Basic Concept Search

curl -X GET "https://api.omophub.com/v1/search/concepts?query=hypertension&page_size=10" \
  -H "Authorization: Bearer YOUR_API_KEY"

import requests

params = {"query": "hypertension", "page_size": 10}
response = requests.get(
    "https://api.omophub.com/v1/search/concepts",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    params=params
)
concepts = response.json()

import { OMOPHub } from '@omophub/omophub-node';

const client = new OMOPHub();
const { data: concepts } = await client.search.basic('hypertension', { pageSize: 10 });

curl -X GET "https://api.omophub.com/v1/search/concepts?query=diabetes&vocabulary_ids=SNOMED&domain_ids=Condition" \
  -H "Authorization: Bearer YOUR_API_KEY"

import requests

params = {
    "query": "diabetes",
    "vocabulary_ids": "SNOMED",
    "domain_ids": "Condition"
}
response = requests.get(
    "https://api.omophub.com/v1/search/concepts",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    params=params
)
filtered_concepts = response.json()

{
  "success": true,
  "data": [
    {
      "concept_id": 320128,
      "concept_name": "Essential hypertension",
      "concept_code": "59621000",
      "vocabulary_id": "SNOMED",
      "domain_id": "Condition",
      "concept_class_id": "Clinical Finding",
      "standard_concept": "S",
      "match_score": 0.95,
      "match_type": "exact",
      "valid_start_date": "1970-01-01",
      "valid_end_date": "2099-12-31"
    },
    {
      "concept_id": 316866,
      "concept_name": "Hypertensive disorder",
      "concept_code": "38341003",
      "vocabulary_id": "SNOMED",
      "domain_id": "Condition",
      "concept_class_id": "Clinical Finding",
      "standard_concept": "S",
      "match_score": 0.89,
      "match_type": "partial",
      "valid_start_date": "1970-01-01",
      "valid_end_date": "2099-12-31"
    },
    {
      "concept_id": 4124681,
      "concept_name": "Accelerated hypertension",
      "concept_code": "48146000",
      "vocabulary_id": "SNOMED",
      "domain_id": "Condition",
      "concept_class_id": "Clinical Finding",
      "standard_concept": "S",
      "match_score": 0.85,
      "match_type": "fuzzy",
      "valid_start_date": "1970-01-01",
      "valid_end_date": "2099-12-31"
    }
  ],
  "meta": {
    "pagination": {
      "page": 1,
      "page_size": 10,
      "total_items": 247,
      "total_pages": 25,
      "has_next": true,
      "has_previous": false
    },
    "search": {
      "query": "hypertension",
      "total_results": 247,
      "filters_applied": {
        "vocabulary_ids": ["SNOMED", "ICD10CM"]
      }
    },
    "request_id": "req_search_123",
    "timestamp": "2024-12-22T10:00:00Z",
    "vocab_release": "2025.2"
  }
}

Overview

Search for medical concepts using text queries. This endpoint provides fast, relevant results across all supported vocabularies using advanced search algorithms.

For clinical concept lookups, add vocabulary_ids=SNOMED and/or domain_ids=Condition filters. Without filters, results include all vocabularies (LOINC questionnaires, administrative codes, etc.) which may not be the clinical concepts you expect.This endpoint performs keyword matching - best for exact medical terms like “myocardial infarction” or concept codes like “E11.9”. For natural language queries like “heart attack” or “high blood sugar”, use Semantic Search which uses neural embeddings to understand clinical meaning and returns results ranked by similarity.

Query Parameters

string

required

Search query text (1-100 characters)

string

Filter search to specific vocabularies (comma-separated)
Examples: SNOMED, SNOMED,ICD10CM, RXNORM,NDC

string

Filter by medical domains (comma-separated)
Examples: Condition, Condition,Procedure, Drug,Device

integer

default:"1"

Page number (1-based indexing)

integer

default:"20"

