Public Health

Addressing Climate Change Impacts on Vector-Borne Infectious Diseases: Can Artificial Intelligence Help?

Human-induced climate change caused by fossil fuel-derived greenhouse gas emissions has disrupted global ecosystems, shifted disease-vector populations, and expanded vector-borne diseases into previously unaffected areas. As these changes occur, traditional epidemiological surveillance and control mechanisms face major challenges. Artificial intelligence (AI) is an emerging technology in epidemiol...

Comparing Generative Artificial Intelligence vs Experts for Detection of Catheter-Associated Urinary Tract Infection.

A retrospective cohort study compared generative artificial intelligence (GenAI) versus infection control expert for catheter-associated urinary tract infection (CAUTI) detection. Sensitivity was 95.2% (20/21), and specificity was 76.2% (16/21), improving to 90% with expert review of GenAI output. GenAI is a promising tool to assist in CAUTI surveillance.

Exploring the toxicological impact of N-nitrosodimethylamine (NDMA) exposure on bladder urothelial carcinoma (BLCA) through network toxicology, machine learning, and multi-dimensional bioinformatics analysis.

BACKGROUND: N-Nitrosodimethylamine (NDMA), classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC), is ubiquitous...

Synthetic health data in Canada: A scoping review of methods, applications, and data sources.

BACKGROUND: Access to provincial health-related data for multi-jurisdictional studies in Canada is restricted by privacy laws. Synthetic data (SD), wh...

Targeting the MPO/LCN2/GMPPB axis in IBS-depression comorbidity: Integrated multi-omics and bidirectional network pharmacology for precision diagnostics and therapeutics.

BACKGROUND: Irritable Bowel Syndrome (IBS) and Major Depressive Disorder (MDD) exhibit high comorbidity, driven by dysregulation of gut-brain axis int...

Improving drug identification in overdose death surveillance by using clinical natural language processing models.

The rising rate of drug-related deaths in the United States, largely driven by fentanyl, requires timely and accurate surveillance. However, critical ...

Machine learning for detection of subclinical mastitis: A Bayesian approach incorporating diagnostic test properties.

The large amount of data collected through automatic milking systems (AMS) may be used for early detection of intramammary infections and become instr...

Update on Non-Biological and RNA-Based Therapeutics in Chronic Inflammatory Diseases: Precision Medicine Through Small Molecules.

In the last decades, critical advancements in research technology and knowledge on disease mechanisms steered therapeutic approaches for chronic infla...

Commercial AI platforms improve reproducibility but fail to meet clinical precision thresholds for intracranial aneurysm measurement: implications for serial surveillance.

BACKGROUND: Intracranial aneurysm affect 3-7% of the global population, with rupture causing > 80% of non-traumatic subarachnoid hemorrhage and approx...

Machine learning model predicts prognosis in SARS-CoV-2-infected autoimmune encephalitis patients.

OBJECTIVE: To assess the neurological prognosis of patients with autoimmune encephalitis (AE) after severe acute respiratory syndrome coronavirus 2 (S...

A lightweight convolutional neural network architecture for violence detection in video sequences.

The escalation of violent incidents in high-density public environments such as political assemblies, concerts, and sports arenas necessitates the dev...

Factors influencing long-term outcomes in lung transplantation: effective monitoring and comprehensive intervention.

PURPOSE OF REVIEW: Achieving long-term survival after lung transplantation remains a major challenge. Outcome determinants have expanded beyond pathop...

Improving pandemic prediction: integrating Physics-Informed Neural Networks and symbolic regression for COVID-19 modeling.

This study refines the SIDARTHE model for Italy's COVID-19 outbreak using a hybrid, data-driven framework. A two-stage approach compares Maximum Likel...

Controversies in Computer-Assisted Detection in Colonoscopy.

BACKGROUND: Artificial intelligence (AI) applications in endoscopy, particularly computer-aided detection (CADe), have shown consistent benefit in ran...

Ethical Risks and Structural Implications of AI-Mediated Medical Interpreting.

Artificial intelligence (AI) is increasingly used to support medical interpreting and public health communication, yet current systems introduce serio...

Impacts of delayed influenza vaccination on clinical outcomes in ICU-admitted patients with influenza: A retrospective cohort study.

BACKGROUND: Influenza vaccination is an effective measure for reducing the risk of severe influenza infection. However, the task of achieving adequate...

Integrated single-cell analyses of affinity-tested B cells enable the identification of a gene signature to predict antibody affinity.

Advancements in single-cell technologies and deep sequencing have expanded the B cell repertoire available for antibody discovery. However, selecting ...

Prognostic Significance of Lymph Node Ratio for Overall Survival in Patients with Lymph Node-Positive Bladder Cancer Post-Radical Cystectomy and Chemotherapy: A Multicenter Cohort Analysis.

BACKGROUND: The lymph node ratio (LNR) is gaining recognition as a prognostic biomarker for various malignant neoplasms. However, its prognostic role ...

Predicting genetic evolution of viruses to identify suitable vaccines using artificial intelligence.

The evolution of the viruses is rapidly becoming a global challenge to the creation of vaccines since the new variants are often capable of escaping t...

Forecasting Local Surges in COVID-19 Hospitalizations through Adaptive Decision Tree Classifiers.

IntroductionDuring the COVID-19 pandemic, many communities across the United States experienced surges in hospitalizations, which strained the local h...