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Big Data and the Precision Medicine Revolution

dc.contributor.authorHopp, Wallace J.
dc.contributor.authorLi, Jun
dc.contributor.authorWang, Guihua
dc.date.accessioned2018-08-23T13:23:44Z
dc.date.available2018-08-23T13:23:44Z
dc.date.issued2018-04
dc.identifier1386en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/145441
dc.description.abstractThe big data revolution is making vast amounts of information available in all sectors of the economy including health care. One important type of data that is particularly relevant to medicine is observational data from actual practice. In comparison to experimental data from clinical studies, observational data offers much larger sample sizes and much broader coverage of patient variables. Properly combining observational data with experimental data can facilitate precision medicine by enabling detection of heterogeneity in patient responses to treatments and tailoring of health care to the specific needs of individuals. However, because it is high-dimensional and uncontrolled, observational data presents unique methodological challenges. The modeling and analysis tools of the production and operations management field are well-suited to these challenges and hence POM scholars are critical to the realization of precision medicine with its many benefits to society.en_US
dc.subjectbig dataen_US
dc.subjectprecision medicineen_US
dc.subjectobservational dataen_US
dc.subjectmachine learningen_US
dc.subjectcasual inferenceen_US
dc.subject.classificationOperations and Management Scienceen_US
dc.titleBig Data and the Precision Medicine Revolutionen_US
dc.typeWorking Paperen_US
dc.subject.hlbsecondlevelManagementen_US
dc.subject.hlbtoplevelBusiness
dc.contributor.affiliationumRoss School of Businessen_US
dc.contributor.affiliationumcampusAnn Arbor
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/145441/1/1386_Hopp.pdf
dc.owningcollnameBusiness, Stephen M. Ross School of - Working Papers Series


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