内容简介:Read more about internals and extendability in theThere are two general ways of searching for patients with specific properties. The first one is to search by coding system:The second one is by text. The searched tags are CodeableConcept.text, Coding.displ
ml-on-fhir
A work in progress library that fuses the HL7 FHIR standard with scikit-learn
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) ) )
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