Number of results per page (max: 1000)

string

Specific vocabulary release version to query
Example: 2025.1

curl -X GET "https://api.omophub.com/v1/search/concepts?query=hypertension&page_size=10" \
  -H "Authorization: Bearer YOUR_API_KEY"

import requests

params = {"query": "hypertension", "page_size": 10}
response = requests.get(
    "https://api.omophub.com/v1/search/concepts",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    params=params
)
concepts = response.json()

import { OMOPHub } from '@omophub/omophub-node';

const client = new OMOPHub();
const { data: concepts } = await client.search.basic('hypertension', { pageSize: 10 });

curl -X GET "https://api.omophub.com/v1/search/concepts?query=diabetes&vocabulary_ids=SNOMED&domain_ids=Condition" \
  -H "Authorization: Bearer YOUR_API_KEY"

import requests

params = {
    "query": "diabetes",
    "vocabulary_ids": "SNOMED",
    "domain_ids": "Condition"
}
response = requests.get(
    "https://api.omophub.com/v1/search/concepts",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    params=params
)
filtered_concepts = response.json()

{
  "success": true,
  "data": [
    {
      "concept_id": 320128,
      "concept_name": "Essential hypertension",
      "concept_code": "59621000",
      "vocabulary_id": "SNOMED",
      "domain_id": "Condition",
      "concept_class_id": "Clinical Finding",
      "standard_concept": "S",
      "match_score": 0.95,
      "match_type": "exact",
      "valid_start_date": "1970-01-01",
      "valid_end_date": "2099-12-31"
    },
    {
      "concept_id": 316866,
      "concept_name": "Hypertensive disorder",
      "concept_code": "38341003",
      "vocabulary_id": "SNOMED",
      "domain_id": "Condition",
      "concept_class_id": "Clinical Finding",
      "standard_concept": "S",
      "match_score": 0.89,
      "match_type": "partial",
      "valid_start_date": "1970-01-01",
      "valid_end_date": "2099-12-31"
    },
    {
      "concept_id": 4124681,
      "concept_name": "Accelerated hypertension",
      "concept_code": "48146000",
      "vocabulary_id": "SNOMED",
      "domain_id": "Condition",
      "concept_class_id": "Clinical Finding",
      "standard_concept": "S",
      "match_score": 0.85,
      "match_type": "fuzzy",
      "valid_start_date": "1970-01-01",
      "valid_end_date": "2099-12-31"
    }
  ],
  "meta": {
    "pagination": {
      "page": 1,
      "page_size": 10,
      "total_items": 247,
      "total_pages": 25,
      "has_next": true,
      "has_previous": false
    },
    "search": {
      "query": "hypertension",
      "total_results": 247,
      "filters_applied": {
        "vocabulary_ids": ["SNOMED", "ICD10CM"]
      }
    },
    "request_id": "req_search_123",
    "timestamp": "2024-12-22T10:00:00Z",
    "vocab_release": "2025.2"
  }
}

Search Features

1. Fuzzy Matching

Handles typos and misspellings automatically.

2. Synonym & Alias Matching

Matches concept synonyms and alternate names when include_synonyms=true.

3. Relevance Scoring

Results ranked by relevance with match scores.

4. Multi-vocabulary

Searches across all vocabularies simultaneously.

5. Fast Performance

Optimized for sub-second response times.

Common Use Cases

1. Clinical Coding

Find appropriate codes for clinical documentation.

2. Data Standardization

Map free-text clinical notes to standard concepts.

3. Quality Assurance

Validate and standardize existing coded data.

4. Research Applications

Find concepts for cohort definitions and outcome measures.

API Reference

Search

Concepts

Concept Hierarchy

Mappings

FHIR Resolver

Relationships

Domains & Classifications

Vocabularies

Overview

Query Parameters

Search Features

1. Fuzzy Matching

2. Synonym & Alias Matching

3. Relevance Scoring

4. Multi-vocabulary

5. Fast Performance

Common Use Cases

1. Clinical Coding

2. Data Standardization

3. Quality Assurance

4. Research Applications

​Overview

​Query Parameters

​Search Features

​1. Fuzzy Matching

​2. Synonym & Alias Matching

​3. Relevance Scoring

​4. Multi-vocabulary

​5. Fast Performance

​Common Use Cases

​1. Clinical Coding

​2. Data Standardization

​3. Quality Assurance

​4. Research Applications

Overview

Query Parameters

Search Features

1. Fuzzy Matching

2. Synonym & Alias Matching

3. Relevance Scoring

4. Multi-vocabulary

5. Fast Performance

Common Use Cases

1. Clinical Coding

2. Data Standardization

3. Quality Assurance

4. Research Applications