[en] 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.
Research center :
CIPSE - Centre de recherche interdisciplinaire en Psychophysiologie et Electrophysiologie de la cognition
Disciplines :
Laboratory medicine & medical technology Mathematics
Author, co-author :
Itani, Sarah ; Université de Mons > Faculté Polytechnique > Mathématique et Recherche opérationnelle
Lecron, Fabian ; Université de Mons > Faculté Polytechnique > Service de Management de l'Innovation Technologique
Fortemps, Philippe ; Université de Mons > Faculté Polytechnique > Service de Management de l'Innovation Technologique
Language :
English
Title :
A Gender-Differentiated MR-Sort Model for Diagnosis Aid of Attention Deficit Hyperactivity Disorder (ADHD)
Publication date :
22 November 2018
Event name :
DA2PL'2018 - From Multiple Criteria Decision Aid to Preference Learning
Event place :
Poznan, Poland
Event date :
2018
Research unit :
F113 - Management de l'Innovation Technologique F151 - Mathématique et Recherche opérationnelle
Research institute :
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique
