Rui Zhang, PhD, FAMIA

Associate Professor and McKnight Presidential Fellow, Department of Pharmaceutical Care & Health Systems, Associate Professor, Core Faculty, Institute for Health Informatics, Institute for Health Informatics (IHI) Member, NLP/IE Group

Artificial Intelligence
Consumer Health Informatics
Health Data Science
Natural Language Processing/NLP
Pharmacy Informatics
Rui Zhang

7-115A Weaver-Densford Hall
308 Harvard St. SE
Minneapolis, MN 55455
United States

Dr. Zhang is McKnight Presidential Fellow and Associated Professor at the University of Minnesota. He has joint appointments in the Department of Pharmaceutical Care & Health System (PCHS) and the Institute for Health Informatics (IHI). He leads the Clinical Informatics PhD track in Health Informatics graduate program, and also faculty in the graduate program of Data Science, Bioinformatics and Computational Biology (BICB). He is the Director of Natural Language Processing (NLP) services in BPIC of CTSI.  Dr. Zhang’s research focuses on biomedical informatics, especially artificial intelligence (AI), biomedical and clinical NLP, knowledge representation and pharmacy informatics. Dr. Zhang’s research focuses on the development of novel AI methods to analyze biomedical big data, including biomedical literature, electronic health records (EHRs), patient-generated data and knowledge bases. His research include but not limited to: i) the secondly analysis of EHR data for patient care, ii) pharmacovigilance knowledge discovery through mining biomedical literature and iii) creation of knowledge base through database integration, terminology and ontology. 

 His research program is funded by federal agencies such as National Institutes of Health and Agency for Health and Research Quality, as well as industries such as Medtronic Inc. His work has been recognized on a national scale including Journal of Biomedical Informatics Editor’s Choice, nominated for Distinguished paper in AMIA Annual Symposium andMarco Ramoni Distinguished Paper Award for Translational Bioinformatics, as well as highlighted by The Wall Street Journal. Dr. Zhang has served on several NIH study sections, the Editorial Board for Journal of American Medical Informatics Association (JAMIA), Chair of AMIA Student working group, and co-chaired international workshops, such as HealthNLP and Semantics-powered Health data analytics (SEPDA). Recently, Dr. Zhang was inducted into the Fellow of AMIA (FAMIA).


Awards & Recognition

  • 2016 - Best Poster Award in BHI-2016 IEEE International Conference on Biomedical and Health Informatics
  • 2015 - Nominated as Marco Ramoni Distinguished Paper Award for Translational Bioinformatics
  • 2014 - Manuscript selected as Student Paper Competition Finalist in AMIA Joint Summits
  • 2013-14 - Health Information and Management Systems Society Scholarship (HIMSS), Minnesota Chapter
  • 2013 - Manuscript selected as Student Paper Competition Finalist in MEDINFO2013 (international, IMIA)
  • 2012-13 - Doctoral Dissertation Fellowship (All-University Competition), University of Minnesota
  • 2011, 2013 - Fellow, International Partnership in Health Informatics Education (IPHIE) Master Class
  • 2017 - PhD student’s manuscript (senior author) won the second place in the Student Paper Competition, AMIA Translational Bioinformatics (San Francisco, March 2017)
  • 2018 - Post-doctorate associate’s manuscript (senior author) nominated as a Student Paper Competition Finalist, AMIA Translational Bioinformatics (San Francisco, March 2018)
  • 2019 - PhD student’s manuscript (senior author) won the third place in the Student Paper Competition, AMIA Informatics Summit (San Francisco, March 2017)



PhD, University of Minnesota, (Health Informatics), 2013

MS, University of Iowa, (Chemistry), 2008; University of Iowa, (Informatics), 2010

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Research Summary/Interests

  • Natural Language Processing
  • Text Mining
  • Literature-based Discovery
  • Translational Informatics
  • Statistic Analysis
  • Machine Learning



Research Funding Grants

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)
Role: Co-investigator
Funding Agency: NIH/NCATS (Blazar)
Project Dates: 03/2018-02/2023
This is a center grant to enhance clinical and translational science in UMN. My role is to receive requests for NLP informatics consultation and provide expert consulting to investigators who need to use or whose projects would benefit from biomedical NLP methods.

An Informatics Framework for Discovery and Ascertainment of Drug-Supplement Interactions
Role: Principal Investigator
Funding Agency: NIH/NCCIH 1 R01 AT009457 (Zhang)
Project Dates: 04/2017–03/2021
This grant is to develop a translational informatics framework to enable the discovery of DSIs by linking scientific evidence from the biomedical literature and clinical evidence from our EHR system.

