Protocol for the automatic extraction of epidemiological information via a pre-trained language model

The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention measures. Here we present a protocol for using CCIE, a COVID-19 Cases Information Extraction system based on the pre-trained language model.(1) We de...

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Detalles Bibliográficos
Autores principales: Wang, Zhizheng, Liu, Xiao Fan, Du, Zhanwei, Wang, Lin, Wu, Ye, Holme, Petter, Lachmann, Michael, Lin, Hongfei, Wang, Zhuoyue, Cao, Yu, Wong, Zoie S.Y., Xu, Xiao-Ke, Sun, Yuanyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328978/
https://www.ncbi.nlm.nih.gov/pubmed/37393610
http://dx.doi.org/10.1016/j.xpro.2023.102392
Descripción
Sumario:The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention measures. Here we present a protocol for using CCIE, a COVID-19 Cases Information Extraction system based on the pre-trained language model.(1) We describe steps for preparing supervised training data and executing python scripts for named entity recognition and text category classification. We then detail the use of machine evaluation and manual validation to illustrate the effectiveness of CCIE. For complete details on the use and execution of this protocol, please refer to Wang et al.(2)

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