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dc.contributor.authorManley, Geoffreyen
dc.contributor.authorStaudenmayer, Kristanen
dc.contributor.authorCohen, Mitchellen
dc.contributor.authorMadden, Michael G.en
dc.contributor.authorMorabito, Dianeen
dc.contributor.authorAleks, Normen
dc.contributor.authorRussell, Stuarten
dc.identifier.citationProbabilistic 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.description.abstractWe 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.subjectProbabilistic modellingen
dc.subjectCritical care medicineen
dc.subjectIntensive care unitsen
dc.subjectMultisensor data fusionen
dc.subject.lcshCritical care medicineen
dc.subject.lcshIntensive care unitsen
dc.subject.lcshMultisensor data fusionen
dc.titleProbabilistic Modeling of Sensor Artifacts in Critical Careen
dc.typeConference Paperen

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