Primary Care

Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms

Diabetic Retinopathy (DR) is a serious and common complication of diabetes, caused by prolonged high blood sugar levels that damage the small retinal blood vessels. If left untreated, DR can progress to retinal vein occlusion and stimulate abnormal blood vessel growth, significantly increasing the risk of blindness. Traditional diabetes diagnosis methods often utilize convolutional neural networ...

Safe Screening Rules for Group OWL Models

Group Ordered Weighted $L_{1}$-Norm (Group OWL) regularized models have emerged as a useful procedure for high-dimensional sparse multi-task learning with correlated features. Proximal gradient methods are used as standard approaches to solving Group OWL models. However, Group OWL models usually suffer huge computational costs and memory usage when the feature size is large in the high-dimension...

Machine learning reveals connections between preclinical type 2 diabetes subtypes and brain health.

Previous research has established type 2 diabetes mellitus as a significant risk factor for various disorders, adversely impacting human health. While...

An AI-powered Public Health Automated Kiosk System for Personalized Care: An Experimental Pilot Study

Background: The HERMES Kiosk (Healthcare Enhanced Recommendations through Artificial Intelligence & Expertise System) is designed to provide persona...

AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening

Resume screening is a critical yet time-intensive process in talent acquisition, requiring recruiters to analyze vast volume of job applications whi...

Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomics Features from Multimodal Retinal Images

This study aimed to develop a machine learning (ML) algorithm capable of determining cardiovascular risk in multimodal retinal images from patients ...

Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models

Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings, presenting opportunities for automated detection to identify pati...

Stroke Disease Classification Using Machine Learning with Feature Selection Techniques

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models...

Graphical Models and Efficient Inference Methods for Multivariate Phase Probability Distributions

Multivariate phase relationships are important to characterize and understand numerous physical, biological, and chemical systems, from electromagne...

Navigating Quality and Innovation: Actor-Network Theory and Hybrid Assemblages in Midwifery Practice, Implications of Maternity Early Warning Tools and Artificial Intelligence.

Midwifery philosophy views childbearing as primarily normal, indicative of a woman's overall health. Midwifery practice focuses on supporting the huma...

Implementation of A New, Mobile Diabetic Retinopathy Screening Model Incorporating Artificial Intelligence in Remote Western Australia.

OBJECTIVE: Diabetic retinopathy (DR) screening rates are poor in remote Western Australia where communities rely on outdated primary care-based retina...

Prediction of Verbal Abilities From Brain Connectivity Data Across the Lifespan Using a Machine Learning Approach.

Compared to nonverbal cognition such as executive or memory functions, language-related cognition generally appears to remain more stable until later ...

Automated Bi-Ventricular Segmentation and Regional Cardiac Wall Motion Analysis for Rat Models of Pulmonary Hypertension.

Artificial intelligence-based cardiac motion mapping offers predictive insights into pulmonary hypertension (PH) disease progression and its impact on...

Integrating Large Language Models with Human Expertise for Disease Detection in Electronic Health Records

Objective: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performa...

Improving Diseases Predictions Utilizing External Bio-Banks

Machine learning has been successfully used in critical domains, such as medicine. However, extracting meaningful insights from biomedical data is o...

A Scalable Framework for Evaluating Health Language Models

Large language models (LLMs) have emerged as powerful tools for analyzing complex datasets. Recent studies demonstrate their potential to generate u...

Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network

Early and accurate diagnosis of pulmonary hypertension (PH) is essential for optimal patient management. Differentiating between pre-capillary and p...

Congenital Heart Disease Classification Using Phonocardiograms: A Scalable Screening Tool for Diverse Environments

Congenital heart disease (CHD) is a critical condition that demands early detection, particularly in infancy and childhood. This study presents a de...

Identification of hypertension subtypes using microRNA profiles and machine learning.

OBJECTIVE: Hypertension is a major cardiovascular risk factor affecting about 1 in 3 adults. Although the majority of hypertension cases (∼90%) are cl...

What is the role of human decisions in a world of artificial intelligence: an economic evaluation of human-AI collaboration in diabetic retinopathy screening

As Artificial intelligence (AI) has been increasingly integrated into the medical field, the role of humans may become vague. While numerous studies...