Alzheimer's disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions
Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …
disorder among the elderly and is a leading cause of dementia. AD results in significant …
Robust temporal smoothness in multi-task learning
Multi-task learning models based on temporal smoothness assumption, in which each time
point of a sequence of time points concerns a task of prediction, assume the adjacent tasks …
point of a sequence of time points concerns a task of prediction, assume the adjacent tasks …
Automatic temporal relation in multi-task learning
Multi-task learning with temporal relation is a common prediction method for modelling the
evolution of a wide range of systems. Considering the inherent relations between multiple …
evolution of a wide range of systems. Considering the inherent relations between multiple …
Explainable tensor multi-task ensemble learning based on brain structure variation for Alzheimer's Disease dynamic prediction
Machine learning approaches for predicting Alzheimer's disease (AD) progression can
substantially assist researchers and clinicians in developing effective AD preventive and …
substantially assist researchers and clinicians in developing effective AD preventive and …
Multi-task time series forecasting based on graph neural networks
X Han, Y Huang, Z Pan, W Li, Y Hu, G Lin - Entropy, 2023 - mdpi.com
Accurate time series forecasting is of great importance in real-world scenarios such as
health care, transportation, and finance. Because of the tendency, temporal variations, and …
health care, transportation, and finance. Because of the tendency, temporal variations, and …
Modeling alzheimer's disease progression via amalgamated magnitude-direction brain structure variation quantification and tensor multi-task learning
Machine learning (ML) techniques for predicting the progression of Alzheimer's disease
(AD) can greatly assist researchers and clinicians in establishing effective AD prevention …
(AD) can greatly assist researchers and clinicians in establishing effective AD prevention …
Efficient multi-task learning with adaptive temporal structure for progression prediction
In this paper, we propose a novel efficient multi-task learning formulation for the class of
progression problems in which its state will continuously change over time. To use the …
progression problems in which its state will continuously change over time. To use the …
Broad Multitask Learning System With Group Sparse Regularization
The broad learning system (BLS) featuring lightweight, incremental extension, and strong
generalization capabilities has been successful in its applications. Despite these …
generalization capabilities has been successful in its applications. Despite these …
Integrating automatic temporal relation graph into multi-task learning for alzheimer's disease progression prediction
Alzheimer's disease (AD), the most prevalent dementia, gradually reduces the cognitive
abilities of patients while also posing a significant financial burden on the healthcare system …
abilities of patients while also posing a significant financial burden on the healthcare system …
Advancing Point-of-Care Diagnosis: Digitalizing Combinatorial Biomarker Signals for Lupus Nephritis
To improve the efficiency and patient coverage of the current healthcare system, user-
friendly novel homecare devices are urgently needed. In this work, we developed a …
friendly novel homecare devices are urgently needed. In this work, we developed a …