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Xiangyang Ye Headshot

Xiangyang Ye, MA, MS, PHDuma

Academic Information

Departments College of Pharmacy , Research Associate Professor - Pharmacotherapy

Academic Office Information

xiangyang.ye@hsc.utah.edu

801-587-9276

Research Interests

  • Health Economics and Outcomes Research (HEOR)
  • Advanced Statistical Analysis and Predictive Modeling
  • Application of Artificial Intelligence in Medical Contexts
  • Clinical Informatics and Data Integration
  • Development of Clinical Decision Support (CDS) Systems of Personalized Medicine

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

RELATED LINKS

FAR Webpage

College of Pharmacy

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

  1. 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 

  2. 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

  3. 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

    More Selected Publications