Graph Attention Networks Unveil Determinants of Intra- and Inter-city Health Disparity

• URL: http://arxiv.org/abs/2210.10142v2
• Date: Thu, 20 Oct 2022 20:23:55 GMT
• Title: Graph Attention Networks Unveil Determinants of Intra- and Inter-city Health Disparity
• Authors: Chenyue Liu (1), Chao Fan (2), Ali Mostafavi (1) ((1) Urban Resilience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, United States, (2) Glenn Department of Civil Engineering, Clemson University, Clemson, South Carolina, United States)
• Abstract summary: Multiple heterogeneous urban features could modulate the prevalence of diseases across different neighborhoods in cities and across different cities. This study examines features related to socio-demographics, population activity, mobility, and the built environment to examine intra- and inter-city disparity in prevalence of four disease types: obesity, diabetes, cancer, and heart disease.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the determinants underlying variations in urban health status is important for informing urban design and planning, as well as public health policies. Multiple heterogeneous urban features could modulate the prevalence of diseases across different neighborhoods in cities and across different cities. This study examines heterogeneous features related to socio-demographics, population activity, mobility, and the built environment and their non-linear interactions to examine intra- and inter-city disparity in prevalence of four disease types: obesity, diabetes, cancer, and heart disease. Features related to population activity, mobility, and facility density are obtained from large-scale anonymized mobility data. These features are used in training and testing graph attention network (GAT) models to capture non-linear feature interactions as well as spatial interdependence among neighborhoods. We tested the models in five U.S. cities across the four disease types. The results show that the GAT model can predict the health status of people in neighborhoods based on the top five determinant features. The findings unveil that population activity and built-environment features along with socio-demographic features differentiate the health status of neighborhoods to such a great extent that a GAT model could predict the health status using these features with high accuracy. The results also show that the model trained on one city can predict health status in another city with high accuracy, allowing us to quantify the inter-city similarity and discrepancy in health status. The model and findings provide novel approaches and insights for urban designers, planners, and public health officials to better understand and improve health disparities in cities by considering the significant determinant features and their interactions.

Related papers

• Impact on Public Health Decision Making by Utilizing Big Data Without Domain Knowledge [17.73578632982445]
  New data sources, and artificial intelligence (AI) methods are becoming plentiful, and relevant to decision making in many societal applications. This work illustrates important issues of robustness and model specification for informing effective allocation of interventions using new data sources.
  arXiv  Detail & Related papers  (2024-02-08T21:03:34Z)
• Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting [64.80538055623842]
  sociodemographic prompting is a technique that steers the output of prompt-based models towards answers that humans with specific sociodemographic profiles would give. We show that sociodemographic information affects model predictions and can be beneficial for improving zero-shot learning in subjective NLP tasks.
  arXiv  Detail & Related papers  (2023-09-13T15:42:06Z)
• Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures [65.62256987706128]
  Compartmental epidemiological models categorize individuals based on their disease status. We propose an ABM architecture that combines an adapted SEIRD model with a decision-making model for citizens. We illustrate the designed model by examining the progression of SARS-CoV-2 infections in A Coruna, Spain.
  arXiv  Detail & Related papers  (2023-07-28T08:01:05Z)
• Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features [1.4010916616909745]
  Age, minority status, and population density are among the most influential factors in cancer prevalence. Increasing green space and reducing developed areas and total emissions could alleviate cancer prevalence.
  arXiv  Detail & Related papers  (2023-06-20T18:56:37Z)
• An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model [0.966840768820136]
  This study seeks to expand our existing Urban Population Health Observatory (UPHO) system. A cohesive approach that employs machine learning and semantic/logical inference reveals pathways leading to diseases. The application of UPHO could help reduce health disparities and improve urban population health.
  arXiv  Detail & Related papers  (2022-07-26T15:59:22Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities [7.148078723492643]
  We propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components. We show that embeddings generated by a model trained on a different county can capture 50% to 60% of the emergent spatial structure in another county.
  arXiv  Detail & Related papers  (2021-10-24T07:13:14Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• Health Status Prediction with Local-Global Heterogeneous Behavior Graph [69.99431339130105]
  Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. We propose to model the behavior-related multi-source data streams with a local-global graph. We take experiments on StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.
  arXiv  Detail & Related papers  (2021-03-23T11:10:04Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Exposure Density and Neighborhood Disparities in COVID-19 Infection Risk: Using Large-scale Geolocation Data to Understand Burdens on Vulnerable Communities [1.2526963688768453]
  This study develops a new method to quantify neighborhood activity levels at high spatial and temporal resolutions. We define exposure density as a measure of both the localized volume of activity in a defined area and the proportion of activity occurring in non-residential and outdoor land uses.
  arXiv  Detail & Related papers  (2020-08-04T15:41:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.