Geriatrics

Exploring polypharmacy with artificial intelligence: data analysis protocol.

BACKGROUND: Polypharmacy is common among older adults and it represents a public health concern, due to the negative health impacts potentially associated with the use of several medications. However, the large number of medication combinations and sequences of use makes it complicated for traditional statistical methods to predict which therapy is genuinely associated with health outcomes. The pr...

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study.

This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective, (1) this study explored the predictors of senile anxiety using base models and meta models. (2) This study presented decision tree visualization that could help psychiatric consultants and primary physicians easily interpret the path of predicting h...

Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data.

Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve...

Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment.

BACKGROUND & AIMS: Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural...

Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer's Disease or Mild Cognitive Impairment.

OBJECTIVES: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain gen...

Exoskeleton robots for lower limb assistance: A review of materials, actuation, and manufacturing methods.

The field of robot-assisted physical rehabilitation and robotics technology for providing support to the elderly population is rapidly evolving. Lower...

ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features.

Background and Objective Electrocardiogram (ECG) quality assessment is significant for automatic diagnosis of cardiovascular disease and reducing the ...

Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits.

The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In partic...

AIoT-Enabled Rehabilitation Recognition System-Exemplified by Hybrid Lower-Limb Exercises.

Ubiquitous health management (UHM) is vital in the aging society. The UHM services with artificial intelligence of things (AIoT) can assist home-isola...

An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging.

While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lack...

Domain-Adaptive Fall Detection Using Deep Adversarial Training.

Fall detection (FD) systems are important assistive technologies for healthcare that can detect emergency fall events and alert caregivers. However, i...

The Impact of Artificial Intelligence on Traditional Chinese Medicine.

Traditional Chinese Medicine (TCM) is a well-established medical system with a long history. Currently, artificial intelligence (AI) is rapidly expand...

A comparative study of perioperative and survival outcomes of robot-assisted radical cystectomy in patients over 80 and under 80 years old.

BACKGROUND: Radical cystectomy (RC) is the standard treatment for bladder cancer, but the safety and efficacy of this treatment for elderly people nee...

Human-in-the-Loop Low-Shot Learning.

We consider a human-in-the-loop scenario in the context of low-shot learning. Our approach was inspired by the fact that the viability of samples in n...

Evaluating the Efficacy of Povidone-Iodine Solution Infection Prophylaxis in Immediate Tissue Expander-Based Breast Reconstruction: A Controlled Retrospective Analysis.

There is currently no consensus among plastic surgeons regarding the optimal infection prophylaxis for immediate tissue expander placement following ...

The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease.

The disease Alzheimer is an irrepressible neurologicalbrain disorder. Earlier detection and proper treatment of Alzheimer's disease can help for brain...

Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy.

Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prev...

Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive...

BBNet: A Novel Convolutional Neural Network Structure in Edge-Cloud Collaborative Inference.

Edge-cloud collaborative inference can significantly reduce the delay of a deep neural network (DNN) by dividing the network between mobile edge and c...

Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging.

Deep learning algorithms for left ventricle (LV) segmentation are prone to bias towards the training dataset. This study assesses sex- and age-depende...