Abstract
Interest in machine-learning applications within medicine has been growing, but few studies have progressed to deployment in patient care. We present a framework, context and ultimately guidelines for accelerating the translation of machine-learning-based interventions in health care. To be successful, translation will require a team of engaged stakeholders and a systematic process from beginning (problem formulation) to end (widespread deployment).
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Change history
19 September 2019
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Acknowledgements
The authors would like to thank the participants in the MLHC Conference 2018 (http://www.mlforhc.org), specifically the organizers and participants of the pre-meeting workshop that served as the genesis for this manuscript, for providing valuable feedback on the initial ideas through a panel discussion.
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J.W., F.D.-V., D.K. and K.J. are on the board of Machine Learning for Healthcare, a non-profit organization that hosts a yearly academic meeting; they are reimbursed for registration and travel expenses. F.D.-V. consults for DaVita, a healthcare company. S.T.-I. serves on the board of Scients (https://scients.org/) and is reimbursed for travel expenses. S.S. is a founder of, and holds equity in, Bayesian Health. The results of the study discussed in this publication could affect the value of Bayesian Health. This arrangement has been reviewed and approved by Johns Hopkins University in accordance with its conflict-of-interest policies. S.S. is a member of the scientific advisory board for PatientPing. M. Sendak is a named inventor of the Sepsis Watch deep-learning model, which was licensed from Duke University by Cohere Med, Inc. M. Sendak does not hold any equity in Cohere Med, Inc. M. Saeed is a founder and Chief Medical Officer at HEALTH at SCALE Technologies and holds equity in this company. P.O. consults for Roche-Genentech, from whom she has received travel reimbursement and consulting fees of less than $4,000/year. A.G., K.H., M.G. and V.L. have no conflicts to declare.
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Wiens, J., Saria, S., Sendak, M. et al. Do no harm: a roadmap for responsible machine learning for health care. Nat Med 25, 1337–1340 (2019). https://doi.org/10.1038/s41591-019-0548-6
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DOI: https://doi.org/10.1038/s41591-019-0548-6
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