Auditing complex concepts in overlapping subsets of SNOMED.
Limited resources and the sheer volume of concepts make auditing a large terminology, such as SNOMED CT, a daunting task. It is essential to devise techniques that can aid an auditor by automatically identifying concepts that deserve attention. A methodology for this purpose based on a previously introduced abstraction network (called the p-area taxonomy) for a SNOMED CT hierarchy is presented. The methodology algorithmically gathers concepts appearing in certain overlapping subsets, defined exclusively with respect to the p-area taxonomy, for review. The results of applying the methodology to SNOMED's Specimen hierarchy are presented. These results are compared against a control sample composed of concepts residing in subsets without the overlaps. With the use of the double bootstrap, the concept group produced by our methodology is shown to yield a statistically significant higher proportion of error discoveries.
Wang
Yue
Y
NJIT, Newark, NJ, USA.
Wei
Duo
D
Xu
Junchuan
J
Elhanan
Gai
G
Perl
Yehoshua
Y
Halper
Michael
M
Chen
Yan
Y
Spackman
Kent A
KA
Hripcsak
George
G
D000465:Algorithms; D001185:Artificial Intelligence; D054869:Clinical Audit / Q000379:methods*; D019300:Medical Errors / Q000517:prevention & control*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D039061:Systematized Nomenclature of Medicine; D009626:Terminology as Topic; D014481:United States