Implications of mappings between ICD clinical diagnosis codes and Human Phenotype Ontology terms
<|reference_start|>Implications of mappings between ICD clinical diagnosis codes and Human Phenotype Ontology terms: 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.<|reference_end|>
@article{tan2024implications,
    title={Implications of mappings between ICD clinical diagnosis codes and Human
  Phenotype Ontology terms},
    author={Amelia LM Tan, Rafael S Gonc{c}alves, William Yuan, Gabriel A Brat,
  The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Robert
  Gentleman, Isaac S Kohane},
    journal={arXiv preprint arXiv:2407.08874},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08874},
    primaryClass={cs.DB}
}