Latest AI and machine learning research in public health & policy for healthcare professionals.
Ease of access to big data and automated analysis tools can facilitate the rapid generation of poorly designed epidemiological studies, which collectively pose a risk to the quality of medical literature. Member organizations of the TriNetX network have the ability to mass-produce retrospective cohort studies at speed using the federated data network's statistical power and streamlined analytics p...
PURPOSE: Ensemble machine learning (ML) demonstrated potential for improving predictions based on big health care data. We developed and validated interpretable ensemble ML models in evaluating the nonadherence and nonpersistence of biological or targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA). METHODS: This retrospective study used 5% Medicare cl...
Air pollution (AP), intensified by industrialization and urbanization, is a key environmental factor linked to rheumatoid arthritis (RA). However, its...
Digital dermatology, which is defined as the use of digital technologies that leverage individual- and population-level skin data to improve the diagn...
CONTEXT: Obesity is an independent risk factor for chronic kidney disease, and accurate estimation of the glomerular filtration rate (GFR) is crucial....
BACKGROUND: Cardiovascular disease prevention relies on accurate risk assessment; however, existing scores are imprecise. Routine imaging may be oppor...
INTRODUCTION: Active post-vaccination surveillance is vital for ensuring vaccine safety, particularly in monitoring Adverse Events of Special Interest...
The algal-bacterial symbiotic communities within the submerged macrophyte phyllosphere exhibit significant potential for lake restoration. However, th...
BACKGROUND: The illegal use of opioids has emerged as a major global public health concern, contributing to widespread addiction and a growing number ...
A key challenge in medical decision making is learning treatment policies for patients with limited observational data. This challenge is particularly...
BACKGROUND: Despite the growing use of digital platforms for sexual health education, many tools fail to meet the needs of LGBTQ+ (lesbian, gay, bisex...
BACKGROUND: The exponential growth of medical data and advancements in artificial intelligence (AI) have accelerated the development of data-driven he...
Rapid and effective decision-making is critical in public health emergencies, where resource allocation must balance multiple objectives under uncerta...
BACKGROUND: Scurvy persists as a significant adult medical condition, frequently overlooked due to its mimicry of more common diseases. This review ai...
Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality in Africa, accounting for over 1 million deaths annually. As CVD preva...
Methane production from wastewater sludge via anaerobic digestion is a complex process and a disturbance in any one of the microbial stages can lead t...
BACKGROUND: High naevus counts and ultraviolet photodamage are strong risk factors for melanoma. However, whole-of-body measures fail to capture varia...
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...
In 2018, Medicare established coverage and reimbursement for its first service using artificial intelligence (AI): computed tomography (CT) fractional...