AIMC Topic: Residence Characteristics

Clear Filters Showing 1 to 10 of 41 articles

Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, and Dubai.

Scientific reports
Urban food landscapes significantly influence dietary habits and health outcomes, with disparities in food access contributing to obesity, particularly in socioeconomically disadvantaged neighborhoods. This study presents a data-driven approach to as...

GPS-based street-view greenspace exposure and wearable assessed physical activity in a prospective cohort of US women.

The international journal of behavioral nutrition and physical activity
BACKGROUND: Increasing evidence positively links greenspace and physical activity (PA). However, most studies use measures of greenspace, such as satellite-based vegetation indices around the residence, which fail to capture ground-level views and da...

Exploring the complex associations between community public spaces and healthy aging: an explainable analysis using catboost and SHAP.

BMC public health
BACKGROUND: As global aging accelerates, community public spaces (CPS) are increasingly recognized as vital for promoting healthy aging. However, existing research often employs linear analytical methods or focuses on single health dimensions, overlo...

Evaluating the predictive performance of different data sources to forecast overdose deaths at the neighborhood level with machine learning in Rhode Island.

Preventive medicine
OBJECTIVES: To evaluate the predictive performance of different data sources to forecast fatal overdose in Rhode Island neighborhoods, with the goal of providing a template for other jurisdictions interested in predictive analytics to direct overdose...

Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation.

Medical care
OBJECTIVE: To understand the variation in artificial intelligence/machine learning (AI/ML) adoption across different hospital characteristics and explore how AI/ML is utilized, particularly in relation to neighborhood deprivation.

How do neighborhood environments impact adolescent health: a comprehensive study from subjective and objective perspectives using machine learning method.

Frontiers in public health
Existing studies have established a linear relationship between urban environments and adolescent health, but the combined impacts of subjective and objective environments on multi-dimensional health status (including physical and mental health) have...

Assessing greenspace and cardiovascular health through deep-learning analysis of street-view imagery in a cohort of US children.

Environmental research
BACKGROUND: Accurately capturing individuals' experiences with greenspace at ground-level can provide valuable insights into their impact on children's health. However, most previous research has relied on coarse satellite-based measurements.

Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.

Journal of substance use and addiction treatment
BACKGROUND: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-leve...

Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populational Study from Quebec, Canada.

American journal of clinical dermatology
BACKGROUND: Psoriasis is a major global health burden affecting ~ 60 million people worldwide. Existing studies on psoriasis focused on individual-level health behaviors (e.g. diet, alcohol consumption, smoking, exercise) and characteristics as drive...