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    Federating distributed clinical data for the prediction of adverse hypotensive events

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    Author
    Stell, A; Sinnott, R; Jiang, J; Donald, R; Chambers, I; Citerio, G; Enblad, P; Gregson, B; Howells, T; Kiening, K; ...
    Date
    2009
    Source Title
    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
    Publisher
    Royal Society, The
    University of Melbourne Author/s
    Sinnott, Richard; Stell, Anthony
    Affiliation
    Melbourne Research
    Metadata
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    Document Type
    Journal Article
    Citations
    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. (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.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/30028
    DOI
    10.1098/rsta.2009.0138
    Description

    This 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/

    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.
    Keywords
    Information Systems

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