Rui Zhang, PhD
Dr. Zhang was jointly hired as an Assistant Professor in the Department of Pharmaceutical Care & Health System (PCHS) and the Institute for Health Informatics (IHI). He obtained his PhD degree in Health Informatics from University of Minnesota and a master’s degree in both Informatics and Chemistry from University of Iowa.
Dr. Zhang has extensive education background and research experience in the field of health and biomedical informatics, especially biomedical natural language processing and text mining. His research interests include the secondly analysis of electronic health record (EHR) data for patient care as well as pharmacovigilance knowledge discovery through mining a large scale of biomedical literature.
Dr. Zhang has published over 30 peer-review publications and is the Principal Investigator on a NIH R01 research award entitled “An Informatics Framework for Discovery and Ascertainment of Drug-Supplement Interactions” (2017-2021).
He is also the recipient of UMN grant-in-aid award and UMII on the horizon grant. His work has been recognized on a national and international scale including Journal of Biomedical Informatics (JBI) Editor’s Choice (2014), a nomination for Marco Ramoni Distinguished Paper Award for Translational Bioinformatics (2015), and best student papers in American Medical Informatics Association (AMIA) Annual Symposium (2011), MEDINFO (2013), and AMIA Joint Summits (2014).
In additional, his work on mining 23 million biomedical publications to discover drug-supplement interactions has been highlighted by The Wall Street Journal and Fox News.
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
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, Institute for Health Informatics (IHI)
Faculty, Data Science
PhD, University of Minnesota, (Health Informatics), 2013
MS, University of Iowa, (Chemistry), 2008; University of Iowa, (Informatics), 2010
- 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)
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
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
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
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
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. 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 (in press)
2. 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 (in press)
3. He X, Zhang R, Rizvi R, Vasilakes J, Yang X, Guo Y, He Z, Prosperi M, Bian J. Prototyping an Interactive Visualization of Dietary Supplement Knowledge Graph. IEEE International conference on Bioinformatics and Biomedicine2018. (in press)
4. Ma S, Zhang R, Shanahan L, Munroe J, Horn S, Speedie S Estimating New York Heart Association Classification for Heart Failure Patients from Information in the Electronic Health Record. IEEE International conference on Bioinformatics and Biomedicine 2018. (in press)
5. Vasilakes J, Rizvi R, Melton G, Pakhomov S, Zhang R. Evaluating Active Learning Methods for Annotating Semantic Predications Extracted from MEDLINE. Journal of American Medical Informatics Association Open.2018:1(2):275-282. (In Press).
6. Zhang R, Ma S, Shanahan L, Munroe J, Horn S, Speedie S. Discovering and Identifying New York Heart Association Classification from Electronic Health Records. BMC Medical Informatics and Decision Making. 2018, 18 (Suppl 2): 48.
7. Zhang R, Meng J, Lian Q, Chen X, Bauman B, Chu H, Segura B, Roy S. Prescription opioids are associated with higher mortality in patients diagnosed with sepsis: a retrospective cohort study using electronic health records. PLoS ONE. 2018 Jan 2;13(1):e0190362.
8. Fan Y, Zhang R, Using Natural Language Processing Methods to Classify Use Status of Dietary Supplements in Clinical Notes. BMC Medical Informatics and Decision Making, 2018, 18 (Suppl 2): 51.
9. Breitenstein M, Liu H, Maxwell K, Pathak J, Zhang R. Electronic health record phenotypes for precision medicine: perspectives and caveats from treatment of breast cancer at a single institution. Clinical and Translational Science. 2018 Jan;11(1):85-92.
10. Rizvi R, Adam T, Lindemann E, Vasilakes J, Pakhomov S, Melton G, Zhang R. Comparing Exisiting Resources to Represent Dietary Supplements. AMIA Joint Summit CRI. 2018 (selected as Student Paper Competition Finalist)
11. Zhang R, Ma S, Shanahan L, Munroe J, Horn S, Speedie S. Automatic Methods to Extract New York Heart Association Classification from Clinical Notes. IEEE International conference on Bioinformatics and Biomedicine. 2017:1277-1280.
12. Fan Y, He L, Zhang R. Evaluating Automatic Methods to Extract Patients’ Supplement Use from Clinical Reports. IEEE International conference on Bioinformatics and Biomedicine. 2017:1239-1242.
13. Jian Z, Guo X, Lou S, Ma H, Zhang S, Zhang R, Lei J. A Cascaded Approach for Chinese Clinical Text De-Identification with Less Annotation Effort. Journal of Biomedical Informatics, 2017; 73: 76-83.
14. Zhang R, Simon G, Yu F. Advancing Alzheimer's Research: A Review of Big Data Promises. International Journal of Medical Informatics. 2017;106:48-56.
15. Sun D, Simon G, Skube S, Blaes A, Melton GB, Zhang R, Causal Phenotyping for Susceptibility to Cardiotoxicity from Antineoplastic Breast Cancer Medications. Proceedings of the American Medical Informatics Association Symposium. 2017:1638-1647.
16. Zhang R, Pakhomov S, Arsoniadis E, Lee T, Wang Y, Melton G. Detecting clinically relevant new information in clinical notes across specialties and settings, BMC Medical Informatics and Decision Making, 2017 (17) Sp 2:68.
