Combined State and Parameter Estimation of Human Intracranial Hemodynamics

We describe an application of probabilistic modeling and inference framework that is capable of analyzing sensor data in an intensive care unit setting. We are specifically interested in the intracranial hemodynamics. We show that using a probabilistic description of the system and sensor models in addition to the stateof-the-art statistical learning machinery can lead to an accurate real-time decision support mechanism.

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