Public Health & Policy

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

BACKGROUND: Mental disorders are increasingly prevalent, leading to increased medical expenditures. ...

Editorial: Mental health, epidemiology and machine learning.

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...

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, whi...

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

Adolescents worldwide are increasingly affected by mental health disorders, with anxiety disorders, ...

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

is an opportunistic pathogen that can infect humans, animals and aquatic species, which is widely d...

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

PURPOSE: Epidemiologic studies are important in assessing risk factors of mortality. Machine learnin...

SVM directed machine learning classifier for human action recognition network.

Understanding human behavior and human action recognition are both essential components of effective...

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 l...

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 compl...

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, w...

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 r...

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...

Real-time prediction system for prevention of acute renal failure based on AI model.

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 ...

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 artific...

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 st...

Fraud detection in healthcare claims using machine learning: A systematic review.

OBJECTIVE: Identifying fraud in healthcare programs is crucial, as an estimated 3%-10% of the total ...

Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.

Alzheimer's disease (AD) is a brain disorder that causes memory loss and behavioral and thinking pro...

Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.

BACKGROUND: Machine learning (ML) is increasingly used in population and public health to support ep...

Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.

We investigate methods that improve the estimation of indoor gamma ray dose rates at locations where...