Probabilistic Modeling of Sensor Artifacts in Critical Care
Madden, Michael G.
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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.
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.