Implications of mappings between ICD clinical diagnosis codes and Human Phenotype Ontology terms
Amelia LM Tan, Rafael S Gon\c{c}alves, William Yuan, Gabriel A Brat,, The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Robert, Gentleman, Isaac S Kohane

TL;DR
This study assesses the alignment between ICD diagnosis codes and HPO terms, revealing limited interoperability which impacts data integration in rare disease research.
Contribution
It provides an empirical analysis of ICD-HPO mappings, highlighting the extent of their coverage and implications for EHR data integration.
Findings
Only 2.2% of ICD codes map to HPO in UMLS.
Less than 50% of ICD codes in EHR data have HPO mappings.
Frequent ICD codes tend to have HPO mappings, rare ones do not.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBiomedical Text Mining and Ontologies · Genomics and Rare Diseases · Semantic Web and Ontologies
