Public Health

Artificial intelligence for modelling infectious disease epidemics.

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease m...

Redefining prostate cancer care: innovations and future directions in active surveillance.

PURPOSE OF REVIEW: This review provides a critical analysis of recent advancements in active surveillance (AS), emphasizing updates from major international guidelines and their implications for clinical practice.

[Digital innovations in vaccination communication].

Despite the significant success of vaccinations, increasing vaccine hesitancy poses a threat to public health, making effective vaccination communicat...

Machine-learning approaches to predict individualized treatment effect using a randomized controlled trial.

Recent advancements in machine learning (ML) for analyzing heterogeneous treatment effects (HTE) are gaining prominence within the medical and epidemi...

Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning.

Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management, yet existing methods are often context-specific...

Of Lyme disease and machine learning in a One Health world.

OBJECTIVE: Lyme disease is a vector-borne emerging zoonosis in Ontario driven by human population growth and climate change. Lyme disease is also a pr...

Understanding Citizens' Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI.

BACKGROUND: The COVID-19 pandemic continues to hold an important place in the collective memory as of 2024. As of March 2024, >676 million cases, 6 mi...

SHASI-ML: a machine learning-based approach for immunogenicity prediction in vaccine development.

INTRODUCTION: Accurate prediction of immunogenic proteins is crucial for vaccine development and understanding host-pathogen interactions in bacterial...

Machine learning-enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta.

The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential...

Application of artificial intelligence and machine learning in bovine respiratory disease prevention, diagnosis, and classification.

Bovine respiratory disease (BRD) is the leading infectious disease in cattle, resulting in significant economic losses and welfare concerns in beef an...

Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media.

OBJECTIVE: Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of imm...

A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data.

Accurate, real-time forecasts of influenza hospitalizations would facilitate prospective resource allocation and public health preparedness. State-of-...

Using ChatGPT for medical education: the technical perspective.

BACKGROUND: The chatbot application Bennie and the Chats was introduced due to the outbreak of COVID-19, which is aimed to provide substitution for te...

Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study.

BACKGROUND: Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availabi...

Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data.

Regarding the transportation of people, commodities, and other items, aeroplanes are an essential need for society. Despite the generally low danger a...

Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists' Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study.

BACKGROUND: The COVID-19 pandemic has significantly strained health care systems globally, leading to an overwhelming influx of patients and exacerbat...

Evaluation of the potential value of artificial intelligence (AI) in public health using fluoride intake as the example.

AIM: We aimed to test whether and how ChatGPT understood the epidemiological problems related to fluoride intake and whether ChatGPT could produce nov...

Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak.

BACKGROUND: Controlling re-emerging outbreaks such as COVID-19 is a critical concern to global health. Disease forecasting solutions are extremely ben...

CLEAR: Multimodal Human Activity Recognition via Contrastive Learning Based Feature Extraction Refinement.

Human activity recognition (HAR) has become a crucial research area for many applications, such as Healthcare, surveillance, etc. With the development...

Synergistic modeling of hemorrhagic dengue fever: Passive immunity dynamics and time-delay neural network analysis.

Dengue fever poses a formidable epidemiological challenge, particularly for vulnerable groups such as infants. This research paper establishes a mathe...