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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：
10246186
负责人：
Elsie Gyang Ross
金额：
$ 16.12万
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/10246186
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 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 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 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 base biomedical informatics care burden career career development clinical center clinical data warehouse clinical implementation clinical trial implementation cohort computing resources cost cost effective cost outcomes cost-effectiveness evaluation data analysis pipeline deep learning algorithm design disease diagnosis disorder risk electronic 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