Public Health

Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.

Acinetobacter baumannii, particularly in its multidrug-resistant (MDR) and carbapenem-resistant (CRAB) forms, has become a major global health concern due to its ability to survive in hospital environments, acquire resistance rapidly, and cause severe infections with high mortality. Conventional diagnostic and therapeutic strategies remain slow, imprecise, and difficult to implement, especially in...

Geospatial insights and artificial intelligence in West Nile virus disease Epidemiology: The Italy case study.

OBJECTIVES: Although artificial intelligence, including machine learning offers transformative potential, its use in infectious disease epidemiology has been understudied. Emerging infectious diseases including WNV infection remains a leading cause of mortality globally. The study aimed to provide a decade-long insight into the global trends on WNV outbreaks. STUDY DESIGN: The study offers a ML-po...

Hybrid Modeling of Cercospora Leaf Spot Epidemiology: Integrating Mechanistic and Machine Learning Approaches Using Remote-Sensing and Environmental Data.

Despite advances in modeling and sensing, no study has previously integrated mechanistic, meteorological, and uncrewed aerial vehicle (UAV) data into ...

Modernizing public health surveillance for global health security leveraging AI.

An electronic public health surveillance (e-PHS) embracing One Health and participatory approaches will collect and analyze data at the human-animal-e...

Predicting skeletal fluorosis severity using machine learning across diverse fluoride-exposed populations in China.

Skeletal fluorosis (SF) is a chronic metabolic bone disease resulting from long-term excessive fluoride exposure, affecting millions worldwide. Conven...

Characterising responses in group-housed pigs to Salmonella typhimurium infection through integrated computer vision-based behavioural monitoring and statistical analyses.

BACKGROUND: Health monitoring is crucial for early disease detection and prompt intervention to mitigate the disease. Computer vision is one of the no...

Artificial Intelligence and the Future of Healthcare-Associated Infection Surveillance: Bridging Precision, Partnership, Practice, and People.

Using Generative Artificial Intelligence for Healthcare-Associated Infection Surveillance.

Generative artificial intelligence (GenAI) is becoming widespread in society but has had limited impact on medicine. GenAI can rapidly digest large do...

Longitudinal prediction of BMI using explainable AI: integrating polygenic scores, maternal, early-life and familial factors.

BACKGROUND/OBJECTIVES: This study aimed to predict body mass index (BMI) trajectories from childhood to early adulthood using explainable artificial i...

A pan-European assessment of multi-sector drivers of human hantavirus risk: climate, biodiversity, and socio-economic factors as key determinants.

The landscape of emerging zoonoses is being rapidly reshaped by concurrent climate change, environmental transformation, and biodiversity loss. These ...

Mining antibody functionality via AI-guided structural landscape profiling.

Despite substantial progress in single-cell screening techniques, antibody (Ab) repertoires still remain enigmatic. Here we show that Ab sequences can...

Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning.

Toward AI foundation models for epidemics: Promise, challenges, and paths forward.

Foundation models-large AI systems pretrained on broad, heterogeneous data-are transforming scientific discovery. These models (e.g., GPT, GenCast, Al...

Machine learning and the role of the vaginal and fecal microbiome in miscarriage: a matched case-control study.

Miscarriage occurs in approximately 15% of all pregnancies, and recent studies have suggested a potential role of the microbiome. A nested case-contro...

Colonoscopy surveillance in Lynch syndrome: what it prevents and what it does not.

Lynch syndrome (LS), synonymous with hereditary non-polyposis colorectal cancer (HNPCC), is caused by germline pathogenic variants in MLH1, MSH2, MSH6...

A comparative study of classical and neural network based optimal control in dengue transmission dynamics.

This study develops and analyses a mathematical model that represents dengue transmission between human and mosquito populations. The human population...

A scalable framework for population-level precision health: Insights from sleep disorders.

Assessing population-level risk patterns of sleep disorders is challenging. Many studies rely on hospital-based samples, which can be biased toward in...

Advancements in Screening Strategies for Early-Onset Colorectal Cancer (EOCRC).

Screening for early-onset colorectal cancer is a growing public health concern, driven by a rising incidence in younger adults. As trends shift, scree...

Trends, Antimicrobial Resistance, and Source Attribution of Campylobacter jejuni and C. coli in Beijing: An 8-Year Molecular Surveillance Study.

OBJECTIVES: Beijing, a large urban center with complex food supply chains, faces increasing challenges from foodborne Campylobacter infections. Howeve...

Integrating explainable AI and One Health: a new frontier in combating infectious diseases.

Infectious diseases (IDs) remain a major threat to global health and societal stability. Because most emerging IDs in humans are zoonotic in origin an...