Path Parameters
The source vocabulary identifier
Example:
Example:
SNOMED
, ICD10CM
, RxNorm
The target vocabulary identifier
Example:
Example:
ICD10CM
, ICD10
, HCPCS
Query Parameters
Filter mappings to specific domains
Example:
Example:
Condition,Drug,Procedure
Filter mappings to specific concept classes
Example:
Example:
Clinical Finding,Procedure
Filter by specific mapping relationship types
Default:
Example:
Default:
Maps to,Mapped from
Example:
Maps to,Maps to value
Filter by standard concept status
Options:
Options:
source
, target
, both
, either
, none
Include mapping quality scores and metadata
Include statistical analysis of the mapping set
Include analysis of unmapped concepts (gaps)
Minimum mapping quality score (0-1)
Example:
Example:
0.8
Filter by mapping equivalence types
Example:
Example:
exact,broader,narrower
Only include mappings between active concepts
Sort mappings by specified field
Options:
Options:
source_concept_id
, target_concept_id
, source_concept_name
, mapping_quality
Sort order for mappings
Options:
Options:
asc
, desc
Number of mappings to return per page (max 5000)
Page number for pagination (1-based)
Response
Information about the source and target vocabularies
Array of mapping relationships between the vocabularies
Statistical analysis of the vocabulary mappings (when include_statistics=true)
Analysis of unmapped concepts (when include_gaps=true)
Response metadata and pagination information
Usage Examples
Basic Vocabulary Mappings
Get all mappings between two vocabularies:High-Quality Condition Mappings
Get high-quality mappings for conditions:Comprehensive Mapping Analysis
Get detailed statistics and gap analysis:Drug Mappings
Get mappings between drug vocabularies:Standard Concept Mappings Only
Get mappings involving only standard concepts:Related Endpoints
- Get Concept Mappings - Mappings for specific concepts
- Get Mapping Coverage - Overall mapping coverage analysis
- Get Vocabularies - Available vocabularies
- Search Concepts - Find concepts across vocabularies
Notes
- Large vocabulary pairs may return many thousands of mappings - use pagination appropriately
- Coverage analysis helps identify gaps in cross-vocabulary translation capabilities
- Quality scores help prioritize the most reliable mappings for production use
- Gap analysis reveals unmapped concepts that may require custom mapping rules
- Temporal analysis shows mapping evolution and maintenance patterns
- Some vocabulary pairs have asymmetric coverage (better mapping in one direction)
- Official mappings are generally more reliable than algorithmic or community mappings
- Domain and concept class filtering can significantly improve query performance