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.

Usage (taken from our demo notebook)

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

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:

# 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:

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.

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.

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) ) )