Geriatrics

Robust Prediction of Drug Interactions using Chemical Descriptors.

Polypharmacy requires accurate prediction of drug-drug interactions to prevent adverse events, yet existing models often lack reliability and explainability. We propose T-DDI, a descriptor-based deep learning framework for multi-class drug-drug interaction prediction. Rather than relying on complex graph embeddings, T-DDI uses explicit physicochemical descriptors and an uncertainty-aware estimator...

A comprehensive evaluation of seven osteoporosis screening tools.

UNLABELLED: This review evaluates the sensitivity, specificity, and predictive values of seven osteoporosis screening tools in populations such as postmenopausal women, older adults, and patients with specific diseases. These tools offer advantages, including high sensitivity (facilitating early detection of osteoporosis), high cost-effectiveness, and the ability to be customized according to dise...

A feature-efficient dual-task machine learning framework for predicting bone mineral density and osteoporosis stratification in resource-constrained environments.

Osteoporosis is a chronic skeletal disorder characterized by progressive bone mineral density (BMD) loss and structural deterioration, significantly i...

Mapping Non-Homologous Pocket Compatibilities to Identify Hidden Drug-Target Relationships: A Pocket Hopping Framework.

Predicting small molecule-protein interactions across nonhomologous proteins remains challenging because shared ligand recognition is often not eviden...

MAGMED: A clinically grounded LLM-guided synthetic augmentation framework for admission-time ICU mortality prediction.

BACKGROUND: Mortality risk prediction for elderly intensive care unit (ICU) patients with severe infections remains challenging due to limited sample ...

Predicting severe COVID-19 in elderly patients using routine laboratory indicators: Diagnostic accuracy of machine learning models.

To enable early warning of severe 2019 coronavirus disease in elderly patients, this study collected routine laboratory indicators and clinical parame...

Enhancing ultrasound training for breast cancer diagnosis: a controlled study of AI-assisted learning.

OBJECTIVES: This study aimed to develop and evaluate an AI-assisted teaching platform to enhance diagnostic competency in breast ultrasound. The goal ...

Precision Medicine for Anticoagulation Strategies in the Cath Lab: Part 2.

PURPOSE OF REVIEW: Intraprocedural anticoagulation during percutaneous coronary intervention (PCI) remains particularly challenging in high-risk and u...

An interpretable machine learning model integrating ultrasound and clinical variables for predicting osteoporosis in patients with rheumatoid arthritis.

OBJECTIVES: Rheumatoid arthritis (RA) significantly increases the risk of osteoporosis (OP) and fractures, yet dual-energy X-ray absorptiometry (DXA) ...

Rethinking aging biomarkers in HIV: from measures to meaning.

PURPOSE OF REVIEW: People with HIV (PWH) on effective antiretroviral therapy continue to experience a disproportionate burden of age-related comorbidi...

Machine learning for predicting full-count FDG PET brain images from low-count acquisitions in suspected dementia: a clinical and quantitative evaluation.

Objective.Artificial intelligence methods for denoising low-count FDG PET brain images are usually evaluated using image quality metrics alone, with l...

Walking stability prediction for pedestrians using gait energy images and hybrid deep and few-shot learning models.

The prediction and recognition of unstable human walking patterns are of high importance for active video surveillance, smart environments, and assist...

Aging-driven transcriptional programs in diabetic kidney disease: multi-omics discovery of diagnostic biomarkers and drug-repurposing targets.

BACKGROUND: Diabetic kidney disease (DKD) is the primary global cause of end-stage renal disease. However, the aging-related gene networks driving its...

A hybrid framework for predicting fall impact forces using pose-estimated kinematics and descriptive information.

Falls are the leading cause of hip fractures among older adults; however, direct measurement of hip impact force during laboratory falls is challengin...

Deep Model Families for EEG-Based Multi-Class Dementia Classification.

Deep learning (DL) has shown considerable promise for EEG-based dementia assessment; however, rigorous cross-family comparisons under leakage-free and...

Follicle Identification in Primate Ovaries Via Machine Learning†.

Studies of ovarian health and aging rely on estimates of the ovarian reserve, i.e. the number of healthy ovarian follicles. We present a machine learn...

Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.

Artificial intelligence (AI) has emerged as a transformative tool for improving the detection, prediction, and prevention of adverse drug reactions (A...

Long-Sequence Lower Limb Action Recognition via Action-Specific Attention and Logic Correction for Daily Rehabilitation.

Accurate and robust intent recognition is the cornerstone of human-machine collaboration in rehabilitation, particularly for controlling Supernumerary...

Identifying sex-specific sub-phenotypes of Alzheimer's disease progression using longitudinal electronic health records.

BACKGROUND: Alzheimer's Disease (AD) is a complex neurodegenerative disorder, with women comprising nearly two-thirds of individuals with AD. However,...

Parental burnout and children's short-form video and online game use: A specification curve and interpretable deep learning analysis.

Parental burnout has many negative effects on both parents and their children; however, research on the relation between parental burnout and children...