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    A clinical grid infrastructure supporting adverse hypotensive event prediction

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    A clinical grid infrastructure supporting adverse hypotensive event prediction (927.6Kb)

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    Author
    STELL, ANTHONY; SINNOTT, RICHARD; Jiang, Jipu
    Date
    2009
    Source Title
    9th IEEE/ACM International Symposium on Cluster Computing and the Grid
    Publisher
    IEEE Computer Society
    University of Melbourne Author/s
    Sinnott, Richard; Stell, Anthony
    Metadata
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    Document Type
    Conference Paper
    Citations
    Stell, A., Sinnott, R., & Jiang, J. (2009). A clinical grid infrastructure supporting adverse hypotensive event prediction. In 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, Shanghai, China.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/28800
    Description

    © 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Abstract
    The condition of hypotension - where a person's arterial blood pressure drops to an abnormally low level - is a common and potentially fatal occurrence in patients under intensive care. As medical interventions to treat such events are typically reactive and often aggressive, there would be great benefit in having a prediction system that can warn health-care professionals of an impending event and thereby allow them to provide non-invasive, preventative treatments. This paper describes the progress of the EU FP7 funded Avert-IT project, which is developing just such a system using Bayesian neural network learning technology based upon an integrated, real-time data grid infrastructure, which draws together heterogeneous data-sets from six clinical centres across Europe.
    Keywords
    hypotension; data-grids; performance; security

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