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

• Published in: iScience 2022-10, Vol.25 (10), p.105079, Article 105079
• Main 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
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Health sciences; Machine learning; Virology
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2022.105079

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. [Display omitted] •We propose a method to obtain epidemiological information from COVID-19 case reports•The extracted information has 80% matching rate with the gold-standard manual coding•We provide an online platform that can analyze epidemiological statistics in real time Health sciences; Virology ; Artificial intelligence; Machine learning;