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• W2156598943 abstract "Causal probabilistic networks provide a natural framework for representation of medical knowledge, allowing clinical experts to encode assumptions about causal dependencies between stochastic variables. Application in medical decision support has produced promising results. However, model features and parameters may vary geo- or demographically. Therefore methods are needed that allow for easy adjustment of the model to a change in conditions. We present a method to represent causal probabilistic networks generically that maximizes the transferability of a models relevance and completeness, when moved from one environment to another, and illustrate application of the method with an example from a medical decision support system." @default.
• W2156598943 created "2016-06-24" @default.
• W2156598943 creator A5040061165 @default.
• W2156598943 creator A5042047206 @default.
• W2156598943 date "2008-02-01" @default.
• W2156598943 modified "2023-09-23" @default.
• W2156598943 title "Generic causal probabilistic networks: A solution to a problem of transferability in medical decision support" @default.
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• W2156598943 doi "https://doi.org/10.1016/j.cmpb.2007.10.015" @default.
• W2156598943 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18096268" @default.
• W2156598943 hasPublicationYear "2008" @default.
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