Artificial Intelligence for Healthcare Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World
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Published online by Cambridge University Press: 21 April 2022
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- Artificial Intelligence for HealthcareInterdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World, pp. 1 - 12Publisher: Cambridge University PressPrint publication year: 2022
References
American Public Health Association, 2019, Topics and issues. www.apha.org/topics-and-issues.Google Scholar
Aswani, A, Olfat, M., 2018, Designing algorithms to increase fairness in artificial intelligence, SIAM News, 51(10), 1,3.Google Scholar
Atkinson, MP, Su, Z., Alphey, N., et al. 2007, Analyzing the control of mosquito-borne diseases by a dominant lethal genetic system, Proceedings of the National Academy of Sciences, 104(22), 9540–9545.Google Scholar
Beam, A.L., Kohane, A.S., 2018, Big data and machine learning in health care, JAMA, 319(13), 1317–1318Google Scholar
Brandeau, M.L., Lee, H.L., Owens, D.K., Sox, C.H., Wachter, R.M, 1991, A policy model of human immunodeficiency virus screening and intervention, Interfaces, 21(3), 5–25.Google Scholar
Bringsjord, S., Govindarajulu, N.S., 2018, Artificial intelligence. In: The Stanford Encyclopedia of Philosophy (Fall 2018 edition), Zalta, E.N. (ed.). https://plato.stanford.edu/archives/fall2018/entries/artificial-intelligence.Google Scholar
CDC Foundation, 2019, Public health in action. www.cdcfoundation.org/what-public-health.Google Scholar
Centers for Disease Control and Prevention, 2019a, Organizational chart. www.cdc.gov/about/pdf/organization/cdc-org-chart.pdf.Google Scholar
Centers for Disease Control and Prevention Foundation, 2019b, Who we are/what is public health. www.cdcfoundation.org/what-public-healthGoogle Scholar
Demoncourt, F. Lee, J.Y., Uzuner, O., Szolovits, P., 2017, De-identification of patient notes with recurrent neural networks, Journal of the American Medical Informatics Association, 24, 596–606.CrossRefGoogle Scholar
Engineer, F.G., Keskinocak, P., Pickering, L.K., 2009, OR practice – catch-up scheduling for childhood vaccination, Operations Research, 57(6), 1307–1319.Google Scholar
Hales, C.M., Carroll, M.D., Fryar, C.D., Ogden, C.L., 2017, Prevalence of obesity among adults and youth: United States, 2015–2016, NCHS Data Brief, No. 288, October 2017. www.cdc.gov/nchs/data/databriefs/db288.pdf.Google Scholar
Hinton, G., 2018, Deep learning – a technology with the potential to transform health care, JAMA, 320(11), 1101–1102.Google Scholar
Israni, S.T., Verghese, A., 2019, Humanizing artificial intelligence, JAMA, 321(1), 29–30CrossRefGoogle ScholarPubMed
Jacobson, S.H., Sewell, E.C., 2008, A web-based tool for designing vaccine formularies for childhood immunization in the United States, JAMA, 15(5), 611–619.Google Scholar
King, D.M., Jacobson, S.H., 2017, Random acts of violence: examining probabilistic independence of mass killing events in the United States, Victims and Violence, 32(6), 1014–1023.Google Scholar
Lee, E.K., Maheshwary, Mason J., Glisson, S., 2006, Large-scale dispensing for emergency response to bioterrorism and infectious-disease outbreak, Interfaces, 36(6), 591–607.CrossRefGoogle Scholar
Long, E.F., Brandeau, M.L., 2009, OR’s next top model: decision models for infectious disease control, TutORials in Operations Research, 123–138.Google Scholar
Maddox, T.M., Rumsfeld, J.S., Payne, P.R.O., 2019, Questions for artificial intelligence in health care, JAMA, 321(1), 31–32.CrossRefGoogle ScholarPubMed
Mak, K.K., Lee, K., Park, C., 2019, Applications of machine learning in addiction studies: a systematic review, Psychiatry Research, 275, 53–60.Google Scholar
McCall, B., 2020, COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread, The Lancet Digital Health, 2(4), e166–e167.Google Scholar
Naylor, C.D., 2018, On the prospects for a (deep) learning health care system, JAMA, 320(11), 1099–1100.CrossRefGoogle ScholarPubMed
Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S., 2019, Dissecting racial bias in an algorithm used to manage the health of populations, Science, 366(6464), 447–453.CrossRefGoogle Scholar
Patel, V.L., Shortliffe, E.H., Stefanelli, M., et al., 2009, The coming of age of artificial intelligence in medicine, Artificial Intelligence in Medicine, 46(1), 5–17.CrossRefGoogle ScholarPubMed
Scheinker, D., Valencia, A., Rodriguez, F., 2019, Identification of factors associated with variation in us county-level obesity prevalence rates using epidemiologic vs machine learning models, JAMA Network Open, 2(4), e192884–e192884.Google Scholar
Shah, NR., 2019, Healthcare in 2030: will artificial intelligence replace physicians? Annals Internal Medicine, 170, 407–408. doi:10.7326/M19-0344Google Scholar
Silver, D.S., Hubert, T., Schrittwieser, J., et al., 2018, A general reinforcement learning algorithm that masters chess, shogi and go through self play, Science 362, 1140–1144.Google Scholar
Stead, W.W., 2018, Clinical implications and challenges of artificial intelligence and deep learning, JAMA, 320(11), 1107–1108.Google Scholar
Thiebout, R., Thiessard, F., 2018, Artificial intelligence in public health and epidemiology, IMIA Yearbook of Medical Informatics, 27(1):207–209.Google Scholar
Visweswaran, S., Colditz, J.B., O’Halloran, P., et al., 2020, Machine learning classifiers for twitter surveillance of vaping: comparative machine learning study, Journal of Medical Internet Research, 22(8), e17478.CrossRefGoogle ScholarPubMed
Wahl, B., Cossy-Gantner, A., Germann, S., Schwalbe, N.R., 2018, Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Global Health, 3, 1–7.Google Scholar
Weniger, B.G., Chen, R.T., Jacobson, S.H., et al., 1998, Addressing the challenges to immunization practice with an economic algorithm for vaccine selection, Vaccine, 16(19), 1885–1897.Google Scholar
Zaric, G.S., Bravata, D.M., Cleophas Holty, J.E., et al., 2008, Modeling the logistics of response to anthrax bioterrorism, Medical Decision Making, 28(3), 332–350.Google Scholar
Zeevi, D., Korem, T., Zmora, N., et al., 2015, Personalized nutrition by prediction of glycemic responses, Cell, 163(5), 1079–1094.Google Scholar