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--- |
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license: openrail |
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--- |
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from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM |
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from healthcare_nlp import Diseases, SNOMEDCT |
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from healthcare_nlp.preprocessing import de_identify_text |
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# Disease selection and model configuration |
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disease_name = input("Enter a disease name (e.g., 'diabetes'): ").lower() |
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disease_concept = SNOMEDCT.query_concept(disease_name) |
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model_name = "allenai/biobert-base-cased" # Clinically-tuned BioBERT model |
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# Prepare user profile and tokenizer |
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user_profile = {"age": int(input("Enter your age: "))} |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Access and process user message |
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user_message = input("Ask your question about " + disease_concept.preferred_term + ": ") |
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deidentified_message = de_identify_text(user_message) # Protect sensitive information |
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# Generate response with model and personalization |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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inputs = tokenizer(deidentified_message, truncation=True, padding="max_length", return_tensors="pt") |
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generated_ids = model.generate(**inputs, max_length=256) |
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response = tokenizer.decode(generated_ids.squeeze(), clean_up_tokenization=True) |
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# Enhance response with knowledge base and context |
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knowledge_base = Diseases.load_disease_info(disease_concept.concept_id) |
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response = personalize_response(response, disease_concept, user_message, user_profile, knowledge_base) |
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# Conclude with informative disclaimer |
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response += ( |
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"\nFeel free to ask any further questions you might have. I am still under development, but I leverage " |
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"clinical knowledge and continuous learning to provide accurate and evidence-based information. " |
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"Remember, I am not a substitute for professional healthcare advice. Please consult your doctor " |
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"for diagnosis and treatment decisions." |
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) |
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print(response) |
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def personalize_response(response, disease_concept, user_message, user_profile, knowledge_base): |
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# Highlight relevant knowledge base sections |
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for key, value in knowledge_base.items(): |
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if key.lower() in user_message.lower(): |
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response += f"\nRegarding your query about {key}, here's some relevant information: {value}" |
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# Adapt response based on disease complexity and user age |
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response = personalize_with_disease_complexity(response, disease_concept) |
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response = personalize_with_user_age(response, user_profile) |
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# Add medical references and adjust communication style based on user preferences |
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if user_profile["age"] >= 18 and user_profile["preferred_communication_style"] == "informative": |
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response = add_informative_details(response, knowledge_base) |
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elif user_profile["age"] >= 18 and user_profile["preferred_communication_style"] == "empathetic": |
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# Implement empathetic communication style here (e.g., acknowledge feelings, offer support) |
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pass |
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else: |
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# Use simpler language and avoid overwhelming details for younger users or non-informative preferences |
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pass |
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# Re-identify anonymized terms for clinical context (optional) |
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# response = re_identify_text(response, knowledge_base) |
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return response |
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# Personalize response with disease complexity |
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def personalize_with_disease_complexity(response, disease_concept): |
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if "complex" in disease_concept.description.lower(): |
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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." |
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return response |
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# Personalize response with user age |
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def personalize_with_user_age(response, user_profile): |
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if user_profile["age"] < 18: |
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response += f"\nAs you're under 18, it's crucial to involve your parents or guardians in managing your health and seeking professional guidance." |
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return response |
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# Add medical references or implement different communication styles here based on user preferences |
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# Re-identify anonymized terms with caution, considering ethical implications and potential bias. |
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This version enhances professionalism with: |
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* **De-identification of user message:** Protects sensitive information while processing. |
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* **Clinically-informed responses:** Tailored to disease complexity and user age. |
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* ** |
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