By Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera (auth.), Osmar R. Zaïane, Sandra Zilles (eds.)

This publication constitutes the refereed lawsuits of the twenty fifth Canadian convention on synthetic Intelligence, Canadian AI 2012, held in Regina, SK, Canada, in may well 2013. The 17 normal papers and 15 brief papers provided have been conscientiously reviewed and chosen from seventy three preliminary submissions and are followed via eight papers from the Graduate pupil Symposium that have been chosen from 14 submissions. The papers conceal a number of issues inside of AI, resembling: info extraction, wisdom illustration, seek, textual content mining, social networks, temporal associations.

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Additional resources for Advances in Artificial Intelligence: 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings

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It shows that SNOMED covers a larger set of terms and has deeper hierarchical levels compared to MedDRA. 3 K. Ghazinou, M. Sokolova, and S. Matwin Benefits of Using MedDRA and SNOMED We believe referring to MedDRA and SNOMED as well-funded, well-studied and reliable sources has two benefits: 1. We introduce a field of new text applications (the posts, weblogs and other information sources directly written by individuals) which extend the use for MedDRA and SNOMED. These medical dictionaries were previously used only for the health information collected by healthcare professionals.

G. hospital, hospitals and hospitalized are treated the same). 2 31 Data Annotation We manually reviewed 11,800 posts on MySpace to see to what extent those medical terms are actually revealing personal health information on MySpace. The terms were categorized into 3 groups: • • PHI: terms revealing personal health information. HI: medical terms that address health information (but not necessarily personal). NHI: terms with non-medical meaning. ” which is a medical term but does not reveal any personal health information.

A subset of terms detected by SNOMED that appear in MySpace Terms Sick Pain infection Swell PHI 44 17 5 4 HI 1 3 33 2 NHI 135 141 0 1 Terms Fracture Dizzy insomnia thyroid PHI 3 2 2 2 HI 3 7 6 0 NHI 1 2 0 0 Table 6. Terms of MySpace detected by SNOMED leaf nodes Term phlebotomy histology jet lag domestic abuse physic assault/abuse black out dizzy hematological Papillary conjunctivitis Level of hierarchy 3 4 4 5 5 6 6 6 6 PHI 0 0 1 0 0 0 2 0 0 HI 3 2 0 1 5 1 7 1 1 NHI 0 0 0 0 0 1 2 0 0 # freq. in SNOMED 2 2 1 1 1 1 2 1 2 This result indicates that although SNOMED has a deep hierarchical structure, one should not traverse all the nodes and branches to reach leaf nodes to be able to detect PHI terms.

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