Identifying Radiological Findings Related to COVID-19 from Medical Literature

TL;DR

This paper develops NLP methods to analyze global COVID-19 literature, extracting consistent radiological findings related to the disease to aid diagnosis and treatment.

Contribution

The paper introduces a novel NLP approach to synthesize radiological findings from diverse COVID-19 studies, addressing population bias and conflicting results.

Findings

Identified key radiological features associated with COVID-19

Reconciled conflicting study results to produce unbiased conclusions

Applied methods successfully to the CORD-19 dataset

Abstract

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.