Artificial Pancreas Machine Learning for Adaptive Insulin Dosing
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Solution Overview
Problem
Conventional insulin therapy for diabetes is cumbersome and burdensome, requiring significant user intervention and oversight to adjust basal and bolus insulin doses, often leading to non-adherence and ineffective glucose level management.
Innovation Solution
An artificial pancreas system incorporating a wearable glucose monitoring device, insulin delivery system, and computing device that uses machine learning to automatically determine and deliver insulin doses to maintain glucose levels within a target range, optimizing the insulin therapy process over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional insulin therapy techniques are used with manual adjustment processes, then users can determine insulin doses, but the process takes months and is cumbersome
Solution Approach 1:
The system enables self-service by using machine learning models that automatically learn and adapt to each user's unique glucose response patterns over time. The algorithm autonomously determines optimal insulin doses without requiring manual intervention or knowledge from the user, transforming a months-long manual process into an automated self-learning system that continuously optimizes therapy
Solution Approach 2:
The patent replaces the mechanical manual adjustment process with an intelligent automated system. Instead of users manually tracking glucose levels and consulting adjustment tables, a machine learning algorithm processes glucose data, learns individual response patterns, and automatically calculates optimal insulin dosing parameters, substituting human cognitive effort with computational intelligence
2Adaptability or versatility
If users manually monitor and adjust insulin therapy parameters, then therapy can be customized, but users experience significant burden and non-adherence
Solution Approach 1:
The system performs self-service by automatically adapting to each user's unique physiological characteristics through continuous learning. The machine learning model autonomously customizes therapy parameters based on individual glucose responses without requiring user effort to track, record, or adjust settings, thereby maintaining high adaptability while eliminating operational burden
Solution Approach 2:
The system implements continuous feedback loops where glucose monitor data is fed back to the machine learning algorithm, which then adjusts insulin dosing recommendations. This automated feedback mechanism enables real-time customization of therapy based on actual physiological responses, eliminating the need for manual monitoring while maintaining high adaptability to individual user needs
3Extent of automation
If machine learning algorithms are used to automatically determine insulin doses, then the process is automated, but the system complexity increases
Solution Approach 1:
The system achieves high automation by integrating multiple functions into a unified machine learning platform that handles data collection, processing, pattern recognition, and dosing calculation. The single algorithm performs what would otherwise require multiple separate devices and manual processes, managing system complexity through functional integration rather than proliferation of components
Data Source
AI summary
Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.