Holistics 3.0 for Health

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dc.contributor.authorLary, David J.en_US
dc.contributor.authorWoolf, Stevenen_US
dc.contributor.authorFaruque, Fazlayen_US
dc.contributor.authorLePage, James P.en_US
dc.contributor.utdAuthorLary, David J.en_US
dc.date.accessioned2017-03-31T19:45:47Z
dc.date.available2017-03-31T19:45:47Z
dc.date.created2014-07-24en_US
dc.date.issued2014-07-24en_US
dc.description.abstractHuman health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting new era is dawning where we are simultaneously collecting multiple datasets to describe many aspects of health, wellness, human activity, environment and disease. Valuable insights from these datasets can be extracted using massively multivariate computational techniques, such as machine learning, coupled with geospatial techniques. These computational tools help us to understand the topology of the data and provide insights for scientific discovery, decision support and policy formulation. This paper outlines a holistic paradigm called Holistics 3.0 for analyzing health data with a set of examples. Holistics 3.0 combines multiple big datasets set in their geospatial context describing as many areas of a problem as possible with machine learning and causality, to both learn from the data and to construct tools for data-driven decisions.en_US
dc.description.sponsorshipDoD Telemedicine & Advanced Technology Research Center (TATRC) (award no. W81XWH-11-2-0165); NASA (award no NNX11AL18G); The California Endowment (grant no. 20111592); National Institute of Enviromental Health Sciences (grant no, R21ES019713).en_US
dc.identifier.bibliographicCitationLary, David John, Steven Woolf, Fazlay Faruque, and James P. LePage. 2014. "Holistics 3.0 for Health." ISPRS International Journal of Geo-Information 3(3), doi:10.3390/ijgi3031023en_US
dc.identifier.issn2220-9964en_US
dc.identifier.issue3en_US
dc.identifier.urihttp://hdl.handle.net/10735.1/5344
dc.identifier.volume3en_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.urihttp://dx.doi.org/10.3390/ijgi3031023
dc.rightsCC BY 3.0 (Attribution)en_US
dc.rights©2014 The Authorsen_US
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en_US
dc.sourceISPRS International Journal of Geo-Information
dc.subjectGeospatial dataen_US
dc.subjectMachine learningen_US
dc.subjectBig dataen_US
dc.subjectRemote sensingen_US
dc.subjectHolistics 3.0en_US
dc.subjectData-driven decisionsen_US
dc.subjectRemote-sensingen_US
dc.subjectAir quality--United Statesen_US
dc.subjectSpectroradiometeren_US
dc.subjectForest firesen_US
dc.subjectModis (Spectroradiometer)en_US
dc.subjectPhysical geographyen_US
dc.titleHolistics 3.0 for Healthen_US
dc.type.genreArticleen_US

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