AIMC Topic: Nutrition Surveys

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Machine Learning-Based Biomarker Identification for Early Diagnosis of Metabolic Dysfunction-Associated Steatotic Liver Disease.

The Journal of clinical endocrinology and metabolism
CONTEXT: Metabolic dysfunction-associated steatotic liver disease (MASLD) is an umbrella term for simple hepatic steatosis and the more severe metabolic dysfunction-associated steatohepatitis. The current reliance on liver biopsy for diagnosis and a ...

Assessing the effect of perfluoroalkyl and polyfluoroalkyl substances on cardiovascular-kidney-metabolic syndrome: Insights from an interpretable machine learning model.

The Science of the total environment
Cardiovascular-kidney-metabolic syndrome (CKM) and its association with exposure to emerging pollutants, particularly perfluoroalkyl and polyfluoroalkyl substances (PFAS), present significant challenges for environmental public health and risk predic...

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.

Investigating the potential risk of cadmium exposure on Osteoporosis: An integrated multi-omics approach.

Ecotoxicology and environmental safety
Osteoporosis (OP) is a chronic progressive bone disease, and its occurrence and development under cadmium exposure remain unclear. This study aims to explore the role of cadmium exposure in the pathogenesis of OP through a comprehensive analysis of m...

Continual learning across population cohorts with distribution shift: insights from multi-cohort metabolic syndrome identification.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study aims to tackle the critical challenge of adapting deep learning (DL) models for deployment in real-world healthcare settings, specifically focusing on catastrophic forgetting due to distribution shifts between hospital and non-h...

Associations of the Hs-CRP/HDL-C ratio with stroke among US adults: Evidence from NHANES 2015-2018.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND: The high-sensitivity C-reactive protein (Hs-CRP)-to-high-density lipoprotein cholesterol (HDL-C) ratio, which integrates insights into inflammation and lipid metabolism, serves as a comprehensive indicator. The association between this ra...

Development and validation of an interpretable machine learning model for predicting hyperuricemia risk: Based on environmental chemical exposure.

Ecotoxicology and environmental safety
Hyperuricemia is a global health concern, with environmental chemicals as risk factors. This study used data of multiple environmental chemical exposures from the 2011-2012 cycle of the National Health and Nutrition Examination Survey (NHANES) to dev...

Enhancing osteoporosis risk prediction using machine learning: A holistic approach integrating biomarkers and clinical data.

Computers in biology and medicine
Osteoporosis (OP) affects approximately 18 % of the global population, with osteoporosis-associated fractures impacting up to 37 million people annually. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its limita...

Machine learning-based prediction of hearing loss: Findings of the US NHANES from 2003 to 2018.

Hearing research
The prevalence of hearing loss (HL) has emerged as an escalating public health concern globally. The objective of this study was to leverage data from the National Health and Nutritional Examination Survey (NHANES) to develop an interpretable predict...