Abstract
BackgroundIdentifying partial mappings between two terminologies is of special importance when one terminology is finer-grained than the other, as is the case for the Human Phenotype Ontology (HPO), mainly used for research purposes, and SNOMED CT, mainly used in healthcare.ObjectivesTo investigate and contrast lexical and logical approaches to deriving partial mappings between HPO and SNOMED CT.Methods1) Lexical approach—We identify modifiers in HPO terms and attempt to map demodified terms to SNOMED CT through UMLS; 2) Logical approach—We leverage subsumption relations in HPO to infer partial mappings to SNOMED CT; 3) Comparison—We analyze the specific contribution of each approach and evaluate the quality of the partial mappings through manual review.ResultsThere are 7358 HPO concepts with no complete mapping to SNOMED CT. We identified partial mappings lexically for 33 % of them and logically for 82 %. We identified partial mappings both lexically and logically for 27 %. The clinical relevance of the partial mappings (for a cohort selection use case) is 49 % for lexical mappings and 67 % for logical mappings.ConclusionsThrough complete and partial mappings, 92 % of the 10,454 HPO concepts can be mapped to SNOMED CT (30 % complete and 62 % partial). Equivalence mappings between HPO and SNOMED CT allow for interoperability between data described using these two systems. However, due to differences in focus and granularity, equivalence is only possible for 30 % of HPO classes. In the remaining cases, partial mappings provide a next-best approach for traversing between the two systems. Both lexical and logical mapping techniques produce mappings that cannot be generated by the other technique, suggesting that the two techniques are complementary to each other. Finally, this work demonstrates interesting properties (both lexical and logical) of HPO and SNOMED CT and illustrates some limitations of mapping through UMLS.
Highlights
In parallel to the deep sequencing effort enabled by Generation Sequencing technologies, a need for deep phenotyping has emerged [1]
Through complete and partial mappings, 92 % of the 10,454 Human Phenotype Ontology (HPO) concepts can be mapped to SNOMED CT (30 % complete and 62 % partial)
Equivalence mappings between HPO and SNOMED CT allow for interoperability between data described using these two systems
Summary
In parallel to the deep sequencing effort enabled by Generation Sequencing technologies, a need for deep phenotyping has emerged [1]. Electronic health record (EHR) data coded with SNOMED CT are increasingly used as a resource for cohort selection (e.g., for selecting patients exhibiting a specific phenotype defined in reference to HPO). In this case, a mapping between SNOMED CT and HPO is key to bridging between datasets annotated to different terminologies. The version of HPO used in this investigation is the (stable) OWL version downloaded on January 21, 2015 (build #1337) from the HPO website (http://www.humanphenotype-ontology.org/) It contains 10,589 classes (concepts) and 16,807 names (terms) for phenotypes, including 6218 exact synonyms in addition to one preferred term for each class
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