Overview This endpoint finds medical concepts that are semantically similar to a specific concept identified by its OMOP concept ID. It uses the same advanced machine learning algorithms as the POST version but optimized for concept-to-concept similarity matching, making it ideal for discovering related concepts when you have a specific starting point.
Path Parameters The OMOP concept ID to find similar concepts for
Query Parameters Target vocabularies to search within (comma-separated) Examples : SNOMED, SNOMED,ICD10CM, RXNORM,NDC
Clinical domains to focus the similarity search (comma-separated) Examples : Condition,Procedure, Drug,Device
Concept classes to include in similarity search (comma-separated) Examples : Clinical Finding,Procedure, Ingredient,Brand Name
Minimum similarity score threshold (0.0 to 1.0) Higher values = More strict similarity matching
Maximum number of similar concepts to return
Include similarity scores in the response
Include explanations for why concepts are considered similar
Filter to standard concepts only Options : S (standard), C (classification), NULL (non-standard)
Include invalid/deprecated concepts in similarity search
Similarity algorithm to use Options : semantic (embedding-based), lexical (text-based), hybrid (combined)
Exclude the source concept from results
Response Indicates if the request was successful
Error information (present only on error responses) Human-readable error message
Additional error details or context
Contains the similar concepts search results Information about the source concept used for similarity search OMOP concept ID of the source concept
Primary name of the source concept
Source vocabulary identifier
Clinical domain classification
Standard concept designation (‘S’, ‘C’, or null for non-standard)
Array of concepts similar to the source concept Vocabulary-specific concept code
Source vocabulary identifier
Clinical domain classification
Similarity score between 0.0 and 1.0
Reason for similarity match (if requested)
Standard concept designation (‘S’, ‘C’, or null for non-standard)
Alternative names for the concept
Information about the similarity search The concept ID used as the similarity source
Similarity algorithm applied
Minimum similarity threshold applied
Total concepts evaluated for similarity
Number of similar concepts returned
Search processing time in milliseconds
curl -X GET "https://api.omophub.com/v1/search/similar/44054006?vocabulary_ids=SNOMED,ICD10CM&similarity_threshold=0.8&max_results=10&include_scores=true&include_explanations=true" \ -H "Authorization: Bearer YOUR_API_KEY"
{ "success" : true , "data" : { "source_concept" : { "concept_id" : 44054006 , "concept_name" : "Type 2 diabetes mellitus" , "concept_code" : "44054006" , "vocabulary_id" : "SNOMED" , "domain_id" : "Condition" , "concept_class_id" : "Clinical Finding" , "standard_concept" : "S" }, "similar_concepts" : [ { "concept_id" : 201826 , "concept_name" : "Type 2 diabetes mellitus without complications" , "concept_code" : "E11.9" , "vocabulary_id" : "ICD10CM" , "domain_id" : "Condition" , "concept_class_id" : "4-char nonbill code" , "similarity_score" : 0.92 , "explanation" : "High similarity - more specific variant of the same condition" , "standard_concept" : null , "synonyms" : [ "Type II diabetes mellitus uncomplicated" ] }, { "concept_id" : 443729 , "concept_name" : "Diabetes mellitus type 2 with hyperglycemia" , "concept_code" : "443729" , "vocabulary_id" : "SNOMED" , "domain_id" : "Condition" , "concept_class_id" : "Clinical Finding" , "similarity_score" : 0.89 , "explanation" : "High similarity - related complication of the same condition" , "standard_concept" : "S" , "synonyms" : [ "Type 2 DM with elevated glucose" ] }, { "concept_id" : 432367 , "concept_name" : "Diabetes mellitus type 2 with diabetic nephropathy" , "concept_code" : "E11.21" , "vocabulary_id" : "ICD10CM" , "domain_id" : "Condition" , "concept_class_id" : "5-char billing code" , "similarity_score" : 0.86 , "explanation" : "High similarity - complication of the same condition" , "standard_concept" : null , "synonyms" : [ "Type 2 DM with kidney disease" ] } ], "search_metadata" : { "source_concept_id" : 44054006 , "algorithm_used" : "hybrid" , "similarity_threshold" : 0.8 , "total_candidates" : 125000 , "results_returned" : 3 , "processing_time_ms" : 187 } } }
Usage Examples Basic Similarity by ID Find concepts similar to Type 2 Diabetes (concept ID 44054006):
GET /v1/search/similar/44054006?max_results= 5 & similarity_threshold = 0.7 Cross-Vocabulary Similarity Find similar concepts across multiple vocabularies:
GET /v1/search/similar/1308216?vocabulary_ids=SNOMED,ICD10CM,LOINC & domain_ids = Condition,Measurement & include_explanations = true Drug Similarity Search Find similar pharmaceutical concepts:
GET /v1/search/similar/1503297?vocabulary_ids=RXNORM & domain_ids = Drug & concept_class_ids = Ingredient,Clinical Drug & similarity_threshold = 0.8 High-Precision Similarity Get only highly similar concepts with detailed scoring:
GET /v1/search/similar/443390?similarity_threshold= 0.9 & max_results = 10 & include_scores = true & algorithm = semantic
Concept-to-concept similarity is typically faster than query-based similarityHybrid algorithm provides best balance of accuracy and performanceSemantic algorithm is most accurate but computationally intensiveCaching is applied for frequently requested concept similarities
