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dc.contributor.authorBadawi, Omar
dc.contributor.authorBrennan, Thomas Patrick
dc.contributor.authorCeli, Leo Anthony G.
dc.contributor.authorFeng, Mengling
dc.contributor.authorGhassemi, Marzyeh
dc.contributor.authorIppolito, Andrea
dc.contributor.authorJohnson, Alistair
dc.contributor.authorMayaud, Louis
dc.contributor.authorMoody, George B.
dc.contributor.authorMoses, Christopher
dc.contributor.authorNaumann, Tristan Josef
dc.contributor.authorNikore, Vipan
dc.contributor.authorPimentel, Marco
dc.contributor.authorPollard, Tom J.
dc.contributor.authorSantos, Mauro
dc.contributor.authorStone, David J.
dc.contributor.authorZimolzak, Andrew
dc.contributor.authorMark, Roger G
dc.date.accessioned2015-01-23T16:17:03Z
dc.date.available2015-01-23T16:17:03Z
dc.date.issued2014-08
dc.identifier.issn2291-9694
dc.identifier.urihttp://hdl.handle.net/1721.1/93168
dc.description.abstractWith growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines--including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology--gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.en_US
dc.language.isoen_US
dc.publisherJMIR Publicationsen_US
dc.relation.isversionofhttp://dx.doi.org/10.2196/medinform.3447en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/en_US
dc.sourceJMIRen_US
dc.titleMaking Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conferenceen_US
dc.typeArticleen_US
dc.identifier.citationBadawi, Omar, Thomas Brennan, Leo Anthony Celi, Mengling Feng, Marzyeh Ghassemi, Andrea Ippolito, Alistair Johnson, et al. “Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference.” JMIR Medical Informatics 2, no. 2 (2014): e22.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering & Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorBrennan, Thomas Patricken_US
dc.contributor.mitauthorCeli, Leo Anthony G.en_US
dc.contributor.mitauthorFeng, Menglingen_US
dc.contributor.mitauthorGhassemi, Marzyehen_US
dc.contributor.mitauthorMark, Roger Greenwooden_US
dc.contributor.mitauthorNaumann, Tristanen_US
dc.relation.journalJMIR Medical Informaticsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsBadawi, Omar; Brennan, Thomas; Celi, Leo Anthony; Feng, Mengling; Ghassemi, Marzyeh; Ippolito, Andrea; Johnson, Alistair; Mark, Roger G; Mayaud, Louis; Moody, George; Moses, Christopher; Naumann, Tristan; Nikore, Vipan; Pimentel, Marco; Pollard, Tom J; Santos, Mauro; Stone, David J; Zimolzak, Andrewen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-6349-7251
dc.identifier.orcidhttps://orcid.org/0000-0002-6318-2978
dc.identifier.orcidhttps://orcid.org/0000-0003-2150-1747
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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