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

Zhizheng Wang; Xiao Fan Liu; Zhanwei Du; Lin Wang; Ye Wu; Petter Holme; Michael Lachmann; Hongfei Lin; Zhuoyue Wang; Yu Cao; Zoie S.Y. Wong; Xiao-Ke Xu; Yuanyuan Sun

doi:10.1016/j.xpro.2023.102392

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

Zhizheng Wang
, Xiao Fan Liu
, Zhanwei Du
, Lin Wang
, Ye Wu
, Petter Holme
, Michael Lachmann
, Hongfei Lin
, Zhuoyue Wang
, Yu Cao
, Zoie S.Y. Wong^*
, Xiao-Ke Xu^*
, Yuanyuan Sun^*

^*Corresponding author for this work

Department of Media and Communication

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

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Abstract

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 © 2023 The Authors.

Original language	English
Article number	102392
Journal	STAR Protocols
Volume	4
Issue number	3
Online published	1 Jul 2023
DOIs	https://doi.org/10.1016/j.xpro.2023.102392
Publication status	Published - 15 Sept 2023

Research Keywords

Clinical Protocol
Computer Sciences
Health Sciences

Publisher's Copyright Statement

This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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10.1016/j.xpro.2023.102392

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title = "Protocol for the automatic extraction of epidemiological information via a pre-trained language model",

abstract = "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 {\textcopyright} 2023 The Authors.",

keywords = "Clinical Protocol, Computer Sciences, Health Sciences",

author = "Zhizheng Wang and Liu, \{Xiao Fan\} and Zhanwei Du and Lin Wang and Ye Wu and Petter Holme and Michael Lachmann and Hongfei Lin and Zhuoyue Wang and Yu Cao and Wong, \{Zoie S.Y.\} and Xiao-Ke Xu and Yuanyuan Sun",

year = "2023",

month = sep,

day = "15",

doi = "10.1016/j.xpro.2023.102392",

language = "English",

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TY - JOUR

T1 - Protocol for the automatic extraction of epidemiological information via a pre-trained language model

AU - Wang, Zhizheng

AU - Liu, Xiao Fan

AU - Du, Zhanwei

AU - Wang, Lin

AU - Wu, Ye

AU - Holme, Petter

AU - Lachmann, Michael

AU - Lin, Hongfei

AU - Wang, Zhuoyue

AU - Cao, Yu

AU - Wong, Zoie S.Y.

AU - Xu, Xiao-Ke

AU - Sun, Yuanyuan

PY - 2023/9/15

Y1 - 2023/9/15

N2 - 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 © 2023 The Authors.

AB - 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 © 2023 The Authors.

KW - Clinical Protocol

KW - Computer Sciences

KW - Health Sciences

UR - https://www.scopus.com/pages/publications/85164021849

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85164021849&origin=recordpage

U2 - 10.1016/j.xpro.2023.102392

DO - 10.1016/j.xpro.2023.102392

M3 - RGC 21 - Publication in refereed journal

C2 - 37393610

SN - 2666-1667

VL - 4

JO - STAR Protocols

JF - STAR Protocols

IS - 3

M1 - 102392

ER -

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

Abstract

Research Keywords

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