omophub 1.6.0

Python SDK for OMOPHub - Medical Vocabulary API with semantic search

OMOPHub Python SDK

Query millions standardized medical concepts via simple Python API

Access SNOMED CT, ICD-10, RxNorm, LOINC, and 90+ OHDSI ATHENA vocabularies without downloading, installing, or maintaining local databases.

Documentation · API Reference · Examples

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Why OMOPHub?

Working with OHDSI ATHENA vocabularies traditionally requires downloading multi-gigabyte files, setting up a database instance, and writing complex SQL queries. OMOPHub eliminates this friction.

Traditional Approach	With OMOPHub
Download 5GB+ ATHENA vocabulary files	pip install omophub
Set up and maintain database	One API call
Write complex SQL with multiple JOINs	Simple Python methods
Manually update vocabularies quarterly	Always current data
Local infrastructure required	Works anywhere Python runs

Installation

pip install omophub

Quick Start

from omophub import OMOPHub

# Initialize client (uses OMOPHUB_API_KEY env variable, or pass api_key="...")
client = OMOPHub()

# Get a concept by ID
concept = client.concepts.get(201826)
print(concept["concept_name"])  # "Type 2 diabetes mellitus"

# Search for concepts across vocabularies
results = client.search.basic("metformin", vocabulary_ids=["RxNorm"], domain_ids=["Drug"])
for c in results["concepts"]:
    print(f"{c['concept_id']}: {c['concept_name']}")

# Map ICD-10 code to SNOMED
mappings = client.mappings.get_by_code("ICD10CM", "E11.9", target_vocabulary="SNOMED")

# Navigate concept hierarchy
ancestors = client.hierarchy.ancestors(201826, max_levels=3)

FHIR-to-OMOP Resolution

Resolve FHIR coded values to OMOP standard concepts in one call:

# Single FHIR Coding → OMOP concept + CDM target table
result = client.fhir.resolve(
    system="http://snomed.info/sct",
    code="44054006",
    resource_type="Condition",
)
print(result["resolution"]["target_table"])  # "condition_occurrence"
print(result["resolution"]["mapping_type"])  # "direct"

# ICD-10-CM → traverses "Maps to" automatically
result = client.fhir.resolve(
    system="http://hl7.org/fhir/sid/icd-10-cm",
    code="E11.9",
)
print(result["resolution"]["standard_concept"]["vocabulary_id"])  # "SNOMED"

# Batch resolve up to 100 codings
batch = client.fhir.resolve_batch([
    {"system": "http://snomed.info/sct", "code": "44054006"},
    {"system": "http://loinc.org", "code": "2339-0"},
    {"system": "http://www.nlm.nih.gov/research/umls/rxnorm", "code": "197696"},
])
print(f"Resolved {batch['summary']['resolved']}/{batch['summary']['total']}")

# CodeableConcept with vocabulary preference (SNOMED wins over ICD-10)
result = client.fhir.resolve_codeable_concept(
    coding=[
        {"system": "http://snomed.info/sct", "code": "44054006"},
        {"system": "http://hl7.org/fhir/sid/icd-10-cm", "code": "E11.9"},
    ],
    resource_type="Condition",
)
print(result["best_match"]["resolution"]["source_concept"]["vocabulary_id"])  # "SNOMED"

Semantic Search

Use natural language queries to find concepts using neural embeddings:

# Natural language search - understands clinical intent
results = client.search.semantic("high blood sugar levels")
for r in results["results"]:
    print(f"{r['concept_name']} (similarity: {r['similarity_score']:.2f})")

# Filter by vocabulary and set minimum similarity threshold
results = client.search.semantic(
    "heart attack",
    vocabulary_ids=["SNOMED"],
    domain_ids=["Condition"],
    threshold=0.5
)

# Iterate through all results with auto-pagination
for result in client.search.semantic_iter("chronic kidney disease", page_size=50):
    print(f"{result['concept_id']}: {result['concept_name']}")

Bulk Search

Search for multiple terms in a single API call — much faster than individual requests:

# Bulk lexical search (up to 50 queries)
results = client.search.bulk_basic([
    {"search_id": "q1", "query": "diabetes mellitus"},
    {"search_id": "q2", "query": "hypertension"},
    {"search_id": "q3", "query": "aspirin"},
], defaults={"vocabulary_ids": ["SNOMED"], "page_size": 5})

for item in results["results"]:
    print(f"{item['search_id']}: {len(item['results'])} results")

# Bulk semantic search (up to 25 queries)
results = client.search.bulk_semantic([
    {"search_id": "s1", "query": "heart failure treatment options"},
    {"search_id": "s2", "query": "type 2 diabetes medication"},
], defaults={"threshold": 0.5, "page_size": 10})

Similarity Search

Find concepts similar to a known concept or natural language query:

# Find concepts similar to a known concept
results = client.search.similar(concept_id=201826, algorithm="hybrid")
for r in results["results"]:
    print(f"{r['concept_name']} (score: {r['similarity_score']:.2f})")

