Adaptive Multi-Agent Chemotherapy Optimization using Hybrid SDRE-ILC Control with Real-Time Patient Response
Abstract
A novel approach combining State-Dependent Riccati Equation (SDRE) control with Iterative Learning Control (ILC) for personalized cancer treatment optimization. The system uses multiple autonomous agents to adaptively adjust chemotherapy dosing based on real-time patient response data while maintaining prescribed performance boundaries.
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Research Gap Analysis
Current approaches lack real-time adaptation to patient responses while maintaining strict safety bounds. No existing system combines SDRE control, ILC, and multi-agent consensus for chemotherapy optimization.
Adaptive Multi-Agent Chemotherapy Optimization using Hybrid SDRE-ILC Control
Motivation
Current chemotherapy protocols often follow standardized schedules that don't account for individual patient responses or temporal variations in tumor behavior. While recent research has explored optimal control methods for chemotherapy scheduling, these approaches typically don't adapt in real-time to patient responses or maintain strict performance boundaries. There's a critical need for adaptive, personalized treatment strategies that can optimize drug delivery while ensuring patient safety.
Proposed Approach
The proposed system combines three key technologies:
- SDRE Control Framework
- Implements non-linear state-space modeling of tumor dynamics
- Incorporates real-time patient vitals and tumor markers
- Maintains prescribed performance boundaries for patient safety
- Iterative Learning Control (ILC)
- Adapts treatment strategies based on previous cycles' outcomes
- Handles varying treatment durations and initial conditions
- Optimizes drug timing and dosage based on learned responses
- Multi-Agent Decision System
- Multiple control agents monitor different aspects of patient health
- Consensus-based decision making for treatment adjustments
- Distributed security measures to ensure system reliability
The system operates in a closed-loop fashion, continuously monitoring patient responses and adjusting treatment parameters while maintaining prescribed performance boundaries. Real-time data from blood tests, imaging, and vital signs inform the control decisions.
Expected Outcomes
- Improved treatment efficacy through personalized dosing
- Reduced side effects via optimal drug timing
- Better management of patient-specific constraints
- Verifiable performance guarantees within safety bounds
- Adaptive learning across treatment cycles
Potential Applications
- Personalized chemotherapy treatment optimization
- Clinical decision support systems
- Drug development and clinical trials
- Extension to other chronic disease treatments
- Training simulations for medical professionals
Proposed Methodology
Develop a hybrid control system integrating SDRE-based optimization with ILC learning capabilities, managed by multiple autonomous agents that ensure consensus-based decision making while maintaining prescribed performance boundaries.
Potential Impact
Could significantly improve chemotherapy outcomes through personalized treatment optimization, reduce side effects, and provide a framework for adaptive medical treatment protocols across various diseases.