Geriatrics

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 task in chromatography based biotechnology. Construction of peak-picking neural networks by deep learning was, however, hampered from the preparation of exact peak-picked or "labeled" chromatograms since the exact start and end points were often unclear in overlapping peaks in real chromatograms. We ...

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 Alzheimer's disease (AD). This work included 76 SCD individuals with two (baseline and 7 years later) neuropsychological evaluations and a baseline T1-weighted structural MRI. A machine learning-based model was trained based on 198 baseline neuroimaging (morphometric) features and a battery of 25 clinica...

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 erythema, is associated with diabetic neuropathy. In cli...

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 evades detection in the early stages of the conditio...

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 develop a fully automatic method based on deep co...

Prediction of Promiscuity Cliffs Using Machine Learning.

Compounds with the ability to interact with multiple targets, also called promiscuous compounds, provide the basis for polypharmacological drug discov...

Effects on sleep from group activity with a robotic seal for nursing home residents with dementia: a cluster randomized controlled trial.

OBJECTIVES: Sleep disturbances are common in people with dementia and increase with the severity of the disease. Sleep disturbances are complex and ca...

The path to international medals: A supervised machine learning approach to explore the impact of coach-led sport-specific and non-specific practice.

Research investigating the nature and scope of developmental participation patterns leading to international senior-level success is mainly explorativ...

Transfer learning in deep neural network based under-sampled MR image reconstruction.

In Magnetic Resonance Imaging (MRI), the success of deep learning-based under-sampled MR image reconstruction depends on: (i) size of the training dat...

The effect of a social robot intervention on sleep and motor activity of people living with dementia and chronic pain: A pilot randomized controlled trial.

OBJECTIVE: To investigate the effect of a social robot intervention on sleep and motor activity in nursing home residents living with dementia and chr...

Are we ready for artificial intelligence health monitoring in elder care?

BACKGROUND: The world is experiencing a dramatic increase in the aging population, challenging the sustainability of traditional care models that have...

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model.

Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative diso...

Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is the most common type of dementia that can seriously affect a person's ability to perform daily a...

An Artificial Neural Network Model for Assessing Frailty-Associated Factors in the Thai Population.

Frailty, one of the major public health problems in the elderly, can result from multiple etiologic factors including biological and physical changes ...

Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: A machine learning-based prediction model by using a big database.

Artificial intelligence is increasingly being adopted in medical fields to predict various outcomes. In particular, chronic kidney disease (CKD) is pr...

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning.

Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform su...

Automatic fall detection using region-based convolutional neural network.

The common classifiers usually used to detect fall incidents depend on building and maintaining complex feature extraction for accurate machine learni...

Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia.

Dementia is a neurodegenerative disorder that affects the older adult population. To date, no cure or treatment to change its course is available. Sin...

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

INTRODUCTION: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neur...

Robotic Rehabilitation in Spinal Cord Injury: A Pilot Study on End-Effectors and Neurophysiological Outcomes.

Robot-aided gait training (RAGT) has been implemented to provide patients with spinal cord injury (SCI) with a physiological limb activation during ga...