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

• 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
• 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

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）和 斯坦福大学的医学（生物医学信息学研究），过渡她所必需的支持 从初级研究员到转化生物医学信息学领域的独立外科医生科学家。博士 罗斯是一位血管外科医生，拥有卫生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跨学科培训以开发和实施 机器学习工具将使心血管患者提供精确的高价值护理 疾病。她的职业发展活动的重点是推进她翻译信息学发现的能力 1）完成可行的临床工具，完成教学课程，以加深和扩大她对深度的了解 学习算法，临床试验和实施科学，2）设计和进行她的第一个 独立人类受试者临床研究评估机器学习的性能 技术，3）实施和评估电子健康记录（EHR）筛查的影响 识别潜在血管疾病的工具，以及4）加强她以前的成本效益分析培训 为了使她未来的目的评估主动，自动疾病的相关成本和实用性 筛选。候选人召集了一个指导团队，其中包括生物医学Nigam Shah博士 结合机器学习，文本挖掘和医学本体的信息学专家，以实现学习 医疗保健系统； Kenneth Mahaffey博士在心血管临床试验中的世界专家；保罗博士 Heidenreich是实施科学专家，重点是使用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 研究提案以使用机器学习和EHR数据评估的候选人的先前工作为基础 并预测心血管疾病的结果。候选人现在建议表征 使用EHR数据鉴定患有周围动脉疾病（PAD）患者的机器学习算法（AIM 1），评估学习分类模型的表现是否比识别传统风险因素更好 未经诊断的垫子在潜在的患者队列中（AIM 2），并实施基于EHR的筛选工具 鉴定患有未诊断垫的患者，并评估诊断和治疗效果（AIM 3）。完成 拟议的研究将导致一种基于EHR的新型筛选工具，用于识别未诊断的 血管疾病可以通过早期和早期和 更具侵略性的医疗管理。这项研究还将构成R01申请的基础 奖励的结束是进行多站点随机控制的临床试验，以评估EHR-的影响 基于主动的垫筛选。 呢 呢 呢