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Exploring the potential of large language models in identifying metabolic dysfunction-associated steatotic liver disease: A comparative study of non-invasive tests and artificial intelligence-generated responses.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND AND AIMS: This study sought to assess the capabilities of large language models (LLMs) in identifying clinically significant metabolic dysfunction-associated steatotic liver disease (MASLD).

Advancing non-alcoholic fatty liver disease prediction: a comprehensive machine learning approach integrating SHAP interpretability and multi-cohort validation.

Frontiers in endocrinology
INTRODUCTION: Non-alcoholic fatty liver disease (NAFLD) represents a major global health challenge, often undiagnosed because of suboptimal screening tools. Advances in machine learning (ML) offer potential improvements in predictive diagnostics, lev...

Modeling health risks using neural network ensembles.

PloS one
This study aims to demonstrate that demographics combined with biometrics can be used to predict obesity related chronic disease risk and produce a health risk score that outperforms body mass index (BMI)-the most commonly used biomarker for obesity....

Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning.

Ecotoxicology and environmental safety
There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter scoring machine learning (ML) techniques were developed using National Health and Nutrition Examination Survey data to predict osteoporosis based on hea...

Artificial intelligence modeling of biomarker-based physiological age: Impact on phase 1 drug-metabolizing enzyme phenotypes.

CPT: pharmacometrics & systems pharmacology
Age and aging are important predictors of health status, disease progression, drug kinetics, and effects. The purpose was to develop ensemble learning-based physiological age (PA) models for evaluating drug metabolism. National Health and Nutrition E...

Development and validation of a prediction model for ED using machine learning: according to NHANES 2001-2004.

Scientific reports
Erectile Dysfunction (ED) is a form of sexual dysfunction in males that imposes significant health and financial burdens globally. Despite its high prevalence, diagnosing ED remains challenging due to the limitations of current diagnostic methods and...

Machine learning model for age-related macular degeneration based on heavy metals: The National Health and Nutrition Examination Survey 2005 to 2008.

Scientific reports
Age-related macular degeneration (AMD) is the leading cause of blindness in older people in developed countries. It has been suggested that heavy metal exposure may be associated with the development of AMD, but most studies have focused on the effec...

Using interpretable machine learning methods to identify the relative importance of lifestyle factors for overweight and obesity in adults: pooled evidence from CHNS and NHANES.

BMC public health
BACKGROUND: Overweight and obesity pose a huge burden on individuals and society. While the relationship between lifestyle factors and overweight and obesity is well-established, the relative contribution of specific lifestyle factors remains unclear...

Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology.

Ecotoxicology and environmental safety
BACKGROUND: Cardiovascular disease (CVD) remains a leading cause of mortality globally. Environmental pollutants, specifically volatile organic compounds (VOCs), have been identified as significant risk factors. This study aims to develop a machine l...

An explainable machine learning estimated biological age based on morphological parameters of the spine.

GeroScience
Accurately estimating biological age is beneficial for measuring aging and predicting risk. It is widely accepted that the prevalence of spine compression increases significantly with age. However, biological age based on vertebral morphological data...