Bioterrorism

From Research to Impact: Assessing a Decade of CDC’s Public Health Science by Topic Area, 2014-2023

This study provides an objective, in-depth overview of a large body of science output addressing public health. We apply topic modeling and bibliometric tools to explore the relevance and impact of a decade of CDC-authored publications. We identified 34,104 scientific publications from 2014-2023 with ≥1 CDC-affiliated author using Science Clips, a CDC library database. We applied a large language ...

A simple feed forward neural network to predict the 2025 outbreak of measles in the USA

Measles is a highly contagious viral disease associated with a variety of severe complications. Since 1963, widespread usage of a highly effective vaccine has made measles a largely preventable disease. However, recent rises in vaccine hesitancy in the United States has seen increasing incidence of measles cases, including an ongoing (22nd March 2025) outbreak originating in Gaines County, Texas. ...

ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL METHODS FOR MODELLING AND FORECASTING INFLUENZA AND INFLUENZA-LIKE ILLNESS: A SCOPING REVIEW

The persistnt resurgence of influence and influenza-like illness despite concerted vaccination interventions is a global health burden, thus necessita...

Leveraging probabilistic forecasts for dengue preparedness and control: the 2024 Dengue Forecasting Sprint in Brazil

Forecast models are a key decision-support tool for public health authorities in managing epi- demics, feeding into early warning systems, scenario ev...

AI-detected tumor-infiltrating lymphocytes for predicting outcomes in anti-PD1 based treated melanoma

Easy and accessible biomarkers to predict response to immune checkpoint inhibition (ICI)-treated melanoma are limited. To evaluate artificial intellig...

Machine Learning-Enabled EEG Biomarkers Predict Divergent Antidepressant and Placebo Response in a Clinical Trial of Major Depression

Major depressive disorder (MDD) is a heterogeneous neuropsychiatric disorder with highly variable antidepressant outcomes. In randomized controlled tr...

Reproducible Generative AI Evaluation for Healthcare: A Clinician-in-the-Loop Approach

To develop and apply a reproducible methodology for evaluating generative artificial intelligence powered systems in healthcare, addressing the gap be...

Leveraging Machine Learning and Clinical Data to Predict Response to Intralesional Corticosteroids in Keloid Patients

Intralesional corticosteroid injections (ILCS) are a common treatment for keloid lesions; however, many patients exhibit resistance, and some experien...

Transcriptomics-Driven Machine Learning Models Accurately Predict Chemotherapy Response in Muscle-invasive Bladder Cancer

Muscle-invasive bladder cancer (MIBC) is associated with poor predictability of response to cisplatin-based neoadjuvant chemotherapy (NAC). Consequent...

AI Implementation in U.S. Healthcare and Its Association With Elder Mortality and Quality of Care

Hospitals are increasingly adopting artificial intelligence (AI) tools in clinical care. However, their overall impact on the health of older adults r...

Dengue forecasting and outbreak detection in Brazil using LSTM: integrating human mobility and climate factors

Dengue fever is a major global health concern, with Brazil experiencing recurrent and severe outbreaks due to its favorable climate factors, socio-env...

Diagnostic accuracy of a high-throughput multiplex immunoassay for the detection of Mpox virus infection and MVA-BN vaccination up to two years after exposure

Mpox, caused by mpox virus (MPXV), has gained global attention following the 2022 Clade IIb outbreak and the emergence of two novel Clade I lineages i...

Enhancing Pandemic Prediction: A Deep Learning Approach Using Transformer Neural Networks and Multi-Source Data Fusion for Infectious Disease Forecasting

The Covid-19 pandemic has highlighted the urgent need for accurate prediction of pandemic trends. We propose a deep learning model for predicting Covi...

Enhancing Fairness in Diabetes Prediction Systems through Smart User Interface Design

Artificial intelligence (AI) in chronic disease prediction often exhibits algorithmic biases, hindering equitable healthcare delivery. This study aims...

Evaluating the accuracy and consistency of ChatGPT for the management of type 2 diabetes: A cross-sectional study

Large language models (LLMs) have fundamentally changed how patients and clinicians retrieve information; however, it is unclear how accurate and cons...

Associations Between Meteorological Factors and Influenza A/B Incidence in Subtropical China: A Six-Year Surveillance Study with Deep Learning Modelling for Influenza Early Warning

Influenza burden in subtropical regions like southeastern China is shaped by meteorological factors-driven complex transmission patterns that differ f...

Resting-State Functional Connectivity of the Fronto-Limbic and Default Mode Networks as Predictors of Antidepressant Response in Major Depressive Disorder

Major depressive disorder (MDD) is a leading cause of disability worldwide, yet treatment response to antidepressants remains highly variable, with a ...

Evaluating Feature Selection Methods and Feature Contributions for Cardiovascular Disease Risk Prediction

Cardiovascular disease (CVD) remains the foremost contributor to global illness and death, underscoring the critical need for effective tools that can...

Evaluating Large Language Model Diagnostic Performance on JAMA Clinical Challenges via a Multi-Agent Conversational Framework

Standard clinical LLM benchmarks use multiple-choice vignettes that present all information up front, unlike real encounters where clinicians iterativ...

Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer

Deep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to ...