Predicting Alzheimer's disease progression using deep recurrent neural networks
Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical di...
Main Authors: | Minh Nguyen, Tong He, Lijun An, Daniel C. Alexander, Jiashi Feng, B.T. Thomas Yeo |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2020-11-01
|
Series: | NeuroImage |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920306893 |
Similar Items
-
Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics
by: Tong He, et al.
Published: (2020-02-01) -
Deep recurrent model for individualized prediction of Alzheimer’s disease progression
by: Wonsik Jung, et al.
Published: (2021-08-01) -
Temporal Spiking Recurrent Neural Network for Action Recognition
by: Wei Wang, et al.
Published: (2019-01-01) -
On Recurrent and Deep Neural Networks
by: Pascanu, Razvan
Published: (2015) -
A Deep Recurrent Neural Network-Based Explainable Prediction Model for Progression from Atrophic Gastritis to Gastric Cancer
by: Hyon Hee Kim, et al.
Published: (2021-07-01)