AIM: In this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.
Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of dust particles in the lungs, is characterized by chronic pulmonary inflammation and progressive fibrosis, potentially leading to respiratory and/or he...
Exposure to charcoal biomass (CB) pollutants affects the cardiorespiratory system. We assessed cardiopulmonary responses (CPR) to exercise in charcoal producers (CPs) compared to farmers and evaluated the prevalence of exercise-induced bronchoconstri...
OBJECTIVE: Workers exposed to dust for extended periods may experience varying degrees of cognitive impairment. However, limited research exists on the associated risk factors. This study aims to identify key variables using machine learning algorith...
BACKGROUND:  Given the high prevalence of hearing loss among truck drivers, using artificial neural networks (ANNs) to predict and detect contributing factors early can aid managers significantly.
Critical reviews in biomedical engineering
39612267
Many reflexologists employ outdated concepts that do not align with modern anatomy, physiology, and biophysics. Those concepts undermine physicians' confidence in their diagnosis. This study aims to improve the quality of medical care for workers in ...
Journal of pharmaceutical and biomedical analysis
40024027
Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for early risk prediction. This study aimed to develop a predictive model for occupational As exposure and hypertension using metabolomics and machine lea...
International journal of occupational safety and ergonomics : JOSE
39935238
Occupational risk assessment involves examining and ranking the risks and hazards in a production or service facility, focusing on workplace health and safety. This study aims to address the deficiencies of traditional methods by applying a fuzzy log...
This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk factors via machine learning techniques. Data from 14,145 workers (627 with occupational noise-induce...
Environmental health : a global access science source
40380224
INTRODUCTION: Artificial intelligence (AI) has the potential to significantly enhance workplace safety and mitigate occupational radiation exposure risks by improving the accuracy of assessment and management of these hazards. This study aims to revi...