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dc.contributor.authorStupple, Aaron
dc.contributor.authorSingerman, David
dc.contributor.authorCeli, Leo Anthony G.
dc.date.accessioned2020-04-28T18:40:23Z
dc.date.available2020-04-28T18:40:23Z
dc.date.issued2019-01-29
dc.identifier.issn2398-6352
dc.identifier.urihttps://hdl.handle.net/1721.1/124903
dc.description.abstractIf anyone doubts the explosive growth of interest in digital medicine, consider a recent conference and workshop in Beijing, jointly organized by the People’s Liberation Army General Hospital and MIT Critical Data to showcase the opportunities and challenges of applying machine learning to the kind of data routinely collected during the provision of care.1 In person, 500 attendees heard a keynote and panels and participated in a health data hackathon. Online, however, the event was streamed to more than one million unique viewers. ©2020 [First paragraph]en_US
dc.description.sponsorshipNational Institute of Health NIBIB R01 (grant no. EB017205)en_US
dc.publisherSpringer Natureen_US
dc.relation.isversionof10.1038/s41746-019-0079-zen_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceNatureen_US
dc.titleThe reproducibility crisis in the age of digital medicineen_US
dc.typeArticleen_US
dc.identifier.citationStupple, Aaron, David Singerman, and Leo Anthony Celi, "The reproducibility crisis in the age of digital medicine." npj Digital Medicine 2, 1 (Jan. 2019): no. 2 doi 10.1038/s41746-019-0079-z ©2019 Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering & Scienceen_US
dc.relation.journalnpj Digital Medicineen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.date.submission2019-07-25T12:19:57Z
mit.journal.volume2en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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