Population Carb Count Modeling for Closed-Loop Insulin Dosing
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Solution Overview
Problem
Manual carbohydrate counting for insulin delivery is tedious and prone to errors, leading to undesirable consequences such as insulin under-delivery or over-delivery, which can affect glucose management in diabetes patients.
Innovation Solution
Implementing default carbohydrate consumption counts based on population carbohydrate consumption models, adjusted by demographic and personal characteristics, to determine insulin dosage, with safety limits and closed-loop control for accurate insulin delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual carbohydrate counting is used for insulin delivery, then insulin dosage can be determined, but the process is tedious and prone to errors leading to insulin under-delivery or over-delivery
Solution Approach 1:
The system automatically determines carbohydrate consumption counts without requiring manual input from the user. The insulin delivery device autonomously calculates insulin dosage based on population carbohydrate consumption models and individual characteristics, eliminating the need for users to manually count carbohydrates while maintaining accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of carbohydrate counting with an automated computational system. The device uses population carbohydrate consumption models and individual characteristics data to automatically calculate carbohydrate intake and determine insulin dosage, substituting human manual operation with algorithmic computation.
2Reliability
If manual carbohydrate counting is used, then insulin dosage can be calculated, but errors lead to insulin under-delivery or over-delivery affecting glucose management
Solution Approach 1:
The system incorporates feedback mechanisms where glucose monitoring data and actual carbohydrate consumption patterns are fed back into the population carbohydrate consumption model. This allows the system to continuously refine and improve the accuracy of carbohydrate count predictions, thereby enhancing insulin delivery reliability over time.
Solution Approach 2:
The patent establishes population carbohydrate consumption models in advance that incorporate individual characteristics. These pre-established models enable the system to quickly and accurately determine carbohydrate consumption and insulin dosage without manual counting, improving both reliability and speed of insulin delivery decisions.
3Productivity
If default carbohydrate consumption counts based on population models are used, then efficiency is improved, but individual variability in carbohydrate consumption must be accounted for
Solution Approach 1:
The system applies local quality by customizing the population carbohydrate consumption model for each individual based on their specific characteristics such as age, weight, activity level, and dietary patterns. This allows the automated system to maintain high efficiency while adapting to individual variations in carbohydrate consumption, ensuring personalized and accurate insulin dosing.
Data Source
AI summary
Techniques disclosed herein relates generally to default carbohydrate consumption counts based on population carbohydrate consumption models. In one example, the techniques involve receiving, from a device, at least one characteristic of a person; accessing a population carbohydrate consumption model which relates the at least one characteristic with carbohydrate intake for a population; determining, based on applying the population carbohydrate consumption model to the at least one characteristic, at least one of: a preliminary carbohydrate count for the person or a default carbohydrate consumption count for the person; and causing delivery of insulin to the person based on communicating, to the device, at least one of: the preliminary carbohydrate count for the person or the default carbohydrate consumption count for the person.