Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis.
Details
Serval ID
serval:BIB_B41DE817A7AB
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis.
Journal
Journal of personalized medicine
ISSN
2075-4426 (Print)
ISSN-L
2075-4426
Publication state
Published
Issued date
11/12/2021
Peer-reviewed
Oui
Volume
11
Number
12
Pages
1349
Language
english
Notes
Publication types: Journal Article ; Review
Publication Status: epublish
Publication Status: epublish
Abstract
Multiple sclerosis (MS) manifests heterogeneously among persons suffering from it, making its disease course highly challenging to predict. At present, prognosis mostly relies on biomarkers that are unable to predict disease course on an individual level. Machine learning is a promising technique, both in terms of its ability to combine multimodal data and through the capability of making personalized predictions. However, most investigations on machine learning for prognosis in MS were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked. This review aims to boost the field of machine learning for cognitive prognosis in MS by means of an introduction to machine learning and its pitfalls, an overview of important elements for study design, and an overview of the current literature on cognitive prognosis in MS using machine learning. Furthermore, the review discusses new trends in the field of machine learning that might be adopted for future studies in the field.
Keywords
Multiple sclerosis, prognosis, cognition, machine learning, artificial intelligence, multiple sclerosis
Pubmed
Web of science
Open Access
Yes
Create date
11/01/2024 18:05
Last modification date
18/01/2024 15:01