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Federating distributed clinical data for the prediction of adverse hypotensive events

Lookup NU author(s): Dr Barbara Gregson

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Abstract

The 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.


Publication metadata

Author(s): Stell 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

Publication type: Article

Publication status: Published

Journal: Philosophical Transactions of the Royal Society, A: Mathematical, Physical and Engineering Sciences

Year: 2009

Volume: 367

Issue: 1898

Pages: 2679-2690

ISSN (print): 1364-503X

ISSN (electronic): 1471-2962

Publisher: The Royal Society Publishing

URL: http://dx.doi.org/10.1098/rsta.2009.0042

DOI: 10.1098/rsta.2009.0042

Notes: Erratum appears in Philos Transact A Math Phys Eng Sci. 2009 Aug 28;367(1901):3331. DOI: 10.1098/rsta.2009.0138


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