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Update app.py
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app.py
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import streamlit as st
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from fhirclient import client
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from fhirclient.models import *
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from fhiry.fhirsearch import Fhirsearch
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settings = {
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'app_id': 'my_web_app',
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'api_base': 'http://hapi.fhir.org/baseR4'
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}
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smart = client.FHIRClient(settings=settings)
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# Define the FHIR search
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fs = Fhirsearch(fhir_base_url='http://hapi.fhir.org/baseR4')
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# Define the Streamlit app
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def app():
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# Create a text area for the HL7 message input
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hl7_input = st.text_area('Enter HL7 message', height=200)
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# For example, you could use the hl7apy library for parsing HL7 messages
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# and mapping the data elements to FHIR elements
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# This step is highly dependent on the structure and content of your HL7 messages
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# and requires careful consideration and testing
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# Here we assume that the HL7 message has already been parsed and mapped to FHIR elements
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patient = Patient()
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patient.name = [HumanName({'given': ['John'], 'family': 'Doe'})]
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patient.gender = 'male'
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patient.birthDate = '1990-01-01'
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# Save the FHIR resource to the server
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response = smart.create(patient)
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# Display the response
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st.write(response)
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my_fhir_search_parameters = {
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"code": "http://snomed.info/sct|39065001",
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}
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df = fs.search(resource_type="Condition", search_parameters=my_fhir_search_parameters)
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st.write(df)
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if __name__ == '__main__':
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app()
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from fhiry.fhirsearch import Fhirsearch
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fs = Fhirsearch(fhir_base_url = "http://fhir-server:8080/fhir")
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my_fhir_search_parameters = {
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"code": "http://snomed.info/sct|39065001",
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}
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df = fs.search(resource_type = "Condition", search_parameters = my_fhir_search_parameters)
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print(df.info())
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