Quality assurance of chemical ingredient classification for the National Drug File - Reference Terminology.

Abstract:

: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.

journal_name

J Biomed Inform

authors

Zheng L,Yumak H,Chen L,Ochs C,Geller J,Kapusnik-Uner J,Perl Y

doi

10.1016/j.jbi.2017.07.013

subject

Has Abstract

pub_date

2017-09-01 00:00:00

pages

30-42

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(17)30169-7

journal_volume

73

pub_type

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