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The COVID-19 pandemic swept across the world rapidly, infecting millions ofpeople. An efficient tool that can accurately recognize important clinicalconcepts of COVID-19 from free text in electronic health records (EHRs) will bevaluable to accelerate COVID-19 clinical research. To this end, this study aimsat adapting the existing CLAMP natural language processing tool to quicklybuild COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8attributes (body location, severity, temporal expression, subject, condition,uncertainty, negation, and course) from clinical text. The extractedinformation is also mapped to standard concepts in the Observational MedicalOutcomes Partnership common data model. A hybrid approach of combining deeplearning-based models, curated lexicons, and pattern-based rules was applied toquickly build the COVID-19 SignSym from CLAMP, with optimized performance. Ourextensive evaluation using 3 external sites with clinical notes of COVID-19patients, as well as the online medical dialogues of COVID-19, shows COVID-19Sign-Sym can achieve high performance across data sources. The workflow usedfor this study can be generalized to other use cases, where existing clinicalnatural language processing tools need to be customized for specificinformation needs within a short time. COVID-19 SignSym is freely accessible tothe research community as a downloadable package(https://clamp.uth.edu/covid/nlp.php) and has been used by 16 healthcareorganizations to support clinical research of COVID-19.

COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model