Latest AI and machine learning research in public health for healthcare professionals.
Mathematical modeling has become an indispensable tool for understanding, predicting, and controlling the spread of infectious diseases. Over the years, a wide variety of models have been developed to analyze disease dynamics and forecast epidemic trajectories. Deterministic and stochastic frameworks provide quantitative insights into transmission mechanisms and allow for rigorous evaluation of pu...
The globally distributed zoonotic protozoan parasite Toxoplasma gondii presents escalating conservation and public health challenges through expanding wildlife reservoir infections. This systematic review with meta-analysis, integrated with machine learning approaches (Random Forest and SHAP analysis), aims to quantify aggregated prevalence, identify key epidemiological drivers, and map infection ...
The malnutrition-based chronic disease (MBCD) model for use in adults to optimize nutrition care is a framework based on epidemiological classifiers, ...
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with approximately 25-30 % of cases exhibiting a familial compo...
Artificial intelligence (AI) is rapidly reshaping the landscape of health care, from clinical diagnostics and disease surveillance to the prediction o...
Artificial Intelligence (AI) has the potential to revolutionize biosecurity, health security, biodefense, and pandemic preparedness by offering ground...
Ease of access to big data and automated analysis tools can facilitate the rapid generation of poorly designed epidemiological studies, which collecti...
AIMS/HYPOTHESIS: Data-driven subtyping of type 2 diabetes has not been translated into clinical practice due to the lack of routine fasting glucose an...
Emerging research links the gut, skin, and oral microbiomes to allergies, with serine proteases (SPs) identified as potential allergens. This study le...
Air pollution (AP), intensified by industrialization and urbanization, is a key environmental factor linked to rheumatoid arthritis (RA). However, its...
OBJECTIVES: This study identifies predictors of severe COVID-19 following completion of two-dose primary series of the AZD1222 COVID-19 vaccine, emplo...
INTRODUCTION: Active post-vaccination surveillance is vital for ensuring vaccine safety, particularly in monitoring Adverse Events of Special Interest...
Cancer immunotherapy is increasingly moving toward personalized, precision-based strategies, with cancer vaccines emerging as a promising approach to ...
BACKGROUND: The exponential growth of medical data and advancements in artificial intelligence (AI) have accelerated the development of data-driven he...
Introduction. Misinformation is a barrier to immunization. The objective was to describe and categorize vaccine-related myths reported by healthcare p...
Rapid and effective decision-making is critical in public health emergencies, where resource allocation must balance multiple objectives under uncerta...
Event-based surveillance systems have proven critical for early detection of disease outbreaks by analyzing informal data sources, and the integration...
IMPORTANCE: Depression most commonly first emerges during adolescence, making early prevention critical. While school-based mindfulness training (SBMT...
BACKGROUND: The convergence of digital health and One Health represents an emergent paradigm in global health governance. While widely discussed in hi...
BACKGROUND: The growing use of artificial intelligence (AI) chatbots for seeking health-related information is concerning, as they were not originally...