Public Health

Machine Learning forecasting of dengue in São Paulo using virtual data augmentation and urban incident predictors: Addressing the exceptional surge of cases in 2024.

Dengue remains one of the most critical vector-borne diseases globally, with Brazil consistently reporting the highest incidence in the Americas. The 2024 epidemic was the largest in the country's history, with São Paulo as its epicenter. This study developed and evaluated a machine learning (ML) framework to forecast dengue under extreme epidemic conditions, focusing on enhancing model robustness...

The escalating threat of multidrug-resistant organisms: COVID-19 impact, global burden, and the Taiwanese experience.

INTRODUCTION: The COVID-19 pandemic has intensified global health challenges, including a silent but escalating crisis: multidrug-resistant organisms (MDROs). As healthcare systems strained under viral outbreaks, infection control and antimicrobial stewardship efforts suffered, accelerating the spread of antimicrobial resistance (AMR). AREAS COVERED: This review examines the indirect effects of th...

Trust at risk: Why public health must lead the use of AI in pandemic preparedness.

Artificial intelligence (AI) is poised to play a transformative role in pandemic preparedness, with the potential to enhance surveillance, risk assess...

Leveraging a large language model to support expansion of surveillance activities to include cardiovascular implantable device infections in a large, integrated national healthcare system.

BACKGROUND: Surveillance activities are emerging as exemplar use cases for large language models (LLMs) in health care. The aim of this study was to e...

Multidimensional analysis and predictive modeling of cognitive decline risk in the United States using propensity score matching and machine learning.

Cognitive decline, an early indicator of neurodegenerative disorders, presents a growing public health challenge. This study aimed to integrate causal...

A review of AI/ML approaches in wastewater surveillance advancement.

Wastewater-based epidemiology (WBE) has emerged as a powerful tool for early detection and monitoring of infectious diseases, particularly during pand...

Using Artificial Intelligence Methods to Evaluate the Effect of the National Cytomegalovirus Awareness Month on the Content and Sentiment of Social Media Posts: Infodemiology Study.

BACKGROUND: The month of June has been recognized as the National Cytomegalovirus (CMV) Awareness Month since 2011 in the United States. Established b...

Evolving strategies may facilitate oral pathology and cancer surveillance, monitoring, and detection for patients at risk.

BACKGROUND: Oral cancers account for considerable morbidity and mortality worldwide, and their incidence has increased in the past several years, desp...

Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.

Vancomycin is a critical glycopeptide antibiotic for treating severe infections caused by Gram-positive bacteria, particularly MRSA and Clostridioides...

Large Language Models Improve Scene Invariant Detection of Behaviours of Risk in Dementia Residential Care Across Multiple Surveillance Camera Views.

Behavioural and psychological symptoms of dementia pose challenges to the safety and well-being of individuals in residential care. The integration of...

Personalized surveillance in giant congenital melanocytic nevus, including the role of AI, histopathology and MC1R genotyping: A case report.

Giant congenital melanocytic nevi (GCMN) are rare pigmented skin lesions present at birth that carry an increased risk of malignant melanoma and neuro...

Microclimates, land cover, and socioeconomic vulnerability shape Anopheles hotspots in Maryland, USA.

BACKGROUND: Anopheles mosquitoes pose notable public health concerns as competent vectors of malaria and other diseases. Although malaria is no longer...

The use of artificial intelligence in decreasing vaccine skepticism and hesitancy.

PURPOSE OF REVIEW: Recent advances in artificial intelligence (AI) coincide with decreases in confidence in vaccines. This review examines studies tha...

Using artificial intelligence to advance pediatric infection prevention and control.

PURPOSE OF REVIEW: Recent literature describes the deployment of different artificial intelligence (AI) technologies to potentially support infection ...

Liver cancer risk stratification using deep learning on nationwide longitudinal health screening data: a retrospective cohort study.

BACKGROUND: Current liver cancer screening in Korea focuses on viral hepatitis or cirrhosis, despite rising risks from metabolic and alcohol-related l...

Charting exposomethics: a roadmap for the ethical foundations of the human exposome project.

BACKGROUND: The Human Exposome Project (HEP) aims to chart lifelong environmental exposures and their biological consequences, furnishing the environm...

A novel hypothetical protein (SAUSA300_1684) confers excellent protection against multi-drug-resistant Staphylococcus aureus infection in the murine model.

Staphylococcus aureus is a leading cause of nosocomial infections, including sepsis, bacteraemia, pneumonia, and endocarditis, and continues to pose a...

Using Latent Dirichlet Allocation Topic Modeling to Uncover Latent Research Topics and Trends in Renal Cell Carcinoma: Bibliometric Review.

BACKGROUND: Renal cell carcinoma (RCC) is a common, often lethal kidney cancer that originates in the renal cortex. Its incidence is rising, and major...

Predictive modeling and spatiotemporal analysis of TB in Argentina: Advancing control efforts through machine learning.

OBJECTIVES: To improve prediction and understanding of TB dynamics in Argentina, identifying key risk factors and high-incidence areas to inform surve...

Machine learning-based typing of Clostridium botulinum group III by FT-IR spectroscopy.

This study aimed to investigate the utility of Fourier-Transform Infrared Spectroscopy (FT-IRS) for differentiating Clostridium botulinum (C. botulinu...