Probabilistic Expert Systems for Reasoning in Clinical Depressive Disorders
Ojeme, Blessing, Audrey Mbogho and Thomas Meyer (2016) Probabilistic Expert Systems for Reasoning in Clinical Depressive Disorders. In Sayed-Mouchaweh, Moamar and Yun Raymond Fu, Eds. Proceedings 15th IEEE International Conference on Machine Learning and Applications, Anaheim, California, USA.
Like other real-world problems, reasoning in clinical depression presents cognitive challenges for clinicians. This is due to the presence of co-occuring diseases, incomplete data, uncertain knowledge, and the vast amount of data to be analysed. Current approaches rely heavily on the experience, knowledge, and subjective opinions of clinicians, creating scalability issues. Automating this process requires a good knowledge representation technique to capture the knowledge of the domain experts, and multidimensional inferential reasoning approaches that can utilise a few bits and pieces of information for efficient reasoning. This study presents knowledge-based system with variants of Bayesian network models for efficient inferential reasoning, translating from available fragmented depression data to the desired information in a visually interpretable and transparent manner. Mutual information, a Conditional independence test-based method was used to learn the classifiers.
|EPrint Type:||Conference Paper|
|Subjects:||I Computing Methodologies: I.2 ARTIFICIAL INTELLIGENCE|
|Deposited By:||Meyer, Thomas|
|Deposited On:||29 November 2016|