Xiangyang Ye, PhD, is a Research Associate Professor. He received his MS degree in Statistics from Michigan State University and his PhD in Biomedical Informatics from the University of Utah. His research primarily focuses on advancing healthcare through innovative methodologies, specifically: 1) leveraging predictive modeling, machine learning, and deep neural networks to empower patient awareness and optimize outcomes; 2) utilizing electronic health records (EHR) and medical terminologies and standards to enhance the quality of care and improve healthcare accessibility; and 3) employing advanced statistical modeling and artificial intelligence in creating tailored clinical decision support systems for personalized medicine.
POSITIONS HELD
2023 - present: Research Associate Professor, Department of Pharmacotherapy, University of Utah
2016 - 2023: Biostatistician/Research Associate, Department of Internal Medicine, University of Utah
2007 - 2016: Research Analyst/Biostatistician, Department of Pharmacotherapy, University of Utah
2005 - 2007: Research Analyst, Department of Epidemiology, Michigan State University
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Education History
| Graduate Training |
East China Normal University |
MA, Education Statistics and Measurement |
|---|---|---|
|
Michigan State University |
MS, Statistics | |
| Doctoral Training |
University of Utah |
PhD, Biomedical Informatics |
Selected Publications
Journal Article
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Ye X, Zeng TQ, Facelli JC, Brixner DI, Conway M, Bray BE. Predicting Optimal Hypertension Treatment Pathways Using Recurrent Neural Networks. Int J Med Inform. 2020 Jul;139:104122. doi: 10.1016/j.ijmedinf.2020.104122. Epub 2020 Mar 21. 32339929
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Roland CL, Ye X, Stevens V, Oderda GM. The Prevalence and cost of Medicare beneficiaries diagnosed and at risk for opioid abuse, dependence and poisoning. Journal of Managed Care & Specialty Pharmacy. 2019 Jan;25(1):18-27. doi: 10.18553/jmcp.2019.25.1.018 PMID: 30589633
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Unni S, Quek RG, Biskupiak J, Lee VC, Ye X, Gandra SR. Assessment of statin therapy, LDL-C levels, and cardiovascular events among high-risk patients in the United States. Journal of Clinical Lipidology. 10(1):63–71.e3. PMID: 26892122