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Probabilistic Modeling of Sensor Artifacts in Critical Care

ARAN - Access to Research at NUI Galway

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dc.contributor.author Manley, Geoffrey en
dc.contributor.author Staudenmayer, Kristan en
dc.contributor.author Cohen, Mitchell en
dc.contributor.author Madden, Michael G. en
dc.contributor.author Morabito, Diane en
dc.contributor.author Aleks, Norm en
dc.contributor.author Russell, Stuart en
dc.date.accessioned 2009-05-22T09:20:53Z en
dc.date.available 2009-05-22T09:20:53Z en
dc.date.issued 2008 en
dc.identifier.citation Probabilistic Modeling of Sensor Artifacts in Critical Care , Norm Aleks and Stuart Russell (UC Berkeley), Michael G. Madden (NUI, Galway), Diane Morabito, Geoffrey Manley, Kristan Staudenmayer, and Mitchell Cohen (UC San Francisco). International Conference on Machine Learning, Workshop on Machine Learning in Health Care Applications, Helsinki, July 2008. en
dc.identifier.uri http://hdl.handle.net/10379/201 en
dc.description.abstract We describe an application of probabilistic modeling and inference technology to the problem of analyzing sensor data in the setting of an intensive care unit (ICU). In particular, we consider the arterial-line blood pressure sensor, which is subject to frequent data artifacts that cause false alarms in the ICU and make the raw data almost useless for automated decision making. The problem is complicated by the fact that the sensor data are acquired at fixed intervals whereas the events causing data artifacts may occur at any time and have durations that may be significantly shorter than the data collection inter- val. We show that careful modeling of the sensor, combined with a general technique for detecting sub-interval events and estimating their duration, enables effective detection of artifacts and accurate estimation of the underlying blood pressure values. en
dc.language.iso en en
dc.subject Probabilistic modelling en
dc.subject Critical care medicine en
dc.subject Intensive care units en
dc.subject Multisensor data fusion en
dc.subject.lcsh Critical care medicine en
dc.subject.lcsh Intensive care units en
dc.subject.lcsh Multisensor data fusion en
dc.title Probabilistic Modeling of Sensor Artifacts in Critical Care en
dc.type Conference Paper en

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