Geriatrics

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

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Showing 2668-2688 of 7,277 articles
A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer's disease.

There is a limited evaluation of an independent linguistic battery for early diagnosis of Mild Cogni...

Factors that affect younger and older adults' causal attributions of robot behaviour.

Stereotypes are cognitive shortcuts that facilitate efficient social judgments about others. Just as...

Leveraging Contextual Information in Extracting Long Distance Relations from Clinical Notes.

Relation extraction from biomedical text is important for clinical decision support applications. In...

Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications.

The identification of drug-drug interactions (DDIs) is important for patient safety; yet, compared t...

Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm.

OBJECTIVE: Predicting the risk of falls in advance can benefit the quality of care and potentially r...

Collective effects of long-range DNA methylations predict gene expressions and estimate phenotypes in cancer.

DNA methylation of various genomic regions has been found to be associated with gene expression in d...

The Effect of Using PARO for People Living With Dementia and Chronic Pain: A Pilot Randomized Controlled Trial.

OBJECTIVES: To evaluate the effect of interaction with a robotic seal (PARO) on pain and behavioral ...

A novel CNN based Alzheimer's disease classification using hybrid enhanced ICA segmented gray matter of MRI.

Predicting Alzheimer's Disease (AD) from Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) h...

A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder.

BACKGROUND: Concomitant use of complementary, multimodal imaging measures and neurocognitive measure...

End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation.

Electro-stimulation or modulation of deep brain regions is commonly used in clinical procedures for ...

A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment.

BACKGROUND: The detection of Alzheimer's Disease (AD) in its formative stages, especially in Mild Co...

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening.

OBJECTIVE: Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In th...

Measuring the impact of age, gender and dementia on communication-robot interventions in residential care homes.

AIM: The primary aim of this study was to examine the impact of age, gender and the stage of dementi...

Space-independent community and hub structure of functional brain networks.

Coordinated brain activity reflects underlying cognitive processes and can be modeled as a network o...

An End-to-End Multi-Task Deep Learning Framework for Skin Lesion Analysis.

Automatic skin lesion analysis of dermoscopy images remains a challenging topic. In this paper, we p...

Multimodal Data Analysis of Alzheimer's Disease Based on Clustering Evolutionary Random Forest.

Alzheimer's disease (AD) has become a severe medical challenge. Advances in technologies produced hi...

On the localness modeling for the self-attention based end-to-end speech synthesis.

Attention based end-to-end speech synthesis achieves better performance in both prosody and quality ...

Decoding rejuvenating effects of mechanical loading on skeletal aging using in vivo μCT imaging and deep learning.

Throughout the process of aging, dynamic changes of bone material, micro- and macro-architecture res...

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning.

INTRODUCTION: Developing cross-validated multi-biomarker models for the prediction of the rate of co...

Towards an ontology of cognitive processes and their neural substrates: A structural equation modeling approach.

A key challenge in the field of cognitive neuroscience is to identify discriminable cognitive functi...

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