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.gitattributes

@@ -32,3 +32,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+LoincTableCore.csv filter=lfs diff=lfs merge=lfs -text
+PanelsAndForms-ACW1208Labeled.csv filter=lfs diff=lfs merge=lfs -text
+PanelsAndForms.csv filter=lfs diff=lfs merge=lfs -text

LoincTableCore.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0ebc67fe7ec52a031d2157f2938e01ee4c68a75331fa95b01ad13b2338df009c
+size 23786041

PanelsAndForms-ACW1208Labeled.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:830bed108c436e46fbca26ec4b0cb449cb113bb15159de961f36289c9a6036ef
+size 12613127

PanelsAndForms.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a5c70a92806a63c42d8af98200601102c4e27361179a368611e3ff0dfc43beb1
+size 27840189

app.py

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+import os
+import pandas as pd
+import gradio as gr
+# SNOMEDCT Download https://www.nlm.nih.gov/healthit/snomedct/us_edition.html
+# LOINC Download https://loinc.org/downloads/
+# ECQM for Value Set Measures and Quality Reporting: https://vsac.nlm.nih.gov/download/ecqm?rel=20220505&res=eh_only.unique_vs.20220505.txt
+# SNOMED Nurse Subset https://www.nlm.nih.gov/healthit/snomedct/index.html?_gl=1*36x5pi*_ga*MTI0ODMyNjkxOS4xNjY1NTY3Mjcz*_ga_P1FPTH9PL4*MTY2Nzk4OTI1My41LjEuMTY2Nzk4OTY5Ni4wLjAuMA..
+
+def MatchLOINC(name):
+    basedir = os.path.dirname(__file__)
+    pd.set_option("display.max_rows", None)
+    data = pd.read_csv(f'LoincTableCore.csv')    
+    swith=data.loc[data['COMPONENT'].str.contains(name, case=False, na=False)]
+    return swith
+    
+def MatchLOINCPanelsandForms(name):
+    basedir = os.path.dirname(__file__)
+    data = pd.read_csv(f'PanelsAndForms.csv')     
+    swith=data.loc[data['ParentName'].str.contains(name, case=False, na=False)]
+    return swith
+    
+def MatchSNOMED(name):
+    basedir = os.path.dirname(__file__)
+    data = pd.read_csv(f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
+    swith=data.loc[data['term'].str.contains(name, case=False, na=False)]
+    return swith
+
+def MatchOMS(name):
+    basedir = os.path.dirname(__file__)
+    data = pd.read_csv(f'SnomedOMS.csv')   
+    swith=data.loc[data['SNOMED CT'].str.contains(name, case=False, na=False)]
+    return swith
+
+
+def MatchICD10(name):
+    basedir = os.path.dirname(__file__)
+    data = pd.read_csv(f'ICD10Diagnosis.csv')   
+    swith=data.loc[data['Description'].str.contains(name, case=False, na=False)]
+    return swith
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        name = gr.Textbox(label="Enter a term or word to match and find LOINC, SNOMED and OMS clinical terminologies.")
+
+
+    with gr.Row():
+        button1 = gr.Button("LOINC Terminology")
+        button2 = gr.Button("LOINC Panels and Forms")
+        button3 = gr.Button("SNOMED Clinical Terminology")
+        button4 = gr.Button("SNOMED and OMS Clinical Terminology")
+        button5 = gr.Button("ICD10 Diagnosis Clinical Terminology")
+    
+    with gr.Row():
+        output1 = gr.DataFrame(label="LOINC Terminology")
+    with gr.Row():
+        output2 = gr.DataFrame(label="LOINC Assessment Panels")
+    with gr.Row():
+        output3 = gr.DataFrame(label="SNOMED Terminology")
+    with gr.Row():
+        output4 = gr.DataFrame(label="SNOMED and OMS Terminology")
+    with gr.Row():
+        output5 = gr.DataFrame(label="ICD10 Diagnosis Clinical Terminology")
+
+    button1.click(fn=MatchLOINC, inputs=name, outputs=output1)
+    button2.click(fn=MatchLOINCPanelsandForms, inputs=name, outputs=output2)
+    button3.click(fn=MatchSNOMED, inputs=name, outputs=output3)
+    button4.click(fn=MatchOMS, inputs=name, outputs=output4)
+    button5.click(fn=MatchICD10, inputs=name, outputs=output5)
+
+demo.launch(debug=True)