Journal
ISCIENCE
Volume 25, Issue 10, Pages -Publisher
CELL PRESS
DOI: 10.1016/j.isci.2022.105079
Keywords
-
Categories
Funding
- National Natural Science Foundation of China [61773091, 62173065, 72025405, 82041020, 91846301]
- Liaoning Revitalization Talents Program [XLYC1807106]
- Grand Challenges ICODA pilot initiative
- Minderoo Foundation
- Japan Society for the Promotion of Science KAKENHI [18H03336]
- Fundamental Research Funds for the Central Universities [DUT22ZD205]
- Bill & Melinda Gates Foundation
Ask authors/readers for more resources
This paper proposes a computational framework that can automatically extract epidemiological information from open-access COVID-19 case reports, and provides an open-access online platform to implement the algorithm.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available