AIMC Topic: United States

Clear Filters Showing 91 to 100 of 1391 articles

The value of triglyceride-glucose index-related indices in evaluating migraine: perspectives from multi-centre cross-sectional studies and machine learning models.

Lipids in health and disease
BACKGROUND: This study employed representative data from the U.S. and China to delve into the correlation among migraine prevalence, the triglyceride‒glucose index, a marker of insulin resistance, and the composite indicator of obesity.

Construction and validation of a risk prediction model for chronic obstructive pulmonary disease (COPD): a cross-sectional study based on the NHANES database from 2009 to 2018.

BMC pulmonary medicine
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major global public health concern, and early screening and identification of high-risk populations are critical for reducing the disease burden. Although several studies have explored the...

Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015-2018.

BMC public health
Cardiometabolic Multimorbidity (CMM) has emerged as one of the primary threats to human health globally due to its high incidence, disability, and mortality rates. Accurate identification of CMM patterns is crucial for CMM classification and health m...

Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning.

BMC public health
In recent years, there has been a growing body of literature on identifying effective determinants for modeling the spatial variation of overdose rates, addressing this emerging public health concern globally. We compiled a range of widely recognized...

Assessment of drug induced hyperuricemia and gout risk using the FDA adverse event reporting system.

Scientific reports
Hyperuricemia, the key pathological basis of gout, is increasingly prevalent worldwide. While lifestyle factors contribute, various medications also play a role. However, their specific risks and mechanisms remain inadequately studied. Disproportiona...

Quality assessment of large language models' output in maternal health.

Scientific reports
Optimising healthcare is linked to broadening access to health literacy in Low- and Middle-Income Countries. The safe and responsible deployment of Large Language Models (LLMs) may provide accurate, reliable, and culturally relevant healthcare inform...

Exploring the link between the ZJU index and sarcopenia in adults aged 20-59 using NHANES and machine learning.

Scientific reports
Sarcopenia, characterized by progressive loss of muscle mass and function, is a growing public health concern. The ZJU index, a novel metabolic marker, integrates lipid metabolism and glucose regulation parameters. While its association with metaboli...

Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES.

BMC medical informatics and decision making
OBJECTIVE: Using 2005-2018 NHANES data, this study examined the association between the visceral fat metabolism score (METS-VF) and heart failure (HF) prevalence in U.S. adults, leveraging machine learning (LightGBM/XGBoost) and SHAP for classficatio...

Screening of Glaucoma in High-Risk Minority Populations.

Journal of glaucoma
PRECIS: This chapter reviews the current recommendations on screening for open angle glaucoma in Black and Hispanic populations. Strategies for increasing case-finding and decreasing cost, with emphasis on evolving technologies, are presented.

Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data.

JMIR research protocols
BACKGROUND: Suicide in local jails occurs at a higher rate than in the general population, requiring improvements to risk screening methods. Current suicide risk screening practices in jails are insufficient: They are commonly not conducted using val...