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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.

Publication ,  Journal Article
Wang, J; Abu-El-Rub, N; Gray, J; Pham, HA; Zhou, Y; Manion, FJ; Liu, M; Song, X; Xu, H; Rouhizadeh, M; Zhang, Y
Published in: Journal of the American Medical Informatics Association : JAMIA
June 2021

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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Published In

Journal of the American Medical Informatics Association : JAMIA

DOI

EISSN

1527-974X

ISSN

1067-5027

Publication Date

June 2021

Volume

28

Issue

6

Start / End Page

1275 / 1283

Related Subject Headings

  • Symptom Assessment
  • Natural Language Processing
  • Medical Informatics
  • Information Storage and Retrieval
  • Humans
  • Electronic Health Records
  • Deep Learning
  • COVID-19
  • 46 Information and computing sciences
  • 42 Health sciences
 

Citation

APA
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Wang, J., Abu-El-Rub, N., Gray, J., Pham, H. A., Zhou, Y., Manion, F. J., … Zhang, Y. (2021). 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. Journal of the American Medical Informatics Association : JAMIA, 28(6), 1275–1283. https://doi.org/10.1093/jamia/ocab015
Wang, Jingqi, Noor Abu-El-Rub, Josh Gray, Huy Anh Pham, Yujia Zhou, Frank J. Manion, Mei Liu, et al. “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.Journal of the American Medical Informatics Association : JAMIA 28, no. 6 (June 2021): 1275–83. https://doi.org/10.1093/jamia/ocab015.
Wang J, Abu-El-Rub N, Gray J, Pham HA, Zhou Y, Manion FJ, et al. 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. Journal of the American Medical Informatics Association : JAMIA. 2021 Jun;28(6):1275–83.
Wang, Jingqi, et al. “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.Journal of the American Medical Informatics Association : JAMIA, vol. 28, no. 6, June 2021, pp. 1275–83. Epmc, doi:10.1093/jamia/ocab015.
Wang J, Abu-El-Rub N, Gray J, Pham HA, Zhou Y, Manion FJ, Liu M, Song X, Xu H, Rouhizadeh M, Zhang Y. 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. Journal of the American Medical Informatics Association : JAMIA. 2021 Jun;28(6):1275–1283.
Journal cover image

Published In

Journal of the American Medical Informatics Association : JAMIA

DOI

EISSN

1527-974X

ISSN

1067-5027

Publication Date

June 2021

Volume

28

Issue

6

Start / End Page

1275 / 1283

Related Subject Headings

  • Symptom Assessment
  • Natural Language Processing
  • Medical Informatics
  • Information Storage and Retrieval
  • Humans
  • Electronic Health Records
  • Deep Learning
  • COVID-19
  • 46 Information and computing sciences
  • 42 Health sciences