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Toward a multimodal multitask model for neurodegenerative diseases diagnosis and progression prediction

Titre	Toward a multimodal multitask model for neurodegenerative diseases diagnosis and progression prediction
Publication Type	Conference Paper
Year of Publication	2021
Authors	Lahrichi, S, Rhanoui, M, Mikram, M, Asri, BE
Conference Name	Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021
Mots-clés	Alzheimer's disease, Comparative studies, Data Science, Detection models, Diagnosis, Early prediction, Forecasting, Hospital data processing, Learning methods, Learning systems, Multi-task model, Neurodegenerative diseases, Patients' conditions, Time dependent
Abstract	Recent studies on modelling the progression of Alzheimer's disease use a single modality for their predictions while ignoring the time dimension. However, the nature of patient data is heterogeneous and time dependent which requires models that value these factors in order to achieve a reliable diagnosis, as well as making it possible to track and detect changes in the progression of patients' condition at an early stage. This article overviews various categories of models used for Alzheimer's disease prediction with their respective learning methods, by establishing a comparative study of early prediction and detection Alzheimer's disease progression. Finally, a robust and precise detection model is proposed. Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
URL	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111735908&doi=10.5220%2f0010600003220328&partnerID=40&md5=1f255250606020cc06dfdc4c73cc29c0
DOI	10.5220/0010600003220328