Public Health

Transforming clinical toxicology in Singapore: four decades of progress and beyond.

By examining key milestones, challenges and future directions, this review chronicles the evolution of clinical toxicology in Singapore into a recognised subspeciality and thriving community of practice. Poisoning trends have transitioned alongside socioeconomic changes from agricultural toxins to pharmaceuticals, substance misuse and prescription drugs. Currently, toxicology services have expande...

Modeling Zero-Dose Children in Ethiopia: A Machine Learning Perspective on Model Performance and Predictor Variables.

BACKGROUND: Despite progress in childhood vaccination, many children in low- and middle-income countries, including Ethiopia, remain unvaccinated, presenting a significant public health challenge. The Immunization Agenda 2030 (IA2030) seeks to halve the number of unvaccinated children by identifying at-risk populations, but effective strategies are limited. This study leverages machine learning (M...

Interpretable machine learning unveils non-linear inflammatory thresholds and synergistic interactions in post-burn hypertrophic scarring: development of an intelligent clinical decision support system.

Hypertrophic scarring (HS) following severe burns remains a persistent rehabilitative challenge, yet traditional linear prediction models fail to capt...

A novel, standardised approach to balancing effectiveness, efficiency and utility of surveillance AI prediction models for hospitalised patients using sepsis prediction as an exemplar.

OBJECTIVE: To introduce a novel, standardised approach to evaluating AI prediction models in balancing effectiveness, efficiency and utility, using a ...

Machine learning-driven identification of serotype-independent pneumococcal vaccine candidates using samples from human infection challenge studies.

Identifying conserved, immunogenic proteins that confer protection against Streptococcus pneumoniae (pneumococcus) colonization could enable developme...

Predicting nonresponse to sexual identity question in youth risk behavior surveillance: A machine learning analysis of complex survey data.

PURPOSE: To compare seven machine learning (ML) models developed to predict non-response to the sexual identity question in the 2023 Youth Risk Behavi...

How ready are we to use artificial intelligence in our fight against Antimicrobial resistance? An ESGAID and EAAS perspective.

INTRODUCTION: Antimicrobial resistance (AMR) remains one of the greatest threats to global health, requiring innovative approaches to antibiotic disco...

Incorporating artificial intelligence into imaging for surveillance and diagnosis of liver cancer: Innovations, challenges, and clinical translation.

Primary liver cancer, mainly hepatocellular carcinoma and intrahepatic cholangiocarcinoma, remains a leading cause of cancer mortality worldwide. Earl...

Computational framework and machine learning approach to fractional order soil helminth infections disease model for control mechanism.

Soil-transmitted helminth (STH) infections are exceedingly common, estimated to afflict 1.5 billion individuals worldwide, or 24% of the total populat...

The Real Cost in Context: Comparative Surveillance of E-Cigarette Prevention Campaigns on Twitter (2018-2023).

INTRODUCTION: E-cigarette prevention campaigns face visibility and sustainability challenges on social media, where provaping content dominates, and c...

Predicting antimicrobial resistance in Staphylococcus aureus using machine learning: Insights from a five-year surveillance study.

Staphylococcus aureus is a leading cause of both community- and hospital-acquired infections, and the growing prevalence of antimicrobial resistance c...

Highly efficient and durable Th1-biased protective immunity from a novel mRNA vaccine against RSV via AI-assisted antigen screening.

Respiratory syncytial virus (RSV) is a leading cause of severe respiratory illness, imposing a significant burden on global health and society. Here, ...

Interepidemic Rift Valley fever in East Africa: the recent risk landscape and projected impacts of global change.

Rift Valley fever (RVF) is a zoonotic disease that causes sporadic, multi-country epidemics. However, there is limited understanding of RVF virus circ...

Interdisciplinary expertise to advance human-centered explainable AI.

The field of artificial intelligence (AI) is rapidly influencing health and healthcare, but bias and inadequate subgroup performance persists. Previou...

Machine learning-driven nanoparticle-enhanced paper chromogenic array sensor approach for detecting sub-lethally injured Salmonella in low moisture food.

Detection of sub-lethally injured pathogens is critical for improving food safety, particularly given regulatory recommendations that microbiological ...

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 ...

Strengthening funding and integration for NTD elimination: insights from the 2025 Annual Meeting of National NTD Programme Managers in the WHO African Region.

The 2025 Annual Meeting of National Neglected Tropical Disease (NTD) Programme Managers (2025 PMM) in the WHO African Region convened stakeholders in ...

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 ...

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...