Geriatrics

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

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Subcategories: Alzheimer's Disease Medicare
Showing 1996-2016 of 7,246 articles
Therapeutic use of the humanoid robot, Telenoid, with older adults: A critical interpretive synthesis review.

This review sought to critically evaluate the use of the teleoperated humanoid robotic communication...

Dietary administration of D-chiro-inositol attenuates sex-specific metabolic imbalances in the 5xFAD mouse model of Alzheimer's disease.

Increasing evidence shows that hypothalamic dysfunction, insulin resistance, and weight loss precede...

Dementia risk predictions from German claims data using methods of machine learning.

INTRODUCTION: We examined whether German claims data are suitable for dementia risk prediction, how ...

Histopathology-Based Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning.

Oral squamous cell carcinoma (OSCC) is prevalent around the world and is associated with poor progno...

Fault Recognition Method Based on Attention Mechanism and the 3D-UNet.

Oil and gas reservoirs are of great significance for economic benefits. Faults act as important cond...

A non-invasive approach to monitor anemia during long-duration spaceflight with retinal fundus images and deep learning.

During spaceflight, astronauts can experience significantly higher levels of hemolysis. With future ...

Machine Learning for Healthcare Wearable Devices: The Big Picture.

Using artificial intelligence and machine learning techniques in healthcare applications has been ac...

A Novel Approach of Feature Space Reconstruction with Three-Way Decisions for Long-Tailed Text Classification.

Text classification is widely studied by researchers in the natural language processing field. Howev...

A Tightly Coupled LiDAR-Inertial SLAM for Perceptually Degraded Scenes.

Realizing robust six degrees of freedom (6DOF) state estimation and high-performance simultaneous lo...

Answering medical questions in Chinese using automatically mined knowledge and deep neural networks: an end-to-end solution.

BACKGROUND: Medical information has rapidly increased on the internet and has become one of the main...

Deep learning -- promises for 3D nuclear imaging: a guide for biologists.

For the past century, the nucleus has been the focus of extensive investigations in cell biology. Ho...

Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation.

Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks. The impac...

End-to-End Lip-Reading Open Cloud-Based Speech Architecture.

Deep learning technology has encouraged research on noise-robust automatic speech recognition (ASR)....

Prediction of femoral strength of elderly men based on quantitative computed tomography images using machine learning.

Hip fracture is the most common complication of osteoporosis, and its major contributor is compromis...

Assessment of the predictive potential of cognitive scores from retinal images and retinal fundus metadata via deep learning using the CLSA database.

Accumulation of beta-amyloid in the brain and cognitive decline are considered hallmarks of Alzheime...

Multigraph classification using learnable integration network with application to gender fingerprinting.

Multigraphs with heterogeneous views present one of the most challenging obstacles to classification...

A Survey of End-to-End Driving: Architectures and Training Methods.

Autonomous driving is of great interest to industry and academia alike. The use of machine learning ...

Deep Learning Enabled Diagnosis of Children's ADHD Based on the Big Data of Video Screen Long-Range EEG.

Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children....

CIPHER-SC: Disease-Gene Association Inference Using Graph Convolution on a Context-Aware Network With Single-Cell Data.

Inference of disease-gene associations helps unravel the pathogenesis of diseases and contributes to...

Deep learning for automatic segmentation of paraspinal muscle on computed tomography.

BACKGROUND: Muscle quantification is an essential step in sarcopenia evaluation.

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