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2018-11-22 - Colloque/Article dans les actes avec comité de lecture - Anglais - 2 page(s)

Itani Sarah , Lecron Fabian , Fortemps Philippe , "A Gender-Differentiated MR-Sort Model for Diagnosis Aid of Attention Deficit Hyperactivity Disorder (ADHD)" in DA2PL'2018 - From Multiple Criteria Decision Aid to Preference Learning, Poznan, Pologne, 2018

  • Codes CREF : Modèles mathématiques d'aide à la décision (DI1151), Ingénierie biomédicale (DI3900)
  • Unités de recherche UMONS : Management de l'Innovation Technologique (F113), Mathématique et Recherche opérationnelle (F151)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech)
  • Centres UMONS : Centre de recherche interdisciplinaire en Psychophysiologie et Electrophysiologie de la cognition (CIPsE)

Abstract(s) :

(Anglais) The present paper deals with the use of decision-making models for medical diagnosis assistance. Such a specific issue requires to consider some parameters, e.g. the nature of the pathology and the related known facts. These factors may lead to the necessity of bringing some nuances to the basic formulation of a decision-making model. In this work, we addressed Attention Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder for which the current agreement rate between clinicians on diagnosis is still to be improved. In that respect, we considered the MR-Sort model which is highly valued for its efficiency and readability. As previous studies report gender-based differences in the neurophysiology of ADHD, we propose a reformulation of the MR-Sort model. It provides interesting prediction rates in comparison to the recent literature.