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dc.contributor.authorStell, A
dc.contributor.authorSinnott, R
dc.contributor.authorJiang, J
dc.contributor.authorDonald, R
dc.contributor.authorChambers, I
dc.contributor.authorCiterio, G
dc.contributor.authorEnblad, P
dc.contributor.authorGregson, B
dc.contributor.authorHowells, T
dc.contributor.authorKiening, K
dc.contributor.authorNilsson, P
dc.contributor.authorRagauskas, A
dc.contributor.authorSahuquillo, J
dc.contributor.authorPiper, I
dc.date.available2014-05-22T00:11:00Z
dc.date.issued2009
dc.identifierpii: 367/1898/2679
dc.identifier.citationStell, A., Sinnott, R., Jiang, J., Donald, R., Chambers, I., Citerio, G., Enblad, P., Gregson, B., Howells, T., Kiening, K., Nilsson, P., Ragauskas, A., Sahuquillo, J. & Piper, I. (2009). Federating distributed clinical data for the prediction of adverse hypotensive events. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367 (1901), pp.2679-2690. https://doi.org/10.1098/rsta.2009.0138.
dc.identifier.issn1471-2962
dc.identifier.urihttp://hdl.handle.net/11343/30028
dc.descriptionThis is a pre-print of an article published in Royal Society of London Philosophical Transactions A: Mathematical, Physical and Engineering Sciences 2009 published by The Royal Society. This version is reproduced under the journal’s author licence agreement. http://rsta.royalsocietypublishing.org/
dc.description.abstractThe ability to predict adverse hypotensive events, where a patient's arterial blood pressure drops to abnormally low (and dangerous) levels, would be of major benefit to the fields of primary and secondary health care, and especially to the traumatic brain injury domain. A wealth of data exist in health care systems providing information on the major health indicators of patients in hospitals (blood pressure, temperature, heart rate, etc.). It is believed that if enough of these data could be drawn together and analysed in a systematic way, then a system could be built that will trigger an alarm predicting the onset of a hypotensive event over a useful time scale, e.g. half an hour in advance. In such circumstances, avoidance measures can be taken to prevent such events arising. This is the basis for the Avert-IT project (http://www.avert-it.org), a collaborative EU-funded project involving the construction of a hypotension alarm system exploiting Bayesian neural networks using techniques of data federation to bring together the relevant information for study and system development.
dc.languageEnglish
dc.publisherRoyal Society, The
dc.subjectInformation Systems
dc.titleFederating distributed clinical data for the prediction of adverse hypotensive events
dc.typeJournal Article
dc.identifier.doi10.1098/rsta.2009.0138
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentMelbourne Research
melbourne.source.titlePhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
melbourne.source.volume367
melbourne.source.issue1901
melbourne.source.pages2679-2690
dc.description.pagestart2679
melbourne.publicationid158651
melbourne.elementsid331699
melbourne.contributor.authorSinnott, Richard
melbourne.contributor.authorStell, Anthony
dc.identifier.eissn1471-2962
melbourne.accessrightsOpen Access


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