Associate Professor, Department of Surgery, University of California, San Diego.
Dr. Gyang-Ross is an Associate Professor of Surgery at UCSD. As a vascular surgeon-scientist, her lab focuses on using big data and advanced analytics to improve the care of vascular patients. Her current research focuses on using electronic health records and machine learning to automate the identification of patients with vascular disease and to recommend guideline-based therapies.
Older adults in communities of color face significant health disparities in diagnosing peripheral artery disease (PAD), with darker skin masking early changes and biases causing delays in care. Blacks are 1.5-2.5 times more likely to have PAD, experiencing lower Ankle Brachial Index (ABI) values and physical function at diagnosis. Socioeconomic status and gender misconceptions further compound the issue. Recognizing this critical unmet need of her patients, vascular surgeon, and data scientist Dr. Elsie Ross developed a novel machine learning model to facilitate early diagnosis of PAD, which outperformed both a random forest and traditional logistic regression model (average AUCs 0.96, 0.91 and 0.81, respectively; P< 0.0001) during initial testing. In this RCMAR pilot project, Dr. Ross aims to refine the model for diverse populations, validating its performance on older adults from Black/African American and Latinx communities, creating an “ethno-PAD” model. She will also investigate barriers to PAD diagnosis and medication compliance in these populations. More broadly, this project underscores the importance of designing AI models with applicability to communities of color in mind.