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 URL We believe COVID-19 SignSym will provide fundamental supports to the secondary use of EHRs, thus accelerating the global research of COVID-19.
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 Sign-Sym 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 URL and has been used by 16 URLanizations to support clinical research of COVID-19.
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,
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will be significantly
(EHRs) will be
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various applications of
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clinical
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the existing clinical NLP tool CLAMP was quickly adapted to
this study aims at adapting the existing CLAMP natural language processing tool to quickly build
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information and generated an automated tool called COVID-19
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and
COVID-19
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eight attributes such as temporal information and negations
8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course)
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clinical
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Observational Medical Outcomes Partnership
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. 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
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OHDSI OMOP. Evaluation on clinical notes and medical dialogues demonstrated promising results. It
COVID-19 patients, as well as the online medical dialogues of COVID-19, shows COVID-19 Sign-Sym 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
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research
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of APIs URL We believe COVID-19 SignSym will provide fundamental supports to the secondary use of EHRs, thus accelerating the global
URL and has been used by 16 URLanizations to support clinical