  Coronavirus disease 2019 (COVID-19) has infected more than one million
individuals all over the world and caused more than 55,000 deaths, as of April
3 in 2020. Radiological findings are important sources of information in
guiding the diagnosis and treatment of COVID-19. However, the existing studies
on how radiological findings are correlated with COVID-19 are conducted
separately by different hospitals, which may be inconsistent or even
conflicting due to population bias. To address this problem, we develop natural
language processing methods to analyze a large collection of COVID-19
literature containing study reports from hospitals all over the world,
reconcile these results, and draw unbiased and universally-sensible conclusions
about the correlation between radiological findings and COVID-19. We apply our
method to the CORD-19 dataset and successfully extract a set of radiological
findings that are closely tied to COVID-19.
