Geriatrics

Novel Alzheimer's disease-associated variants and genetic interactions identified from UK biobank whole-exome sequencing data using IBI-DT.

Alzheimer's disease (AD) is a highly heritable neurodegenerative disorder whose genetic architecture remains incompletely understood, particularly with respect to rare variants and higher-order interactions. We applied Individualized Bayesian Inference-Decision Tree (IBI-DT) to 790,000 whole-exome sequencing variants from 8,292 unrelated White British individuals in the UK Biobank, using genome-wi...

Uncertainty-aware deep kernel learning: An end-to-end approach for crack localization in turbine blades.

Early and reliable crack localization in jet turbine blades is important for structural health monitoring in aerospace systems. This study presents an uncertainty-aware Deep Kernel Learning (DKL) framework that integrates a deep residual feature extractor with a Sparse Variational Gaussian Process (SVGP) model for crack location regression from modal frequency data. The model is trained end-to-end...

Risk prediction models based on machine learning for emergence delirium in elderly patients undergoing spine surgery: development and validation study.

BACKGROUND: Emergence delirium (ED) is a common complication in elderly patients undergoing surgery for degenerative spinal disease (DSD) and is assoc...

[Deep learning-driven high-frequency ultrasound method for skin aging assessment].

Ultraviolet radiation is a primary external factor contributing to skin photoaging, as it induces cellular deoxyribonucleic acid damage and collagen d...

Using AI to identify in-hospital falls: a comparative analysis with incident reports and manual chart review.

INTRODUCTION: Falls remain a problem in hospitalised patients, especially in older adults. In-hospital falls occasionally lead to injuries. Currently,...

Reinforcement learning-driven adaptive game therapy for cognitive impairment patients with improved vision transformer based detection model.

BACKGROUND: Cognitive impairment is the growing challenge that requires early diagnosis and personalized management of neurodegenerative conditions li...

Machine learning and plasma growth differentiation factor-15 (GDF15) for early risk stratification of heart failure progression in a long-latency doxorubicin cardiomyopathy model.

BACKGROUND: Anthracycline-induced cardiotoxicity is a major cause of late heart failure (HF) in cancer survivors. Yet early identification of individu...

Both chronological age and individual differences in aging are the two indispensable components for predicting biological age.

Due to the lack of a gold standard for biological age (BA), existing studies usually adopt chronological age (CA) as the training label when construct...

Predicting SIRS after PCNL using machine learning: the joint impact of sarcopenia and staghorn stones.

PURPOSE: To develop and validate machine learning models for predicting systemic inflammatory response syndrome (SIRS) after percutaneous nephrolithot...

Machine-Learned Leftmost Hessian Eigenvectors for Robust Transition State Finding.

The reliable determination of transition states (TSs) benefits from second-order information for robust convergence and validation, but the computatio...

Expression patterns of risk genes associated with three evolutionarily relevant syndromes in rhesus macaque and human brains.

Depressive disorder (DD), Alzheimer's disease (AD), and schizophrenia (SZ) are evolutionarily relevant traits that disrupt neural networks supporting ...

Enhancing enterprise decision support via swin transformer-based OCR-free information extraction.

Extracting key information from vast amounts of documents and data plays a crucial role in knowledge graph construction, intelligence analysis, decisi...

Aggregating XAI-based explanations to identify spectral-spatial patterns in CNN-based resting-state EEG classification.

Convolutional neural networks (CNNs) achieve high performance in electroencephalographic (EEG) classification tasks; however, their decision-making me...

Bayesian-optimized machine learning models for classifying metabolic syndrome control among NCD patients in Bangladesh hospitals.

Control of metabolic syndrome (MS) is still a major challenge for patients suffering from Non-communicable diseases (NCDs) in Bangladesh. In this stud...

Artificial Intelligence, Cognitive Aging, and the Late-Career Surgeon: A Narrative Review.

BackgroundThe surgical workforce in the United States is aging while artificial intelligence (AI) tools are increasingly integrated into clinical prac...

Development and validation of machine-learning diagnostic models for identifying frailty in older adults with tuberculosis: a multicentre observational study protocol.

BACKGROUND: The Frailty in Tuberculosis (FIT) study aims to assess frailty in older adults with tuberculosis (TB) using machine learning (ML) to devel...

Real-World Generalizability of Alzheimer's Volumetric MRI Machine-Learning Models: External Validation with British Data.

PURPOSE: Assessing generalizability and performance of machine learning models in clinical settings is crucial. In this study, we aimed to test our mo...

Exploring Non-Embodied AI-Based Digital Companions for Older Adults in Aging and Care Contexts: Protocol for a Scoping Review.

BACKGROUND: Conversational artificial intelligence (AI) technologies are increasingly positioned as a response to social isolation, loneliness, and un...

Segmenting Older Adults by Their Acceptance of Digital Health Care Devices: Cross-Sectional Study Using the Augmented Technology Acceptance Model and K-Means Clustering.

BACKGROUND: Population aging has become a critical global challenge, with South Korea entering a super-aged society and facing rapidly increasing heal...

Comprehensive analysis of interactions between brain aging and late-onset psychoses using heuristic mapping models.

BACKGROUND: Advanced brain aging is closely associated with late-onset psychoses, including bipolar disorder(BD), schizophrenia(SP), and major depress...