Meal Model Subcategories for Insulin Dosage
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Solution Overview
Problem
Diabetic patients face challenges in managing blood glucose levels during meals due to the need for estimating carbohydrate intake and insulin dosage, leading to potential hypoglycemic or hyperglycemic states, which can be risky and harmful.
Innovation Solution
A system and method utilizing meal models to automatically determine insulin dosage based on meal size, composition, and timing, reducing user involvement by selecting a meal model from a database and adjusting parameters such as delay, extend, and delivery constraints to maintain blood glucose within a target range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users estimate carbohydrate intake and insulin dosage manually, then user control and flexibility are maintained, but estimation errors lead to hypoglycemic or hyperglycemic states
Solution Approach 1:
The system enables self-service by automatically detecting meal ingestion through sensors and algorithms, selecting appropriate meal models, and calculating insulin dosages without requiring user estimation or manual input. The closed-loop system monitors glucose levels continuously and adjusts insulin delivery autonomously, freeing users from the burden of manual meal management while maintaining reliable blood glucose control.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring blood glucose levels through sensors and using this real-time data to adjust insulin dosage calculations. The meal models are refined based on observed glucose responses, creating a closed-loop control system that learns from user-specific patterns and improves accuracy over time, thereby enhancing reliability without increasing user burden.
2Ease of operation
If a closed loop system reduces user engagement, then ease of operation improves, but the system requires complex meal models and parameters to maintain accuracy
Solution Approach 1:
The system segments meal management into distinct components: meal detection modules that identify when eating occurs, meal model libraries that categorize different meal types with characteristic parameters, and dosage calculation engines that compute insulin requirements. This segmentation allows the complex task of meal management to be divided into manageable sub-tasks handled by specialized algorithmic modules, reducing overall system complexity while maintaining automation.
Solution Approach 2:
The system manages complexity by dynamically adjusting parameters within meal models based on real-time glucose data and historical patterns. Rather than requiring fixed complex models for every scenario, the system modifies parameters such as absorption rates, peak times, and insulin sensitivity factors adaptively, allowing a core set of meal models to handle diverse meal types through parameter variation rather than requiring separate complex models for each meal type.
3Productivity
If insulin dosage is calculated using fixed meal models, then calculation speed improves, but adaptability to individual user responses deteriorates
Solution Approach 1:
The system transitions from static fixed meal models to dynamic adaptive models that evolve based on user-specific responses. Meal models incorporate time-varying parameters that adjust according to observed glucose patterns, individual metabolic responses, and contextual factors. This dynamic approach maintains calculation speed by using pre-computed model structures while adapting parameters in real-time to match individual user characteristics, achieving both speed and personalization.
Solution Approach 2:
The system performs preliminary actions by pre-loading multiple meal models with characteristic parameters for different meal types before they are needed. When a meal is detected, the system quickly selects the most appropriate pre-prepared model and begins dosage calculations immediately. Historical user response data is also pre-processed to identify personal patterns, enabling rapid adaptation without delaying the insulin dosage calculation, thus maintaining productivity while improving adaptability.
Data Source
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AI summary
Disclosed are examples that include receiving information related to ingestion of a meal. The information may include a coarse indication of a size of the meal, a relative time of ingestion of the meal, and a general composition indication of the meal. A blood glucose measurement value received within a predetermined time range of a relative time of ingestion of the meal may be identified. Settings for a delay and an extend parameter and a delivery constraint may be determined. A meal model may be modified using the determined settings for the delay parameter, the extend parameter, and the delivery constraint. The modified meal model may be used to determine a dose of insulin to be delivered in response to the received information. An instruction indicates a determined dose of insulin to be delivered may be output for delivery to a drug delivery device.