Public Health & Policy

Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.

Accurate detection and prevalence estimation of behavioral health conditions, such as opioid use disorder (OUD), are crucial for identifying at-risk individuals, determining treatment needs, monitoring prevention and intervention efforts, and recruiting treatment-naive participants for clinical trials. The availability of extensive health data, combined with advancements in machine learning (ML) f...

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 and dairy production systems. Traditional diagnostic methods for BRD typically rely on clinical observations and diagnostic laboratory tests, which can be time consuming with moderate diagnostic sensitivity. In recent years, machine learning (ML) and A...

Machine learning predicts selected cat diseases using insurance data amid challenges in interpretability.

OBJECTIVE: To develop models for prediction of the onset of specific diseases in cats using pet insurance data and to evaluate their predictive perfor...

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-...

Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review.

BACKGROUND: Low back pain is the leading cause of disability worldwide with a significant socioeconomic burden; artificial intelligence (AI) has prove...

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...

REMED-T2D: A robust ensemble learning model for early detection of type 2 diabetes using healthcare dataset.

Early diagnosis and timely treatment of diabetes are critical for effective disease management and the prevention of complications. Undiagnosed diabet...

Advances in the application of human-machine collaboration in healthcare: insights from China.

In the context of the technological revolution and the digital intelligence era, the contradiction between the rising incidence of diseases and the un...

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...

Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the forme...

Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning.

In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular fat deposition during cattle fattening stage throug...

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...

Artificial Intelligence in Ischemic Heart Disease Prevention.

PURPOSE OF REVIEW: This review discusses the transformative potential of artificial intelligence (AI) in ischemic heart disease (IHD) prevention. It e...

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...

Machine Learning-Based Risk Factor Analysis and Prediction Model Construction for the Occurrence of Chronic Heart Failure: Health Ecologic Study.

BACKGROUND: Chronic heart failure (CHF) is a serious threat to human health, with high morbidity and mortality rates, imposing a heavy burden on the h...

Risk factor assessment of prediabetes and diabetes based on epidemic characteristics in new urban areas: a retrospective and a machine learning study.

To explore in depth the characteristics of the risk factors for diabetes and prediabetes pathogenesis and progression in special regions. We investiga...

Elucidating the Mechanism of VVTT Infection Through Machine Learning and Transcriptome Analysis.

The vaccinia virus (VV) is extensively utilized as a vaccine vector in the treatment of various infectious diseases, cardiovascular diseases, immunode...