Mike Conway, PhD

Research Interests

  • Clinical Natural Language Processing
  • Natural Language Processing
  • Public Health Informatics
  • Consumer Health Informatics
  • Health Informatics
  • Bioethics


  • English

Academic Information

  • Departments: Biomedical Informatics - Assistant Professor

Academic Office Information

  • 801-581-4080
  • 421 Wakara Way
    Salt Lake City, UT 84108

Academic Bio

Since earning his PhD from the University of Sheffield's Department of Computer Science in 2007, Dr Conway's research has focused on using informatics methods -- particularly natural language processing -- to address research questions in population health. His recent work, funded by the NIH/National Institute on Drug Abuse and NIH/National Library of Medicine, utilizes social media and clinical notes to investigate both how social media users discuss their substance use (particularly focusing on cannabis, combustible tobacco, and electronic nicotine delivery systems) and how clinicians document substance use in the electronic health record.

Dr Conway has held research positions at the National Institute of Informatics (Japan), the University of Pittsburgh, Mayo Clinic, and the University of California San Diego. He is currently a tenure-track assistant professor in the Department of Biomedical Informatics at the University of Utah.

Research Statement

Dr Conway's research interests focus on the application of computational methods (e.g. natural language processing & machine learning) to problems in population and behavioral health.

Lab website: https://pbhig.chpc.utah.edu/

Google Scholar: https://scholar.google.com/citations?user=uiJso6MAAAAJ&hl=en

Email address: mike.conway@utah.edu

Education History

Type School Degree
Doctoral Training Sheffield University
Computer Science

Global Impact

Education History

Type School Degree Country
Doctoral Training Sheffield University
Computer Science
Ph.D. United Kingdom

Selected Publications

Journal Article

  1. Conway M, Hu M, Chapman WW (2019). Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data. Yearb Med Inform, 28(1), 208-217.
  2. Conway M, Keyhani S, Christensen L, South BR, Vali M, Walter LC, Mowery DL, Abdelrahman S, Chapman WW (2019). Moonstone: a novel natural language processing system for inferring social risk from clinical narratives. Journal of Biomedical Semantics, 10(1), 6.
  3. Zolnoori M, Fung KW, Patrick TB, Fontelo P, Kharrazi H, Faiola A, Shah ND, Shirley Wu YS, Eldredge CE, Luo J, Conway M, Zhu J, Park SK, Xu K, Moayyed H (2019). The PsyTAR dataset: From patients generated narratives to a corpus of adverse drug events and effectiveness of psychiatric medications. Data Brief, 24, 103838.
  4. Conway M, Mowery DL, South BR, Stoddard GJ, Chapman WW, Patterson OV, Zhu SH (2018). Documentation of ENDS Use in the Veterans Affairs Electronic Health Record (2008-2014). Am J Prev Med, 56(3), 474-475.
  5. Zolnoori M, Fung KW, Patrick TB, Fontelo P, Kharrazi H, Faiola A, Wu YSS, Eldredge CE, Luo J, Conway M, Zhu J, Park SK, Xu K, Moayyed H, Goudarzvand S (2019). A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications. J Biomed Inform, 90, 103091.
  6. Chen AT, Taylor-Swanson L, Buie RW, Park A, Conway M (2018). Characterizing Websites That Provide Information About Complementary and Integrative Health: Systematic Search and Evaluation of Five Domains. Interact J Med Res, 7(2), e14.
  7. Conway M, Mowery D, Ising A, Velupillai S, Doan S, Gunn J, Donovan M, Wiedeman C, Ballester L, Soetebier K, Tong C, Burkom H (2018). Cross Disciplinary Consultancy to Bridge Public Health Technical Needs and Analytic Developers: Negation Detection Use Case. OJPHI, 10(2), e209.
  8. Hurst S, Conway M (2018). Exploring Physician Attitudes Regarding Electronic Documentation of E-cigarette Use: A Qualitative Study. Tob Use Insights, 11, 1179173X18782879.
  9. Belyeu JR, Nicholas TJ, Pedersen BS, Sasani TA, Havrilla JM, Kravitz SN, Conway ME, Lohman BK, Quinlan AR, Layer RM (2018). SV-plaudit: A cloud-based framework for manually curating thousands of structural variants. Gigascience, 7(7).
  10. Park A, Conway M (2018). Tracking Health Related Discussions on Reddit for Public Health Applications. AMIA Annu Symp Proc, 2017, 1362-1371.
  11. Park A, Conway M (2018). Harnessing Reddit to Understand the Written-Communication Challenges Experienced by Individuals With Mental Health Disorders: Analysis of Texts From Mental Health Communities. J Med Internet Res, 20(4), e121.
  12. Park A, Conway M, Chen AT (2017). Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach. Comput Human Behav, 78, 98-112.
  13. Doan S, Ritchart A, Perry N, Chaparro JD, Conway M (2017). How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets. JMIR Public Health Surveill, 3(2), e35.
  14. Park A, Conway M (2017). Longitudinal Changes in Psychological States in Online Health Community Members: Understanding the Long-Term Effects of Participating in an Online Depression Community. J Med Internet Res, 19(3), e71.
  15. Mowery D, Smith H, Cheney T, Stoddard G, Coppersmith G, Bryan C, Conway M (2017). Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study. J Med Internet Res, 19(2), e48.
  16. Doing-Harris K, Mowery DL, Daniels C, Chapman WW, Conway M (2017). Understanding patient satisfaction with received healthcare services: A natural language processing approach. AMIA Annu Symp Proc, 2016, 524-533.
  17. Park A, Zhu SH, Conway M (2017). The Readability of Electronic Cigarette Health Information and Advice: A Quantitative Analysis of Web-Based Information. JMIR Public Health Surveill, 3(1), e1.