AIMC Topic: Alzheimer Disease
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Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jun 11, 2024
INTRODUCTION: Most people with Alzheimer's disease and related dementia (ADRD) also suffer from two or more chronic conditions, known as multiple chronic conditions (MCC). While many studies have investigated the MCC patterns, few studies have consid...
Unveiling Immune-related feature genes for Alzheimer's disease based on machine learning.
Frontiers in immunology
Jun 10, 2024
The identification of diagnostic and therapeutic biomarkers for Alzheimer's Disease (AD) remains a crucial area of research. In this study, utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we identified RHBDF2 and TNFRSF1...
Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease.
Neuroinformatics
Jun 7, 2024
Tensor-based representations are being increasingly used to represent complex data types such as imaging data, due to their appealing properties such as dimension reduction and the preservation of spatial information. Recently, there is a growing lit...
CLADSI: Deep Continual Learning for Alzheimer's Disease Stage Identification Using Accelerometer Data.
IEEE journal of biomedical and health informatics
Jun 6, 2024
Alzheimer's disease (AD) is a neurodegenerative disorder that can cause a significant impairment in physical and cognitive functions. Gait disturbances are also reported as a symptom of AD. Previous works have used Convolutional Neural Networks (CNNs...
Integrating AI in fighting advancing Alzheimer: diagnosis, prevention, treatment, monitoring, mechanisms, and clinical trials.
Current opinion in structural biology
Jun 4, 2024
The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these...
Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement.
Genome medicine
Jun 4, 2024
BACKGROUND: Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic da...
Exploring Brain Effective Connectivity Networks Through Spatiotemporal Graph Convolutional Models.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning me...
A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology Counting.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
The detection and segmentation of stained cells and nuclei are essential prerequisites for subsequent quantitative research for many diseases. Recently, deep learning has shown strong performance in many computer vision problems, including solutions ...
Hypergraph Structural Information Aggregation Generative Adversarial Networks for Diagnosis and Pathogenetic Factors Identification of Alzheimer's Disease With Imaging Genetic Data.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Alzheimer's disease (AD) is a neurodegenerative disease with profound pathogenetic causes. Imaging genetic data analysis can provide comprehensive insights into its causes. To fully utilize the multi-level information in the data, this article propos...
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