| Abstract
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Belief networks provide a causal probabili … Belief networks provide a causal probabilistic framework for the representation of medical knowledge. We have developed VPnet, a belief-network model of the pathophysiology of patients in the intensive-care unit (ICU), and have incorporated this belief-network in a system _VentPlan_ that assists in the care and monitoring of patients in the ICU. VPnet converts patient observations into probability distributions for a set of physiological parameters used by VentPlan's mathematical model. VPnet represents the uncertainty of data observations explicitly and implements a model of increasing uncertainty as the time from an observation increases. We have evaluated VPnet using sets of inputs corresponding to a variety of clinical states, and we show calculated physiologic parameter distributions appropriate for the clinical state. Evaluation of complex belief-network models is difficult due to the lack of a gold standard for comparison, and because there is a large number of possible sets of input states. e number of possible sets of input states.
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| Author
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Geoffrey W. Rutledge +,
Stig K. Andersen +,
Jeanette X. Polaschek +,
Lawrence M. Fagan +
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| Bibtype
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techreport +
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| Institution
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Knowledge Systems, AI Laboratory +
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| Key
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KSL-90-49 +
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| Modification dateThis property is a special property in this wiki.
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1 May 2009 13:40:26 +
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| Number
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KSL-90-49 +
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| Tag
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Computer science +
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| Title
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A Belief Network Model for Interpretation of ICU Data +
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| Tr id
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KSL-90-49 +
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| Year
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1990 +
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| Categories |
Technical Report,
Publication,
KSL Technical Report
|
