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Using Artificial Intelligence to Enable Early Identification and Treatment of Peripheral Artery Disease

利用人工智能实现外周动脉疾病的早期识别和治疗

基本信息

批准号：
10907378
负责人：
Elsie Gyang Ross
金额：
$ 16.09万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10907378
关键词：
Adoption Adult Affect Age Algorithms American Applications Grants Artificial Intelligence Award Awareness Blood Vessels Cardiovascular Diseases Cardiovascular system Caring Cessation of life Characteristics Classification Clinical Clinical Research Clinical Trials Cohort Studies Cost Analysis Cost Effectiveness Analysis Costs and Benefits Current Procedural Terminology Codes Data Data Set Diagnosis Disease Disease Outcome Early Diagnosis Early identification Early treatment Electronic Health Record Enrollment Ensure Event Foundations Future Goals Grant Health Health Services Research Healthcare Healthcare Systems Image Informatics Intervention Knowledge Learning Logistic Regressions Longevity Machine Learning Medical Medicare Medicine Mentorship Modeling Morbidity - disease rate Myocardial Infarction Newly Diagnosed Noise Notification Ontology Operative Surgical Procedures Outcome Patient-Focused Outcomes Patients Performance Peripheral arterial disease Physicians Postdoctoral Fellow Quality of Care Randomized Randomized Controlled Clinical Trials Recommendation Records Research Research Personnel Research Proposals Resources Risk Risk Factors Scientist Screening procedure Site Specialist Specificity Stroke Structure Surgeon Symptoms Technology Testing Text Time Training Translating United States National Institutes of Health Universities Vascular Diseases Work biomedical informatics care burden career career development clinical center clinical data warehouse clinical implementation cohort computing resources cost cost effective cost-effectiveness evaluation data analysis pipeline deep learning algorithm design disease diagnosis disorder risk electronic health data high risk human subject human very old age (85+)implementation science improved limb loss machine learning algorithm mortality new technology novel post-doctoral training premature prevent professor prospective random forest randomized trial recurrent neural network research study risk stratification screening text searching tool treatment effect

项目摘要

ABSTRACT The purpose of this award is to provide Dr. Elsie Ross, Assistant Professor of Surgery (Vascular Surgery) and Medicine (Biomedical Informatics Research) at Stanford University, the support necessary to transition her from a junior investigator into an independent surgeon-scientist in translational biomedical informatics. Dr. Ross is a vascular surgeon with an advanced degree in health services research and postdoctoral training in biomedical informatics. Her long-term goal is to combine her interdisciplinary training to develop and implement machine learning tools that will enable the delivery of precise, high-value care to patients with cardiovascular diseases. Her career development activities focus on advancing her ability to translate informatics discoveries into viable clinical tools by 1) completing didactic courses to deepen and expand her knowledge of deep learning algorithms, clinical trials and implementation science, 2) designing and conducting her first independent human subjects clinical research study evaluating the performance of machine learning technology, 3) implementing and evaluating the effects of an electronic health record (EHR)-based screening tool to identify latent vascular disease, and 4) strengthening her previous training in cost-effectiveness analysis to enable her future aim of evaluating the associated costs and utility of pro-active, automated disease screening. The candidate has convened a mentorship team that includes Dr. Nigam Shah, a biomedical informatics expert who combines machine learning, text-mining and medical ontologies to enable a learning health care system; Dr. Kenneth Mahaffey a world-expert in cardiovascular clinical trials; and Dr. Paul Heidenreich, an expert in implementation sciences with a focus on the use of EHR interventions to improve care quality for cardiovascular patients and evaluating the cost-effectiveness of new technologies. The research proposal builds on the candidate's prior work with using machine learning and EHR data to evaluate and predict cardiovascular disease outcomes. The candidate now proposes to characterize the performance of machine learning algorithms in identifying patients with peripheral artery disease (PAD) using EHR data (Aim 1), evaluate whether learned classification models perform better than traditional risk factors for identification of undiagnosed PAD in a prospective patient cohort (Aim 2), and implement an EHR-based screening tool to identify patients with undiagnosed PAD and evaluate the diagnosis and treatment effects (Aim 3). Completion of the proposed research will result in a novel, EHR-based screening tool for identification of undiagnosed vascular disease that can decrease PAD-related cardiovascular morbidity and mortality through earlier and more aggressive medical management. This research will also form the basis for an R01 application before the end of the award to conduct a multi-site randomized-controlled clinical trial to evaluate the impact of EHR- based proactive PAD screening. ! ! !

摘要该奖项的目的是提供博士埃尔西罗斯，外科助理教授（血管外科）和医学（生物医学信息学研究）在斯坦福大学，必要的支持，以过渡她从一个初级研究员转变为一个独立的外科医生，生物医学信息学的科学家。博士罗斯是一名血管外科医生，拥有卫生服务研究高级学位和博士后培训，生物医学信息学她的长期目标是联合收割机，她的跨学科培训，开发和实施机器学习工具，能够为心血管疾病患者提供精确，高价值的护理疾病她的职业发展活动侧重于提高她翻译信息学发现的能力通过1）完成教学课程，以加深和扩展她对深度治疗的知识，学习算法，临床试验和实施科学，2）设计和进行她的第一个评估机器学习性能的独立人类受试者临床研究技术，3）实施和评估基于电子健康记录（EHR）的筛查的效果工具，以确定潜在的血管疾病，和4）加强她以前的培训，在成本效益分析以实现她未来的目标，即评估主动自动化疾病的相关成本和效用，筛选候选人召集了一个导师团队，其中包括生物医学博士Nigam Shah 信息学专家，他将机器学习、文本挖掘和医学本体结合起来，医疗保健系统; Kenneth Mahaffey博士是心血管临床试验的世界专家; Paul Heidenreich是实施科学专家，专注于使用EHR干预措施来改善心血管患者的护理质量并评估新技术的成本效益。的研究计划建立在候选人之前的工作基础上，使用机器学习和EHR数据来评估并预测心血管疾病的结果。候选人现在提议描述使用EHR数据识别外周动脉疾病（PAD）患者的机器学习算法（目的 1）评估学习的分类模型是否比传统的风险因素更好地识别在前瞻性患者队列中未诊断的PAD（目标2），并实施基于EHR的筛查工具，识别未确诊的PAD患者，评价诊断和治疗效果（目的3）。完成拟议的研究将产生一种新的，基于EHR的筛查工具，用于识别未确诊的血管疾病可以通过早期和早期治疗降低PAD相关的心血管发病率和死亡率，更积极的医疗管理。这项研究也将成为R 01申请的基础，结束的奖项进行多站点随机对照临床试验，以评估EHR的影响- 基于主动PAD筛查。 ! ! !