Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Knowledge Engineering in Health Informatics
Knowledge Engineering in Health Informatics
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Construction of Large-Scale Bayesian Networks by Local to Global Search
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Bayesian Networks for Knowledge-Based Authentication
IEEE Transactions on Knowledge and Data Engineering
Automated troubleshooting of a UMTS-WLAN test platform
Proceedings of the 4th International Conference on Testbeds and research infrastructures for the development of networks & communities
Constructing Bayesian networks for criminal profiling from limited data
Knowledge-Based Systems
Comparing risks of alternative medical diagnosis using Bayesian arguments
Journal of Biomedical Informatics
Automated troubleshooting of a UMTS-WLAN test platform
Mobile Networks and Applications
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Pattern Recognition Letters
An algorithm for cooperative learning of bayesian network structure from data
CSCWD'04 Proceedings of the 8th international conference on Computer Supported Cooperative Work in Design I
Probabilistic Information Structure of Human Walking
Journal of Medical Systems
One backward inference algorithm in bayesian networks
PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
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ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Journal of Medical Systems
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The paper discusses several knowledge engineering techniques for the construction of Bayesian networks for medical diagnostics when the available numerical probabilistic information is incomplete or partially correct. This situation occurs often when epidemiological studies publish only indirect statistics and when significant unmodeled conditional dependence exists in the problem domain. While nothing can replace precise and complete probabilistic information, still a useful diagnostic system can be built with imperfect data by introducing domain-dependent constraints. We propose a solution to the problem of determining the combined influences of several diseases on a single test result from specificity and sensitivity data for individual diseases. We also demonstrate two techniques for dealing with unmodeled conditional dependencies in a diagnostic network. These techniques are discussed in the context of an effort to design a portable device for cardiac diagnosis and monitoring from multimodal signals.