Geriatrics

Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool.

BACKGROUND: Adequate bowel preparation is crucial for effective colonoscopy, especially in elderly patients who face a high risk of inadequate preparation. This study develops and validates a machine learning model to predict bowel preparation adequacy in elderly patients before colonoscopy.

Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand.

This study aims to construct a prediction model for the demand for medical and daily care services of the elderly and to explore the factors that affect the demand for medical and daily care services of the elderly. In this study, a questionnaire survey on the demand for medical and daily care services of 1291 elderly was conducted using multi-stage stratified whole cluster random sampling. SPSS21...

A comprehensive interpretable machine learning framework for mild cognitive impairment and Alzheimer's disease diagnosis.

An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD...

Develop and validate machine learning models to predict the risk of depressive symptoms in older adults with cognitive impairment.

BACKGROUND: Cognitive impairment and depressive symptoms are prevalent and closely interrelated mental health issues in the elderly. Traditional metho...

Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?

OBJECTIVE: This study was designed to establish a diagnostic model for osteoporosis by collecting clinical information from patients with and without ...

PrOsteoporosis: predicting osteoporosis risk using NHANES data and machine learning approach.

OBJECTIVES: Osteoporosis, prevalent among the elderly population, is primarily diagnosed through bone mineral density (BMD) testing, which has limitat...

Using statistical modelling and machine learning in detecting bone properties: A systematic review protocol.

INTRODUCTION: Osteoporosis, a common condition characterised by decreased bone mass and microarchitectural deterioration, leading to increased fractur...

Dementia Overdiagnosis in Younger, Higher Educated Individuals Based on MMSE Alone: Analysis Using Deep Learning Technology.

BACKGROUND: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatme...

Machine learning to detect Alzheimer's disease with data on drugs and diagnoses.

BACKGROUND: Integrating machine learning with medical records offers potential for early detection of Alzheimer's disease (AD), enabling timely interv...

Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.

Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is...

MCNEL: A multi-scale convolutional network and ensemble learning for Alzheimer's disease diagnosis.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) significantly threatens community well-being and healthcare resource allocation due to its high inc...

Ensemble-learning approach improves fracture prediction using genomic and phenotypic data.

UNLABELLED: This study presents an innovative ensemble machine learning model integrating genomic and clinical data to enhance the prediction of major...

Multitask Deep Learning Models of Combined Industrial Absorption, Distribution, Metabolism, and Excretion Datasets to Improve Generalization.

The optimization of absorption, distribution, metabolism, and excretion (ADME) profiles of compounds is critical to the drug discovery process. As suc...

An explainable machine learning-based prediction model for sarcopenia in elderly Chinese people with knee osteoarthritis.

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disease that leads to loss of muscle mass and function, resulting in adverse heal...

Alzheimer's disease prediction using 3D-CNNs: Intelligent processing of neuroimaging data.

Alzheimer's disease (AD) is a severe neurological illness that demolishes memory and brain functioning. This disease affects an individual's capacity ...

A comprehensive approach to anticipating the progression of mild cognitive impairment.

The immersive experience provided by our approach empowers researchers with an intuitive exploration of brain structures. Within the brain's central n...

Deep learning-based classification of dementia using image representation of subcortical signals.

BACKGROUND: Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the common...

Mechanical Structure Design and Motion Simulation Analysis of a Lower Limb Exoskeleton Rehabilitation Robot Based on Human-Machine Integration.

Population aging is an inevitable trend in contemporary society, and the application of technologies such as human-machine interaction, assistive heal...

A Lightweight Deep Convolutional Neural Network Extracting Local and Global Contextual Features for the Classification of Alzheimer's Disease Using Structural MRI.

Recent advancements in the classification of Alzheimer's disease have leveraged the automatic feature generation capability of convolutional neural ne...

Learning Consistent Semantic Representation for Chest X-ray via Anatomical Localization in Self-Supervised Pre-Training.

Despite the similar global structures in Chest X-ray (CXR) images, the same anatomy exhibits varying appearances across images, including differences ...