Sunyang Fu


Assistant Professor & Associate Director of Team Science, Center for Translational AI Excellence and Applications in Medicine (TEAM-AI), McWilliams School of Biomedical Informatics, UTHealth Houston

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Email: Sunyang.Fu@uth.tmc.edu


News

Mar 18 2024: Received NIDUS II LOI Award.

Mar 17 2024: Awarded AMIA IS 24 SPC Award.

Sep 1 2023: Selected as a Leadership Fellow by the NIH AIM-AHEAD initiative.

Jul 1 2023: Joined UTHealth, McWilliams School of Biomedical Informatics as a faculty member.

Mar 18 2023: Featured by the American Society for Clinical Pharmacology and Therapeutics and Clinical and Translational Science, as well as Mayo Clinic's Research Magazine.

About

I am an Assistant Professor and Associate Director of Team Science at the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI), McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston. I am also affiliated with UTHealth Institute on Aging, Network for Investigation of Delirium: Unifying Scientists, and Mayo Clinic, Division of Epidemiology. Additionally, I was selected as the 2023-24 Leadership Fellow with the National Institute of Health’s Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD). The overarching goal of my research is to accelerate, improve and govern the secondary use of Electronic Health Records (EHRs) for clinical and translational research toward high throughput, reproducible, fair, and trustworthy discoveries. I have significant collaborative research experience in aging, cancer, and musculoskeletal diseases and procedures. Previously, I was a Sr. Data Science Analyst at the Department of AI and Informatics, Mayo Clinic. I obtained my Ph.D. at the University of Minnesota, M.H.I. at the University of Michigan, and B.B.A. at the University of Iowa.

I am looking for talented graduate (PhD and Master) students and postdoctoral fellows who are interested in:

  • Aging informatics (e.g., delirium, frailty, ADRD, functional status, and nursing home).
  • Clinical NLP (e.g., LLM + knowledge graph, federated learning, hybrid NLP framework, and multisite evaluation).
  • Real-world data (e.g., data quality, reporting standard, decentralized digital trials, multisite EHR data heterogeneity).

Selected Publications

FedFSA: Hybrid and federated framework for functional status ascertainment across institutions.https://doi.org/10.1016/j.jbi.2024.104623

Recommended Practices and Ethical Considerations for Natural Language Processing-Assisted Observational Research: A Scoping Review. https://doi.org/10.1111/cts.13463
(Featured by Translational Bytes, Mayo Clinic Research Magazine, and American Society for Clinical Pharmacology and Therapeutics) and adopted by the OHDSI community

Clinical concept extraction: A methodology review. https://doi.org/10.1016/j.jbi.2020.103526
(JBI most cited articles, covered 10 years of research on clinical NLP methodology)

Assessment of Data Quality Variability across Two EHR Systems through a Case Study of Post-Surgical Complications.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285181/
(Best Paper Competition 2nd place)

Ascertainment of delirium status using natural language processing from electronic health records. https://doi.org/10.1093/gerona/glaa275
(Featured by The Gerontological Society of America)

Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction. https://doi.org/10.1186/s12911-020-1072-9
(Adopted by The National Center for Data to Health (CD2H) as best practices)