By Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera (auth.), Osmar R. Zaïane, Sandra Zilles (eds.)
This e-book constitutes the refereed court cases of the twenty fifth Canadian convention on synthetic Intelligence, Canadian AI 2012, held in Regina, SK, Canada, in may well 2013. The 17 average papers and 15 brief papers offered have been conscientiously reviewed and chosen from seventy three preliminary submissions and are followed by means of eight papers from the Graduate pupil Symposium that have been chosen from 14 submissions. The papers disguise numerous themes inside of AI, resembling: details extraction, wisdom illustration, seek, textual content mining, social networks, temporal associations.
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Additional info for Advances in Artificial Intelligence: 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings
A a d b d b c c Fig. 2. Bayesian networks B (left) and B (right) Table 3. 5 CPT Structure It is instructive to review that, when evidence is not considered, each potential built by VE is a CPT. A topological ordering  is an ordering ≺ of the variables in a Bayesian network B so that for every arc (vi , vj ) in B, vi precedes vj in ≺. For example, c ≺ d ≺ i ≺ g ≺ s ≺ l ≺ j ≺ h is a topological ordering of the directed acyclic graph in Figure 1, but d ≺ c ≺ i ≺ g ≺ h ≺ l ≺ j ≺ s is not. Recall this feature of Bayesian networks, p(vi |P (vi )).
The clinical findings node has 29,724 sub-nodes (19,349 diseases and disorders, 10,375 findings) and the node procedure has 15,078 sub-nodes. So in total we have selected 44,802 nodes out of 353,154. Fig. 1. A sample of the SNOMED hierarchy Table 2. MedDRA and SNOMED hierarchical structure Dictionary MedDRA SNOMED # total nodes 11,400 353,154 # unique sub-selected nodes 8,561 44,802 average depth level 3 6 Table 2 depicts a brief comparison between MedDRA and SNOMED hierarchical structure. It shows that SNOMED covers a larger set of terms and has deeper hierarchical levels compared to MedDRA.
245–257. Springer, Heidelberg (2010) 4. : Expert Systems and Probabilistic Network Models. Springer, New York (1997) 5. : Introduction to Algorithms. MIT Press, Cambridge (2009) 6. : Modeling and Reasoning with Bayesian Networks. Cambridge University Press, New York (2009) 7. : Bayesian Networks and Inﬂuence Diagrams. Springer, New York (2008) 8. : Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009) 9. : A Diﬀerential Semantics of Lazy AR Propagation. In: 21st Conference on Uncertainty in Artiﬁcial Intelligence, pp.