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

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 e

SUBMITTER: Wang Z 

PROVIDER: S-EPMC9441477 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature