Create @fhir.predict() decorator for simplified ML model deployment #143
Description
Add a convenience decorator that simplifies deploying ML models as FHIR endpoints by abstracting away the FHIR resource boilerplate.
Current approach (verbose):
from fhir.resources.reference import Reference
from fhir.resources.riskassessment import RiskAssessment
from healthchain.gateway import HealthChainAPI, FHIRGateway
app = HealthChainAPI(title="Sepsis Risk Assessment")
fhir = FHIRGateway()
fhir.add_source("epic", "fhir://epic.org/api/FHIR/R4/test")
@fhir.transform(RiskAssessment)
def sepsis_risk_transform(id: str, source: str | None = None) -> RiskAssessment:
predicted_score = round(random(), 3)
return RiskAssessment(
status="completed",
subject=Reference(reference=f"Patient/{id}"),
prediction=[{"probabilityDecimal": predicted_score}],
)Proposed approach (cleaner):
@fhir.predict(resource=RiskAssessment)
def sepsis_risk(patient_id: str) -> float:
"""Just return the prediction score, decorator handles FHIR formatting."""
# Load your model and make prediction
return model.predict(patient_data) # Or round(random(), 3) for testingGoal:
Create a @fhir.predict() decorator that wraps user prediction functions and automatically:
- Handles FHIR resource construction
- Manages patient references
- Formats prediction output correctly
- Supports both sync and async functions
Implementation Location:
Add to healthchain/gateway/fhir/base.py or create healthchain/gateway/fhir/decorators.py
Acceptance Criteria:
- Decorator accepts
resourceparameter (start with RiskAssessment support) - User function just returns prediction score (float/dict)
- Automatically creates proper FHIR resource structure
- Follows existing
@fhir.transform()pattern for consistency - Works with FastAPI/uvicorn (registers as endpoint)
- Includes type hints and docstring
- Add unit tests in
tests/gateway/ - Add to
healthchain/gateway/__init__.pyexports
API Design:
def predict(
resource: Type[DomainResource],
status: str = "completed",
**kwargs
) -> Callable:
"""Decorator to simplify ML model deployment as FHIR endpoints.
Args:
resource: FHIR resource type (e.g., RiskAssessment)
status: Default status for the resource
**kwargs: Additional resource fields
Returns:
Decorated function that returns proper FHIR resource
Example:
@fhir.predict(resource=RiskAssessment)
def predict_sepsis(patient_id: str) -> float:
return 0.85 # High risk
"""Test Cases:
def test_predict_decorator_with_float():
@fhir.predict(resource=RiskAssessment)
def simple_prediction(patient_id: str) -> float:
return 0.5
result = simple_prediction("Patient/123")
assert isinstance(result, RiskAssessment)
assert result.prediction[0]["probabilityDecimal"] == 0.5
def test_predict_decorator_with_dict():
@fhir.predict(resource=RiskAssessment)
def complex_prediction(patient_id: str) -> dict:
return {"score": 0.75, "confidence": 0.9}
result = complex_prediction("Patient/123")
assert result.prediction[0]["probabilityDecimal"] == 0.75Resources:
- Study
@fhir.transform()inhealthchain/gateway/fhir/base.py - Use fhir.resource Pydantic models
- FHIR RiskAssessment
Stretch Goals (optional):
- Support for multiple resource types (Observation, DiagnosticReport)
- Built-in model loading from file paths
- Automatic input validation