MedGNN: Capturing the Links Between Urban Characteristics and Medical Prescriptions
Journal:
arXiv
Published Date:
Apr 7, 2025
Abstract
Understanding how urban socio-demographic and environmental factors relate
with health is essential for public health and urban planning. However,
traditional statistical methods struggle with nonlinear effects, while machine
learning models often fail to capture geographical (nearby areas being more
similar) and topological (unequal connectivity between places) effects in an
interpretable way. To address this, we propose MedGNN, a spatio-topologically
explicit framework that constructs a 2-hop spatial graph, integrating
positional and locational node embeddings with urban characteristics in a graph
neural network. Applied to MEDSAT, a comprehensive dataset covering over 150
environmental and socio-demographic factors and six prescription outcomes
(depression, anxiety, diabetes, hypertension, asthma, and opioids) across 4,835
Greater London neighborhoods, MedGNN improved predictions by over 25% on
average compared to baseline methods. Using depression prescriptions as a case
study, we analyzed graph embeddings via geographical principal component
analysis, identifying findings that: align with prior research (e.g., higher
antidepressant prescriptions among older and White populations), contribute to
ongoing debates (e.g., greenery linked to higher and NO2 to lower
prescriptions), and warrant further study (e.g., canopy evaporation correlated
with fewer prescriptions). These results demonstrate MedGNN's potential, and
more broadly, of carefully applied machine learning, to advance
transdisciplinary public health research.
