Reinforcement Learning Drug Injection Control for Anesthetic Stability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Maintaining a patient's anesthetic state during surgery is challenging due to variations in anesthesiologists' skill levels and health conditions, leading to instability in drug injection rates, which can result in unintended consciousness or hemodynamic issues.
Innovation Solution
A device and method using reinforcement learning to control drug injection rates by calculating anesthetic state information, generating a policy model to adjust injection rates of remifentanil and propofol based on target anesthetic states, and predicting expected anesthetic states to stabilize the patient's condition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an anesthesiologist manually adjusts drug injection rate, then the system is simple and easy to operate, but the anesthetic state becomes unstable due to variations in skill level and health condition
Solution Approach 1:
The system automatically adjusts drug injection rates based on real-time anesthetic state information without requiring continuous manual intervention. The control unit autonomously processes BIS/EEG signals, calculates appropriate injection rates, and controls the infusion pump, enabling the system to self-regulate the anesthetic state.
Solution Approach 2:
The system continuously monitors the patient's anesthetic state through BIS or EEG measurements and uses this feedback to dynamically adjust the drug injection rate. The control unit compares the measured anesthetic state information with target values and modifies the injection rate accordingly, creating a closed-loop control system that maintains stable anesthesia.
2Reliability
If drug injection rate is increased to prevent consciousness regain, then patient safety improves, but hemodynamic instability and side effects occur
Solution Approach 1:
The system dynamically adjusts the drug injection rate in real-time based on the patient's current anesthetic state rather than using fixed dosing protocols. The control unit continuously modifies the injection rate according to changes in BIS or EEG values, ensuring the minimum effective dose is administered to prevent consciousness regain while avoiding excessive dosing that causes hemodynamic instability.
Solution Approach 2:
The system changes the injection rate parameter dynamically based on measured anesthetic state information. By adjusting this critical parameter in response to real-time feedback, the system maintains the anesthetic state within the desired range, preventing both under-dosing (consciousness regain) and over-dosing (hemodynamic instability).
Data Source
AI summary
A drug injection control device generates a policy model by learning a change in anesthetic state information due to a drug injection rate set so that anesthetic state information of a patient follows target anesthetic state information, generates a prediction model by learning a change in the anesthetic state information according to a change in the drug injection rate, sets the drug injection rate from the anesthetic state information based on the policy model, and predicts expected anesthetic state information from the set drug injection rate and a previously set drug injection rate based on the prediction model.