Peer-reviewed Publications

Note: _ denotes the first author advised by Dr. Zhang, # indicates co-first author.

  1. Schutte D, Vasilakes J, Bompelli A, Zhou Y, Fiszman M, Kilicoglu H, Bishop J, Adam T, Zhang RDiscovering novel drug-supplement interactions using SuppKG generated from the biomedical literature. Journal of Biomedical Informatics. July 2022.

  2. Zhou S, Wang N, Wang L, Liu H, Zhang R. CancerBERT: a cancer domain specific language model for extracting breast cancer phenotypes from electronic health records. JAMIA. 2022:29(7): 1208-1816.

  3. Singh, E., Bompelli, A., Wan, R. Bian J, Pakhomov S, Zhang RA conversational agent system for dietary supplements use. BMC Med Inform Decis Mak 22, 153 (2022). 

  4. 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. BMC Med Inform Decis Mak 22, 88 (2022). 

  5. Sicheng Zhou,  Dalton Schutte, Aiwen Xing, Jiyang Chen, Julian Wolfson, Zhe He, Fang Yu, Rui Zhang. Identification of dietary supplement use from electronic health records using transformer-based language models. BMC Medical Informatics and decision making. 2022. (in press)

  6. Wang Y, Zhao Y, Schutte D, Bian J, Zhang RDeep Learning Models in Detection of Dietary Supplement Adverse Event Signals from Twitter. JAMIA Open. September 2021. 

  7. 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.

  8. 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.

  9. 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

  10. 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 2021.

  11. 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.

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

  13. Fan Y, Zhou S, Li Y, Zhang RDeep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical TextJournal of American Medical Informatics Association. 2020

  14. 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.

  15. 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).

  16. 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. 

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  21. 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.

  22. 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.

  23. Vasilakes J, Fan Y, Rizvi R, Bompelli A, Bodenreider O, Zhang R. Normalizing Dietary Supplement Product Names Using the RxNorm ModelStud Health Technol Inform. 2019:408-412.

  24. Rizvi R, Wang Y, Nguyen T, Vasilakes J, He Z, Zhang R. Analyzing Social Media Data to Understand Consumers’ Information Needs on Dietary SupplementsStud Health Technol Inform. 2019:323-327.

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

  26. He Zhe, Barrett L, Rizvi R, Payrovnaziri S, Zhang R. Exploring the Discrepancies in Actual and Perceived Benefits of Dietary Supplements Among Obese PatientsStud Health Technol Inform. 2019:1474-1478.

  27. Fan Y, Pakhomov S, McEwan R, Zhao W, Lindermann E, Zhang RUsing Word Embeddings to Expand Terminology of Dietary Supplements on Clinical Notes.  Journal of American Medical Informatics Association Open 2019, 2(2): 246–253.

  28. 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.

  29. 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.

  30. 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 GraphIEEE International conference on Bioinformatics and Biomedicine 2018: 1649-1652

  31. 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. 2018:1504-1507.

  32. Vasilakes J, Rizvi R, Melton G, Pakhomov S, Zhang R. Evaluating Active Learning Methods for Annotating Semantic Predications Extracted from MEDLINEJournal of American Medical Informatics Association Open. 2018:1(2):275-282.

  33. 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.

  34. 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.

  35. 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.

  36. 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.
  37. Wang Y, Zhao Y, Bian J, Zhang R.  Detecting signals of associations between dietary supplements use and mental disorders from Twitter. IEEE International Conference on Healthcare Informatics. 2018.

  38. Fan YZhang 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.

  39. Breitenstein M, Liu H, Maxwell K, Pathak J, Zhang RElectronic 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. 

  40. 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)

  41. 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.

  42. 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.
  43. 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.

  44. 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.

  45. 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.

  46. 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.

  47. 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. 

  48. 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.

  49. 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.

  50. 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)

  51. 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.

  52. 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.

  53. 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.

  54. 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.

  55. 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.

  56. 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.

  57. Marc DZhang 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.

  58. 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).  [The Wall Street Journal] 

  59. 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.

  60. 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:Supp.1:103-11.

  61. 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)

  62. 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)

  63. 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)

  64. 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. Appl Clin Inform. 2012;3(4):404-418.

  65. Zhang R, Pakhomov S, Gladding S, Aylward M, Borman-Shoap E, Melton GB. Automated Assessment of Medical Training Evaluation Text. AMIA Annu Symp Proc 2012: 1459-68.

