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