Discovery and Visualization of New Information from Clinical Reports
Role: Co-Investigator
Funding Agency: AHRQ 1 R01 HS022085-01 (Melton-Meaux)
Project Dates: 09/01/2013–08/30/2017
This grant develops and evaluates visualization methods by “highlighting” important information from clinical texts, improving user interface design for clinical texts, and conducts a prospective clinical trial with a tool in the EHR to highlight new, non-redundant information in clinical documents.

NYHA Classification Determination from Electronic Health Records for Medtronic CRT Patients
Role: Co-Investigator
Funding Agency: Medtronic Inc. (Aliferis/Speedie)
Project Dates: 11/2017-09/2018
The goal is to develop NLP methods to detect and predict NYHA classification from EHR data for patients who had Medtronic CRT implant.

Creating a 21st century precision medicine intensive care unit
Role: Co-Investigator
Funding Agency: College of Pharmacy (Skaar)
Project Dates: 10/2017-09/2019
The overall goal of this project is to aims to identify actionable genetic variants in ICU patients and evaluate the relationship between genotypes and drug efficacy as well as adverse drug reactions (ADR) in a real-world setting at the bedside.

Using Electronic Health Records to Validate Literature Discovery-Based Drug-Drug Interactions
Role: Principal Investigator
Funding Agency: University of Minnesota Office of the Vice President for Research Grant-in-Aid (Zhang)
Project Dates: 01/2016–06/2017

Improving Breast Cancer Survivors’ Disease Management Outcomes through Smartphone Apps and Online Health Community
Role: Co-Investigator
Funding Agency: University of Minnesota Office of the Vice President for Research Grant-in-Aid (Gao)
Project Dates: 07/2016–01/2018

Large-scale discovery of drug-supplements interactions in biomedical literature
Role: Principal Investigator
Funding Agency: University of Minnesota Informatics Institute On the Horizon Grant
Project Dates: 07/2014–07/2015


