Glucose Pattern Recognition for Insulin Dosage Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional external infusion pump therapy for diabetes management is plagued by complications, and there is a need for improved systems to analyze glucose level patterns and provide accurate insulin dosages, especially for managing anomalous readings and predicting notification events.
Innovation Solution
A system that uses pattern recognition and filtering algorithms to analyze glucose level readings, adapts anomalous data to established patterns, and calculates insulin dosages based on these patterns, while also predicting notification events such as hyperglycemia or hypoglycemia to initiate proactive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional external infusion pumps are used for insulin delivery, then insulin can be delivered continuously into the body, but the system suffers from several complications that make use less desirable
Solution Approach 1:
The patent replaces the mechanical external infusion pump system with a biological solution using genetically modified beta cells that can be transplanted into the patient. These engineered cells automatically sense glucose levels and secrete insulin in response, eliminating the need for mechanical pumps, infusion tubes, and percutaneous needles while maintaining continuous insulin delivery control.
Solution Approach 2:
The engineered beta cells are designed to autonomously monitor glucose levels in the bloodstream and self-regulate insulin secretion without external control mechanisms. The cells inherently possess the sensing and response capabilities needed, eliminating the need for complex pump control systems and reducing complications associated with external device usage.
2Reliability
If infusion pump therapy is used to provide continuous insulin infusion, then greater control of diabetic condition is achieved, but the system complexity and user burden increase
Solution Approach 1:
The complex mechanical infusion pump system with its associated controls, programming, and monitoring equipment is replaced by a biological system of engineered beta cells that naturally perform glucose sensing and insulin secretion functions, dramatically simplifying the overall therapy system while maintaining glucose control reliability.
Solution Approach 2:
The engineered beta cells autonomously perform all functions of the pump system - sensing glucose levels, determining appropriate insulin dosage, and secreting insulin - without requiring external control mechanisms, programming, or user intervention, thereby eliminating device complexity while achieving reliable glucose control.
3Measurement precision
If pattern recognition algorithms analyze multiple glucose level readings to determine insulin dosage, then more accurate insulin dosing is achieved, but the data processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary analysis of glucose level patterns by identifying common event occurrences (such as meals, exercise, or stress events) and pre-processing the glucose readings in relation to these events. This preliminary organization of data allows for faster subsequent analysis and insulin dosage calculation, reducing the time penalty associated with analyzing multiple glucose readings.
Solution Approach 2:
The patent segments the glucose level data into distinct periods or events (such as pre-meal, post-meal, exercise periods, etc.) and analyzes patterns within each segment separately. This segmentation approach simplifies the overall data processing task by breaking down complex multi-variable analysis into manageable segments, thereby maintaining dosage accuracy while reducing computational time.
Data Source
AI summary
A method of diabetes analysis includes receiving a plurality of glucose level readings for a user. A common event occurrence in at least two of the glucose level readings is determined. The at least two glucose level readings from the common event occurrence onwards in time for a time period is analyzed. A glucose level pattern formed by the at least two glucose level readings having a similar shape is determined. At least one anomalous glucose level reading having the similar shape and not conforming to the glucose level pattern is analyzed. The at least one anomalous glucose level reading is adapted to the pattern to form an adapted glucose level pattern. An insulin dosage for the time period beginning at the common event occurrence is calculated based on the adapted glucose level pattern.