Create @fhir.predict() decorator for simplified ML model deployment #146
+237
−8
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
🧠 Add
@fhir.predict()
Decorator for Simplified ML Model DeploymentCloses : #143
Summary
This PR introduces a new
@fhir.predict()
decorator in theBaseFHIRGateway
to make deploying machine learning models as FHIR-compliant endpoints simpler and more consistent. It abstracts the boilerplate involved in constructing FHIR resources, letting developers focus purely on model inference logic.Key Features
@fhir.predict(resource=RiskAssessment)
float
ordict
) into FHIRRiskAssessment
resources.Implementation Details
healthchain/gateway/fhir/base.py
:_wrap_prediction()
to construct FHIR-compliant resources._execute_handler()
to handle async/sync predictions.predict()
decorator to register ML endpoints.tests/gateway/test_base_fhir_gateway.py
.Example Usage
Automatically serves a FHIR endpoint:
GET /predict/RiskAssessment/{id}
Response:
Checklist