Create app.py

app.py

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+# Import Transformers & Gradio
+from datasets import load_dataset
+from transformers import pipeline
+import gradio as gr
+
+# Load clinical note data
+
+# Print the columns in this dataset and a sample entry.
+from datasets import load_dataset
+ds = load_dataset("AGBonnet/augmented-clinical-notes")
+
+# Loads the JSON column and indicates whether the JSON was valid. Some records contain invalid JSON formatting.
+def load_json_with_status(raw):
+    import json
+    try:
+        parsed = json.loads(raw)
+        return True, parsed
+    except:
+        return False, raw
+
+# View samples of Clinical Notes
+
+train = ds["train"]
+
+# Sample up to 25 of today's patients since we never have more than that per day.
+sampleLengthTrain = min(25, len(ds['train']))
+print("length=", sampleLengthTrain)
+patientSample = {}  # Dictionary keyed by patient ID
+
+for i in range(sampleLengthTrain):
+
+  # A JSON summary contains multiple sections describing the patient's problem and the prescribed tests.
+  patientJsonSummaryStr = ds['train'][i]['summary']
+  patientIdx = ds['train'][i]['idx']
+  patientNote = ds['train'][i]['note']
+  success, parsedJsonSummary = load_json_with_status(patientJsonSummaryStr);
+
+  # Only if the JSON parsing succeeded do we load this record.
+  if success:
+    motivation = parsedJsonSummary['visit motivation']
+    discharge = parsedJsonSummary['discharge']
+    treatments = parsedJsonSummary['treatments']
+    patientSample[patientIdx] = {'note': patientNote, 'visit motivation': motivation, 'summary': parsedJsonSummary, 'discharge': discharge, 'treatments': treatments}
+    #(DEBUG ONLY) #print("idx:", ds['train'][i]['idx'], "summary:", repr(str(patientJsonSummaryStr)))
+
+  # otherwise, skip the record with invalid JSON.
+print("Done. patientSample=", len(patientSample))
+
+# Create functions to use from the UI to show content in dropdowns and textboxes.
+
+def get_motivation_from_patient_id(patientId):
+    return patientSample.get(patientId)['visit motivation']
+
+def get_note_from_patient_id(patientId):
+    return patientSample.get(patientId)['note']
+
+def get_treatment_plan_from_patient_id(patientId):
+    print(patientSample.get(patientId))
+    summary = patientSample.get(patientId)['summary']
+    return ("Treatments: " + str(summary.get('treatments', 'N/A')) +
+            " Discharge notes: " + str(summary.get('discharge', 'N/A')))
+
+
+# Assist medical staff by summarizing treatment plan from their notes.
+
+def json_section_to_text(json_obj):
+    parts = []
+
+    # Simple mapping that includes only non-empty fields.
+    if json_obj.get("visit motivation"):
+        motiviation = "Visit motivation: " + json_obj["visit motivation"]
+        parts.append(motiviation)
+        print(motiviation)
+    if json_obj.get("patient information"):
+        patientInfo = "Patient information: " + json_obj["patient information"]
+        parts.append(patientInfo)
+        print(patientInfo)
+
+    return "\n\n".join(parts)
+
+# Create a question answering pipeline to answer questions about the patient's treatment plan.
+
+from transformers import pipeline, AutoTokenizer
+generator = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+
+
+def summarize_notes(patient_id):
+    import json
+
+    treatment_text = get_treatment_plan_from_patient_id(patient_id);
+    prompt = "what are a few special care instructions to provide to this patient (or not applicable)?"
+
+    result = generator(question=prompt, context=treatment_text)
+    responseGenerator = result['answer']
+
+    return responseGenerator
+
+
+# Build a UI using Gradio to list a sampling of patient's from Today's Office Visits.
+#   - Once selected, display the Visit Motivation and the Clinical Summary
+#   - A Generate
+
+import gradio as gr
+
+# Load patient IDs for the dropdown. The first item will be a blank so that the user must select a patient ID.
+choices = [""] + list(patientSample.keys())
+
+with gr.Blocks(title="Patient Instructions generated from Clinical Notes", theme="soft") as demo:
+  gr.Markdown("# Choose a patient to view their summary, and then generate concise patient instructions.")
+
+  patientDropdown = gr.Dropdown(choices, label="Choose a Patient ID")
+  motivationTextbox = gr.Textbox(lines=4, label="Visit Motivation")
+  summaryTextbox = gr.Textbox(lines=4, label="Clinical summary")
+  patientDropdown.change(get_motivation_from_patient_id, inputs=patientDropdown, outputs=motivationTextbox)
+  patientDropdown.change(get_note_from_patient_id, inputs=patientDropdown, outputs=summaryTextbox)
+
+  # Button to generate summarized patient instructions and display them.
+  patientInstructions = gr.Textbox(lines=5, label="Simplified Next-Steps")
+  generateBtn = gr.Button("Get simplified Next Steps for Patient")
+  generateBtn.click(summarize_notes, inputs=patientDropdown, outputs=patientInstructions)
+
+
+demo.queue().launch(share=True)