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https://github.com/chrisby/ml-on-fhir

A work in progress library that fuses the HL7 FHIR standard with scikit-learn
https://github.com/chrisby/ml-on-fhir

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A work in progress library that fuses the HL7 FHIR standard with scikit-learn

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# ml-on-fhir



## A work in progress library that fuses the HL7 FHIR standard with scikit-learn



Currently this library is only tested on `STU3`, so use with caution when you run an `R4` database. We will look more into compatibility checks in the future. 



Read more about internals and extendability in the [readthedocs](https://ml-on-fhir.readthedocs.io/en/latest/).



### Usage (taken from [our demo notebook](https://github.com/chrisby/ml-on-fhir/blob/master/src/Demo.ipynb))



##### First: Register the base URL of your database with a FHIRCient object:



```python

from fhir_client import FHIRClient

import logging

import pandas as pd



logger = logging.getLogger(__name__)

logging.basicConfig(level=logging.INFO)



client = FHIRClient(service_base_url='https://r3.smarthealthit.org', logger=logger)

```



##### Querying Patients

There are two general ways of searching for patients with specific properties.

The first one is to search by coding system:

```python

# To receive a list of available procedures:

procedures = client.get_all_procedures()

pd.DataFrame([prod.code['coding'][0] for prod in procedures]).drop_duplicates().sort_values(by=['display']).head()



# Now retrieve patients

patients_by_procedure_code = client.get_patients_by_procedure_code("http://snomed.info/sct","73761001")

```



The second one is by text. The searched tags are CodeableConcept.text, Coding.display, or Identifier.type.text:

```python

conditions = client.get_all_conditions()

pd.DataFrame([cond.code['coding'][0] for cond in conditions]).drop_duplicates(subset=['display']).sort_values(by='display', ascending=True).head()



patients_by_condition_text = client.get_patients_by_condition_text("Abdominal pain")

```



One can also load a control group for a specific cohort of patients. The control group is of equal size of the case cohort (min size: 10) and is composed of randomly sampled patients that do not match the original query. Their class is contained in the .case property of the Patient object.

```python

patients_by_condition_text_with_controls = client.get_patients_by_condition_text("Abdominal pain", controls=True)



print("{} are cases and {} are controls".format(len([d for d in patients_by_condition_text_with_controls if d.case]), 

                                                len([d for d in patients_by_condition_text_with_controls if not d.case])))

```



#### Machine Learning

To train a classifier, we need to first tell the `MLOnFHIRClassifier` the type of object which we would like to classify. We can then define features (`feature_attrs`) and labels (`label_attrs`) for our classification task and pass the preprocessor of our current client, so it is clear how to preprocess the features/labels of a patient. We can then simply call `.fit` on the `MLOnFHIRClassifier` instance together with our classifier of choice.



```python

from ml_on_fhir import MLOnFHIRClassifier

from fhir_objects.patient import Patient

from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import accuracy_score, roc_curve, auc



ml_fhir = MLOnFHIRClassifier(Patient, feature_attrs=['birthDate', 'gender'],

                             label_attrs=['case'], preprocessor=client.preprocessor)

X, y, trained_clf = ml_fhir.fit(patients_by_condition_text_with_controls, DecisionTreeClassifier())



from sklearn.metrics import accuracy_score, roc_curve, auc

fpr, tpr, _ = roc_curve(y, trained_clf.predict(X))

print("Prediction accuracy {}".format( auc(fpr, tpr) ) )

```