AIMC Topic: Urban Population
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Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.
Environmental pollution (Barking, Essex : 1987)
Jul 18, 2017
Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air ...
Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning.
PloS one
May 2, 2017
This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image ...
Scaling Laws in City Growth: Setting Limitations with Self-Organizing Maps.
PloS one
Dec 22, 2016
Do scaling relations always provide the means to anticipate the relationships between the size of cities, costs of maintenance, and the socio-economic benefits resulting from their growth? Scaling laws are considered a universal principle that descri...
Vitamin D status, hypertension and body mass index in an urban black community in Mangaung, South Africa.
African journal of primary health care & family medicine
Oct 31, 2016
BACKGROUND: A strong relationship exists between hypertension and body weight. Research has linked both higher blood pressure and body weight with lower vitamin D status.
A Novel Tool for Evaluation of Mild Traumatic Brain Injury Patients in the Emergency Department: Does Robotic Assessment of Neuromotor Performance Following Injury Predict the Presence of Postconcussion Symptoms at Follow-up?
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
Mar 21, 2016
OBJECTIVES: Postconcussion symptoms (PCS) are a common complication of mild traumatic brain injury (TBI). Currently, there is no validated clinically available method to reliably predict at the time of injury who will subsequently develop PCS. The pu...
Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Nov 5, 2015
INTRODUCTION: Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that ...
The urban physical environment and leisure-time physical activity in early midlife: a FinnTwin12 study.
Health & place
Jul 1, 2025
Under the exposome framework, this study examined the relationship between the urban physical environment and leisure-time physical activity during early midlife based on 394 participants (mean age: 37, range 34-40) from the FinnTwin12 cohort, residi...
InclusiViz : Visual Analytics of Human Mobility Data for Understanding and Mitigating Urban Segregation.
IEEE transactions on visualization and computer graphics
Jun 1, 2025
Urban segregation refers to the physical and social division of people, often driving inequalities within cities and exacerbating socioeconomic and racial tensions. While most studies focus on residential spaces, they often neglect segregation across...
Urban-rural disparities in the prevalence and trends of loneliness among Chinese older adults and their associated factors: Evidence from machine learning analysis.
Applied psychology. Health and well-being
Feb 1, 2025
In the context of rapid aging development, exploring the predictive factors of older adults' loneliness and its urban-rural differences is of great significance for promoting the psychological health of older adults. This study selected 30 variables ...
Urban-rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.
Frontiers in public health
Jan 1, 2025
BACKGROUND: Falls among older adults are a significant challenge to global healthy aging. Identifying key factors and differences in fall risks, along with developing predictive models, is essential for differentiated and precise interventions in Chi...
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