OMOP2OBO Condition Occurrence Mappings

OMOP2OBO Condition Occurrence Mappings V1.0

These mappings were created by the OMOP2OBO mapping algorithm (see links below).  OMOP2OBO - the first health system-wide, disease-agnostic mappings between standardized clinical terminologies and eight Open Biomedical Ontology (OBO) Foundry ontologies spanning diseases, phenotypes, anatomical entities, cell types, organisms, chemicals, vaccines, and proteins. These mappings are also the first to be explicitly created using standard terminologies in the Observational Medical Outcomes (OMOP) common data model (CDM), ensuring both semantic and clinical interoperability across a space of N conditions (and N relationships curated in these ontologies).

The mappings in this repository were created between OMOP standard condition occurrence concepts (i.e., SNOMED CT) to the Human Phenotype Ontology (HPO) and the (Mondo). The National Library of Medicine's Unified Medical Language System (UMLS) Semantic Types are first used to filter out all concepts that did not have a biological origin (accidents, injuries, external complications, and findings without clear interpretations). Then, the Semantic Type was used to prioritize the mapping of HPO concepts to findings and symptoms and Mondo to Semantic Types indicative of disease. For these OMOP domains, owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings.

Mapping Details
Mappings included in this set were generated automatically using OMOP2OBO or through the use of a Bag-of-words embedding model using TF-IDF. Cosine similarity is used to compute similarity scores between all pairwise combinations of OMOP and OBO concepts and ancestor concepts. To improve the efficiency of this process, the algorithm searches only the top 𝑛 most similar results and keeps the top 75th percentile among all pairs with scores >= 0.25. Manually created mappings are also included.

Mapping Categories

Automatic One-to-One Concept: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1

Automatic One-to-One Ancestor: Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1

Automatic One-to-Many Concept: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many

Automatic One-to-Many Ancestor: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many

Manual One-to-One: Hand mapping created using expert suggested resources; 1:1

Manual One-to-Many: Hand mapping created using expert suggested resources; 1:Many

Cosine Similarity: score suggested mapping -- manually verified

UnMapped: No suitable mapping or not mapped type

Mapping Statistics
Additional statistics have been provided for the mappings and are shown in the table below. This table presents the counts of OMOP concepts by mapping category and ontology:

Mapping Category
HPO
Mondo

Automatic One-to-One Concept
4767
9097

Automatic One-to-Many Concept
150
885

Cosine Similarity
1375
667

Automatic One-to-One Ancestor
13595
8911

Automatic One-to-Many Ancestor  
38080
40224

Manual
5131
755

Manual One-to-Many
10326
2835

Unmapped
36301
46345

Provenance and Versioning: The V1.0 deposited mappings were created by OMOP2OBO v1.0.0 on October 2022 using the OMOP Common Data Model V5.0 and OBO Foundry ontologies downloaded on September 14, 2020. 

Caveats: The deposited files only contain the mappings that were generated automatically by the algorithm. The manually generated mappings will be deposited with the official preprint manuscript. Please note that these are the original mappings that were created for the preprint. They have not been updated to current versions of the ontologies. In our experience, this should result in very few errors, but we do suggest that you check the ontology concepts used against current versions of each ontology before using them.

Important Resources and Documentation

GitHub: OMOP2OBO

Project Wiki: OMOP2OBO - wiki

Zenodo Community: OMOP2OBO

Preprint Manuscript: 10.5281/zenodo.5716421