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# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
FILE: sample_analyze_healthcare_entities.py
DESCRIPTION:
This sample demonstrates how to detect healthcare entities in a batch of documents.
In this sample we will be a newly-hired engineer working in a pharmacy. We are going to
comb through all of the prescriptions our pharmacy has fulfilled so we can catalog how
much inventory we have.
USAGE:
python sample_analyze_healthcare_entities.py
Set the environment variables with your own values before running the sample:
1) AZURE_LANGUAGE_ENDPOINT - the endpoint to your Language resource.
2) AZURE_LANGUAGE_KEY - your Language subscription key
"""
def sample_analyze_healthcare_entities() -> None:
print(
"In this sample we will be combing through the prescriptions our pharmacy has fulfilled "
"so we can catalog how much inventory we have"
)
print(
"We start out with a list of prescription documents."
)
# [START analyze_healthcare_entities]
import os
import typing
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient, HealthcareEntityRelation
endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
key = os.environ["AZURE_LANGUAGE_KEY"]
text_analytics_client = TextAnalyticsClient(
endpoint=endpoint,
credential=AzureKeyCredential(key),
)
documents = [
"""
Patient needs to take 100 mg of ibuprofen, and 3 mg of potassium. Also needs to take
10 mg of Zocor.
""",
"""
Patient needs to take 50 mg of ibuprofen, and 2 mg of Coumadin.
"""
]
poller = text_analytics_client.begin_analyze_healthcare_entities(documents)
result = poller.result()
docs = [doc for doc in result if not doc.is_error]
print("Let's first visualize the outputted healthcare result:")
for doc in docs:
for entity in doc.entities:
print(f"Entity: {entity.text}")
print(f"...Normalized Text: {entity.normalized_text}")
print(f"...Category: {entity.category}")
print(f"...Subcategory: {entity.subcategory}")
print(f"...Offset: {entity.offset}")
print(f"...Confidence score: {entity.confidence_score}")
if entity.data_sources is not None:
print("...Data Sources:")
for data_source in entity.data_sources:
print(f"......Entity ID: {data_source.entity_id}")
print(f"......Name: {data_source.name}")
if entity.assertion is not None:
print("...Assertion:")
print(f"......Conditionality: {entity.assertion.conditionality}")
print(f"......Certainty: {entity.assertion.certainty}")
print(f"......Association: {entity.assertion.association}")
for relation in doc.entity_relations:
print(f"Relation of type: {relation.relation_type} has the following roles")
for role in relation.roles:
print(f"...Role '{role.name}' with entity '{role.entity.text}'")
print("------------------------------------------")
print("Now, let's get all of medication dosage relations from the documents")
dosage_of_medication_relations = [
entity_relation
for doc in docs
for entity_relation in doc.entity_relations if entity_relation.relation_type == HealthcareEntityRelation.DOSAGE_OF_MEDICATION
]
# [END analyze_healthcare_entities]
print(
"Now, I will create a dictionary of medication to total dosage. "
"I will use a regex to extract the dosage amount. For simplicity sake, I will assume "
"all dosages are represented with numbers and have mg unit."
)
import re
from collections import defaultdict
medication_to_dosage: typing.Dict[str, int] = defaultdict(int)
for relation in dosage_of_medication_relations:
# The DosageOfMedication relation should only contain the dosage and medication roles
dosage_role = next(iter(filter(lambda x: x.name == "Dosage", relation.roles)))
medication_role = next(iter(filter(lambda x: x.name == "Medication", relation.roles)))
try:
dosage_value = int(re.findall(r"\d+", dosage_role.entity.text)[0]) # we find the numbers in the dosage
medication_to_dosage[medication_role.entity.text] += dosage_value
except StopIteration:
# Error handling for if there's no dosage in numbers.
pass
for medication, dosage in medication_to_dosage.items():
print("We have fulfilled '{}' total mg of '{}'".format(
dosage, medication
))
if __name__ == "__main__":
sample_analyze_healthcare_entities()
|
