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Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data
Based on complex adaptive system theory and information theory for investigating heterogeneous situations, this paper develops an outlier knowledge management framework based on three aspects—dimension, object, and situation—for dealing with extreme public health events. In the context of the COVID-...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477628/ https://www.ncbi.nlm.nih.gov/pubmed/32921839 http://dx.doi.org/10.1016/j.seps.2020.100941 |
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author | Xia, Huosong An, Wuyue Li, Jiaze Zhang, Zuopeng (Justin) |
author_facet | Xia, Huosong An, Wuyue Li, Jiaze Zhang, Zuopeng (Justin) |
author_sort | Xia, Huosong |
collection | PubMed |
description | Based on complex adaptive system theory and information theory for investigating heterogeneous situations, this paper develops an outlier knowledge management framework based on three aspects—dimension, object, and situation—for dealing with extreme public health events. In the context of the COVID-19 pandemic, we apply advanced natural language processing (NLP) technology to conduct data mining and feature extraction on the microblog data from the Wuhan area and the imported case province (Henan Province) during the high and median operating periods of the epidemic. Our experiment indicates that the semantic and sentiment vocabulary of words, the sentiment curve, and the portrait of patients seeking help were all heterogeneous in the context of COVID-19. We extract and acquire the outlier knowledge of COVID-19 and incorporate it into the outlier knowledge base of extreme public health events for knowledge sharing and transformation. The knowledge base serves as a think tank for public opinion guidance and platform suggestions for dealing with extreme public health events. This paper provides novel ideas and methods for outlier knowledge management in healthcare contexts. |
format | Online Article Text |
id | pubmed-7477628 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74776282020-09-08 Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data Xia, Huosong An, Wuyue Li, Jiaze Zhang, Zuopeng (Justin) Socioecon Plann Sci Invited Paper Based on complex adaptive system theory and information theory for investigating heterogeneous situations, this paper develops an outlier knowledge management framework based on three aspects—dimension, object, and situation—for dealing with extreme public health events. In the context of the COVID-19 pandemic, we apply advanced natural language processing (NLP) technology to conduct data mining and feature extraction on the microblog data from the Wuhan area and the imported case province (Henan Province) during the high and median operating periods of the epidemic. Our experiment indicates that the semantic and sentiment vocabulary of words, the sentiment curve, and the portrait of patients seeking help were all heterogeneous in the context of COVID-19. We extract and acquire the outlier knowledge of COVID-19 and incorporate it into the outlier knowledge base of extreme public health events for knowledge sharing and transformation. The knowledge base serves as a think tank for public opinion guidance and platform suggestions for dealing with extreme public health events. This paper provides novel ideas and methods for outlier knowledge management in healthcare contexts. Elsevier Ltd. 2022-03 2020-09-08 /pmc/articles/PMC7477628/ /pubmed/32921839 http://dx.doi.org/10.1016/j.seps.2020.100941 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Invited Paper Xia, Huosong An, Wuyue Li, Jiaze Zhang, Zuopeng (Justin) Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data |
title | Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data |
title_full | Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data |
title_fullStr | Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data |
title_full_unstemmed | Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data |
title_short | Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data |
title_sort | outlier knowledge management for extreme public health events: understanding public opinions about covid-19 based on microblog data |
topic | Invited Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477628/ https://www.ncbi.nlm.nih.gov/pubmed/32921839 http://dx.doi.org/10.1016/j.seps.2020.100941 |
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