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  4. Application of Spectral Clustering for the Detection of High Priority Areas of Attention for COVID-19 in Mexico
 
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Application of Spectral Clustering for the Detection of High Priority Areas of Attention for COVID-19 in Mexico

Journal
Computer Science and Health Engineering in Health Services
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
ISSN
1867-8211
1867-822X
Date Issued
2021
Author(s)
Rodríguez Aguilar, Román  
Facultad de Ciencias Económicas y Empresariales - CampCM  
Type
Resource Types::text::conference output::conference proceedings::conference paper
DOI
10.1007/978-3-030-69839-3_9
URL
https://scripta.up.edu.mx/handle/123456789/1782
Abstract
The recent COVID-19 pandemic has represented a great challenge for health systems around the world. That is why it is necessary to propose strategies for prioritizing care and containing the pandemic. This work proposes the use of spectral clustering to characterize high-priority areas of care based on key information on the performance of the pandemic as well as health system variables. The result shows the generation of high priority areas not only due to the deaths observed but also due to the clinical, demographic and health system variables. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
Subjects

COVID-19

Priorization

Spectral cluster

Unsupervised models


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