# Find similar concepts using a natural language query
results = client.search.similar(
    query="medications for high blood pressure",
    algorithm="semantic",
    similarity_threshold=0.6,
    vocabulary_ids=["RxNorm"],
    include_scores=True,
)

Async Support

import asyncio
from omophub import AsyncOMOPHub

async def main():
    async with AsyncOMOPHub() as client:
        concept = await client.concepts.get(201826)
        print(concept["concept_name"])

asyncio.run(main())

Use Cases

ETL & Data Pipelines

Validate and map clinical codes during OMOP CDM transformations:

# Validate that a source code exists and find its standard equivalent
def validate_and_map(source_vocab, source_code):
    concept = client.concepts.get_by_code(source_vocab, source_code)
    if concept["standard_concept"] != "S":
        mappings = client.mappings.get(concept["concept_id"],
                                        target_vocabulary="SNOMED")
        return mappings["mappings"][0]["target_concept_id"]
    return concept["concept_id"]

Data Quality Checks

Verify codes exist and are valid standard concepts:

# Check if all your condition codes are valid
condition_codes = ["E11.9", "I10", "J44.9"]  # ICD-10 codes
for code in condition_codes:
    try:
        concept = client.concepts.get_by_code("ICD10CM", code)
        print(f"OK {code}: {concept['concept_name']}")
    except omophub.NotFoundError:
        print(f"ERROR {code}: Invalid code!")

Phenotype Development

Explore hierarchies to build comprehensive concept sets:

# Get all descendants of "Type 2 diabetes mellitus" for phenotype
descendants = client.hierarchy.descendants(201826, max_levels=5)
concept_set = [d["concept_id"] for d in descendants["concepts"]]
print(f"Found {len(concept_set)} concepts for T2DM phenotype")

Clinical Applications

Build terminology lookups into healthcare applications:

# Autocomplete for clinical coding interface
suggestions = client.concepts.suggest("diab", vocabulary_ids=["SNOMED"], page_size=10)
# Returns: ["Diabetes mellitus", "Diabetic nephropathy", "Diabetic retinopathy", ...]

API Resources

Resource	Description	Key Methods
concepts	Concept lookup and batch operations	get(), get_by_code(), batch(), suggest()
search	Full-text and semantic search	basic(), advanced(), semantic(), similar(), bulk_basic(), bulk_semantic()
hierarchy	Navigate concept relationships	ancestors(), descendants()
mappings	Cross-vocabulary mappings	get(), map()
vocabularies	Vocabulary metadata	list(), get(), stats()
domains	Domain information	list(), get(), concepts()
fhir	FHIR-to-OMOP resolution	resolve(), resolve_batch(), resolve_codeable_concept()

Configuration

client = OMOPHub(
    api_key="oh_xxx",                        # Or set OMOPHUB_API_KEY env var
    base_url="https://api.omophub.com/v1",   # API endpoint
    timeout=30.0,                             # Request timeout (seconds)
    max_retries=3,                            # Retry attempts
    vocab_version="2025.2",                   # Specific vocabulary version
)

Error Handling

import omophub

try:
    concept = client.concepts.get(999999999)
except omophub.NotFoundError as e:
    print(f"Concept not found: {e.message}")
except omophub.AuthenticationError as e:
    print(f"Check your API key: {e.message}")
except omophub.RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after} seconds")
except omophub.APIError as e:
    print(f"API error {e.status_code}: {e.message}")

Type Safety

The SDK is fully typed with TypedDict definitions for IDE autocomplete:

from omophub import OMOPHub, Concept

client = OMOPHub()
concept: Concept = client.concepts.get(201826)

# IDE autocomplete works for all fields
concept["concept_id"]      # int
concept["concept_name"]    # str
concept["vocabulary_id"]   # str
concept["domain_id"]       # str
concept["concept_class_id"] # str

Integration Examples

With Pandas

import pandas as pd

# Search and load into DataFrame
results = client.search.basic("hypertension", page_size=100)
df = pd.DataFrame(results["concepts"])
print(df[["concept_id", "concept_name", "vocabulary_id"]].head())

In Jupyter Notebooks

# Iterate through all results with auto-pagination
for concept in client.search.basic_iter("diabetes", page_size=100):
    process_concept(concept)

Compared to Alternatives

Feature	OMOPHub SDK	ATHENA Download	OHDSI WebAPI
Setup time	1 minute	Hours	Hours
Infrastructure	None	Database required	Full OHDSI stack
Updates	Automatic	Manual download	Manual
Programmatic access	Native Python	SQL queries	REST API

Best for: Teams who need quick, programmatic access to OMOP vocabularies without infrastructure overhead.

Documentation

• Full Documentation
• API Reference
• Examples
• Get API Key

Contributing

We welcome contributions! Please see our Contributing Guide for details.

# Clone and install for development
git clone https://github.com/omopHub/omophub-python.git
cd omophub-python
pip install -e ".[dev]"

# Run tests
pytest

Support

• GitHub Issues
• GitHub Discussions
• Email: [e-mail]
• Website: omophub.com

License

MIT License - see LICENSE for details.

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Built for the OHDSI community