Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
IEEE Transactions on Knowledge and Data Engineering
Building Bayesian Network Models in Medicine: The MENTOR Experience
Applied Intelligence
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Inference in hybrid Bayesian networks using dynamic discretization
Statistics and Computing
Diagnosis of breast cancer using Bayesian networks: A case study
Computers in Biology and Medicine
Journal of Biomedical Informatics
Exploiting missing clinical data in Bayesian network modeling for predicting medical problems
Journal of Biomedical Informatics
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
MUNIN: a causal probabilistic network for interpretation of electromyographic findings
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A Bayesian expert system for the analysis of an adverse drug reaction
Artificial Intelligence in Medicine
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
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This paper explains the role of Bayes Theorem and Bayesian networks arising in a medical negligence case brought by a patient who suffered a stroke as a result of an invasive diagnostic test. The claim of negligence was based on the premise that an alternative (non-invasive) test should have been used because it carried a lower risk. The case raises a number of general and widely applicable concerns about the decision-making process within the medical profession, including the ethics of informed consent, patient care liabilities when errors are made, and the research problem of focusing on 'true positives' while ignoring 'false positives'. An immediate concern is how best to present Bayesian arguments in such a way that they can be understood by people who would normally balk at mathematical equations. We feel it is possible to present purely visual representations of a non-trivial Bayesian argument in such a way that no mathematical knowledge or understanding is needed. The approach supports a wide range of alternative scenarios, makes all assumptions easily understandable and offers significant potential benefits to many areas of medical decision-making.