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

> **NIH NIH K01** · STANFORD UNIVERSITY · 2022 · $161,260

## Abstract

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 ...

## Key facts

- **NIH application ID:** 10472016
- **Project number:** 5K01HL148639-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Elsie Gyang Ross
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $161,260
- **Award type:** 5
- **Project period:** 2019-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10472016

## Citation

> US National Institutes of Health, RePORTER application 10472016, Using artificial intelligence to enable early identification and treatment of peripheral artery disease (5K01HL148639-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10472016. Licensed CC0.

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