Epidemiologic information discovery from open-access COVID-19 case reports via pretrained language model.
iScience
; 25(10): 105079, 2022 Oct 21.
Article
in English
| MEDLINE | ID: covidwho-2007782
ABSTRACT
Although open-access data are increasingly common and useful to epidemiological research, the curation of such datasets is resource-intensive and time-consuming. Despite the existence of a major source of COVID-19 data, the regularly disclosed case reports were often written in natural language with an unstructured format. Here, we propose a computational framework that can automatically extract epidemiological information from open-access COVID-19 case reports. We develop this framework by coupling a language model developed using deep neural networks with training samples compiled using an optimized data annotation strategy. When applied to the COVID-19 case reports collected from mainland China, our framework outperforms all other state-of-the-art deep learning models. The information extracted from our approach is highly consistent with that obtained from the gold-standard manual coding, with a matching rate of 80%. To disseminate our algorithm, we provide an open-access online platform that is able to estimate key epidemiological statistics in real time, with much less effort for data curation.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Case report
/
Observational study
Language:
English
Journal:
IScience
Year:
2022
Document Type:
Article
Affiliation country:
J.isci.2022.105079
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