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Update quantum/optimizer.py

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  1. quantum/optimizer.py +68 -13
quantum/optimizer.py CHANGED
@@ -1,14 +1,69 @@
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- # quantum/optimizer.py
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- def optimize_treatment(query: str):
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- # your stub or real BF-DCQO call
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  return {
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- "input": query,
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- "optimized_treatment_plan": [
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- {"step": 1, "action": "Order CBC, CMP, and ECG"},
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- {"step": 2, "action": "Start IV diuretics"},
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- {"step": 3, "action": "Monitor electrolytes and renal function"},
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- {"step": 4, "action": "Consider ACE inhibitor if stable"}
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- ],
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- "confidence_score": 0.93,
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- "explanation": "Optimized sequence for symptom relief and safety monitoring."
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- }
 
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+ from services.logger import app_logger
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+ from typing import Dict, Any
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+ import time
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+ import random
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+
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+ # This is a STUB for a quantum optimizer.
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+ # In a real scenario, this would interface with a quantum computing service
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+ # or a sophisticated classical optimizer that mimics quantum approaches.
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+
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+ def optimize_treatment(patient_data: Dict[str, Any], current_treatments: list, conditions: list) -> Dict[str, Any]:
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+ """
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+ Stub for a quantum-inspired treatment optimization.
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+
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+ Args:
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+ patient_data (Dict[str, Any]): Relevant patient characteristics.
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+ current_treatments (list): List of current medications/therapies.
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+ conditions (list): List of diagnosed conditions.
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+
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+ Returns:
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+ Dict[str, Any]: A dictionary with optimized treatment suggestions.
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+ """
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+ app_logger.info(f"Quantum optimizer called for patient data: {patient_data}, treatments: {current_treatments}, conditions: {conditions}")
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+
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+ # Simulate a complex computation
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+ time.sleep(random.uniform(1, 3)) # Simulate processing time
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+
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+ # Mocked optimization logic
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+ # This would be replaced by actual quantum annealing, VQE, QAOA, etc. calls
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+ # or complex classical algorithms.
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+
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+ suggestions = []
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+ confidence_score = random.uniform(0.65, 0.95)
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+
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+ if "diabetes" in [c.lower() for c in conditions]:
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+ if "metformin" not in [t.lower() for t in current_treatments]:
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+ suggestions.append({
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+ "action": "ADD",
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+ "medication": "Metformin",
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+ "dosage": "500mg BID",
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+ "rationale": "Standard first-line for type 2 diabetes, potentially enhanced by quantum risk modeling."
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+ })
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+ else:
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+ suggestions.append({
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+ "action": "MONITOR",
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+ "medication": "Metformin",
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+ "rationale": "Continue Metformin. Quantum analysis suggests current efficacy."
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+ })
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+
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+ if "hypertension" in [c.lower() for c in conditions]:
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+ suggestions.append({
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+ "action": "CONSIDER_ADD",
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+ "medication": "Lisinopril",
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+ "dosage": "10mg QD",
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+ "rationale": "ACE inhibitor, effective for hypertension. Quantum combinatorial analysis suggests synergy."
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+ })
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+
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+ if not suggestions:
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+ suggestions.append({
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+ "action": "MAINTAIN",
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+ "medication": "Current Regimen",
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+ "rationale": "Quantum analysis indicates current treatment plan is near-optimal or requires further data."
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+ })
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+
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  return {
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+ "optimized_suggestions": suggestions,
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+ "confidence": f"{confidence_score:.2f}",
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+ "summary": f"Based on quantum-inspired analysis of {len(conditions)} conditions and {len(current_treatments)} current treatments, the following adjustments are suggested.",
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+ "iterations": random.randint(1000, 50000) # Mock "quantum" parameter
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+ }