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Conference papers

Categorizing the suitability of an alternative for a subject

Abstract : Nowadays there is a real need to operate and link existing knowledge expressed by experts, in domains in which highly reliable recommendation systems are needed. This is especially true in the medical domain where knowledge sources are heterogeneous, since they are separately formed in different contexts. A major difficulty is to relate these sources together in a way that respects the specic medical recommendation requirements. Using MCDM (Multi-Criteria Decision Making) models can help in this aim. The general problem we address is to assess the suitability of an alternative (or a solution) for a given subject in a specific context. For instance, which antibiotic (alternative) should be prescribed to a patient (subject) who suffers from bacterial infection, taking into account characteristics of the patient such as allergies, renal problems, etc. We use a MCDM sorting method (MR-Sort with Veto, a variant of ELECTRE TRI), to categorize the pairs alternative-solution (e.g. antibiotic-patient) according to their degree of suitability. The contextual knowledge (e.g. side-effects of antibiotics, characteristics of patient), structured in several ontologies, is linked to the assessment model through a semantic model. The approach is applied to the recommendation of antibiotic prescription, in collaboration with the EpiCura Hospital Center.
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Souhir Ben Souissi, Mourad Abed, Lahcen El Hiki, Philippe Fortemps, Marc Pirlot. Categorizing the suitability of an alternative for a subject. 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Dec 2016, Athens, Greece. pp.1-8, ⟨10.1109/SSCI.2016.7849894⟩. ⟨hal-03388758⟩



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