Name
Abstract | ||
|---|---|---|
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 opensource 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/jamia/ocaa145 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | DocType | Volume |
COVID-19, natural language processing, testing name normalization, LOINC, COVID-19 TestNorm | Journal | 27 |
Issue | ISSN | Citations |
9 | 1067-5027 | 0 |
PageRank | References | Authors |
0.34 | 0 | 20 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Dong | 1 | 0 | 1.01 |
| Jianfu Li | 2 | 0 | 1.35 |
| Ekin Soysal | 3 | 0 | 0.34 |
| Jiang Bian | 4 | 150 | 43.09 |
| Scott L. DuVall | 5 | 385 | 33.47 |
| Elizabeth Hanchrow | 6 | 0 | 0.34 |
| Hongfang Liu | 7 | 1479 | 160.66 |
| Kristine E Lynch | 8 | 0 | 0.34 |
| Michael E. Matheny | 9 | 202 | 33.36 |
| Karthik Natarajan | 10 | 407 | 31.52 |
| Lucila Ohno-Machado | 11 | 1426 | 187.95 |
| Serguei V S Pakhomov | 12 | 471 | 40.62 |
| Ruth Madeleine Reeves | 13 | 0 | 0.34 |
| Amy M Sitapati | 14 | 0 | 0.34 |
| Swapna Abhyankar | 15 | 47 | 7.51 |
| Theresa Cullen | 16 | 0 | 0.34 |
| Jami Deckard | 17 | 0 | 0.34 |
| Xiaoqian Jiang | 18 | 718 | 72.47 |
| Robert Murphy | 19 | 0 | 0.34 |
| Hua Xu | 20 | 650 | 69.76 |