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 could 2013. The 17 ordinary papers and 15 brief papers provided have been conscientiously reviewed and chosen from seventy three preliminary submissions and are followed by way of eight papers from the Graduate pupil Symposium that have been chosen from 14 submissions. The papers disguise various issues inside AI, akin to: details extraction, wisdom illustration, seek, textual content mining, social networks, temporal associations.
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Extra info for Advances in Artificial Intelligence: 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings
For example, GENIA is built for the microbiology domain. Its categories include DNA-metabolism, protein metabolism, and cellular process. Another resource, Medical Subjects Heading (MeSH), is a controlled vocabulary thesaurus, which terms are informative to experts but might not be in use by the general public. The Medical Entities Dictionary (MED) is an ontology containing approximately 60,000 concepts, 208,000 synonyms, and 84,000 hierarchies. This powerful lexical and knowledge resource is designed with medical research vocabulary in mind.
In the sentence "I had my bell rung in the hockey game last night" words bell rung could indicate a concussion). Use methods such as Latent Dirichlet Allocation (LDA) might be a good approach. Furthermore, testing our model on different posts on other social networks such as Facebook or Twitter would be a good research experiment. We want to compare the terms that appear in MedDRA and SNOMED and evaluate their RFPI values. An interesting project would be to develop a user interface or an application plug in to the current social networks such as MySpace, Facebook and Twitter that warns the user about revealing PHI when they use these potentially privacy violating words that we have introduced in this research.
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. I saw a guy chasing someone and screaming at the top of his lungs…” which carries no medical value. In this manner we have manually analyzed and performed manual labeling based on the annotator’s judgment whether the post reveals information about the person who wrote it, or discloses information about other individuals that make them identifiable.