Geriatrics

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

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Subcategories: Alzheimer's Disease Medicare
Showing 652-672 of 7,183 articles
Age group classification based on optical measurement of brain pulsation using machine learning.

Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential fo...

Alzheimer's Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery.

Alzheimer's disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology cha...

The Use of AI in Mental Health Services to Support Decision-Making: Scoping Review.

BACKGROUND: Recent advancements in artificial intelligence (AI) have changed the care processes in m...

Enhanced detection of mild cognitive impairment in Alzheimer's disease: a hybrid model integrating dual biomarkers and advanced machine learning.

Alzheimer's disease (AD) is a complex, progressive, and irreversible neurodegenerative disorder mark...

"Tom and Pepper Lab". Robotics for cognitive stimulation and social skills: A preliminary study.

Dementia affects over 55 million people globally, with early cognitive decline, such as Mild Cogniti...

Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study.

BACKGROUND: Identifying high risk factors and predicting lung cancer incidence risk are essential to...

Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis.

Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's d...

Dual inhibition of AChE and MAO-B in Alzheimer's disease: machine learning approaches and model interpretations.

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases. Given the multifac...

ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus.

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spa...

Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia.

Existing dementia prediction models using non-neuroimaging clinical measures have been limited in th...

Constructing a fall risk prediction model for hospitalized patients using machine learning.

STUDY OBJECTIVES: This study aimed to identify the risk factors associated with falls in hospitalize...

Predicting fall parameters from infant skull fractures using machine learning.

When infants are admitted to the hospital with skull fractures, providers must distinguish between c...

Alzheimer's disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study.

OBJECTIVES: The future emergence of disease-modifying treatments for dementia highlights the urgent ...

MDWConv:CNN based on multi-scale atrous pyramid and depthwise separable convolution for long time series forecasting.

Long time series forecasting has extensive applications in various fields such as power dispatching,...

Adapting to evolving MRI data: A transfer learning approach for Alzheimer's disease prediction.

Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in...

Ecological risks of PFAS in China's surface water: A machine learning approach.

The persistence of per- and polyfluoroalkyl substances (PFAS) in surface water can pose risks to eco...

Kernel representation-based End-to-End network-enabled decoding strategy for precise and medical diagnosis.

Artificial intelligence-assisted imaging biosensors have attracted increasing attention due to their...

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