Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference
Author(s)
Badawi, Omar; Brennan, Thomas Patrick; Celi, Leo Anthony G.; Feng, Mengling; Ghassemi, Marzyeh; Ippolito, Andrea; Johnson, Alistair; Mayaud, Louis; Moody, George B.; Moses, Christopher; Naumann, Tristan Josef; Nikore, Vipan; Pimentel, Marco; Pollard, Tom J.; Santos, Mauro; Stone, David J.; Zimolzak, Andrew; Mark, Roger G; ... Show more Show less![Thumbnail](/bitstream/handle/1721.1/93168/Mark_Making%20big%20data.pdf.jpg?sequence=4&isAllowed=y)
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With 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.
Date issued
2014-08Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
JMIR Medical Informatics
Publisher
JMIR Publications
Citation
Badawi, 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.
Version: Final published version
ISSN
2291-9694