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 (EHRs) will be valuable to accelerate COVID-19 clinical research. To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text. The extracted information is also mapped to standard concepts in the Observational Medical Outcomes Partnership common data model. A hybrid approach of combining deep learning-based models, curated lexicons, and pattern-based rules was applied to quickly build the COVID-19 SignSym from CLAMP, with optimized performance. Our extensive evaluation using 3 external sites with clinical notes of COVID-19 patients, as well as the online medical dialogues of COVID-19, shows COVID-19 SignSym can achieve high performance across data sources. The workflow used for this study can be generalized to other use cases, where existing clinical natural language processing tools need to be customized for specific information needs within a short time. COVID-19 SignSym is freely accessible to the research community as a downloadable package (https://clamp.uth.edu/covid/nlp.php) and has been used by 16 healthcare organizations to support clinical research of COVID-19.

中文翻译：

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COVID-19 SignSym：通用临床 NLP 工具的快速改编，用于识别 COVID-19 体征和症状并将其标准化为 OMOP 通用数据模型


COVID-19 大流行迅速席卷全球，感染了数百万人。一种能够从电子健康记录 (EHR) 中的自由文本中准确识别 COVID-19 重要临床概念的有效工具，对于加速 COVID-19 临床研究将具有重要意义。为此，本研究旨在采用现有的 CLAMP 自然语言处理工具来快速构建 COVID-19 SignSym，它可以提取 COVID-19 体征/症状及其 8 个属性（身体位置、严重程度、时间表达、主题、病情、临床文本中的不确定性、否定性和过程）。提取的信息还映射到观察医疗结果合作伙伴通用数据模型中的标准概念。应用将基于深度学习的模型、精选词典和基于模式的规则相结合的混合方法，从 CLAMP 快速构建 COVID-19 SignSym，并优化性能。我们使用 3 个外部站点以及 COVID-19 患者的临床记录以及 COVID-19 的在线医疗对话进行的广泛评估表明，COVID-19 SignSym 可以跨数据源实现高性能。本研究使用的工作流程可以推广到其他用例，其中现有的临床自然语言处理工具需要在短时间内针对特定信息需求进行定制。 COVID-19 SignSym 可作为可下载包免费供研究界使用 (https://clamp.uth.edu/covid/nlp.php)，并已被 16 个医疗机构用于支持 COVID-19 的临床研究。