Surveillance

Machine learning-based prediction of antibiotic resistance gene distribution in agricultural soils under different climate change scenarios.

Antibiotic resistance genes (ARGs) in agricultural soils represent a major public health concern, as climate change is believed to augment their dissemination and abundance. Understanding the impact of future climate change scenarios on ARG abundance is essential to implement predictive and proactive One Health strategies. In this study, a total of 2301 soil samples from 67 countries across six co...

Systematic review of foundation models for structured electronic health records.

PURPOSE: Foundation models pretrained on structured electronic health record (EHR) data promise improved predictive performance, sample efficiency and resilience to distribution shifts. However, model design, scale and use remain unclear. Objectives were to characterize foundation models pretrained on structured EHR data; examine temporal trends in model application and scale, architecture and des...

Leveraging clinical epidemiology concepts to strengthen machine learning fairness evaluations.

OBJECTIVES: The increasing use of machine learning (ML) in clinical care makes fairness a central issue. Fairness, defined as the absence of dispariti...

Automating infection indicator extraction in home healthcare through instruction-tuned large language models.

OBJECTIVE: Home healthcare (HHC) clinical notes contain critical infection indicators that clinicians need in structured "indicator + context" pairs. ...

Preparing Midwives for the Digital Future: A Qualitative Study on Academic Midwives' Perspectives on Artificial Intelligence in Education.

AIM: The aim of this study was to explore the perspectives of midwifery academics regarding the integration of Artificial Intelligence (AI) into midwi...

Undergraduate medical students and artificial intelligence: A global systematic review and meta-analysis using a knowledge-attitude-practice framework.

BACKGROUND: Artificial intelligence (AI), including deep learning and large language models, is increasingly reshaping healthcare and medical educatio...

A cannabis use Reddit dataset for aspect-based sentiment analysis.

Using publicly accessible Reddit posts, we developed a manually annotated dataset for traditional and aspect-based sentiment analysis (ABSA) of cannab...

Development and External Validation of a Large Language Model-Based Clinical Decision Support System for Colonoscopy Surveillance Intervals.

BACKGROUND: Adherence to guideline-based colonoscopy surveillance intervals remains suboptimal. Large language models (LLMs) show promise for automati...

Does lens opacity matter? The effect of cataract on deep learning based cardiovascular disease risk scores from fundus photos.

PURPOSE: To investigate the effect of cataracts on a deep learning (DL) model for cardiovascular disease (CVD) risk prediction. METHODS: This retrospe...

Digital and technology-enabled approaches in dietary assessment: addressing bias, error, and feasibility in population- and community-based research.

Dietary intake data are essential for understanding diet-disease relationships, informing policy, and evaluating nutrition interventions. This is part...

Analyzing Global Threat of Lumpy Skin Disease Virus Infection: A Comparative Study of a Time Series Model and Machine Learning Models Across 10 High-Risk Countries.

Lumpy Skin Disease (LSD), caused by the Lumpy Skin Disease Virus (LSDV), which is a part of the family of Poxviridae, subfamily Chordopoxviridae, and ...

Machine-learning prediction of 30-day infection-related hospitalization in advanced CKD.

BACKGROUND: Patients with advanced chronic kidney disease (CKD), defined as having an estimated glomerular filtration rate (eGFR) < 45 ml/min/1.73 m²,...

Development and external validation of multiple machine learning-based models for breast cancer-specific survival prediction in postoperative patients with invasive breast cancer: a study based on the SEER database and an external cohort.

BACKGROUND: Patients with invasive breast cancer (IBC) account for the vast majority of breast cancer cases and exhibit significant heterogeneity; hen...

Automated evidence surveillance with AI-enabled preranking with cutoff in living guideline maintenance: a simulation study.

OBJECTIVES: Living guidelines are an emerging approach to ensure timely synthesis of the research evidence. However, pragmatic methods for maintenance...

Digital epidemiology investments, Saudi Arabia.

PROBLEM: Traditional epidemiological surveillance methods are often limited by delays in reporting and fragmented data systems. Saudi Arabia faces add...

Shift-Specific Patterns of Nursing Workloads in the Emergency Department: AI Powered Analysis.

AIM: To identify and differentiate workload patterns across shifts and to provide evidence for optimizing nursing workforce allocation in emergency de...

Advances in rapid microbiological testing for animal diseases: A review.

Animal pathogenic microorganisms destabilize livestock economies and jeopardize human health through zoonotic transmission and foodborne illness. Trad...

Fluid-based biomarkers of amyotrophic lateral sclerosis: recent advances and future prospects.

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder with no definitive cure. The absence of specific diagnostic biomarkers...

AI-microbial hybrid biosensors: the next generation of intelligent detection systems.

The convergence of artificial intelligence (AI) and microbial biosensor technology is transforming pathogen detection, environmental surveillance, ant...

Compliance with the CONSORT AI Extension in Orthopaedic Randomized Controlled Trials.

BACKGROUND: The utilization of artificial intelligence (AI), such as machine learning, in health care has risen exponentially, underscoring the necess...