Geriatrics

Latest AI and machine learning research in geriatrics for healthcare professionals.

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The use of geroprotectors to prevent multimorbidity: Opportunities and challenges.

Over 60 % of people over the age of 65 will suffer from multiple diseases concomitantly but the comm...

Deep learning for digitizing highly noisy paper-based ECG records.

Electrocardiography (ECG) is essential in many heart diseases. However, some ECGs are recorded by pa...

Realizing 5G- and AI-based doctor-to-doctor remote diagnosis: opportunities, challenges, and prospects.

Fifth Generation (5G) mobile communications technology became available in Japan as of the end of Ma...

Deep convolutional neural network based on adaptive gradient optimizer for fault detection in SCIM.

Early fault detection in squirrel cage induction motor (SCIM) can minimize the downtime and maximize...

Predicting brain age with complex networks: From adolescence to adulthood.

In recent years, several studies have demonstrated that machine learning and deep learning systems c...

A Comprehensive Analysis of MicroRNAs in Human Osteoporosis.

MicroRNAs (miRNAs) are single-stranded RNA molecules that control gene expression in various process...

Associated Learning: Decomposing End-to-End Backpropagation Based on Autoencoders and Target Propagation.

Backpropagation (BP) is the cornerstone of today's deep learning algorithms, but it is inefficient p...

Development of a prognostic model for mortality in COVID-19 infection using machine learning.

Coronavirus disease 2019 (COVID-19) is a novel disease resulting from infection with severe acute re...

Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements.

OBJECTIVE: This retrospective and exploratory study tested the accuracy of artificial neural network...

Parkinson's Disease Tremor Detection in the Wild Using Wearable Accelerometers.

Continuous in-home monitoring of Parkinson's Disease (PD) symptoms might allow improvements in asses...

Automated detection of cerebral microbleeds in MR images: A two-stage deep learning approach.

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagn...

Fake metabolomics chromatogram generation for facilitating deep learning of peak-picking neural networks.

Finding peaks in chromatograms and determining their start and end points (peak picking) is a core t...

Prediction of 7-year's conversion from subjective cognitive decline to mild cognitive impairment.

Subjective cognitive decline (SCD) is a high-risk yet less understood status before developing Alzhe...

Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality.

Rubeosis faciei diabeticorum, caused by microangiopathy and characterized by a chronic facial erythe...

Detection of Mild Cognitive Impairment Through Natural Language and Touchscreen Typing Processing.

Mild cognitive impairment (MCI), an identified prodromal stage of Alzheimer's Disease (AD), often ev...

lncRNAKB, a knowledgebase of tissue-specific functional annotation and trait association of long noncoding RNA.

Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology ...

Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks.

OBJECTIVE: To explore the application of deep learning in patients with primary osteoporosis, and to...

LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion.

Recent studies uncover that subcellular location of long non-coding RNAs (lncRNAs) can provide signi...

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