Complexity measures to track the evolution of a SNOMED hierarchy.
SNOMED CT is an extensive terminology with an attendant amount of complexity. Two measures are proposed for quantifying that complexity. Both are based on abstraction networks, called the area taxonomy and the partial-area taxonomy, that provide, for example, distributions of the relationships within a SNOMED hierarchy. The complexity measures are employed specifically to track the complexity of versions of the Specimen hierarchy of SNOMED before and after it is put through an auditing process. The pre-audit and post-audit versions are compared. The results show that the auditing process indeed leads to a simplification of the terminology's structure.
Wei
Duo
D
NJIT, Newark, NJ, USA.
Wang
Yue
Y
Perl
Yehoshua
Y
Xu
Junchuan
J
Halper
Michael
M
Spackman
Kent A
KA
Spackman
Kent
K
D000465:Algorithms; D001185:Artificial Intelligence; D008485:Medical Audit; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D039061:Systematized Nomenclature of Medicine; D014481:United States