  Objective: Integrating EHR data with other resources is essential in rare
disease research due to low disease prevalence. Such integration is dependent
on the alignment of ontologies used for data annotation. The International
Classification of Diseases (ICD) is used to annotate clinical diagnoses; the
Human Phenotype Ontology (HPO) to annotate phenotypes. Although these
ontologies overlap in biomedical entities described, the extent to which they
are interoperable is unknown. We investigate how well aligned these ontologies
are and whether such alignments facilitate EHR data integration.
  Materials and Methods: We conducted an empirical analysis of the coverage of
mappings between ICD and HPO. We interpret this mapping coverage as a proxy for
how easily clinical data can be integrated with research ontologies such as
HPO. We quantify how exhaustively ICD codes are mapped to HPO by analyzing
mappings in the UMLS Metathesaurus. We analyze the proportion of ICD codes
mapped to HPO within a real-world EHR dataset.
  Results and Discussion: Our analysis revealed that only 2.2% of ICD codes
have direct mappings to HPO in UMLS. Within our EHR dataset, less than 50% of
ICD codes have mappings to HPO terms. ICD codes that are used frequently in EHR
data tend to have mappings to HPO; ICD codes that represent rarer medical
conditions are seldom mapped.
  Conclusion: We find that interoperability between ICD and HPO via UMLS is
limited. While other mapping sources could be incorporated, there are no
established conventions for what resources should be used to complement UMLS.
