番茄社区

Dillon Chrimes

Dillon Chrimes
Position
Assistant Teaching Professor
Health Information Science
Contact
250-472-4474
Credentials

BSc (UVic), BSc (Alberta), MSc (UVic), MSc (Ume氓, Sweden), PhD (Ume氓, Sweden)

Area of expertise

Dr. Dillon Chrimes is currently not available to supervise graduate students.

Dr. Dillon Chrimes is an Assistant Teaching Professor at the School of Health Information Science, 番茄社区, BC, Canada. Dillon has 20 years experience learning and working in academia, government and industries. Understanding health informatics is important in the pursuit of digital health and patient care. Dillon is a proponent of health information science as measurements with analysis for assessment and management of the measured values in a framework or model of applications with health informatics in health care. Dillon has language proficiency in Greek, Japanese and Swedish.

  • Artificial Intelligence in Health Care
  • BioMedical Informatics Interoperability
  • Data Analytics and Visualizations
  • Digital Health Platforms
  • Electronic Health Records
  • Health Big Data
  • Hospital System and Integrating Platform
  • Surveillance and Usability of Health Informatics
  • Virtual Care in Health IT
  1. Craig Kuziemsky, Dillon Chrimes, Simon Minshall, Michael Mannerow, Francis Lau. (2024). AI Quality Standards in Health Care: Rapid Umbrella Review. J Med Internet Res 2024;26:e54705, DOI: .
  2. Dillon Chrimes. Using Decision Trees as an Expert System for Clinical Decision Support for COVID-19. (2023). Interact J Med Res (JMIR) 2023;12:e42540, doi: .
  3. Dillon Chrimes, Emile Keruzore. (2023). Text mining using clinical terms in electronic records of annual falls of patients in home community care. 2023 IEEE International Conference on Big Data (BigData). DOI: .
  4. Dillon Chrimes, Chanhee Kim. (2023). Comparison of MIMIC-III and MIMIC-IV for big data analytics of health informatics. 2023 IEEE International Conference on Big Data (BigData). DOI: .
  5. Dillon Chrimes, Ivan Tang. (2023). Big data usability text mining of publicly available YouTube electronic health record (EHR) tutorials. 2023 IEEE International Conference on Big Data (BigData). DOI: .
  6. Dillon Chrimes, Chanhee Kim. 2022. Review of publically available health big data sets. 2022 IEEE International Conference on Big Data (BigData). DOI: .
  7. Dillon Chrimes. 2022. Big data analytics of predicting annual US Medicare billing claims with health services. 2022 IEEE International Conference on Big Data (BigData). DOI:
  8. Chrimes D, Zamani H, Spenser C, Westwood A (2021) Decision-Support Expert System to Assess Severe COVID-19. COVID-19 Pandemic: Case Studies & Opinions 02(03): 279–303. .
  9. Kuo A, Chrimes D, Qin P, Zamani H. A Hadoop/MapReduce Based Platform for Supporting Health Big Data Analytics. Studies Health Technol Inform 257 (2019): 229-235.
  10. Chrimes D, Zamani H, Moa B, Kuo A. Importance of Simulations of Hadoop/MapReduce-Based Platform to Support its Usability of Big Data Analytics in Healthcare. Athens Journal of Technology and Engineering 5:3 (2018), 197-222.
  11. Chrimes D, Moa B, Zamani H, Kuo A. Interactive Big Data Analytics Platform for Healthcare and Clinical Services. Global Journal of Engineering Sciences 1(1): 2018. GJES. ISSN: 2641-2039.
  12. Chrimes D, Kuo A, Moa B, Hu W. Towards a Real-time Big Data Analytics Platform for Health Applications. International Journal of Big Data Intelligence4:2 (2017), 61-80.
  13. Chrimes D, Moa B, Kuo A, Kushniruk A. Operational Efficiencies and Simulated Performance of Big Data Analytics Platform over Billions of Patient Records of a Hospital System. Advances in Science, Technology and Engineering Systems Journal 2:1 (2017), 23-41.
  14. Kuo A, Chrimes D, Moa B, Hu W. Design and Construction of a Big Data Analytics Framework for Health Applications. IEEE Proceedings International Conference on Smart City/SocialCom/SustainCom together with DataCom 2015 and SC2 2015, Chengdu, China. (2015): 631-636.
  15. Chrimes D, Kitos N, Kurshniruk A, Mann D. Usability testing of Avoiding Diabetes Thru Action Plan Targeting (ADAPT) decision support for integrating care-based counseling of pre-diabetes in an electronic health record. International Journal of Medical Informatics 83:9 (2014), 636-647.
  16. Li A, Kannry J, Kurshniruk A, Chrimes D, McGuinn T, Mann D. (2012). Integrating usability testing and think-aloud protocol analysis with go-live clinical simulations in evaluating clinical decision support. International Journal of Medical Informatics 81 (2012), 761-772.