Using artificial intelligence to enable early identification and treatment of peripheral artery disease

摘要 该奖项的目的是为外科(血管外科)助理教授埃尔西·罗斯博士提供 斯坦福大学医学(生物医学信息学研究)，过渡所需的支持 从初级研究员转变为翻译生物医学信息学领域的独立外科医生兼科学家。Dr。 罗斯是一名血管外科医生，拥有卫生服务研究和博士后培训方面的高级学位。 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，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 通过完成授课课程来加深和扩展她的深层知识，从而成为可行的临床工具 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体临床研究 技术，3)实施和评估基于电子健康记录(EHR)的筛查效果 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 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 放映。这位候选人已经召集了一个导师团队，其中包括生物医学专家尼加姆·沙阿博士 信息学专家，他将机器学习、文本挖掘和医学本体结合在一起，使学习成为可能 卫生保健系统；世界心血管临床试验专家Kenneth Mahaffey博士；以及Paul博士 海登瑞克，实施科学专家，专注于使用电子健康记录干预措施来改善 对心血管病人的护理质量和新技术的成本效益进行评估。这个 研究计划建立在候选人使用机器学习和电子病历数据进行评估的先前工作的基础上 并预测心血管疾病的结果。这位候选人现在提议将 利用EHR数据识别外周动脉疾病(PAD)患者的机器学习算法 1)，评估学习的分类模型是否比传统的风险因素识别效果更好 未来患者队列中未诊断的PAD(目标2)，并实施基于EHR的筛查工具以 确定未诊断的PAD患者并评估其诊断和治疗效果(目标3)。完成 将产生一种新的、基于电子病历的筛查工具，用于识别未诊断的 血管疾病可以通过更早和更早地减少与PAD相关的心血管疾病的发病率和死亡率 更积极的医疗管理。这项研究也将成为R01在 奖项结束后，进行一项多地点随机对照临床试验，以评估EHR- 基于主动式PAD筛查。 好了！ 好了！ 好了！

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. ! ! !