Exploring Deep Learning-based Approaches for Predicting Concept Names in SNOMED CT

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Cui

2020

Self Cite

Biomedical terminologies have been increasingly used in modern biomedical research and applications to facilitate data management and ensure semantic interoperability. As part of the evolution process, new concepts are regularly added to biomedical terminologies in response to the evolving domain knowledge and emerging applications. Most existing concept enrichment methods suggest new concepts via directly importing knowledge from external sources. In this paper, we introduced a lexical method based on formal concept analysis (FCA) to identify potentially missing concepts in a given terminology by leveraging its intrinsic knowledge -concept names. We first construct the FCA formal context based on the lexical features of concepts. Then we perform multistage intersection to formalize new concepts and detect potentially missing concepts. We applied our method to the Disease or Disorder sub-hierarchy in the National Cancer Institute (NCI) Thesaurus (19.08d version) and identified a total of 8,983 potentially missing concepts. As a preliminary evaluation of our method to validate the potentially missing concepts, we further checked whether they were included in any external source terminology in the Unified Medical Language System (UMLS). The result showed that 592 out of 8,937 potentially missing concepts were found in the UMLS.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Lexical-based Formal Concept Analysis Method to Identify Missing Concepts in the NCI Thesaurus

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Cui

2020

Self Cite

“…In one study, we explored 3 deep learning models to predict proper names of SNOMED CT concepts complying with the terminology’s naming convention. 16 Other works have leveraged deep learning to place new concepts in the SNOMED CT hierarchy given that the new concept’s name is known. 17–19 We also investigated whether deep learning could aid in automatically validating the suggested missing is-a relations in SNOMED CT obtained by nonlattice-based auditing approaches.…”

Section: Background and Significancementioning

confidence: 99%

A deep learning approach to identify missingis-arelations in SNOMED CT

Abeysinghe

Journal of the American Medical Informatics Association

Bernstam

et al. 2022

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 containment pattern. The model is a binary classifier leveraging concept name features, hierarchical features, enriched lexical attribute features, and logical definition features. We introduce a cross-validation inspired approach to identify missing is-a relations among all hierarchically unrelated containment concept-pairs. Results We trained and applied our model on the Clinical finding subhierarchy of SNOMED CT (September 2019 US edition). Our model (based on the validation sets) achieved a precision of 0.8164, recall of 0.8397, and F1 score of 0.8279. Applying the model to predict actual missing is-a relations, we obtained a total of 1661 potential candidates. Domain experts performed evaluation on randomly selected 230 samples and verified that 192 (83.48%) are valid. Conclusions The results showed that our deep learning approach is effective in uncovering missing is-a relations between containment concept-pairs in SNOMED CT.

“…Purely from the concept name itself, the machine will not be able to know that this concept refers to a malignant neoplasm of the soft tissue or bone. In addition, concept names are defined manually by curators of biomedical ontologies and inconsistencies may exist during the naming process [22], which may further affect the subsumption checking.…”

Section: Lexical Features and Role Definitions In Biomedical Ontologiesmentioning

confidence: 99%

Detecting missing IS-A relations in the NCI Thesaurus using an enhanced hybrid approach

BMC Med Inform Decis Mak

Abeysinghe

Sioutos

et al. 2020

Self Cite

Background The National Cancer Institute (NCI) Thesaurus provides reference terminology for NCI and other systems. Previously, we proposed a hybrid prototype utilizing lexical features and role definitions of concepts in non-lattice subgraphs to identify missing IS-A relations in the NCI Thesaurus. However, no domain expert evaluation was provided in our previous work. In this paper, we further enhance the hybrid approach by leveraging a novel lexical feature—roots of noun chunks within concept names. Formal evaluation of our enhanced approach is also performed. Method We first compute all the non-lattice subgraphs in the NCI Thesaurus. We model each concept using its role definitions, words and roots of noun chunks within its concept name and its ancestor’s names. Then we perform subsumption testing for candidate concept pairs in the non-lattice subgraphs to automatically detect potentially missing IS-A relations. Domain experts evaluated the validity of these relations. Results We applied our approach to 19.08d version of the NCI Thesaurus. A total of 55 potentially missing IS-A relations were identified by our approach and reviewed by domain experts. 29 out of 55 were confirmed as valid by domain experts and have been incorporated in the newer versions of the NCI Thesaurus. 7 out of 55 further revealed incorrect existing IS-A relations in the NCI Thesaurus. Conclusions The results showed that our hybrid approach by leveraging lexical features and role definitions is effective in identifying potentially missing IS-A relations in the NCI Thesaurus.