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. 2021 Jun 12;28(6):1275-1283.
doi: 10.1093/jamia/ocab015.

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

Jingqi Wang  1   2 , Noor Abu-El-Rub  3 , Josh Gray  4 , Huy Anh Pham  1 , Yujia Zhou  2 , Frank J Manion  1 , Mei Liu  3 , Xing Song  5 , Hua Xu  2 , Masoud Rouhizadeh  4 , Yaoyun Zhang  1
Affiliations

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

Jingqi Wang et al. J Am Med Inform Assoc. .

Abstract

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.

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Figures

Figure 1.
Figure 1.
An overview of the NLP pipeline for COVID-19 sign/symptom extraction and normalization.
Figure 2.
Figure 2.
Information model of COVID-19 signs/symptoms and their attributes.
Figure 3.
Figure 3.
An output illustration of the COVID-19 sign/symptom extraction tool.
Figure 4.
Figure 4.
A Venn diagram of lexicon overlap among MIMIC-III, UTP notes, and medical dialogues for COVID-19 signs/symptoms.
See this image and copyright information in PMC

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References

    1. WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/ Accessed January 10, 2020.
    1. Coronavirus in the U.S.:Latest Map and Case Count., https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html Accessed February 23, 2021.
    1. Kupferschmidt K, Cohen J. WHO launches global megatrial of the four most promising coronavirus treatments. Science | AAAS; 2020. https://www.sciencemag.org/news/2020/03/who-launches-global-megatrial-fo...
    1. Open science initiatives related to the COVID-19. OECD. https://community.oecd.org/docs/DOC-172520 Accessed January 10, 2020.
    1. COVID-19 Datasets and Machine Learning Projects. https://www.kaggle.com/tags/covid19/ Accessed January 10, 2020.

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