TY - JOUR
T1 - Quality assurance of chemical ingredient classification for the National Drug File – Reference Terminology
AU - Zheng, Ling
AU - Yumak, Hasan
AU - Chen, Ling
AU - Ochs, Christopher
AU - Geller, James
AU - Kapusnik-Uner, Joan
AU - Perl, Yehoshua
N1 - Funding Information:
We thank Michael Lincoln of the VHA and Mark Erlbaum of Apelon for sharing with us their insights into NDF-RT structure and its history. Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA190779. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© 2017 Elsevier Inc.
PY - 2017/9
Y1 - 2017/9
N2 - The National Drug File – Reference Terminology (NDF-RT) is a large and complex drug terminology consisting of several classification hierarchies on top of an extensive collection of drug concepts. These hierarchies provide important information about clinical drugs, e.g., their chemical ingredients, mechanisms of action, dosage form and physiological effects. Within NDF-RT such information is represented using tens of thousands of roles connecting drugs to classifications. In previous studies, we have introduced various kinds of Abstraction Networks to summarize the content and structure of terminologies in order to facilitate their visual comprehension, and support quality assurance of terminologies. However, these previous kinds of Abstraction Networks are not appropriate for summarizing the NDF-RT classification hierarchies, due to its unique structure. In this paper, we present the novel Ingredient Abstraction Network (IAbN) to summarize, visualize and support the audit of NDF-RT's Chemical Ingredients hierarchy and its associated drugs. A common theme in our quality assurance framework is to use characterizations of sets of concepts, revealed by the Abstraction Network structure, to capture concepts, the modeling of which is more complex than for other concepts. For the IAbN, we characterize drug ingredient concepts as more complex if they belong to IAbN groups with multiple parent groups. We show that such concepts have a statistically significantly higher rate of errors than a control sample and identify two especially common patterns of errors.
AB - The National Drug File – Reference Terminology (NDF-RT) is a large and complex drug terminology consisting of several classification hierarchies on top of an extensive collection of drug concepts. These hierarchies provide important information about clinical drugs, e.g., their chemical ingredients, mechanisms of action, dosage form and physiological effects. Within NDF-RT such information is represented using tens of thousands of roles connecting drugs to classifications. In previous studies, we have introduced various kinds of Abstraction Networks to summarize the content and structure of terminologies in order to facilitate their visual comprehension, and support quality assurance of terminologies. However, these previous kinds of Abstraction Networks are not appropriate for summarizing the NDF-RT classification hierarchies, due to its unique structure. In this paper, we present the novel Ingredient Abstraction Network (IAbN) to summarize, visualize and support the audit of NDF-RT's Chemical Ingredients hierarchy and its associated drugs. A common theme in our quality assurance framework is to use characterizations of sets of concepts, revealed by the Abstraction Network structure, to capture concepts, the modeling of which is more complex than for other concepts. For the IAbN, we characterize drug ingredient concepts as more complex if they belong to IAbN groups with multiple parent groups. We show that such concepts have a statistically significantly higher rate of errors than a control sample and identify two especially common patterns of errors.
KW - Auditing NDF-RT
KW - Auditing chemical ingredients
KW - Chemical Ingredients hierarchy
KW - Drug terminology
KW - NDF-RT quality assurance
KW - National Drug File – Reference Terminology
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U2 - 10.1016/j.jbi.2017.07.013
DO - 10.1016/j.jbi.2017.07.013
M3 - Article
C2 - 28723580
AN - SCOPUS:85025701939
SN - 1532-0464
VL - 73
SP - 30
EP - 42
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
ER -