AIMC Topic: Nutrition Surveys
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More science friction for less science fiction.
PLoS biology
May 1, 2025
AI-ready health datasets can be exploited to generate many research articles with potentially limited scientific value. A study in PLOS Biology highlights this problem, by describing a recent, sudden explosion in papers analyzing the NHANES health da...
Explosion of formulaic research articles, including inappropriate study designs and false discoveries, based on the NHANES US national health database.
PLoS biology
May 1, 2025
With the growth of artificial intelligence (AI)-ready datasets such as the National Health and Nutrition Examination Survey (NHANES), new opportunities for data-driven research are being created, but also generating risks of data exploitation by pape...
Exploring the Impact of PA and Sedentary Behavior on Gout Risk in Hyperuricemia: Insights From Machine Learning and SHAP Analysis.
International journal of rheumatic diseases
Apr 1, 2025
BACKGROUND: Individuals with hyperuricemia (HUA) are widely recognized as being at increased risk for gout. This study aimed to investigate how physical activity (PA) duration and sedentary duration impact gout risk in individuals with HUA and to dev...
Building a cancer risk and survival prediction model based on social determinants of health combined with machine learning: A NHANES 1999 to 2018 retrospective cohort study.
Medicine
Feb 7, 2025
The occurrence and progression of cancer is a significant focus of research worldwide, often accompanied by a prolonged disease course. Concurrently, researchers have identified that social determinants of health (SDOH) (employment status, family inc...
The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects.
Frontiers in public health
Jan 1, 2025
INTRODUCTION: This study examines the synergistic effects of multi-pollutant exposure on hepatic lipid accumulation in non-alcoholic fatty liver disease (NAFLD) through the application of an explainable machine learning framework. This approach addre...
Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels.
Frontiers in public health
Jan 1, 2025
BACKGROUND: Environmental exposure to heavy metals, such as arsenic, cadmium, and lead, is a known risk factor for cardiovascular diseases.
Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation?
PloS one
Jan 1, 2025
PURPOSE: We aim to evaluate various proxy selection methods within the context of high-dimensional propensity score (hdPS) analysis. This study aimed to systematically evaluate and compare the performance of traditional statistical methods and machin...
Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey.
Environmental health and preventive medicine
Jan 1, 2025
BACKGROUND: Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as the heart, brain, and kidneys. Our goal is to predict the risk of new onset hypertension using machin...
Machine learning-driven risk assessment of coronary heart disease: Analysis of NHANES data from 1999 to 2018.
Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
Aug 28, 2024
OBJECTIVES: The high incidence of coronary artery heart disease (CHD) poses a significant burden and challenge to public health systems globally. Effective prevention and early diagnosis of CHD have become key strategies to alleviate this burden. Thi...
Comparative analysis of machine learning models for efficient low back pain prediction using demographic and lifestyle factors.
Journal of back and musculoskeletal rehabilitation
Jan 1, 2024
BACKGROUND: Low back pain (LBP) is one of the most frequently occurring musculoskeletal disorders, and factors such as lifestyle as well as individual characteristics are associated with LBP.
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