Geriatrics

Potentially inappropriate polypharmacy is an important predictor of 30-day emergency hospitalisation in older adults: a machine learning feature validation study.

BACKGROUND: Machine learning (ML) models in healthcare are crucial for predicting clinical outcomes, and their effectiveness can be significantly enhanced through improvements in accuracy, generalisability, and interpretability. To achieve widespread adoption in clinical practice, risk factors identified by these models must be validated in diverse populations.

MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility

Healthcare decision-making represents one of the most challenging domains for Artificial Intelligence (AI), requiring the integration of diverse knowledge sources, complex reasoning, and various external analytical tools. Current AI systems often rely on either task-specific models, which offer limited adaptability, or general language models without grounding with specialized external knowledge...

MotionPersona: Characteristics-aware Locomotion Control

We present MotionPersona, a novel real-time character controller that allows users to characterize a character by specifying attributes such as phys...

S3CE-Net: Spike-guided Spatiotemporal Semantic Coupling and Expansion Network for Long Sequence Event Re-Identification

In this paper, we leverage the advantages of event cameras to resist harsh lighting conditions, reduce background interference, achieve high time re...

Generative AI for Urban Design: A Stepwise Approach Integrating Human Expertise with Multimodal Diffusion Models

Urban design is a multifaceted process that demands careful consideration of site-specific constraints and collaboration among diverse professionals...

S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation

The latest advancements in multi-modal large language models (MLLMs) have spurred a strong renewed interest in end-to-end motion planning approaches...

An end-to-end mass spectrometry data classification model with a unified architecture.

Mass spectrometry, known for its high sensitivity, selectivity, rich structural information, and rapid analysis capabilities, is widely used in diseas...

Dc-EEMF: Pushing depth-of-field limit of photoacoustic microscopy via decision-level constrained learning

Photoacoustic microscopy holds the potential to measure biomarkers' structural and functional status without labels, which significantly aids in com...

Large Language Model-Based Agents for Automated Research Reproducibility: An Exploratory Study in Alzheimer's Disease

Objective: To demonstrate the capabilities of Large Language Models (LLMs) as autonomous agents to reproduce findings of published research studies ...

PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation

In medical image segmentation, limited external validity remains a critical obstacle when models are deployed across unseen datasets, an issue parti...

Comparative assessment of fairness definitions and bias mitigation strategies in machine learning-based diagnosis of Alzheimer's disease from MR images

The present study performs a comprehensive fairness analysis of machine learning (ML) models for the diagnosis of Mild Cognitive Impairment (MCI) an...

Agentic Robot: A Brain-Inspired Framework for Vision-Language-Action Models in Embodied Agents

Long-horizon robotic manipulation poses significant challenges for autonomous systems, requiring extended reasoning, precise execution, and robust e...

Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imaging

Deep-unrolling and plug-and-play (PnP) approaches have become the de-facto standard solvers for single-pixel imaging (SPI) inverse problem. PnP appr...

Identification of Patterns of Cognitive Impairment for Early Detection of Dementia

Early detection of dementia is crucial to devise effective interventions. Comprehensive cognitive tests, while being the most accurate means of diag...

Predicting and preventing Alzheimer's disease.

With all the advances in both the science of aging and artificial intelligence (AI), we are in a propitious position to accurately and precisely deter...

Comparative Analysis of Feature Extraction Methods and Machine Learning Models for Predicting Osteoporosis Prevalence.

This study systematically examined the impact of three feature selection techniques (Boruta, Extreme gradient boosting (XGBoost), and Lasso) for optim...

A modified TOPSIS algorithm for the assessment of sports quality in higher education using circular pythagorean fuzzy information.

Higher education institutions experience difficulties in sports quality assessment because multiple qualitative and quantitative factors, including sp...

3DGS Compression with Sparsity-guided Hierarchical Transform Coding

3D Gaussian Splatting (3DGS) has gained popularity for its fast and high-quality rendering, but it has a very large memory footprint incurring high ...

Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification

Limited DXA access hinders osteoporosis screening. This proof-of-concept study proposes using widely available knee X-rays for opportunistic Bone Mi...

Single Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive Learning

Although Alzheimer's disease detection via MRIs has advanced significantly thanks to contemporary deep learning models, challenges such as class imb...