Glucose Control Parameter Tuning From Insulin Bolus History
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Solution Overview
Problem
Automated insulin delivery systems require users to manually specify glucose control parameters, which can be difficult and may result in ill-suited settings, leading to frustrating and ineffective glucose control.
Innovation Solution
A medicament delivery system that automatically adjusts glucose control parameters based on the history of medicament bolus deliveries, using an adjustment factor to enhance glucose control by increasing the aggressiveness of insulin delivery settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually specify glucose control parameter values, then the system allows user control over insulin delivery settings, but the parameters may be set at values that are ill-suited for the user and the process becomes confusing and frustrating
Solution Approach 1:
The system automatically adjusts glucose control parameters using bolus delivery data without requiring manual user input. The processor determines the number of boluses delivered over a period, calculates an adjustment factor, and automatically modifies parameters such as target glucose level, weight coefficients, basal delivery rates, and constraints, enabling the system to self-optimize based on actual usage patterns
Solution Approach 2:
The system uses feedback from historical bolus delivery data to automatically adjust glucose control parameters. By analyzing the number of correction boluses and their components over time, the system calculates adjustment factors that modify parameters to better suit the user's needs, creating a closed-loop optimization process
2Reliability
If the system increases the aggressiveness of insulin delivery settings, then glucose control effectiveness improves, but the rate of medicament delivery increases which may cause safety concerns
Solution Approach 1:
The system applies partial adjustment to glucose control parameters by calculating an adjustment factor based on the proportion of correction boluses to total boluses. This partial action approach gradually increases aggressiveness only to the extent needed, avoiding excessive medicament delivery while still improving glucose control effectiveness
Solution Approach 2:
The system modifies multiple glucose control parameters simultaneously including target glucose level, weight coefficients for cost functions, basal delivery rates, and delivery constraints. By changing multiple parameters in coordination rather than single-parameter adjustments, the system achieves better glucose control while distributing the increase in medicament delivery across several adjusted parameters
Data Source
AI summary
A framework may be provided for automatically converting a history of manual medicament bolus deliveries to a user into modifications of one or more glucose control parameters of a medicament delivery system. This may eliminate the need for the user to manually adjust the glucose control parameter(s); instead the glucose control parameter(s) may be programmatically adjusted automatically. The glucose control parameter(s) may include, for example, a target glucose level, an input base basal delivery rate, cost function coefficients, and/or a constraint on maximum delivery amounts of medicament. The automatic adjustments may help to customize the glucose control parameter(s) to the user and to minimize the need for user interactions with the medicament delivery system to adjust glucose control parameter(s).