Infusion System Meal Prediction Using Association Mining
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Solution Overview
Problem
Current infusion pump systems for managing diabetes face challenges in automatically adapting to individual patient variations in insulin response due to factors like meals and activities, leading to potential manual errors and reduced therapy effectiveness.
Innovation Solution
A processor-implemented method and system that uses association mining to generate a predictive association model based on historical patient data, allowing for real-time adjustments in insulin delivery by identifying patterns in meal consumption and other activities to personalize insulin administration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual bolus administration is used to mitigate postprandial hyperglycemia, then the user can control insulin delivery, but manual errors such as miscounting carbohydrates or failing to initiate a bolus in a timely manner reduce therapy effectiveness
Solution Approach 1:
The system performs automatic meal detection and bolus calculation without requiring manual patient input. The processor analyzes sensor data to detect meal events, calculate carbohydrate amounts, and determine appropriate bolus dosages automatically, eliminating manual carbohydrate counting and bolus initiation while improving therapy reliability
Solution Approach 2:
The system continuously monitors sensor data providing feedback about glucose levels and insulin absorption. This feedback loop enables the system to detect meal events in real-time, adjust bolus recommendations based on current glucose trends and insulin action, and improve the timing and accuracy of insulin delivery
2Measurement precision
If a predictive association model is implemented to automatically detect meal consumption, then manual errors are reduced, but the device complexity increases
Solution Approach 1:
The existing infusion pump hardware is extended to perform multiple functions: continuous glucose monitoring, meal detection through pattern recognition, automatic bolus calculation, and insulin delivery. The processor leverages existing sensor capabilities and adds predictive analytics software to create a multi-functional system that detects meal consumption with high accuracy without requiring separate dedicated devices
Solution Approach 2:
The system pre-processes sensor data continuously to establish baseline patterns of glucose response and insulin absorption for each patient. By analyzing historical data and creating predictive models in advance, the system can rapidly detect meal events and calculate appropriate bolus dosages when needed, improving measurement precision while managing computational complexity through progressive learning
Data Source
AI summary
Techniques for monitoring a physiological condition of a patient are provided. In some embodiments, the techniques may involve obtaining a predictive association model associated with the patient, wherein the predictive association model comprises an association of two or more categorical state values that are predictive of the patient consuming a meal. The techniques may further involve obtaining real-time data associated with the patient. The techniques may further involve determining a current state of the patient based at least in part on the real-time data by transforming the real-time data into two or more current state categorical values. The techniques may further involve predicting consumption of a meal in response to determining the two or more current state categorical values match the two or more categorical state values of the association.