COVID-19 has caused thousands of deaths around the world and also resulted in a large international economic disruption. Identifying the pathways associated with this illness can help medical researchers to better understand the properties of the condition. This process can be carried out by analyzing the medical records. It is crucial to develop tools and models that can aid researchers with this process in a timely manner. However, medical records are often unstructured clinical notes, and this poses significant challenges to developing the automated systems. In this article, we propose a pipeline to aid practitioners in analyzing clinical notes and revealing the pathways associated with this disease. Our pipeline relies on topological properties and consists of three steps: 1) pre-processing the clinical notes to extract the salient concepts, 2) constructing a feature space of the patients to characterize the extracted concepts, and finally, 3) leveraging the topological properties to distill the available knowledge and visualize the result. Our experiments on a publicly available dataset of COVID-19 clinical notes testify that our pipeline can indeed extract meaningful pathways.

翻译：COVID-19已经在世界各地造成数千人死亡,并造成了巨大的国际经济混乱。 确定与这种疾病相关的路径可以帮助医学研究人员更好地了解病情的特性。 这一过程可以通过分析医疗记录来进行。 开发工具和模型,能够及时帮助研究人员进行这一过程至关重要。 但是, 医疗记录往往没有结构化的临床记录, 这给开发自动化系统带来了重大挑战。 在文章中, 我们提议建立一个管道, 帮助从业者分析临床笔记和揭示与这种疾病相关的路径。 我们的管道依靠地貌特性, 包括三个步骤:(1) 预处理临床笔记以提取突出的概念;(2) 建立病人特征空间以描述提取的概念;以及(3) 利用病理特性来提取现有的知识并将结果视觉化。 我们对可公开获得的COVID-19临床笔记数据集的实验证明, 我们的管道确实可以提取有意义的路径。