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Structuring raw medical documents with ontology mapping is now the next step for medical intelligence. Deep learning models take as input mathematically embedded information, such as encoded texts. To do so, word embedding methods can represent every word from a text as a fixed-length vector. A formal evaluation of three word embedding methods has been performed on raw medical documents. The data corresponds to more than 12M diverse documents produced in the Rouen hospital (drug prescriptions, discharge and surgery summaries, inter-services letters, etc.). Automatic and manual validation demonstrates that Word2Vec based on the skip-gram architecture had the best rate on three out of four accuracy tests. This model will now be used as the first layer of an AIbased semantic annotator. [ABSTRACT FROM AUTHOR]

Objective. The aim of this paper was to present a practical InfoRoute algorithm and applications developed by CISMeF to perform a contextual information retrieval across multiple medical websites in different health domains. Methods. The algorithm was developed to treat multiple types of queries: natural, Boolean and advanced. The algorithm also generates multiple types of queries: Boolean query, PubMed query or Advanced query. Each query can be extended via an inter alignments relationship from UMLS and HeTOP portal. Results. A web service and two web applications have been developed based on the InfoRoute algorithm to generate linksquery across multiple websites, i.e.: "PubMed" or "ClinicalTrials.org". Conclusion. The InfoRoute algorithm is a useful tool to perform contextual information retrieval across multiple medical websites in both English and French. [ABSTRACT FROM AUTHOR]