Artificial intelligence in psychiatry research, diagnosis, and therapy
J Sun, QX Dong, SW Wang, YB Zheng, XX Liu… - Asian Journal of …, 2023 - Elsevier
Psychiatric disorders are now responsible for the largest proportion of the global burden of
disease, and even more challenges have been seen during the COVID-19 pandemic …
disease, and even more challenges have been seen during the COVID-19 pandemic …
Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review
Introduction Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive
impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast …
impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast …
Multimodal multitask deep learning model for Alzheimer's disease progression detection based on time series data
Early prediction of Alzheimer's disease (AD) is crucial for delaying its progression. As a
chronic disease, ignoring the temporal dimension of AD data affects the performance of a …
chronic disease, ignoring the temporal dimension of AD data affects the performance of a …
Deep learning framework for Alzheimer's disease diagnosis via 3D-CNN and FSBi-LSTM
Alzheimer's disease (AD) is an irreversible progressive neurodegenerative disorder. Mild
cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a …
cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a …
Automatic detection of Alzheimer's disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers
Predicting Alzheimer's disease (AD) progression is crucial for improving the management of
this chronic disease. Usually, data from AD patients are multimodal and time series in …
this chronic disease. Usually, data from AD patients are multimodal and time series in …
Predicting clinical scores for Alzheimer's disease based on joint and deep learning
Alzheimer's disease (AD) is a progressive neurodegenerative disease that often grows in
middle-aged and elderly people with the gradual loss of cognitive ability. Presently, there is …
middle-aged and elderly people with the gradual loss of cognitive ability. Presently, there is …
Robust hybrid deep learning models for Alzheimer's progression detection
The prevalence of Alzheimer's disease (AD) in the growing elderly population makes
accurately predicting AD progression crucial. Due to AD's complex etiology and …
accurately predicting AD progression crucial. Due to AD's complex etiology and …
[HTML][HTML] Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data
N Bhagwat, JD Viviano, AN Voineskos… - PLoS computational …, 2018 - journals.plos.org
Computational models predicting symptomatic progression at the individual level can be
highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD) …
highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD) …
CMC: a consensus multi-view clustering model for predicting Alzheimer's disease progression
Abstract Machine learning has been used in the past for the auxiliary diagnosis of
Alzheimer's Disease (AD). However, most existing technologies only explore single-view …
Alzheimer's Disease (AD). However, most existing technologies only explore single-view …
Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review
Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome
characterized by cognitive impairment severe enough to interfere with activities of daily life …
characterized by cognitive impairment severe enough to interfere with activities of daily life …