Latest AI and machine learning research in public health for healthcare professionals.
Accurately attributing causes of death is vital for global health, yet fewer than 5% of deaths in resource-constrained regions are medically certified. To assign causes to these unlabeled deaths at scale, practitioners traditionally rely on verbal autopsy, using supervised statistical models to classify based on structured survey data. However, modern mortality surveillance increasingly collects r...
Abstract Background Despite a rising global psychiatric burden, a treatment gap persists where the majority of symptomatic individuals remain unmedicated. Traditional epidemiological analyses treat this untreated population as a single, uniform block, obscuring specific barriers to care. This study uses an unsupervised machine learning pipeline to identify distinct socio-behavioural and biological...
Active surveillance (AS) is the preferred strategy for favorable-risk prostate cancer, yet current protocols rely on scheduled repeat biopsies, most o...
Long COVID (LC) affects millions of individuals worldwide, particularly those with preexisting comorbidities. However, whether these comorbidities sho...
Abstract Dengue, malaria, and yellow fever remain major mosquito-borne public health threats in tropical and subtropical regions. Yet most global spat...
Background Quantifying the effect of Shigella diarrhea with and without antibiotic treatment on linear growth faltering is critical to understanding t...
Anthropogenic climate change is reshuffling global biodiversity and accelerating zoonotic spillover at the human-wildlife interface. The 2026 multi-co...
Introduction: Climate change disproportionately affects disadvantaged communities, yet construction workforce education rarely addresses interconnecte...
Vaccination is one of the most effective public health interventions. However, vaccine efficacy varies widely among individuals, as immunity arises fr...
Low-count Positron Emission Tomography (PET) reconstruction is severely hindered by the dissipative nature of prevailing generative models, where the ...
Background Timely assessment, classification, and escalation of public health events are essential for effective outbreak response, yet decision-makin...
Diarrheal disease remains a significant cause of morbidity and mortality in children under five years of age in low and middle-income countries. Ident...
Conventional mosquito surveillance typically relies on contemporaneous data, making it challenging to anticipate future vector surges. To support proa...
Public microbial genomes encode an immense record of biological diversity, evolution and molecular function, but much of this information remains diff...
Images acquired in surveillance environments often suffer from conditions such as low resolution, variations in pose, irregular illumination, and occl...
Background Early outbreak detection often depends on complex, data-intensive models that have limited operational use in sparse surveillance settings....
Abstract Objective Wrist-worn accelerometers are common in large-scale epidemiological studies, but their ability to measure sedentary behaviour in fr...
Advancements in immunogenomics and immuno-oncology have enabled the development of personalized cancer vaccines (PCVs) that target cancer cell-specifi...
Outbreak transmission reconstruction treats epidemiological timing and transmission labels as deterministic ground truth; neither has been systematica...
Text-to-image person re-identification (TIPR) retrieves target persons using natural language descriptions. However, existing methods largely overlook...