Beyond mathematics, statistics, and programming: data science, machine learning, and artificial intelligence competencies and curricula for clinicians, informaticians, science journalists, and researchers

References

• Ancker, J. (2020). The COVID-19 pandemic and the power of numbers. Numeracy, 13(2). https://doi.org/10.5038/1936-4660.13.2.1358
   Google Scholar
• Ancker, J., & Begg, M. (2017). Using visual analogies to teach introductory statistical concepts. Numeracy, 10(2). https://doi.org/10.5038/1936-4660.10.2.7
   Google Scholar
• Dorr, D. A., Adams, L., & Embí, P. (2023). Harnessing the promise of artificial intelligence responsibly. JAMA, 329(16), 1347–1348. https://doi.org/10.1001/jama.2023.2771
   PubMedGoogle Scholar
• Embi, P. J. (2021). Algorithmovigilance-advancing methods to analyze and monitor artificial intelligence-driven health care for effectiveness and equity. JAMA Network Open, 4(4), e214622. https://doi.org/10.1001/jamanetworkopen.2021.4622
   PubMed Web of Science ®Google Scholar
• Gartner, D., Spernaes, I., England, T. J., Behrens, D. A., Buchanan, J., & Harper, P. R. (2022). Evaluation of participants’ reaction and learning in a taught analytics and modelling academy program in U.K.‘s national health service. 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI), 591–596. https://doi.org/10.1109/ICHI54592.2022.00116
   Google Scholar
• Harris, R. (2020, August 25). FDA’s Hahn apologizes for overselling plasma’s benefits as a COVID-19 treatment. NPR. https://www.npr.org/sections/health-shots/2020/08/25/905792261/fdas-hahn-apologizes-for-overselling-plasmas-benefits-as-a-covid-19-treatment
   Google Scholar
• Hoyt, R., & Muenchen, R. (Eds.). (2019). Introduction to biomedical data science. Lulu.com https://www.lulu.com/en/us/shop/robert-muenchen-and-robert-hoyt/introduction-to-biomedical-data-science/ebook/product-1244jgv7.html
   Google Scholar
• Johnson, A. E. W., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T. J., Lehman, B., Moody, B., Gow, L.-W. H., Celi, L. A., Mark, R. G., & Mark, R. G. (2023). MIMIC-IV, a freely accessible electronic health record dataset. Scientific Data, 10(1), 1. https://doi.org/10.1038/s41597-022-01899-x
   PubMed Web of Science ®Google Scholar
• Joyner, M. J., Senefeld, J. W., Klassen, S. A., Mills, J. R., Johnson, P. W., Theel, E. S., Wiggins, C. C., Bruno, K. A., Klompas, A. M., Lesser, E. R., Kunze, K. L., Sexton, M. A., Diaz Soto, J. C., Baker, S. E., Shepherd, J. R. A., van Helmond, N., van Buskirk, C. M., Winters, J. L., Stubbs, J. R., & Casadevall, A. (2020). Effect of convalescent plasma on mortality among hospitalized patients with COVID-19: Initial three-month experience. MedRxiv: The Preprint Server for Health Sciences, 20169359. 2020 08 12, https://doi.org/10.1101/2020.08.12.20169359
   Google Scholar
• Kusner, M. J., & Loftus, J. R. (2020). The long road to fairer algorithms. Nature, 578(7793), 34–36. https://doi.org/10.1038/d41586-020-00274-3
   PubMed Web of Science ®Google Scholar
• Masters, K. (2023). Ethical use of artificial intelligence in health professions education: AMEE Guide No.158. Medical Teacher, 45(6), 574–584. https://doi.org/10.1080/0142159X.2023.2186203
   PubMedGoogle Scholar
• Parks, R. F. (2020). A pedagogic experience in designing a healthcare analytics course: Lessons learned. Information Systems Education Journal, 18(5), 4–15. https://eric.ed.gov/?id=EJ1258237
   Google Scholar
• Peker, M., Özkaraca, O., & Şaşar, A. (2018). Use of orange data mining toolbox for data analysis in clinical decision making: The diagnosis of diabetes disease. In P. K. Pattnaik, A. Swetapadma, & J. Sarraf, (Eds.), Expert system techniques in biomedical science practice (pp. 143–167). IGI Global. https://www.igi-global.com/gateway/book/187083
   Google Scholar
• Vaishnav, D., & Rao, B. R. (2018). Comparison of machine learning algorithms and fruit classification using orange data mining tool. 2018 3rd International Conference on Inventive Computation Technologies (ICICT), 603–607. https://doi.org/10.1109/ICICT43934.2018.9034442
   Google Scholar
• Zikmund-Fisher, B. J. (2019). Helping people know whether measurements have good or bad implications: Increasing the evaluability of health and science data communications. Policy Insights from the Behavioral and Brain Sciences, 6(1), 29–37. https://doi.org/10.1177/2372732218813377
   Google Scholar
• Zikmund-Fisher, B. J., & Ancker, J. S. (2022). Preventing health number confusion through clear communication design. In A. Keselman, C. A. Smith, & A. J. Wilson (Eds.), Combating online health misinformation: A Professional’s guide to helping the public, 29–37. Rowman & Littlefield Publishers.
   Google Scholar