COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes.
COVID-19
COVID-19 TestNorm
LOINC
natural language processing
testing name normalization
Journal
Journal of the American Medical Informatics Association : JAMIA
ISSN: 1527-974X
Titre abrégé: J Am Med Inform Assoc
Pays: England
ID NLM: 9430800
Informations de publication
Date de publication:
01 07 2020
01 07 2020
Historique:
received:
11
03
2020
revised:
11
05
2020
accepted:
17
06
2020
pubmed:
23
6
2020
medline:
6
10
2020
entrez:
23
6
2020
Statut:
ppublish
Résumé
Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.
Identifiants
pubmed: 32569358
pii: 5860833
doi: 10.1093/jamia/ocaa145
pmc: PMC7337837
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1437-1442Subventions
Organisme : NCI NIH HHS
ID : U24 CA194215
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG066749
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002494
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM114612
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR003167
Pays : United States
Organisme : NCATS NIH HHS
ID : U01 TR002062
Pays : United States
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.
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