Machine Learning - Special issue on learning with probabilistic representations
An Expert System for Assigning Patients into Clinical Trials Based on Bayesian Networks
Journal of Medical Systems
Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain
Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain
Machine Learning
Bayesian Networks in Ovarian Cancer Diagnosis: Potentials and Limitations
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Using Bayesian Networks for Diagnostic Reasoning in Penetrating Injury Assessment
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Computers in Biology and Medicine
Diagnosis of breast cancer using Bayesian networks: A case study
Computers in Biology and Medicine
Journal of Biomedical Informatics
Journal of Biomedical Informatics
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
Artificial Intelligence in Medicine
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo
Computer Methods and Programs in Biomedicine
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We show that Bayesian methods can be efficiently applied to the classification of otoneurological diseases and to assess attribute dependencies. A set of 38 otoneurological attributes was employed in order to use a naïve Bayesian probabilistic model and Bayesian networks with different scoring functions for the classification of cases from six otoneurological diseases. Tests were executed on the basis of tenfold crossvalidation. We obtained average sensitivities of 90%, positive predictive values of 92% and accuracies as high as 97%, which is better than our earlier tests with neural networks. Our assessments indicated that Bayesian methods have good power and potential to classify otoneurological patient cases correctly even if this is often a complicated task for the best specialists. Bayesian methods classified the current medical data and knowledge well.