17. Fan Y, Adam T, McEwan R, Pakhomov S, Melton G, Zhang R. Detecting Signals of Interactions between Warfarin and Supplements in Electronic Health Records. Stud Health Techno Inform 2017:370-374.
18. Wang Y, Gunashekar R, Adam T, Zhang R. Mining Adverse Events of Dietary Supplements from Product Labels by Topic Modeling. Stud Health Techno Inform 2017:614-618.
19. Sun D, Sarda G, Skube S, Blaes A, Khairat S, Melton G, Zhang R. Phenotyping and Visualizing Infustion-related Reactions for Breast Cancer Patients. Stud Health Techno Inform 2017:599-603.
20. Fan Y, He L, Pakhomov S, Melton GB, Zhang R. Classifying Supplement Use Status in Clinical Notes. Proceedings of the American Medical Informatics Association Symposium Joint Summit on Translational Science 2017:493-501. (The 2nd place in Student Paper Competition
21. Fan Y*, He L, Zhang R. Classification of Status for Supplement Use in Clinical Notes. Proceedings of the IEEE International conference on Bioinformatics and Biomedicine. 2016: 1054-61.
22. Marc D*, Beattie J, Herasevich, V, Gatewood, L, Zhang R. Assessing Metadata Quality of a Federally Sponsored Health Data Repository. Proceedings of the American Medical Informatics Association Symposium. 2016; 2016: 864–73.
23. Wang Y*, Adam TJ, Zhang R. Term Coverage of Dietary Supplements Ingredients in Product Labels. Proceedings of the American Medical Informatics Association Symposium. 2016: 2053-2061.
24. Zhang R. Healthcare Data Analytics. Chandan K. Reddy and Charu C. Aggarwal. Boca Raton, FL: Chapman & Hall/CRC Press (2015) 724 pp. Journal of Biomedical Informatics. 2015;58:166-7.
25. Zhang R, Manohar N, Arsoniadis E, Wang Y, Adam T, Pakhomov S, Melton GB. Evaluating Term Coverage of Herbal and Dietary Supplements in Electronic Health Records. Proceedings of the American Medical Informatics Association Symposium. 2015:1361-70.
26. Manohar N*, Adam TJ, Pakhomov SV, Melton GB, Zhang R. Evaluation of Herbal and Dietary Supplement Resource Term Coverage. Stud Health Technol Inform 2015;216:785-9.
27. Marc D*, Zhang R, Beattie J, Gatewood LC, Khairat S. Indexing Publicly Available Health Data with Medical Subject Headings (MeSH): An Evaluation of Term Coverage. Stud Health Technol Inform 2015;216:529-33.
28. Zhang R, Adam T, Simon G, Cairelli M, Rindflesch T, Pakhomov S, Melton GB. Mining Biomedical Literature to Explore Interactions between Cancer Drugs and Dietary Supplements. AMIA Joint Summits on Translational Science. 2015:69-73. (Distinguished Paper Award Nominee).
29. Zhang R, Pakhomov S, Janet T Lee Melton GB. Using language models to identify relevant new information in inpatient clinical note. Proceedings of the American Medical Informatics Association Symposium. 2014:1268-76.
30. Zhang R, Cairelli M, Fiszman M, Kilicoglu H, Rindflesch TC, Pakhomov S, Melton GB. Exploiting Literature-derived knowledge and semantics to identify potential prostate cancer drugs. Cancer Informatics. 2014;13(S1):103-11.
31. Zhang R, Cairelli M, Fiszman M, Graciela R, Kilicoglu H, Rindflesch TC, Pakhomov S, Melton GB. Using semantic predications to discover drug-drug interactions from biomedical literature. Journal of Biomedical Informatics. 2014;49:134-47. (Manuscript selected as JBI Editors' Choice)
32. Zhang R, Pakhomov S, Janet T. Lee, Melton GB. Navigating longitudinal clinical notes with an automated method for detecting new information. Studies in Health Technology and Informatics. 2013;192: 754-8. (Manuscript selected as a Student Paper Competition Finalist)
33. Zhang R, Pakhomov S, Gladding S, Aylward M, Borman-Shoap E, Melton GB. Automated assessment of medical training evaluation text. Proceedings of the American Medical Informatics Association Symposium. 2012: 1459-1468.
34. Zhang R, Pakhomov S, Melton GB. Automated identification of relevant new information in clinical narrative. Proceedings of the 2nd ACM SIGHIT International Health Informatics (IHI?12) Symposium. 2012: 837-841.
35. Farri O, Rahman A, Monsen KA, Zhang R, Pakhomov S, Pieczkiewicz DS, Speedie SM, Melton GB. Impact of a prototype visualization tool for new information in EHR clinical documents. Applied Clinical Informatics. 2012; 3(4): 404-418.
36. Zhang R, Pakhomov S, McInnes TB, Melton GB. Evaluating measures of redundancy in clinical texts. Proceedings of the American Medical Informatics Association Symposium. 2011: 1612-1620. (Manuscript selected as a Student Paper Competition Finalist)
37. Zhang R, Wang Y, Melton GB. "Natural Language Processing in Medicine." In Medical Applications of Artificial Intelligence, CRC Press, Taylor & Francis, Boca Raton, Florida, 2013, ISBN: 1439884331
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.”