Geriatrics

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

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Subcategories: Alzheimer's Disease Medicare
Showing 1051-1071 of 7,204 articles
Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning.

This systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evalu...

Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning.

Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive imp...

SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation.

Heart failure (HF) is a significant global public health concern with a high readmission rate, posin...

Making Co-Design More Responsible: Case Study on the Development of an AI-Based Decision Support System in Dementia Care.

BACKGROUND: Emerging technologies such as artificial intelligence (AI) require an early-stage assess...

A machine learning approach to determine the risk factors for fall in multiple sclerosis.

BACKGROUND: Falls in multiple sclerosis can result in numerous problems, including injuries and func...

Computed tomography-based radiomics machine learning models for differentiating enchondroma and atypical cartilaginous tumor in long bones.

To explore the value of CT-based radiomics machine learning models for differentiating enchondroma f...

Using Video Technology and AI within Parkinson's Disease Free-Living Fall Risk Assessment.

Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-...

Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.

Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our...

Additional Rehabilitative Robot-Assisted Gait Training for Ambulation in Geriatric Individuals with Guillain-Barré Syndrome: A Case Report.

We present a case of a 75-year-old Asian woman with Guillain-Barré syndrome (GBS) who underwent a 1-...

Identification of profiles associated with conversions between the Alzheimer's disease stages, using a machine learning approach.

BACKGROUND: The identification of factors involved in the conversion across the different Alzheimer'...

Adopting machine learning to predict ICU delirium.

With neuropsychiatric complications recognized among COVID-19 patients translating into significant ...

Integrating multi-task and cost-sensitive learning for predicting mortality risk of chronic diseases in the elderly using real-world data.

BACKGROUND AND OBJECTIVE: Real-world data encompass population diversity, enabling insights into chr...

Unlocking Tomorrow's Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence.

As an integral aspect of health care, digital technology has enabled modelling of complex relationsh...

Development and External Validation of a Machine Learning-based Fall Prediction Model for Nursing Home Residents: A Prospective Cohort Study.

OBJECTIVES: To develop and externally validate a machine learning-based fall prediction model for am...

Exceptional performance with minimal data using a generative adversarial network for alzheimer's disease classification.

The classification of Alzheimer's disease (AD) using deep learning models is hindered by the limited...

Responsive Hydrogel-Based Modular Microrobots for Multi-Functional Micromanipulation.

Microrobots show great potential in biomedical applications such as drug delivery and cell manipulat...

AI Hesitancy and Acceptability-Perceptions of AI Chatbots for Chronic Health Management and Long COVID Support: Survey Study.

BACKGROUND: Artificial intelligence (AI) chatbots have the potential to assist individuals with chro...

Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury.

BACKGROUND: Convolutional neural network (CNN) can capture the structural features changes of brain ...

An ensemble model for accurate prediction of key water quality parameters in river based on deep learning methods.

Deep learning models provide a more powerful method for accurate and stable prediction of water qual...

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