A deep learning approach to identify missing is-a relations in SNOMED CT.

ABSTRACT:

Objective

SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT.

Materials and methods

Our focus is to identify missing is-a relations between concept-pairs exhibiting a containment pattern (ie, the set of words of one concept being a proper subset of that of the other concept). We use hierarchically related containment concept-pairs as positive instances and hierarchically unrelated containment concept-pairs as negative instances to train a model predicting whether an is-a relation exists between 2 concepts with containme

SUBMITTER: Abeysinghe R 

PROVIDER: S-EPMC9933066 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature