| Literature DB >> 31427808 |
Jenna Wiens1, Suchi Saria2,3,4, Anna Goldenberg5,6,7,8, Mark Sendak9, Marzyeh Ghassemi10,11,12, Vincent X Liu13, Finale Doshi-Velez14, Kenneth Jung15, Katherine Heller16,17, David Kale18, Mohammed Saeed19, Pilar N Ossorio20, Sonoo Thadaney-Israni21.
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).Entities:
Mesh：
Year: 2019 PMID： 31427808 DOI： 10.1038/s41591-019-0548-6
Source DB: PubMed Journal: Nat Med ISSN： 1078-8956 Impact factor: 53.440