AIMC Topic: Residence Characteristics
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Exploring the complex associations between community public spaces and healthy aging: an explainable analysis using catboost and SHAP.
BMC public health
Jul 2, 2025
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
Mar 29, 2025
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...
Interpretable machine learning for identifying overweight and obesity risk factors of older adults in China.
Geriatric nursing (New York, N.Y.)
Jan 4, 2025
OBJECTIVE: To estimate the importance of risk factors on overweight/obesity among older adults by comparing different predictive model.
Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation.
Medical care
Jan 3, 2025
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
Jan 3, 2025
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
Nov 26, 2024
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
Jun 8, 2024
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
Mar 18, 2024
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...
Integrating street view images and deep learning to explore the association between human perceptions of the built environment and cardiovascular disease in older adults.
Social science & medicine (1982)
Oct 11, 2023
Understanding how built environment attributes affect health remains important. While many studies have explored the objective characteristics of built environments that affect health outcomes, few have examined the role of human perceptions of built...
Spatial and deep learning analyses of urban recovery from the impacts of COVID-19.
Scientific reports
Feb 11, 2023
This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests...
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