  66. Zhang R, Pakhomov S, Melton GB. Automated identification of relevant new information in clinical narrative. 2nd ACM SIGHIT Inter Health Inform (IHI) Symp Proc. 2012: 837-41.

  67. 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)

  68. Zhang R, Chen X, Mo M, Wang Z, Zhang M, Liu X. Morphology-controlled growth of crystalline antimony sulfide via a refluxing polyol process. J Cryst Growth. 2004;262(1-4):449-55.

  69. Zhang M, Zhang R, Liu Y, Qian Y. From sheets to fibers: A novel approach to gamma-AlOOH and gamma-Al2O3 1D nanostructures. J Nanosci and Nanotech. 2006;6(5):1437-40.

  70. Xi G, Yu S, Zhang R, Zhang M, Ma D, Qian Y. Crystalline silicon carbide nanoparticles encapsulated in branched wavelike carbon nanotubes: Synthesis and optical properties. J Phys Chem B. 2005;109(27):13200-4.

  71. Ma D, Zhang W, Zhang R, Zhang M, Qian Y. A facile hydrothermal synthesis route to single-crystalline lead iodide nanobelts and nanobelt bundles. J Nanosci and Nanotech. 2005;5(5):810-3.

  72. Chen X, Wang Z, Wang X, Zhang R, Liu X, Lin W. Synthesis of novel copper sulfide hollow spheres generated from copper (II)-thiourea complex. J Cryst Growth. 2004;263(1-4):570-4.

  73. Xi G, Xiong K, Zhao Q, Zhang R, Zhang H, Qian Y. Nucleation-dissolution-recrystallization: A new growth mechanism for t-selenium nanotubes. Cryst Growth Design. 2006;6(2):577-82.

  74. Mu L, Wan J, Ma D, Zhang R, Yu W, Qian Y. A room temperature self-sacrificing template route to Ag2Te fibers. Chem Lett. 2005;34(1):52-3.

  75. Hu HM, Yang BJ, Liu XY, Zhang R, Qian Y. Large-scale growth of porous CuInS2 microsphere. Inorg Chem Comm. 2004:7(4):563-5.

  76. Ma D, Zhang W, Tang Q, Zhang R, Yu W, Qian Y. Large-scale hydrothermal synthesis of SnS2 nanobelts. J Nanosci and Nanotech. 2005;5(5):806-9.

  77. Liu Y, Zhang M, Gao Y, Zhang R, Qian Y. Synthesis and optical properties of cubic In2S3 hollow nanospheres. Mater Chem Phys. 2007;101(2-3):362-6.

  78. Zhang M, Wang Z, Ma D, Zhang R, Qian Y. Large-scale synthesis of antimony nanobelt bundles. J Cryst Growth. 2004;268(1-2):215-21.

  79. Zhang M, Wang ZH, MA DK, Zhang R, Qian Y. Surfactant-assisted controlled synthesis of antimony and bismuth three-dimensional superstructures in different hydrothermal emulsion systems. Austra J Chem. 2005:58(7) 539-543.

  80. Zhang M, Wang ZH, Mo MS, Chen XY, Zhang R, Yu WC, Qian Y. A simple approach to synthesize KNiF3 hollow spheres by solvothermal method. Mater Chem Phys. 2005;89(2-3):373-378.

Peer-reviewed Conference Abstracts

  1. Xi Chen, Michael J Cairelli, Charles Sneiderman, Thomas Rindflesch, Serguei Pakhomov, Genevieve Melton, Rui Zhang. “Applying Active Learning to Semantic Predications in SemMedDB”. IEEE International Conference on Biomedical and Health Informatics. Las Vegas, NV, 2016 (Best Poster Award)

  2. Zhang R, Melton GB. “Natural Language Processing and Standardized Terminologies.” Proceedings of the First Omaha System International Conference. Eagan, Minnesota April 2013.

Book Chapters

  1. Zhang R, Wang Y, Melton GB. “Natural language processing in medicine.” Medical Applications of Artificial Intelligence, CRC Press, Taylor & Francis, Boca Raton, Florida, 2013, ISBN: 1439884331.

  2. Zhang M and Zhang R. “Selected-control growth of antimony and bismuth nanocrystals in solutions.” New Developments in Crystal Growth Research. Nova Science Publishers, New York, 2005, ISBN: 1594545391.