Selected article for: "accuracy achieve and achieve test set accuracy"
Author: Dong, Xiao; Li, Jianfu; Soysal, Ekin; Bian, Jiang; DuVall, Scott L; Hanchrow, Elizabeth; Liu, Hongfang; Lynch, Kristine E; Matheny, Michael; Natarajan, Karthik; Ohno-Machado, Lucila; Pakhomov, Serguei; Reeves, Ruth Madeleine; Sitapati, Amy M; Abhyankar, Swapna; Cullen, Theresa; Deckard, Jami; Jiang, Xiaoqian; Murphy, Robert; Xu, Hua
Title: COVID-19 TestNorm - A tool to normalize COVID-19 testing names to LOINC codes
  • Cord-id: sx2lolfj
  • Document date: 2020_6_22
    • ID: sx2lolfj
    Snippet: Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on 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
    Document: Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on 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 codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from eight 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 it will be a useful tool to support secondary use of EHRs for research on COVID-19.

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