The COVID-19 pandemic swept across the world rapidly infecting millions of people. An efficient tool that can accurately recognize important clinical concepts of COVID-19 from free text in electronic health records will be significantly valuable to accelerate various applications of COVID-19 research. To this end, the existing clinical NLP tool CLAMP was quickly adapted to COVID-19 information and generated an automated tool called COVID-19 SignSym, which can extract and signs/symptoms and their eight attributes such as temporal information and negations from clinical text. The extracted information is also mapped to standard clinical concepts in the common data model of OHDSI OMOP. Evaluation on clinical notes and medical dialogues demonstrated promising results. It is freely accessible to the community as a downloadable package of APIs (https://clamp.uth.edu/covid/nlp.php). We believe COVID-19 SignSym will provide fundamental supports to the secondary use of EHRs, thus accelerating the global research of COVID-19.

本文旨在将现有的CLAMP自然语言处理工具定制为COVID-19 SignSym，能够从临床文本中提取COVID-19的迹象/症状及其8个属性，并映射到标准概念中，以加速COVID-19的临床研究。通过结合深度学习模型、精选的词典和基于模式的规则等混合方法，优化了COVID-19 SignSym的性能，且在多个数据来源中表现良好。

COVID-19 SignSym: 将通用临床 NLP 工具快速适配用于识别和规范 COVID-19 的征象和症状到 OMOP 通用数据模型