ArticleIEEE access : practical innovations, open solutions2020
Analyzing Associations Between Chronic Disease Prevalence and Neighborhood Quality Through Google Street View Images.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Article
- Multi-Task Classification for Improved Health Outcome Prediction Based on Environmental Indicators.IEEE access : practical innovations, open solutions · 2023Article
- Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand.PLoS neglected tropical diseases · 2021Article
- Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes.Public health reports (Washington, D.C. : 1974)Article
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Authors and funding
8 authors.
Funding
Abstract
Deep learning and, specifically, convoltional neural networks (CNN) represent a class of powerful models that facilitate the understanding of many problems in computer vision. When combined with a reasonable amount of data, CNNs can outperform traditional models for many tasks, including image classification. In this work, we utilize these powerful tools with imagery data collected through Google Street View images to perform virtual audits of neighborhood characteristics. We further investigate different architectures for chronic disease prevalence regression through networks that are applied to sets of images rather than single images. We show quantitative results and demonstrate that our proposed architectures outperform the traditional regression approaches.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.