Personalized Insulin Infusion Feedback for Hypoglycemia Control
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Solution Overview
Problem
Existing insulin infusion systems lack the ability to automatically adjust settings in a personalized manner based on patient-specific data to optimize diabetes therapy and mitigate the risk of hypoglycemia.
Innovation Solution
A closed-loop system that adjusts control parameters of an infusion device using patient data to optimize insulin delivery, disproportionately penalizing deviations below target glucose levels to minimize hypoglycemic events, and iteratively determines optimized parameter settings through cost functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fixed settings are used for infusion devices, then device complexity is reduced, but therapy optimization and personalization are insufficient
Solution Approach 1:
The system implements closed-loop feedback by continuously monitoring glucose levels and automatically adjusting infusion pump settings based on real-time data. The optimization system analyzes glucose profiles and provides recommendations for parameter adjustments, creating a feedback cycle that improves therapy optimization without requiring complex manual reconfiguration by the user.
Solution Approach 2:
The optimization system performs self-service by automatically analyzing patient data, generating glucose profiles, and determining optimal parameter settings without requiring manual intervention. The system uses algorithms to process historical data and autonomously recommend personalized parameters, reducing the burden on users while maintaining high adaptability.
2Adaptability or versatility
If automated parameter adjustment is implemented, then therapy personalization is improved, but the risk of hypoglycemia increases if not properly constrained
Solution Approach 1:
The system applies preliminary anti-action by pre-establishing safety constraints and penalty functions that prevent hypoglycemic events before they occur. The cost function includes disproportionately high penalties for glucose levels below target ranges, which guides the optimization algorithm to avoid settings that would cause hypoglycemia, thereby counteracting the risk before automated adjustment can cause harm.
Solution Approach 2:
The system dynamically changes parameters based on patient-specific data patterns. By analyzing historical glucose profiles and adjusting parameters adaptively, the system personalizes therapy while maintaining safety through continuous monitoring and constrained optimization. The parameters are modified based on real-time conditions rather than fixed predetermined values.
3Productivity
If continuous monitoring and analysis of patient data is performed, then therapy optimization is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing historical data in a structured format that enables rapid analysis. By maintaining organized datasets of past glucose readings and responses, the system can quickly generate optimized parameters without performing exhaustive analysis in real-time, thus reducing processing time while maintaining optimization quality.
Solution Approach 2:
The optimization system applies local quality by focusing computational resources on the most critical parameters and time windows. Rather than analyzing all historical data equally, the system identifies and processes only the relevant patterns and recent data points that most impact current therapy decisions, improving productivity while reducing overall processing time.
Data Source
AI summary
Techniques disclosed herein involve automatically adjusting a control parameter for an operating mode of a medical device. In some embodiments, the techniques involve determining a value for the control parameter using data pertaining to a physiological condition of a patient by disproportionately penalizing a physiological parameter when it is below a target range in comparison to when the physiological parameter is above the target range.