Latest AI and machine learning research in primary care for healthcare professionals.
Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk prediction models for five common chronic diseases: diabetes, hypertension, CKD, COPD, and chronic ischemic heart disease. In this study, we present a novel approach for disease risk models by integrating survival analysis with classification techniques. Tr...
The automatic identification of cough segments in audio through the determination of start and end points is pivotal to building scalable screening tools in health technologies for pulmonary related diseases. We propose the application of two current pre-trained architectures to the task of cough activity detection. A dataset of recordings containing cough from patients symptomatic for tuberculosi...
More than one third of adults with diabetes can experience diabetes distress due to the demands of daily self-care. As a cognitive therapy, mindfulnes...
Cardiovascular diseases (CVDs) remain the foremost cause of global morbidity and mortality, driving an urgent need for robust predictive tools that en...
Background Sickle cell disease (SCD) is a common inherited genetic disorder and contributor to global childhood mortality and morbidity. In the Democr...
Background: Pleuroparenchymal fibroelastosis (PPFE) is an upper lobe predominant fibrotic lung abnormality associated with increased mortality in esta...
Purpose Medical imaging typically generates 12- to 16-bit formats, yet conversion to 8-bit is often required. While deep learning has been widely expl...
Importance: Lung cancer mortality in the United States has fallen substantially in recent decades, yet the relative influence of behavioral, environme...
Background and aims Population screening for liver disease in high-risk groups is recommended. Community diagnosis of liver disease is a challenge due...
As of early 2026, over 115 million US adults (more than 1 in 3) have prediabetes, a condition with an annual conversion rate of 5%-10% to type 2 diabe...
In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 ...
Background: Artificial intelligence chatbots (AICs) are increasingly being integrated into scholarly publishing, with the potential to automate routin...
Abstract Background: Systematic reviews (SRs) are essential for evidence-based medicine but require extensive time and resources for abstract screenin...
Background: Cognitive decline and dementia represent major public health challenges in aging populations. Natural language processing (NLP)-augmented ...
Background: Cardiovascular disease remains the leading cause of global morbidity and mortality. The original My Heart Counts smartphone application de...
Rare diseases affect over 300 million people worldwide, yet patients often endure years-long diagnostic delays that limit timely intervention and tria...
Early screening for glaucoma and diabetic retinopathy (DR) is critical to prevent irreversible vision loss, yet remains inaccessible to many underserv...
The human gut microbiome is increasingly explored as a diagnostic indicator for disease, yet machine learning models trained on metagenomic data are o...
Legitimacy screening, the process of verifying the identity and purpose of customers ordering synthetic nucleic acids, is a primary safeguard against ...
Causal discovery has achieved substantial theoretical progress, yet its deployment in large-scale longitudinal systems remains limited. A key obstacle...