Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A munin network for the median nerve-a case study on loops
Applied Artificial Intelligence
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Object-oriented analysis and design with applications (2nd ed.)
Object-oriented analysis and design with applications (2nd ed.)
Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
Object-oriented software construction (2nd ed.)
Object-oriented software construction (2nd ed.)
A design/constraint model to capture design intent
SMA '97 Proceedings of the fourth ACM symposium on Solid modeling and applications
Introduction to ISO 10303—the STEP standard for product data exchange
Journal of Computing and Information Science in Engineering
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
A Causal Probabilistic Network for Optimal Treatment of Bacterial Infections
IEEE Transactions on Knowledge and Data Engineering
Top-Down Construction and Repetetive Structures Representation in Bayesian Networks
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
An empirical comparison of three inference methods
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Operations for learning with graphical models
Journal of Artificial Intelligence Research
MUNIN: a causal probabilistic network for interpretation of electromyographic findings
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Towards the standardized exchange of parameterized feature-based CAD models
Computer-Aided Design
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Generating Bayesian networks from probability logic knowledge bases
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
From certainty factors to belief networks
Artificial Intelligence in Medicine
Paper: Information technology factors in transferability of knowledge based systems in medicine
Artificial Intelligence in Medicine
Paper: Specification of models in large expert systems based on causal probabilistic networks
Artificial Intelligence in Medicine
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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.