def test_rotate_subscription_key(self, resource_group, location,
text_analytics_account,
text_analytics_account_key):
credential = AzureKeyCredential(text_analytics_account_key)
client = TextAnalyticsClient(text_analytics_account, credential)
docs = [{
"id": "1",
"text": "I will go to the park."
}, {
"id": "2",
"text": "I did not like the hotel we stayed at."
}, {
"id": "3",
"text": "The restaurant had really good food."
}]
response = client.begin_analyze_healthcare_entities(
docs, polling_interval=self._interval()).result()
self.assertIsNotNone(response)
credential.update("xxx") # Make authentication fail
with self.assertRaises(ClientAuthenticationError):
response = client.begin_analyze_healthcare_entities(
docs, polling_interval=self._interval()).result()
credential.update(
text_analytics_account_key) # Authenticate successfully again
response = client.begin_analyze_healthcare_entities(
docs, polling_interval=self._interval()).result()
self.assertIsNotNone(response)
def analyze_healthcare_entities(self):
# [START analyze_healthcare_entities]
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient
endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]
text_analytics_client = TextAnalyticsClient(
endpoint=endpoint,
credential=AzureKeyCredential(key),
)
documents = ["Subject is taking 100mg of ibuprofen twice daily"]
poller = text_analytics_client.begin_analyze_healthcare_entities(
documents, show_stats=True)
result = poller.result()
docs = [doc for doc in result if not doc.is_error]
print("Results of Healthcare Entities Analysis:")
for idx, doc in enumerate(docs):
for entity in doc.entities:
print("Entity: {}".format(entity.text))
print("...Category: {}".format(entity.category))
print("...Subcategory: {}".format(entity.subcategory))
print("...Offset: {}".format(entity.offset))
print("...Confidence score: {}".format(
entity.confidence_score))
if entity.data_sources is not None:
print("...Data Sources:")
for data_source in entity.data_sources:
print("......Entity ID: {}".format(
data_source.entity_id))
print("......Name: {}".format(data_source.name))
if len(entity.related_entities) > 0:
print("...Related Entities:")
for related_entity, relation_type in entity.related_entities.items(
):
print("......Entity Text: {}".format(
related_entity.text))
print("......Relation Type: {}".format(relation_type))
print("------------------------------------------")
def analyze_healthcare_entities_with_cancellation(self):
# [START analyze_healthcare_entities_with_cancellation]
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient
endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]
text_analytics_client = TextAnalyticsClient(
endpoint=endpoint,
credential=AzureKeyCredential(key),
)
documents = [
"RECORD #333582770390100 | MH | 85986313 | | 054351 | 2/14/2001 12:00:00 AM | \
CORONARY ARTERY DISEASE | Signed | DIS | Admission Date: 5/22/2001 \
Report Status: Signed Discharge Date: 4/24/2001 ADMISSION DIAGNOSIS: \
CORONARY ARTERY DISEASE. HISTORY OF PRESENT ILLNESS: \
The patient is a 54-year-old gentleman with a history of progressive angina over the past several months. \
The patient had a cardiac catheterization in July of this year revealing total occlusion of the RCA and \
50% left main disease , with a strong family history of coronary artery disease with a brother dying at \
the age of 52 from a myocardial infarction and another brother who is status post coronary artery bypass grafting. \
The patient had a stress echocardiogram done on July , 2001 , which showed no wall motion abnormalities ,\
but this was a difficult study due to body habitus. The patient went for six minutes with minimal ST depressions \
in the anterior lateral leads , thought due to fatigue and wrist pain , his anginal equivalent. Due to the patient's \
increased symptoms and family history and history left main disease with total occasional of his RCA was referred \
for revascularization with open heart surgery."
]
poller = text_analytics_client.begin_analyze_healthcare_entities(
documents)
try:
cancellation_poller = poller.cancel()
cancellation_poller.wait()
except HttpResponseError as e:
# If the operation has already reached a terminal state it cannot be cancelled.
print(e)
else:
print("Healthcare entities analysis was successfully cancelled.")
def analyze_healthcare_entities(self):
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. "
"To simplify matters, we will assume all dosages are in units of mg."
)
# [START analyze_healthcare_entities]
import re
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient
from collections import defaultdict
endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_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(
"In order to find the total dosage for every mentioned medication, "
"let's create a dict, mapping medication name -> total dosage. "
)
medication_to_dosage = defaultdict(int)
print(
"We will start off by extracting all of the dosage entities."
)
dosage_entities = [
entity
for doc in docs
for entity in doc.entities
if entity.category == "Dosage"
]
print(
"Now we traverse the related entities of each dosage entity. "
"We are looking for entities that are related by 'DosageOfMedication'. "
"After that, we're done!"
)
for dosage in dosage_entities:
dosage_value = int(re.findall(r"\d+", dosage.text)[0]) # we find the numbers in the dosage
for related_entity, relation_type in dosage.related_entities.items():
if relation_type == "DosageOfMedication":
medication_to_dosage[related_entity.text] += dosage_value
[
print("We have fulfilled '{}' total mg of '{}'".format(
dosage, medication
))
for medication, dosage in medication_to_dosage.items()
]
def analyze_healthcare_entities(self):
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
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient, HealthcareEntityRelationType, HealthcareEntityRelationRoleType
endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_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 idx, doc in enumerate(docs):
for entity in doc.entities:
print("Entity: {}".format(entity.text))
print("...Normalized Text: {}".format(entity.normalized_text))
print("...Category: {}".format(entity.category))
print("...Subcategory: {}".format(entity.subcategory))
print("...Offset: {}".format(entity.offset))
print("...Confidence score: {}".format(
entity.confidence_score))
if entity.data_sources is not None:
print("...Data Sources:")
for data_source in entity.data_sources:
print("......Entity ID: {}".format(
data_source.entity_id))
print("......Name: {}".format(data_source.name))
if entity.assertion is not None:
print("...Assertion:")
print("......Conditionality: {}".format(
entity.assertion.conditionality))
print("......Certainty: {}".format(
entity.assertion.certainty))
print("......Association: {}".format(
entity.assertion.association))
for relation in doc.entity_relations:
print("Relation of type: {} has the following roles".format(
relation.relation_type))
for role in relation.roles:
print("...Role '{}' with entity '{}'".format(
role.name, 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 ==
HealthcareEntityRelationType.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 = 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 == HealthcareEntityRelationRoleType.
DOSAGE, relation.roles)))
medication_role = next(
iter(
filter(
lambda x: x.name == HealthcareEntityRelationRoleType.
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))
