Cargando…
Predicting Longitudinal Outcomes of Alzheimer’s Disease via a Tensor-Based Joint Classification and Regression Model
Alzheimer’s disease (AD) is a serious neurodegenerative condition that affects millions of people across the world. Recently machine learning models have been used to predict the progression of AD, although they frequently do not take advantage of the longitudinal and structural components associate...
Autores principales: | Brand, Lodewijk, Nichols, Kai, Wang, Hua, Huang, Heng, Shen, Li |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6948350/ https://www.ncbi.nlm.nih.gov/pubmed/31797582 |
Ejemplares similares
-
Bayesian longitudinal tensor response regression for modeling neuroplasticity
por: Kundu, Suprateek, et al.
Publicado: (2023) -
Soft Tensor Regression
por: Papadogeorgou, Georgia, et al.
Publicado: (2021) -
Joint regression and classification via relational regularization for Parkinson’s disease diagnosis
por: Lei, Haijun, et al.
Publicado: (2018) -
Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease
por: Huang, Meiyan, et al.
Publicado: (2017) -
A semi–supervised tensor regression model for siRNA efficacy prediction
por: Thang, Bui Ngoc, et al.
Publicado: (2015)