Geriatrics

Machine Learning-Based Prediction of Delirium and Risk Factor Identification in Intensive Care Unit Patients With Burns: Retrospective Observational Study.

BACKGROUND: The incidence of delirium in patients with burns receiving treatment in the intensive care unit (ICU) is high, reaching up to 77%, and has been associated with increased mortality rates. Therefore, early identification of patients at high risk of delirium onset is essential for improving treatment strategies.

Cold storage surpasses the impact of biological age and donor characteristics on red blood cell morphology classified by deep machine learning.

Assessment of the morphology of red blood cells (RBCs) can improve clinical benefits following blood transfusion. Deep machine learning surpasses traditional microscopy-based classification methods, offering more accurate and consistent results while reducing time and labor intensity. RBCs from teenage males, teenage females, senior males, and senior females were biologically age-profiled or densi...

Enhancing diagnosis prediction with adaptive disease representation learning.

Diagnosis prediction predicts which diseases a patient is most likely to suffer from in the future based on their historical electronic health records...

Differential effects of robot's touch on perceived emotions and the feeling of Kawaii in adults and seniors.

Using social robots is a promising approach for supporting senior citizens in the context of super-aging societies. The essential design factors for a...

Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer's disease biomarkers.

PURPOSE: This study explores the application of machine learning to high-dimensional proteomics datasets for identifying Alzheimer's disease (AD) biom...

Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features.

OBJECTIVES: Sarcopenic obesity (SO), characterized by the coexistence of obesity and sarcopenia, is an increasingly prevalent condition in aging popul...

Neurofind: using deep learning to make individualised inferences in brain-based disorders.

Within precision psychiatry, there is a growing interest in normative models given their ability to parse heterogeneity. While they are intuitive and ...

Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models.

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration and cognitive decline. Early diagnosis is challengin...

Prediction of sarcopenia at different time intervals: an interpretable machine learning analysis of modifiable factors.

OBJECTIVES: This study aims to develop sarcopenia risk prediction models for Chinese older adults at different time intervals and to identify and comp...

Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.

Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze ...

Therapeutic Mechanisms of Medicine Food Homology Plants in Alzheimer's Disease: Insights from Network Pharmacology, Machine Learning, and Molecular Docking.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive function. Currently, there are no...

Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture.

BACKGROUND: Hip fractures have become a significant health concern, particularly among super-aged patients, who were at a high risk of postoperative p...

Estimation of Machine Learning-Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study.

BACKGROUND: The atherosclerotic cardiovascular disease (ASCVD) is associated with dementia. However, the risk factors of dementia in patients with ASC...

CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that significantly impacts health care worldwide, particularly among the elderly ...

Imbalanced Power Spectral Generation for Respiratory Rate and Uncertainty Estimations Based on Photoplethysmography Signal.

Respiratory rate (RR) changes in the elderly can indicate serious diseases. Thus, accurate estimation of RRs for cardiopulmonary function is essential...

Artificial intelligence for osteoporosis detection on panoramic radiography: A systematic review and meta analysis.

INTRODUCTION: Osteoporosis is a disease characterized by low bone mineral density and an increased risk of fractures. In dentistry, mandibular bone mo...

Linguistic cues for automatic assessment of Alzheimer's disease across languages.

BackgroundMost common forms of dementia, including Alzheimer's disease, are associated with alterations in spoken language.ObjectiveThis study explore...

Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study.

BACKGROUND: Understanding the dementia disease trajectory and clinical practice patterns in outpatient settings is vital for effective management. Kno...

Development of weighted residual RNN model with hybrid heuristic algorithm for movement recognition framework in ambient assisted living.

In healthcare applications, automatic and intelligent movement recognition systems in Ambient Assisted Living (AAL) are designed for elderly and disab...

Cognitive performance classification of older patients using machine learning and electronic medical records.

Dementia rates are projected to increase significantly by 2050, posing considerable challenges for healthcare systems worldwide. Developing efficient ...