Latest AI and machine learning research in primary care for healthcare professionals.
A scalable and reliable system is required to analyze the National Health and Nutrition Examination Survey (NHANES) data efficiently to understand hospital utilization risk factors. This study aims to investigate the integration of continuous integration and deployment (CI/CD) practices in data science workflows, specifically focusing on analyzing NHANES data to identify the prevalence of diabet...
Clinical natural language processing (NLP) is increasingly in demand in both clinical research and operational practice. However, most of the state-of-the-art solutions are transformers-based and require high computational resources, limiting their accessibility. We propose a hybrid NLP framework that integrates rule-based filtering, a Support Vector Machine (SVM) classifier, and a BERT-based mo...
Objective: Create precise, structured, data-backed guidelines for type 2 diabetes treatment progression, suitable for clinical adoption. Research ...
Training Neural Networks (NNs) to behave as Model Predictive Control (MPC) algorithms is an effective way to implement them in constrained embedded ...
In recent years, the rapid development of artificial intelligence (AI) has brought innovative opportunities to diabetes management, with significant a...
White matter hyperintensities (WMH) are neuroimaging markers linked to an elevated risk of cognitive decline. WMH severity is typically assessed via v...
Overweight and obesity have emerged as widespread societal challenges, frequently linked to unhealthy eating patterns. A promising approach to enhan...
Frequent and long-term exposure to hyperglycemia (i.e., high blood glucose) increases the risk of chronic complications such as neuropathy, nephropa...
The dead-in-bed syndrome describes the sudden and unexplained death of young individuals with Type 1 Diabetes (T1D) without prior long-term complica...
Refractive error is a significant factor contributing to visual impairment, imposing a relatively large burden on the social economy. Although refract...
We propose and create an incentive based recommendation algorithm aimed at improving the lifestyle of diabetic patients. This algorithm is integrate...
This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,1...
In this study, hypertension is utilized as an indicator of individual vascular damage. This damage can be identified through machine learning techni...
Monitoring maternal and fetal health during pregnancy is crucial for preventing adverse outcomes. While tests such as ultrasound scans offer high ac...
Early life experiences are crucial for health and well-being, influencing physical, emotional, and social development throughout the lifespan. Recent ...
Understanding how urban socio-demographic and environmental factors relate with health is essential for public health and urban planning. However, t...
ML-supported decisions, such as ordering tests or determining preventive custody, often involve binary classification based on probabilistic forecas...
Type 2 Diabetes Mellitus (T2DM) remains a global health challenge, underscoring the need for early and accurate risk prediction. This study presents...
Chronic Kidney Disease (CKD) is a major global health issue which is affecting million people around the world and with increasing rate of mortality...
Systematic reviews are fundamental to evidence-based medicine. Creating one is time-consuming and labour-intensive, mainly due to the need to screen...