Epidemiologic information discovery from open-access COVID-19 case reports via pretrained language model

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

Source：iScience, 2022, 25(10): 105079.

DOI：10.1016/j.isci.2022.105079

• FRAMEWORK

Although open-access data are increasing common and useful to epidemiological research, curation of such datasets is resource-intensive and time-consuming. Despite a major source of COVID-19 data, the regularly disclosed case reports were often written in natural language with 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 language model developed using deep neural networks with training samples compiled using an optimized data annotation strategy. When applying to the COVID-19 case reports collected from mainland China, our novel framework outstrips 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 implement our algorithm, we provide an open-access online platform that can accurately estimate epidemiological statistics in real-time with substantially reduced burden in data curation.

• Institution
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