from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM from healthcare_nlp import Diseases, SNOMEDCT from healthcare_nlp.preprocessing import de_identify_text

Disease selection and model configuration

disease_name = input("Enter a disease name (e.g., 'diabetes'): ").lower() disease_concept = SNOMEDCT.query_concept(disease_name) model_name = "allenai/biobert-base-cased" # Clinically-tuned BioBERT model

Prepare user profile and tokenizer

user_profile = {"age": int(input("Enter your age: "))} tokenizer = AutoTokenizer.from_pretrained(model_name)

Access and process user message

user_message = input("Ask your question about " + disease_concept.preferred_term + ": ") deidentified_message = de_identify_text(user_message) # Protect sensitive information

Generate response with model and personalization

model = AutoModelForSeq2SeqLM.from_pretrained(model_name) inputs = tokenizer(deidentified_message, truncation=True, padding="max_length", return_tensors="pt") generated_ids = model.generate(**inputs, max_length=256) response = tokenizer.decode(generated_ids.squeeze(), clean_up_tokenization=True)

Enhance response with knowledge base and context

knowledge_base = Diseases.load_disease_info(disease_concept.concept_id) response = personalize_response(response, disease_concept, user_message, user_profile, knowledge_base)

Conclude with informative disclaimer

response += ( "\nFeel free to ask any further questions you might have. I am still under development, but I leverage " "clinical knowledge and continuous learning to provide accurate and evidence-based information. " "Remember, I am not a substitute for professional healthcare advice. Please consult your doctor " "for diagnosis and treatment decisions." )

print(response)

def personalize_response(response, disease_concept, user_message, user_profile, knowledge_base): # Highlight relevant knowledge base sections for key, value in knowledge_base.items(): if key.lower() in user_message.lower(): response += f"\nRegarding your query about {key}, here's some relevant information: {value}"

# Adapt response based on disease complexity and user age
response = personalize_with_disease_complexity(response, disease_concept)
response = personalize_with_user_age(response, user_profile)

# Add medical references and adjust communication style based on user preferences
if user_profile["age"] >= 18 and user_profile["preferred_communication_style"] == "informative":
    response = add_informative_details(response, knowledge_base)
elif user_profile["age"] >= 18 and user_profile["preferred_communication_style"] == "empathetic":
    # Implement empathetic communication style here (e.g., acknowledge feelings, offer support)
    pass
else:
    # Use simpler language and avoid overwhelming details for younger users or non-informative preferences
    pass

# Re-identify anonymized terms for clinical context (optional)
# response = re_identify_text(response, knowledge_base)

return response

Personalize response with disease complexity

def personalize_with_disease_complexity(response, disease_concept): if "complex" in disease_concept.description.lower(): response += f"\nPlease note that {disease_concept.preferred_term} is a complex condition. My information offers a starting point, not a definitive explanation. Consult your doctor for a comprehensive understanding." return response

Personalize response with user age

def personalize_with_user_age(response, user_profile): if user_profile["age"] < 18: response += f"\nAs you're under 18, it's crucial to involve your parents or guardians in managing your health and seeking professional guidance." return response

Add medical references or implement different communication styles here based on user preferences

Re-identify anonymized terms with caution, considering ethical implications and potential bias.

This version enhances professionalism with:

De-identification of user message: Protects sensitive information while processing.
Clinically-informed responses: Tailored to disease complexity and user age.