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：
9806796
负责人：
Elsie Gyang Ross
金额：
$ 16.2万
依托单位：
STANFORD UNIVERSITY
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/9806796
关键词：
Address 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 Effectiveness Analysis Cost utility Costs and Benefits Current Procedural Terminology Codes Data Data Analyses Data Set Diagnosis Disease Disease Outcome Early Diagnosis Early identification Early treatment Electronic Health Record Enrollment Ensure Evaluation Event Foundations Future Goals Grant Health Health Services Research Healthcare Healthcare Systems Image Informatics Intervention Knowledge Lead Learning Limb structure Logistic Regressions Longevity Machine Learning Medical Medicare Medicine Mentorship Modeling Morbidity - disease rate Myocardial Infarction Newly Diagnosed Noise Notification Ontology Operative Surgical Procedures Patient-Focused Outcomes Patients Performance Peripheral arterial disease Physicians Quality of Care Randomized Randomized Controlled Clinical Trials Recommendation Records Research Research Personnel Research Proposals Research Training Resources Risk Risk Factors Risk stratification Scientist Screening procedure Sensitivity and Specificity Site Specialist Stroke Structure Surgeon Symptoms Technology Testing Text Time Training Translating United States National Institutes of Health Universities Vascular Diseases Work analysis pipeline base biomedical informatics care burden career career development clinical data warehouse clinical implementation cohort computing resources cost cost effective cost outcomes cost-effectiveness evaluation deep learning algorithm design disease diagnosis disorder risk electronic data high risk human subject human very old age (85+)implementation science improved machine learning algorithm mortality new technology novel post-doctoral training premature prevent professor prospective random forest randomized trial recurrent neural network research study 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. ! ! !

抽象的该奖项的目的是为外科助理教授（血管外科）Elsie Ross 博士提供斯坦福大学医学（生物医学信息学研究），为她的过渡提供必要的支持从一名初级研究员转变为转化生物医学信息学领域的独立外科医生科学家。博士。罗斯是一名血管外科医生，拥有健康服务研究和博士后培训的高级学位生物医学信息学。她的长期目标是结合她的跨学科培训来开发和实施机器学习工具将为心血管患者提供精确、高价值的护理疾病。她的职业发展活动侧重于提高转化信息学发现的能力通过 1) 完成教学课程来加深和扩展她的深层知识，将其转化为可行的临床工具学习算法、临床试验和实施科学，2) 设计和实施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) 的筛查的效果识别潜在血管疾病的工具，以及 4) 加强她之前在成本效益分析方面的培训使她未来的目标是评估主动、自动化疾病的相关成本和效用筛选。该候选人召集了一个导师团队，其中包括生物医学博士 Nigam Shah 博士。信息学专家，结合机器学习、文本挖掘和医学本体论来实现学习医疗保健系统； Kenneth Mahaffey 博士是心血管临床试验领域的世界专家；和保罗博士 Heidenreich，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。这研究提案建立在候选人之前使用机器学习和 EHR 数据进行评估的工作的基础上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 (Aim 1)、评估学习的分类模型是否比传统的风险因素识别表现更好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 患者并评估诊断和治疗效果（目标 3）。完成拟议的研究将产生一种新颖的、基于电子病历的筛查工具，用于识别未确诊的患者血管疾病可以通过早期和早期预防来降低 PAD 相关的心血管发病率和死亡率更积极的医疗管理。这项研究也将构成 R01 申请之前的基础奖励结束后进行多中心随机对照临床试验，以评估 EHR 的影响基于主动 PAD 筛查。！！！