家。霍弗博士正在进行合作研究- 与Cannesson博士和Halperin博士就联合项目发表演讲。Cannesson博士和Halperin博士都有强烈的 有指导个人发展到独立和富有成效的学术生涯的记录。 霍费尔博士将得到由道格拉斯·贝尔博士组成的咨询委员会的协助(他将为 整合来自多个来源的数据)，Mohammed MahBouba博士(提供数据安全方面的支持 和创建企业级分析解决方案)和Jeanine Wiener-Kronish博士(提供关于 围手术期预后预测的相关问题)。 管理CRI的挑战与每年1500多万例术后并发症有关。 然而，花费超过1650亿美元的复方药并没有分数来预测CRI。这项研究将利用独特的 加州大学洛杉矶分校的基础设施，其中整个EMR数据已与生理波形和基因组相结合 超过3万名患者的数据。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。 总而言之，这项建议将为霍费尔博士提供ML方面的技术培训和实践经验。 在使用ML预测围手术期结果方面的重要性。这项研究有可能创造出有助于 临床医生可以预测并避免围手术期的不稳定性，从而改善患者的预后。此外，这一点 该计划将为Hofer博士提供成功竞争专注于使用ML的R01所需的工具 基于各种医疗保健数据的模型，以预测CRI围手术期并发症的下游影响。