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 centered on utilizing Natural Language Processing for applications in public health informatics. First, in Dr Nigel Collier’s research group at the National Institute of Informatics, Japan (2007-2009) where, funded by a Japanese Society for the Promotion of Science postdoctoral fellowship, he contributed to the development of Biocaster, a system that uses NLP to automatically analyse global news texts and blogs, alerting public health practitioners to anomalous health events, and subsequently as a postdoctoral fellow in Dr Wendy Chapman’s Biomedical NLP lab, initially at the Department of Biomedical Informatics at the University of Pittsburgh (2009-2010) and then at the Division of Biomedical Informatics at the University of California San Diego (2011-2013). During this period, Dr Conway's research focused on the development of NLP resources for text mining electronic health records and other clinical data. In addition to his work with Dr Chapman, in the winter of 2010/2011 Dr Conway spent six months in the Division of Biomedical Statistics and Informatics at Mayo Clinic, Rochester, where, supervised by Dr Jyotishman Pathak, he contributed to the development of electronic health record-oriented phenotyping algorithms designed to fully automate the identification of candidates for epidemiological studies and clinical trials. In 2013, Dr Conway spend one year as an Assistant Project Scientist in the Division of Behavioral Health in the Department of Family & Preventive Medicine at the University of California San Diego, funded by the K99 component of a K99/R00 "Pathways to Independence" award entitled Utilzing Social Media as a Resource for Mental Health Surveillance. Since joining the University of Utah in 2014, Dr Conway has, in addition to his R00, been awarded two further federal grants from the National Institute on Drug abuse, and published 41 peer-reviewed articles, including journal papers and computer science conference/workshop papers.

Dr Conway has won several academic awards & prizes. Most recently, a 2018 journal paper published in Computers & Human Behavior was selected by the International Medical Informatics Association Yearbook of Medical Informatics as one of the three best research papers of the year in the “Consumer Health Informatics & Education” section. In terms of service to the wider community, Dr Conway is currently an associate editor of the journal BMC Medical Informatics and Decision Making, and regularly serve on conference program committees(e.g. Computational Linguistics & Clinical Psychology Workshop, International Conference on Healthcare Informatics, Digital Public Health Conference). He regularly peer reviews for a variety of journals, including Journal of Drug Issues, Nature Human Behaviour, Suicide & Life-Threatening Behavior and has reviewed grant proposals for the US National Institutes of Health, the US Department of Defense, the Israeli Science Foundation, and the UK Medical Research Council. From 2016 to 2019, Dr Conway served on the University of Utah's Institutional Review Board.

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.