COVID-19 TestNorm - A tool to normalize COVID-19 testing names to LOINC codes
| Autor: | Amy M. Sitapati, Serguei V. S. Pakhomov, Hongfang Liu, Swapna Abhyankar, Jianfu Li, Theresa Cullen, Robert Murphy, Elizabeth Hanchrow, Hua Xu, Xiaoqian Jiang, Ruth M. Reeves, Jian-Guo Bian, Lucila Ohno-Machado, Michael E. Matheny, Scott L. DuVall, Karthik Natarajan, Kristine E. Lynch, Xiao Dong, Ekin Soysal, Jami Deckard |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Normalization (statistics)
030213 general clinical medicine Testing name normalization Computer science Pneumonia Viral Health Informatics Brief Communication Database normalization 03 medical and health sciences Betacoronavirus 0302 clinical medicine COVID-19 Testing Terminology as Topic Web application Leverage (statistics) Electronic Health Records Humans 030212 general & internal medicine COVID-19 TestNorm Pandemics Natural Language Processing Information retrieval LOINC business.industry End user Clinical Laboratory Techniques SARS-CoV-2 COVID-19 Identifier Test set Logical Observation Identifiers Names and Codes business Coronavirus Infections |
| Zdroj: | Journal of the American Medical Informatics Association : JAMIA Journal of the American Medical Informatics Association |
| ISSN: | 1527-974X 1067-5027 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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