# FHIR Terminology Encoder
This is a [scikit-learn](https://scikit-learn.org/) compatible encoder that uses
a FHIR terminology server to encode ontological features.
It currently supports subsumption relationships and properties.
You supply a scope in the form of a FHIR ValueSet URI, and a FHIR terminology
endpoint.
The result is a multi-hot encoded vector delivered as a sparse matrix, suitable
for input into most models and estimators.
## Installation
```bash
pip install fhir-tx-encoder
```
## Usage
```python
from fhir_tx_encoder import FhirTerminologyEncoder
import numpy as np
encoder = FhirTerminologyEncoder(
scope="http://snomed.info/sct?fhir_vs=ecl/(%3E%3E%20363346000)",
tx_url="http://localhost:8080/fhir",
)
result = encoder.fit_transform(np.array([["399981008", "363346000"]]))
print(f"result.shape: {result.shape}")
print(f"result:\n{result.toarray()}")
```
Which would output:
```
Expanding value set: http://snomed.info/sct?fhir_vs=ecl/(%3E%3E%20363346000)
Expanding (6 items, offset 0, total 6)
Expansion complete
Generating one-hot encoding... (6, 6)
Creating index... 6 items
Applying transitive closure
Batch 1 of 1, 6 items... 15 pairs added
Encoding complete: (6, 6)
encoder.codes_: ['404684003', '64572001', '363346000', '399981008', '55342001', '138875005']
encoder.displays_: ['Clinical finding', 'Disease', 'Malignant neoplastic disease', 'Neoplasm and/or hamartoma', 'Neoplastic disease', 'SNOMED CT Concept']
result.shape: (2, 6)
result:
[[1. 1. 0. 1. 0. 1.]
[1. 1. 1. 1. 1. 1.]]
```
## Important note
This software is currently in alpha. It is not yet ready for production use.
Copyright © 2023, Commonwealth Scientific and Industrial Research Organisation
(CSIRO) ABN 41 687 119 230. Licensed under
the [Apache License, version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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