  1. Silverman G, Sahoo H, Ingraham N, Lupei M, Pusharich M, Usher M, Dries J, Finzel R, Murray E, Sartori J, Simon G, Zhang R, Melton G, Pakhomv S. NLP Methods for Extraction of Symptoms from Unstructured Data for use in Prognostic COVID-19 Analytic Models. Journal of Artificial Intelligence Research. 2021 in press.
  2. He X#Zhang R#, Alpert J, Zhou S, Adam T, Raisa A, Peng Y, Zhang H, Guo Y, Bian J. When text simplification is not enough: could a graph-based visualization facilitate consumers' comprehension of dietary supplement information? Journal of American Medical Informatics Association Open 2021
  3. Anusha Bompelli#, Yanshan Wang#, Ruyuan Wan, Esha Singh, Yuqi Zhou, Lin Xu, David Oniani, Bhavani Singh Agnikula Kshatriya, Joyce (Joy) E. Balls-Berry, and Rui ZhangSocial and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review. Health Data Science (in press).
  4. Zhang R #, Hristovski D#, Schutte D #, Kastrin A#, Fiszman M, Kilicoglu H. Drug Repurposing for COVID-19 via Knowledge Graph CompletionJournal of Biomedical Informatics. Feb 2021. (
  5. Fan Y, Zhou S, Li YZhang R. Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text. Journal of American Medical Informatics Association. 2021: 28(3): 569-577.
  6.  Zhou S, Zhou Y, Bian J, Haynos A, Zhang RExploring Eating Disorder Topics from TwitterJMIR Medical Informatics, 2020;8(10):e18273.
  7. Vasilakes J, Bompelli A, Bishop J, Adam T, Bodenreider O, Zhang R. Assessing the Enrichment of Dietary Supplement Coverage in the UMLS. Journal of American Medical Informatics Association. 2020 (10): 1547-1555.
  8.  Bompelli A, Li J, Xu Y, Wang N, Wang Y, Adam T, Zhe H, Zhang R. Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model. AMIA Annual Symposium. 2020 (in press).
  9. Bompelli A#, Silverman G#, Finzel R, Vasilakes J, Knoll B, Pakhomov S, Zhang R. Comparing NLP Systems to Extract of Eligibility Criteria in Dietary Supplements Clinical Trials using NLP-ADAPT. Artificial Intelligence in Medicine. 2020: 67-77. (
  10. Rizvi R#, Vasilakes J#, Adam T, Melton G, Bishop J, Bian J, Tao C, Zhang R. iDISK: The Integrated Dietary Supplements Knowledge Base. Journal of American Medical Informatics Association. 2020, 27(4): 539-548. (
  11. Yi, Y., Shen, Z., Bompelli, A., Yu, F., Wang, Y., Zhang, R. (2020). Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer’s Disease from Clinical Notes. IEEE ICHI HealthNLP Workshop (vol. 2020). (in press)
  12. He Z, Barrett L, Rizvi R, Tang X, Payrovnaziri S, and Zhang R. Assessing the Use and Perception of Dietary Supplements Among Obese Patients with National Health and Nutrition Examination Survey. AMIA Informatics Summit. 2020: 231-240.
  13.  Singh E, Bompelli A, Yang A, Wang A, Pakhomov S, Zhang R. A Prototype Conversational Agent for Dietary Supplements. IEEE ICHI HealthNLP Workshop (vol. 2020). (in press)
  14. Zhang H, Wheldon C, Dunn A, Tao C, Huo J, Zhang R, Prosperi M, Guo Y, Bian J. Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States. Journal of American Medical Informatics Association. 2020 Feb 1;27(2):225-235.
  15. He X*, Zhang R* (co-first), Rizvi R, Vasilakes J, Yang X, Guo Y, He Z, Prosperi M, Huo J, Alpert J, Bian J. ALOHA: Developing an Interactive Graph-based Visualization for Dietary Supplement Knowledge Graph through User-Centered Design. BMC Med Inform Decis Mak. 2019 Aug 8;19(Suppl 4):150. 
  16. Wang Y, Zhao Y, Bian J, Zhang R.  Detecting Associations between Dietary Supplement Intake and Sentiments within Mental Disorder Tweets. Health Informatics Journal. 2019:1-13.
  17. Zhou S, Zhao Y, Rizvi R, Bian J, Haynos A, Zhang R. Analysis of Twitter to Identify Topics Related to Eating Disorder Symptoms. IEEE International Conference on Health Informatics, 2019 2019:10.1109/ichi.2019.8904863. doi:10.1109/ichi.2019.8904863.
  18. Vasilakes J, Fan Y, Rizvi R, Bompelli A, Bodenreider O, Zhang R. Normalizing Dietary Supplement Product Names Using the RxNorm Model. Stud Health Technol Inform. 2019 408-412.
  19. Rizvi R, Wang Y, Nguyen T, Vasilakes J, He Z, Zhang R. Analyzing Social Media Data to Understand Consumers’ Information Needs on Dietary Supplements. Stud Health Technol Inform. 2019:323-327.
  20. He Zhe, Barrett L, Rizvi R, Payrovnaziri S, Zhang R. Exploring the Discrepancies in Actual and Perceived Benefits of Dietary Supplements Among Obese Patients. Stud Health Technol Inform. 2019:1474-1478.
  21. Zhou S, Zhang X, Zhang R. Identifying Cardiomegaly in ChestX-ray8 using Transfer Learning. Stud Health Technol Inform. 2019:482-486.
  22. Fan Y, Pakhomov S, McEwan R, Zhao W, Lindermann E, Zhang R. Using Word Embeddings to Expand Terminology of Dietary Supplements on Clinical Notes. Journal of American Medical Informatics Association Open. 2019, 2(2):246-253.
  23. Vasilakes J, Rizvi R, Terrence A, Zhang R, Detecting Signals of Dietary Supplement Adverse Events from the CFSAN Adverse Event Reporting System (CAERS). AMIA Informatics Summit. 2019:258-266.
  24. He Z, Rizvi R, Terrence A, Zhang R. Comparing the Study Populations in Dietary Supplement and Drug Clinical Trials for Metabolic Syndrome and Related Disorders. AMIA Informatics Summit. 2019:799-808.
  25.  Wang Y, Zhao Y, Schutte D, Bian J, Zhang R. Deep Learning Models in Detection      of Dietary Supplement Adverse Event Signals from Twitter. JAMIA Open. September2021. 
  26. Shaoo H, Silverman G, Ingraham N, Lupei M, Puskarich M, Finzel R, Sartori J, Zhang R, Knoll B, Liu S, Liu H, Melton B, Tignanelli, Pakhomov S. A fast, resource efficient and reliable rule-based system for COVID-19 symptom identification.JAMIA Open. 2021 August 7.




In The News

“Researchers at the University of Minnesota in Minneapolis are exploring interactions between cancer drugs and dietary supplements, based on data extracted from 23 million scientific publications, according to lead author Rui Zhang, a clinical assistant professor in health informatics. In a study published last year by a conference of the American Medical Informatics Association, he says, they identified some that were previously unknown.”