AIMC Topic: Alzheimer Disease

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Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer's Disease Gene Expression Data.

Journal of Alzheimer's disease : JAD Jan 1, 2020
BACKGROUND: Gene Ontology (GO) is a major bioinformatic resource used for analysis of large biomedical datasets, for example from genome-wide association studies, applied universally across biological fields, including Alzheimer's disease (AD) resear...
Microglia Gene Expression Alzheimer Disease Molecular Sequence Annotation Gene Ontology Encephalitis Humans Computational Biology
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Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation.

Journal of Alzheimer's disease : JAD Jan 1, 2020
BACKGROUND: Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls ...
Humans Biomarkers Neuropsychological Tests Support Vector Machine Brain Aged Electroencephalography Sensitivity and Specificity Alzheimer Disease Aging Brain Waves
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A Machine Learning Framework for Assessment of Cognitive and Functional Impairments in Alzheimer's Disease: Data Preprocessing and Analysis.

The journal of prevention of Alzheimer's disease Jan 1, 2020
The neuropsychological scores and Functional Activities Questionnaire (FAQ) are significant to measure the cognitive and functional domain of the patients affected by the Alzheimer's Disease. Further, there are standardized dataset available today th...
Memory Executive Function Mental Status and Dementia Tests Humans Machine Learning Cognitive Dysfunction Logistic Models Alzheimer Disease Activities of Daily Living Datasets as Topic
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Convolutional Neural Network-based MR Image Analysis for Alzheimer's Disease Classification.

Current medical imaging reviews Jan 1, 2020
BACKGROUND: In this study, we used a convolutional neural network (CNN) to classify Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonanc...
Magnetic Resonance Imaging Brain Mapping Alzheimer Disease Image Interpretation, Computer-Assisted Hippocampus Datasets as Topic Humans Neural Networks, Computer Case-Control Studies Cognitive Dysfunction
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Machine Learning Predictive Models Can Improve Efficacy of Clinical Trials for Alzheimer's Disease.

Journal of Alzheimer's disease : JAD Jan 1, 2020
BACKGROUND: The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and also would be responsive to the therapeutic intervention being studied (i.e., drug arm). ...
Clinical Trials as Topic Humans Clinical Trials, Phase III as Topic Machine Learning Mental Status and Dementia Tests Reproducibility of Results Aged Aged, 80 and over Sensitivity and Specificity Algorithms Research Design Databases, Factual Female Predictive Value of Tests Male Sample Size Cognitive Dysfunction Alzheimer Disease Follow-Up Studies Neuroimaging Magnetic Resonance Imaging
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A Machine Learning Approach to Identify a Circulating MicroRNA Signature for Alzheimer Disease.

The journal of applied laboratory medicine Jan 1, 2020
BACKGROUND: Accurate diagnosis of Alzheimer disease (AD) involving less invasive molecular procedures and at reasonable cost is an unmet medical need. We identified a serum miRNA signature for AD that is less invasive than a measure in cerebrospinal ...
Early Diagnosis Circulating MicroRNA Aged Machine Learning Sensitivity and Specificity Male Brain Transcriptome Gene Expression Profiling Humans Female Cognitive Dysfunction Reproducibility of Results Autopsy Alzheimer Disease
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3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI.

Neuroinformatics Jan 1, 2020
We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state function...
Brain Female Aged Aged, 80 and over Humans Neural Networks, Computer Support Vector Machine Magnetic Resonance Imaging Alzheimer Disease Mental Status and Dementia Tests Male Middle Aged Deep Learning Imaging, Three-Dimensional
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Deep learning for clustering of multivariate clinical patient trajectories with missing values.

GigaScience Nov 1, 2019
BACKGROUND: Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratifica...
Parkinson Disease Alzheimer Disease Disease Progression Deep Learning Models, Neurological Databases, Factual Precision Medicine Female Male Humans
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[Early prognosis of Alzheimer's disease based on convolutional neural networks and ensemble learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Oct 25, 2019
Alzheimer's disease (AD) is a typical neurodegenerative disease, which is clinically manifested as amnesia, loss of language ability and self-care ability, and so on. So far, the cause of the disease has still been unclear and the course of the disea...
Prognosis Humans Neuroimaging Deep Learning Neural Networks, Computer Cognitive Dysfunction Brain Alzheimer Disease
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The Use of Random Forests to Classify Amyloid Brain PET.

Clinical nuclear medicine Oct 1, 2019
PURPOSE: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification.
Supervised Machine Learning Alzheimer Disease Aniline Compounds Positron-Emission Tomography Male Humans Middle Aged Amyloid Prospective Studies Cognitive Dysfunction Female Neuroimaging Brain Image Processing, Computer-Assisted Aged Ethylene Glycols Aged, 80 and over Sensitivity and Specificity
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