EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports

Non-pharmacological interventions (NPIs) are important for controlling infectious diseases such as COVID-19, but their implementation is currently monitored in an ad hoc manner. To address this issue, we present a three-stage machine learning framework called EpiTopics to facilitate the surveillance...

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Detalles Bibliográficos
Autores principales: Wen, Zhi, Zhang, Jingfu, Powell, Guido, Chafi, Imane, Buckeridge, David L., Li, Yue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9189439/
https://www.ncbi.nlm.nih.gov/pubmed/35712009
http://dx.doi.org/10.1016/j.xpro.2022.101463
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author Wen, Zhi
Zhang, Jingfu
Powell, Guido
Chafi, Imane
Buckeridge, David L.
Li, Yue
author_facet Wen, Zhi
Zhang, Jingfu
Powell, Guido
Chafi, Imane
Buckeridge, David L.
Li, Yue
author_sort Wen, Zhi
collection PubMed
description Non-pharmacological interventions (NPIs) are important for controlling infectious diseases such as COVID-19, but their implementation is currently monitored in an ad hoc manner. To address this issue, we present a three-stage machine learning framework called EpiTopics to facilitate the surveillance of NPI. In this protocol, we outline the use of transfer-learning to address the limited number of NPI-labeled documents and topic modeling to support interpretation of the results. For complete details on the use and execution of this protocol, please refer to Wen et al. (2022).
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spelling pubmed-91894392022-06-13 EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports Wen, Zhi Zhang, Jingfu Powell, Guido Chafi, Imane Buckeridge, David L. Li, Yue STAR Protoc Protocol Non-pharmacological interventions (NPIs) are important for controlling infectious diseases such as COVID-19, but their implementation is currently monitored in an ad hoc manner. To address this issue, we present a three-stage machine learning framework called EpiTopics to facilitate the surveillance of NPI. In this protocol, we outline the use of transfer-learning to address the limited number of NPI-labeled documents and topic modeling to support interpretation of the results. For complete details on the use and execution of this protocol, please refer to Wen et al. (2022). Elsevier 2022-06-13 /pmc/articles/PMC9189439/ /pubmed/35712009 http://dx.doi.org/10.1016/j.xpro.2022.101463 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Wen, Zhi
Zhang, Jingfu
Powell, Guido
Chafi, Imane
Buckeridge, David L.
Li, Yue
EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports
title EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports
title_full EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports
title_fullStr EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports
title_full_unstemmed EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports
title_short EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports
title_sort epitopics: a dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9189439/
https://www.ncbi.nlm.nih.gov/pubmed/35712009
http://dx.doi.org/10.1016/j.xpro.2022.101463
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