Semi-Closed Loop Insulin Infusion System With Derivative Prediction
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Solution Overview
Problem
Current insulin infusion systems for diabetics require significant patient interaction and do not efficiently adjust insulin delivery based on real-time and predicted blood glucose levels, leading to suboptimal glucose control.
Innovation Solution
A semi-closed loop infusion system that uses a sensor to monitor blood glucose levels and a derivative predicted algorithm to adjust insulin delivery, suspending or resuming infusion based on predefined thresholds and providing alarm alerts to the patient, thereby simulating the body's natural insulin response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional insulin infusion systems are used, then patient interaction is required for manual adjustments, but glucose control efficiency is suboptimal
Solution Approach 1:
The system enables self-service automation by using the sensor-derived glucose trend and derivative predicted algorithm to automatically determine insulin delivery adjustments without requiring manual patient intervention. The controller autonomously processes sensor data, predicts future glucose levels, and adjusts infusion rates based on predefined target ranges and thresholds.
Solution Approach 2:
The system implements continuous feedback by monitoring sensor-derived glucose levels and trends, comparing them against target ranges, and automatically adjusting insulin delivery. The feedback loop includes real-time sensor data collection, algorithmic processing of glucose trends, and dynamic modification of infusion rates based on predicted future glucose levels.
2Adaptability or versatility
If manual insulin adjustment is used, then system complexity is low, but real-time glucose level adaptation is insufficient
Solution Approach 1:
The system performs preliminary action by using the derivative predicted algorithm to forecast future blood glucose levels before actual hypoglycemia or hyperglycemia occurs. This predictive capability allows the system to proactively adjust insulin delivery to prevent glucose excursions rather than merely reacting to current glucose levels.
Solution Approach 2:
The system implements dynamics by continuously adapting insulin delivery rates based on real-time sensor-derived glucose trends and predicted future levels. The controller dynamically modifies infusion parameters according to the calculated glucose trajectory, enabling flexible adaptation to changing metabolic conditions.
3Extent of automation
If automated insulin delivery is implemented, then patient interaction is reduced, but algorithm complexity increases
Solution Approach 1:
The system replaces manual mechanical adjustment with an automated electronic control system that uses sensor-derived data and mathematical algorithms. The derivative predicted algorithm substitutes for manual patient judgment and adjustment, automatically calculating future glucose levels and determining appropriate insulin delivery modifications.
Data Source
AI summary
An infusion system is for infusing a fluid into the body of a patient. The infusion system includes at least one sensor for monitoring blood glucose concentration of the patient and an infusion device for delivering fluid to the patient. The sensor produces at least one sensor signal input. The infusion device uses the at least one sensor signal input and a derivative predicted algorithm to determine future blood glucose levels. The infusion device delivers fluid to the patient when future blood glucose levels are in a patient's target range. The infusion device is capable of suspending and resuming fluid delivery based on future blood glucose levels and a patient's low shutoff threshold. The infusion device suspends fluid delivery when future blood glucose levels falls below the low shutoff threshold. The infusion device resumes fluid delivery when a future blood glucose level is above the low shutoff threshold.