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dc.contributor.authorHeldt, Thomas
dc.contributor.authorVerghese, George C.
dc.date.accessioned2012-02-03T18:50:02Z
dc.date.available2012-02-03T18:50:02Z
dc.date.issued2010-08
dc.identifier.isbn978-1-4244-4124-2
dc.identifier.isbn978-1-4244-4123-5
dc.identifier.issn1557-170X
dc.identifier.otherINSPEC Accession Number: 11650248
dc.identifier.urihttp://hdl.handle.net/1721.1/69024
dc.description.abstractAs a result of improved hospital information-technology infrastructure and declining costs of storage media, vast amounts of physiological waveform and trend data can now be continuously collected and archived from bedside monitors in operating rooms, intensive care units, or even regular hospital rooms. The real-time or off-line processing of such volumes of high-resolution data, in attempts to turn raw data into clinically actionable information, poses significant challenges. However, it also presents researchers - and eventually clinicians - with unprecedented opportunities to move beyond the traditional individual-channel analysis of waveform data, and towards an integrative patient-monitoring framework, with likely improvements in patient care and safety. We outline some of the challenges and opportunities, and propose strategies for model-based integration of physiological data to improve patient monitoring.en_US
dc.description.sponsorshipNational Institute of Biomedical Imaging and Bioengineering (U.S.) (grant R01 EB001659)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/iembs.2010.5626101en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceIEEEen_US
dc.titleModel-based data integration in clinical environmentsen_US
dc.typeArticleen_US
dc.identifier.citationHeldt, T, and G C Verghese. “Model-based data integration in clinical environments.” IEEE, 2010. 5209-5212. Web. 3 Feb. 2012. © 2010 Institute of Electrical and Electronics Engineersen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Research Laboratory of Electronicsen_US
dc.contributor.departmentHarvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiologyen_US
dc.contributor.approverVergese, George C.
dc.contributor.mitauthorHeldt, Thomas
dc.contributor.mitauthorVerghese, George C.
dc.relation.journalAnnual International Conference of the IEEE Engineering in Medicine and Biology Society, 2010. EMBC 2010.en_US
dc.eprint.versionFinal published versionen_US
dc.identifier.pmid21095826
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsHeldt, T; Verghese, G Cen
dc.identifier.orcidhttps://orcid.org/0000-0002-5930-7694
dc.identifier.orcidhttps://orcid.org/0000-0002-2446-1499
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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