Rui Zhang, PhD, FAMIA

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 electronic health records (EHRs), biomedical literature, patient-generated data, and knowledge bases. His researcher program is funded by federal agencies such as NIH and Agency for Health and Research Quality (AHRQ), as well industries such as Medtronic Inc.

Health Data Science, Artificial Intelligence, Informatics, Natural language processing, Knowledge Discovery.

• Outstanding Teaching and Dedication Certificate, Center for Educational Innovation, University of Minnesota (UMN), 2019
• Named as a McKnight Presidential Fellow, UMN, 2019
• 2019 PhD student’s manuscript (senior author) won the third place in the Student Paper Competition, AMIA Informatics Summit (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)
• 2017 PhD student’s manuscript (senior author) won the second place in the Student Paper Competition, AMIA Translational Bioinformatics (San Francisco, March 2017)
• 2016 Manuscript nominated as AMIA Annual Symposium Distinguished Paper Award (Chicago, November 2016)
• 2016 Research project highlighted on The Wall Street Journal Health Headline “How Your Supplements Interact With Prescription Drugs”. (February 2016)
• 2016 Best Poster Award, IEEE International Conference on Biomedical and Health Informatics (Las Vegas, February 2016)
• 2015 Manuscript (first author) nominated as the Marco Ramoni Distinguished Paper Award for Translational Bioinformatics (San Francisco, March 2015)
• 2014 Manuscript selected as Journal of Biomedical Informatics Editors’ Choice
• 2014 Manuscript (first author) selected as Student Paper Competition Finalist, AMIA Joint Summits on Translational Science (San Francisco, March 2014)
• 2013 Manuscript (first author) selected as Student Paper Competition Finalist, MEDINFO - The14th World Congress on Medical and Health Informatics of the International Medical Informatics Association (Denmark, August 2013)
• 2013 Health Information and Management Systems Society (HIMSS) Scholarship awardee, Minnesota Chapter
• 2011 Manuscript (first author) selected as Student Paper Competition Finalist, AMIA Annual Symposium (DC, November 2011)
• 2011 Fellowship of the International Partnership in Health Informatics Education (IPHIE) 
• Master Class, University of Heidelberg-University of Heilbronn, Germany

• American Medical Informatics Association (AMIA)
• Institute of Electrical and Electronics Engineers (IEEE)

• Chinese
• English

His research include but not limited to: i) the secondly analysis (especially NLP methods) of EHR data for clinical applications; ii) pharmacovigilance knowledge discovery through mining biomedical literature; and iii) creation of knowledge bases through database integration, terminology and ontology.

Detecting synergistic effects of pharmacological and non-pharmacological interventions for AD/ADRD

Role: Principal Investigator
Funding Agency: NIH/NIA 1R01AG078154-01
Project Dates: 07/2022-06/2027
This grant is to develop translational informatics approaches to aggregate, standardize and discover the synergistic effects of pharmacological and non-pharmacological intervention candidates on AD/ADRD using multi-modal data resources (i.e., literature, EHR, clinical trials) followed by animal model validation.

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

Selected publications (full publication list):

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. September 2021.

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.

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.

He X#, Zhang R#, Alpert J, Zhou S, Adam T, Raisa A, Peng Y, Zhang H, Guo Y, Bian J. When text simplication is not enough: could a graph-based visualization facilitate consumers' comprehension of dietary supplement information? Journal of American Medical Informatics Association Open 2021

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 Zhang. Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review. Health Data Science (in press).

Shen Z#, Yoonkwon Y#, Bompelli A, Yu F, Wang Y, Zhang R. Extracting Lifestyle Factors for Alzheimer's Disease from Clinical Notes Using Deep Learning with Weak Supervision. 2021. Under review.

Zhang R #, Hristovski D#, D Schutte D #, Kastrin A#, Fiszman M, Kilicoglu H. Drug Repurposing for COVID-19 via Knowledge Graph Completion. 2021 Journal of Biomedical Informatics.

Zhou S, Zhou Y, Bian J, Haynos A, Zhang R. Exploring Eating Disorder Topics from Twitter. JMIR Med Inform 2020;8(10):e18273.

Fan Y, Zhou S, Li Y, Zhang R. Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text. Journal of American Medical Informatics Association. 2020 https://doi.org/10.1093/jamia/ocaa218.

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 Sep 17;ocaa128. doi: 10.1093/jamia/ocaa128.

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).

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.

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 Apr 1;27(4):539-548. doi: 10.1093/jamia/ocz216.

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; 2020: 231–240.

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.

He X#, Zhang R#, 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. doi: 10.1186/s12911-019-0857-1.

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.

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, 2019:10.1109/ichi.2019.8904863.

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.

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.

Zhou S, Zhang X, Zhang R. Identifying Cardiomegaly in ChestX-ray8 using Transfer Learning. Stud Health Technol Inform. 2019:482-486.

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.

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.

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;2019:258-266.

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; 2019: 799–808.

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):275-28

Pope Z, Zeng N, Zhang R, Lee HY, Gao Z. Effectiveness of Combined Smartwatch and Social Media Intervention on Breast Cancer Survivor Outcomes: Randomized Trial. Medicine & Science in Sports & Exercise, 2018, 50(5S):137.

Pope Z, Zeng N, Zhang R, Lee HY, Gao Z. Effectiveness of Combined Smartwatch and Social Media Intervention on Breast Cancer Survivor Health Outcomes: A 10-Week Pilot Randomized Trial. Journal of clinical medicine, 2018, 7(6), 140.

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.

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.

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

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).

Zhang R, Cairelli M, Fiszman M, Rosemblat G, Kilicoglu H, Rindflesch TC, Pakhomov S, Melton GB. Using semantic predications to uncover drug-drug interactions in clinical data. J Biomed Inform. 2014;49:134-47. (JBI Editors’ Choice)

Zhang R, Pakhomov S, Melton GB. Longitudinal analysis of new information types in clinical notes. AMIA Joint Summits on Translational Science. 2014: 232-7. (Student Paper Competition Finalist)

Zhang R, Pakhomov S, Lee J, Melton GB. Navigating longitudinal clinical notes with an automated method for detecting new information. Stud Health Technol Inform 2013;192: 754-8. (Student Paper Competition Finalist)

Zhang R, Pakhomov S, McInnes TB, Melton GB. Evaluating measures of redundancy in clinical texts. AMIA Annu Symp Proc 2011: 1612-20. (Student Paper Competition Finalist)

“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.”
http://www.wsj.com/articles/what-you-should-know-a...