AIMC Topic: Risk Assessment

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Machine learning-driven prediction of eye irritation toxicity: Integration of in silico and in vitro study.

Toxicology and applied pharmacology
Eye irritation (EI) toxicity poses critical challenges in chemical safety assessment, demanding alternatives to ethically contentious animal testing. We present the first integrative framework combining computational prediction with experimental vali...

Multi-modal models using fMRI, urine and serum biomarkers for classification and risk prognosis in diabetic kidney disease.

Diabetes, obesity & metabolism
BACKGROUND: Functional magnetic resonance imaging (fMRI) is a powerful tool for non-invasive evaluation of micro-changes in the kidneys. This study aims to develop classification and prognostic models based on multi-modal data.

A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning.

Ecotoxicology and environmental safety
3-Monochloropropane-1,2-diol (3-MCPD) and glycidol along with their esters are commonly found in chemical production, wastewater treatment, food processing, and exhibit toxicity. Accurate exposure assessment is essential for evaluating the environmen...

Interpretable machine learning insights into the association between PFAS exposure and diabetes mellitus.

Ecotoxicology and environmental safety
BACKGROUND: Diabetes Mellitus (DM) is a global health concern with rising prevalence, and its link to PFAS exposure remains unclear. No machine learning (ML) models have yet been developed to predict DM based on PFAS exposure.

A multidimensional prediction model for overtraining risk in youth soccer players: Integrating physiological and psychological markers.

Journal of sports sciences
Overtraining syndrome (OTS) poses a critical challenge in youth soccer, particularly during periods of rapid physiological maturation combined with high training demands. This study aimed to develop and validate a multidimensional prediction model fo...

Machine learning algorithms to predict the risk of hyperlipidemia in people with HIV after starting HAART for 6 months.

AIDS (London, England)
OBJECTIVE: The purpose of this study was to use machine learning models to predict the risk of hyperlipidemia in people with HIV (PWH) for 6 months after starting HAART, to improve early intervention efforts and prevent further progression to cardiov...

Computer vision and tactile glove: A multimodal model in lifting task risk assessment.

Applied ergonomics
Work-related injuries from overexertion, particularly lifting, are a major concern in occupational safety. Traditional assessment tools, such as the Revised NIOSH Lifting Equation (RNLE), require significant training and practice for deployment. This...

Prediction of trihalomethane occurrence and cancer risk using interpretable machine learning and virtual data augmentation.

Journal of hazardous materials
Trihalomethanes (THMs) in drinking water are regulated for carcinogenic health risks. However, frequent water quality monitoring imposes significant resource burdens. This study proposes a framework integrating interpretable machine learning (ML) wit...

Advancing wetland groundwater pollution zoning: A novel integration of Monte Carlo health risk modeling and machine learning.

Journal of hazardous materials
Wetlands serve as crucial water reservoirs, providing essential water resources for the surrounding regions. However, elevated ion concentrations in wetland groundwater may pose health risks to local populations. This study focused on Judian Lake and...