Sunyang Fu, PhD

Assistant Professor

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Department of Clinical and Health Informatics

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Contact

Sunyang.Fu@uth.tmc.edu

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NCBI Bibliography

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Sunyang Fu, PhD became an Assistant Professor at McWilliams School of Biomedical Informatics at UTHealth Houston in June of 2023. He is the Associate Director of Team Science at the Center of Translational AI Excellence and Applications in Medicine (TEAM-AI). Prior to joining UTHealth Houston, Dr. Fu worked as a biomedical informatician and data scientist in the Department of AI and Informatics at Mayo Clinic. His research focuses on clinical research informatics and clinical and translational research, with an emphasis on accelerating, improving, and governing the secondary use of Electronic Health Records (EHRs) for high throughput, reproducible, fair, and trustworthy discoveries. Dr. Fu has extensive experience in informatics research, including assessing multi-institutional EHR data quality and heterogeneity, developing natural language processing (NLP) techniques for clinical information extraction, and designing informatics frameworks to optimize clinical research workflows. Additionally, he has significant collaborative research experience in epidemiology, aging, cancer, and musculoskeletal diseases and procedures.

Dr. Fu has maintained a high level of research productivity with 50 peer-reviewed publications in top-tier informatics journals, such as NPJ Digital Medicine, Briefings in Bioinformatics, Bioinformatics, JAMIA, and JBI. He has contributed to the scientific community by serving as an editor for Frontiers in Public Health and as a guest reviewer for Lancet Digital/Regional Health. His research on reproducibility has made a significant impact on clinical and translational science. He has been invited to contribute to the textbook "Clinical Research Informatics", entailing a full chapter related to clinical NLP applications in clinical research, and he has co-edited Chapter 9 of the National Center for Data to Health (CD2H) playbook. Dr. Fu has also served as a committee member for several international conferences including IEEE-BIBM and ACM-BCB. Additionally, he has co-organized several national-level workshops and shared tasks in clinical NLP, including n2c2, OHNLP, HealthNLP, and BioCreative, involving more than 20 institutions worldwide. Dr. Fu is an active member of AMIA, IEEE, and AACR.

• What does the future of your research look like?
  There is a noticeable gap in translational efforts aimed at facilitating the pragmatic implementation of standards, methods, and best practices related to clinical research design and operation. In my research, I envision a strong focus on addressing these translational challenges through quality assessment, workflow optimization, and community engagement
• What does the future of informatics look like?
  With the increasing integration of AI applications into patient care and clinical research workflows, there is a strong need for translational research and seamless integration of real-world evidence. The future of biomedical informatics must tackle challenges concerning AI ethics, fairness, reproducibility, data privacy, and security to ensure valid and trustworthy discoveries.

Selected Publications

1. Fu S, Wen A, Liu H. Clinical Natural Language Processing in Secondary Use of EHR for Research. InClinical Research Informatics 2023 Jun 15 (pp. 433-451). Cham: Springer International Publishing.
2. Fu S, Wang L, Moon S, Zong N, He H, Pejaver V, Relevo R, Walden A, Haendel M, Chute CG, Liu H. Recommended practices and ethical considerations for natural language processing?assisted observational research: A scoping review. Clinical and translational science. 2023 Mar;16(3):398-411.
3. Fu S, Chen D, He H, Liu S, Moon S, Peterson KJ, Shen F, Wang L, Wang Y, Wen A, Zhao Y. Clinical concept extraction: a methodology review. Journal of biomedical informatics. 2020 Sep 1;109:103526.
4. Fu S, Wen A, Schaeferle GM, Wilson PM, Demuth G, Ruan X, Liu S, Storlie C, Liu H. Assessment of data quality variability across two EHR systems through a case study of post-surgical complications. InAMIA Annual symposium proceedings 2022 (Vol. 2022, p. 196). American Medical Informatics Association.
5. Fu S, Lopes GS, Pagali SR, Thorsteinsdottir B, LeBrasseur NK, Wen A, Liu H, Rocca WA, Olson JE, St. Sauver J, Sohn S. Ascertainment of delirium status using natural language processing from electronic health records. The Journals of Gerontology: Series A. 2022 Mar 1;77(3):524-30.
6. Fu S, Leung LY, Raulli AO, Kallmes DF, Kinsman KA, Nelson KB, Clark MS, Luetmer PH, Kingsbury PR, Kent DM, Liu H. Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction. BMC medical informatics and decision making. 2020 Dec;20(1):1-2.