Public Health & Policy

Development of deep learning auto-encoder algorithms for predicting alcohol use in Korean adolescents based on cross-sectional data.

Alcohol is a highly addictive substance, presenting significant global public health concerns, particularly among adolescents. Previous studies have been limited by traditional research methods, making it challenging to encompass diverse risk factors and automate screening or prediction of adolescents' alcohol use. This study aimed to develop prediction algorithms for adolescent alcohol use in Sou...

Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.

Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mechanistic approaches is rapidly advancing, efforts remain fragmented. This scoping review provides a comprehensive overview of emerging integrated model...

A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.

Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of dust particles in the lungs, is characterized by c...

Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach.

Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indi...

Machine-learning-based cost prediction models for inpatients with mental disorders in China.

BACKGROUND: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for i...

Predicting cardiovascular disease in patients with mental illness using machine learning.

BACKGROUND: Cardiovascular disease (CVD) is twice as prevalent among individuals with mental illness compared to the general population. Prevention st...

The Goldilocks Zone: Finding the right balance of user and institutional risk for suicide-related generative AI queries.

Generative artificial intelligence (genAI) has potential to improve healthcare by reducing clinician burden and expanding services, among other uses. ...

Leveraging AI to improve disease screening among American Indians: insights from the Strong Heart Study.

Screening tests for disease have their performance measured through sensitivity and specificity, which inform how well the test can discriminate betwe...

Isolation, identification and characteristics of from diseased rainbow trout ().

is an opportunistic pathogen that can infect humans, animals and aquatic species, which is widely distributed in different aquatic environments and p...

Key risk factors of generalized anxiety disorder in adolescents: machine learning study.

Adolescents worldwide are increasingly affected by mental health disorders, with anxiety disorders, including Generalized Anxiety Disorder (GAD), bein...

Application of machine learning algorithms in an epidemiologic study of mortality.

PURPOSE: Epidemiologic studies are important in assessing risk factors of mortality. Machine learning (ML) is efficient in analyzing multidimensional ...

The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development.

Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investi...

SVM directed machine learning classifier for human action recognition network.

Understanding human behavior and human action recognition are both essential components of effective surveillance video analysis for the purpose of gu...

A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud.

Healthcare insurance fraud imposes a significant financial burden on healthcare systems worldwide, with annual losses reaching billions of dollars. Th...

Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 2.

Part 2 explores the transformative potential of artificial intelligence (AI) in addressing the complexities of headache disorders through innovative a...

Vision Sensor for Automatic Recognition of Human Activities via Hybrid Features and Multi-Class Support Vector Machine.

Over recent years, automated Human Activity Recognition (HAR) has been an area of concern for many researchers due to its widespread application in su...

Leveraging AHP and transfer learning in machine learning for improved prediction of infectious disease outbreaks.

Infectious diseases significantly impact both public health and economic stability, underscoring the critical need for precise outbreak predictions to...

Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset.

Accurate forecasting of claim frequency in automobile insurance is essential for insurers to assess risks effectively and establish appropriate pricin...

Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.

Suicide constitutes a public health issue of major concern. Ongoing progress in the field of artificial intelligence, particularly in the domain of la...

From Outbreaks to Artificial Intelligence: A Comprehensive Review of Monkeypox Virus Epidemiology, Diagnosis, Treatment, Vaccination, and Deep Learning Applications.

After the global impact of the COVID-19 pandemic, concerns over virus transmission have risen. A state of health emergency was declared in 2022 due t...