AI Medical Compendium Journal:
Frontiers in public health

Showing 11 to 20 of 338 articles

Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction.

Frontiers in public health
INTRODUCTION: Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and time factors. Machine learning offers powerful tools...

Factors influencing adoption intentions to use AIGC for health information: findings from SEM and fsQCA.

Frontiers in public health
BACKGROUND: With the rapid advancement of artificial intelligence technologies, AI-generated content (AIGC) was increasingly applied in the health information sector, becoming a vital tool to enhance the efficiency and quality of health information e...

Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.

Frontiers in public health
BACKGROUND: Exposure to heavy metals has been implicated in adverse auditory health outcomes, yet the precise relationships between heavy metal biomarkers and hearing status remain underexplored. This study leverages a machine learning framework to i...

Constructing a screening model to identify patients at high risk of hospital-acquired influenza on admission to hospital.

Frontiers in public health
OBJECTIVE: To develop a machine learning (ML)-based admission screening model for hospital-acquired (HA) influenza using routinely available data to support early clinical intervention.

Machine learning model for age related macular degeneration based on pesticides: the National Health and Nutrition Examination Survey 2007-2008.

Frontiers in public health
Age-related macular degeneration (AMD) is the most common cause of irreversible deterioration of vision in older adults. Previous studies have found that exposure to pesticides can lead to a worsening of AMD. In this paper, information on pesticide e...

A method for predicting postpartum depression via an ensemble neural network model.

Frontiers in public health
INTRODUCTION: Postpartum depression (PPD) has numerous adverse impacts on the families of new mothers and society at large. Early identification and intervention are of great significance. Although there are many existing machine learning classifiers...

Machine learning based association between inflammation indicators (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality in arthritis patients with hypertension: NHANES 1999-2018.

Frontiers in public health
BACKGROUND: This study aimed to evaluate the relationship between CBC-derived inflammatory markers (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality (ACM) risk in arthritis (AR) patients with hypertensive (HTN) using data from the NHANES.

Transforming physical fitness and exercise behaviors in adolescent health using a life log sharing model.

Frontiers in public health
INTRODUCTION: This study investigates the potential of a deep learning-based Life Log Sharing Model (LLSM) to enhance adolescent physical fitness and exercise behaviors through personalized public health interventions.

Exploring the impact of AI on unemployment for people with disabilities: do educational attainment and governance matter?

Frontiers in public health
The current study investigates the impact of artificial intelligence (AI) on unemployment among people with disabilities, focusing on the mediating role of education and the moderating effect of governance quality. Using panel data from 27 high-tech ...

Artificial intelligence in hospital infection prevention: an integrative review.

Frontiers in public health
BACKGROUND: Hospital-acquired infections (HAIs) represent a persistent challenge in healthcare, contributing to substantial morbidity, mortality, and economic burden. Artificial intelligence (AI) offers promising potential for improving HAIs preventi...