AIMC Topic: Alzheimer Disease
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Probiotic and selenium co-supplementation, and the effects on clinical, metabolic and genetic status in Alzheimer's disease: A randomized, double-blind, controlled trial.
Clinical nutrition (Edinburgh, Scotland)
Dec 10, 2018
BACKGROUND AND AIMS: Combined probiotic and selenium supplementation may improve Alzheimer's disease (AD) by correcting metabolic abnormalities, and attenuating inflammation and oxidative stress. This study aimed to determine the effects of probiotic...
Seasonal Variation in Essential Oils Composition and the Biological and Pharmaceutical Protective Effects of Leaves Grown in Tunisia.
BioMed research international
Dec 9, 2018
This research assessed the seasonal variation of the chemical composition and antibacterial and anticholinesterase activities of essential oils extracted from leaves. The leaves organic fractions were also investigated for their biological activitie...
Correlation-Aware Sparse and Low-Rank Constrained Multi-Task Learning for Longitudinal Analysis of Alzheimer's Disease.
IEEE journal of biomedical and health informatics
Dec 6, 2018
Alzheimer's disease (AD), as a severe neurodegenerative disease, is now attracting more and more researchers' attention in the healthcare. With the development of magnetic resonance imaging (MRI), the neuroimaging-based longitudinal analysis is gradu...
Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the...
Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic Multilayer Perceptron Classifier for MCI Identification.
IEEE transactions on medical imaging
Nov 20, 2018
Mild cognitive impairment (MCI) detection is important, such that appropriate interventions can be imposed to delay or prevent its progression to severe stages, including Alzheimer's disease (AD). Brain connectivity network inferred from the function...
A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using F-FDG PET of the Brain.
Radiology
Nov 6, 2018
Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to t...
Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.
Journal of medical systems
Oct 27, 2018
Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one of the most suitable application domains for these techniques since they help model diagnostic info...
Deep learning based low-cost high-accuracy diagnostic framework for dementia using comprehensive neuropsychological assessment profiles.
BMC geriatrics
Oct 3, 2018
BACKGROUND: The conventional scores of the neuropsychological batteries are not fully optimized for diagnosing dementia despite their variety and abundance of information. To achieve low-cost high-accuracy diagnose performance for dementia using a ne...
Alzheimer's disease diagnosis based on multiple cluster dense convolutional networks.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oct 2, 2018
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Structural magnetic resonance images (MRI) play important role to evaluate the brain anatomical changes for AD Diagn...
Instance-Based Representation Using Multiple Kernel Learning for Predicting Conversion to Alzheimer Disease.
International journal of neural systems
Sep 20, 2018
The early detection of Alzheimer's disease and quantification of its progression poses multiple difficulties for machine learning algorithms. Two of the most relevant issues are related to missing data and results interpretability. To deal with both ...
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