论文标题
从医学文献中识别与COVID-19相关的放射学发现
Identifying Radiological Findings Related to COVID-19 from Medical Literature
论文作者
论文摘要
2019年冠状病毒病（Covid-19）已感染了世界各地超过一百万个人，并造成了55,000多人死亡，截至2020年4月3日。放射学发现是指导Covid-19的诊断和治疗的重要信息来源。但是，现有的有关放射学发现与Covid-19的相关性如何相关的研究是由不同的医院分别进行的，这可能是由于人口偏见而导致的，甚至可能是不一致的。为了解决这个问题，我们开发了自然语言处理方法，以分析包含来自世界各地医院的研究报告，调和这些结果，并得出关于放射线学发现与共同199之间的相关性的大量研究报告，并调和这些结果，并调和这些结果。我们将我们的方法应用于脐带-19数据集，并成功提取了一组与COVID-19密切相关的放射学发现。
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.
