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Updated: Aug 8, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[From psychometry to neuropsychological disability in multiple sclerosis: a new brief French cognitive screening
E Sartori1, S Belliard, C Chevrier
1Service de Neurologie, Centre Hospitalier de Bretagne Sud, Lorient. e.sartori@ch-bretagne-sud.fr
Introduction:
Cognitive deficit in multiple sclerosis (MS) is a frequent early feature in the disease course, which conditions patients' overall disability. The goals of this study were to validate a reproducible brief screening battery written in French and to examine cognitive risk profiles in patients with a mild physical disability.
Methods:
Cognitive performances of 40 patients with EDSS <4.5 were compared with those of a control group. The study was completed with an analysis of socio-demographic, clinical and psychological variables (questionnaires).
Results:
Three tests were discriminative with satisfactory predictive values (positive: 88 percent; negative: 96 percent) and a time duration <30 minutes: PASAT (hard condition), backward digit span, learning stage of California Verbal Learning Test. Four variables were associated with cognitive deficit: educational level <11 years, age >40 years, pathological laughing-crying, unemployment.
Conclusions:
Our brief battery is an easy and reproducible tool. Completed with warning signs indicating the need for neuropsychological screening, this tool provides the practitioner with a global means of assessing disease activity and potentially therapeutic efficacy.
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