1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
|
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""
FILE: sample_age_mismatch_inference.py
DESCRIPTION:
The sample_age_mismatch_inference.py module processes a sample radiology document with the Radiology Insights service.
It will initialize a RadiologyInsightsClient, build a Radiology Insights request with the sample document,
submit it to the client, RadiologyInsightsClient, and display
-the Age Mismatch patient ID,
-the Age Mismatch url extension,
-the Age Mismatch offset extension,
-the Age Mismatch length extension, and
-the Age Mismatch evidence
USAGE:
1. Set the environment variables with your own values before running the sample:
- AZURE_HEALTH_INSIGHTS_ENDPOINT - the endpoint to your source Health Insights resource.
- For more details how to use DefaultAzureCredential, please take a look at https://learn.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential
2. python sample_age_mismatch_inference.py
"""
import datetime
import os
import uuid
from azure.identity import DefaultAzureCredential
from azure.healthinsights.radiologyinsights import RadiologyInsightsClient
from azure.healthinsights.radiologyinsights import models
def radiology_insights_sync() -> None:
credential = DefaultAzureCredential()
ENDPOINT = os.environ["AZURE_HEALTH_INSIGHTS_ENDPOINT"]
job_id = str(uuid.uuid4())
radiology_insights_client = RadiologyInsightsClient(endpoint=ENDPOINT, credential=credential)
doc_content1 = """CLINICAL HISTORY:
20-year-old female presenting with abdominal pain. Surgical history significant for appendectomy.
COMPARISON:
Right upper quadrant sonographic performed 1 day prior.
TECHNIQUE:
Transabdominal grayscale pelvic sonography with duplex color Doppler and spectral waveform analysis of the ovaries.
FINDINGS:
The uterus is unremarkable given the transabdominal technique with endometrial echo complex within physiologic normal limits. The ovaries are symmetric in size, measuring 2.5 x 1.2 x 3.0 cm and the left measuring 2.8 x 1.5 x 1.9 cm.\n On duplex imaging, Doppler signal is symmetric.
IMPRESSION:
1. Normal pelvic sonography. Findings of testicular torsion.
A new US pelvis within the next 6 months is recommended.
These results have been discussed with Dr. Jones at 3 PM on November 5 2020."""
# Create ordered procedure
procedure_coding = models.Coding(
system="Http://hl7.org/fhir/ValueSet/cpt-all",
code="USPELVIS",
display="US PELVIS COMPLETE",
)
procedure_code = models.CodeableConcept(coding=[procedure_coding])
ordered_procedure = models.OrderedProcedure(description="US PELVIS COMPLETE", code=procedure_code)
# Create encounter
start = datetime.datetime(2021, 8, 28, 0, 0, 0, 0)
end = datetime.datetime(2021, 8, 28, 0, 0, 0, 0)
encounter = models.PatientEncounter(
id="encounter2",
class_property=models.EncounterClass.IN_PATIENT,
period=models.TimePeriod(start=start, end=end),
)
# Create patient info
birth_date = datetime.date(1959, 11, 11)
patient_info = models.PatientDetails(sex=models.PatientSex.FEMALE, birth_date=birth_date)
# Create author
author = models.DocumentAuthor(id="author2", full_name="authorName2")
create_date_time = datetime.datetime(2024, 2, 19, 0, 0, 0, 0, tzinfo=datetime.timezone.utc)
patient_document1 = models.PatientDocument(
type=models.DocumentType.NOTE,
clinical_type=models.ClinicalDocumentType.RADIOLOGY_REPORT,
id="doc2",
content=models.DocumentContent(source_type=models.DocumentContentSourceType.INLINE, value=doc_content1),
created_at=create_date_time,
specialty_type=models.SpecialtyType.RADIOLOGY,
administrative_metadata=models.DocumentAdministrativeMetadata(
ordered_procedures=[ordered_procedure], encounter_id="encounter2"
),
authors=[author],
language="en",
)
# Construct patient
patient1 = models.PatientRecord(
id="patient_id2",
details=patient_info,
encounters=[encounter],
patient_documents=[patient_document1],
)
# Create a configuration
configuration = models.RadiologyInsightsModelConfiguration(verbose=False, include_evidence=True, locale="en-US")
# Construct the request with the patient and configuration
patient_data = models.RadiologyInsightsJob(
job_data=models.RadiologyInsightsData(patients=[patient1], configuration=configuration)
)
# Health Insights Radiology Insights
try:
poller = radiology_insights_client.begin_infer_radiology_insights(
id=job_id,
resource=patient_data,
)
radiology_insights_result = poller.result()
display_age_mismatch(radiology_insights_result, doc_content1)
except Exception as ex:
raise ex
def display_age_mismatch(radiology_insights_result, doc_content1):
for patient_result in radiology_insights_result.patient_results:
for ri_inference in patient_result.inferences:
if ri_inference.kind == models.RadiologyInsightsInferenceType.AGE_MISMATCH:
print(f"Age Mismatch Inference found")
print(f"Age Mismatch: Patient ID: {patient_result.patient_id}")
Tokens = ""
for extension in ri_inference.extension:
for attribute in dir(extension):
if attribute == "extension":
offset = -1
length = -1
for sub_extension in extension.extension:
for sub_attribute in dir(sub_extension):
if sub_attribute == "url":
if sub_extension.url.startswith("http"):
continue
elif sub_extension.url == "offset":
offset = sub_extension.value_integer
elif sub_extension.url == "length":
length = sub_extension.value_integer
if offset > 0 and length > 0:
evidence = doc_content1[offset : offset + length]
if not evidence in Tokens:
Tokens = Tokens + " " + evidence
print(f"Age Mismatch: Evidence: {Tokens}")
if __name__ == "__main__":
radiology_insights_sync()
|
