Patient-Specific Glucose Prediction With Context-Aware Insulin Control
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Solution Overview
Problem
Existing infusion pump systems struggle to provide personalized and context-sensitive glucose control due to variations in insulin response and patient-specific factors, leading to inconsistent therapy efficacy.
Innovation Solution
A patient-specific database system that utilizes a directed graph data structure to analyze historical patient data, medical records, and insurance claims data to establish causal relationships, enabling personalized predictions and recommendations through machine learning and natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a standardized insulin infusion protocol is used, then device complexity is reduced and ease of operation is improved, but therapy efficacy and glucose control consistency deteriorate due to individual patient variations
Solution Approach 1:
The system dynamically adjusts insulin infusion parameters (rate, timing, dosage) based on real-time glucose measurements and patient-specific response patterns. The protocol transitions from fixed standardized values to adaptive parameters that change according to measured physiological states, resolving the contradiction between operational simplicity and therapy effectiveness.
Solution Approach 2:
The system implements continuous feedback loops where glucose measurements from sensors inform subsequent insulin delivery decisions. The closed-loop control mechanism uses measured glucose levels to automatically adjust infusion rates, creating a self-regulating system that maintains glucose control without requiring complex manual intervention from patients or providers.
2Reliability
If multiple patient-specific variables and contextual factors are incorporated into the control system, then therapy efficacy and personalization are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex control problem into distinct functional modules: glucose sensing, data storage, pattern recognition algorithms, insulin delivery control, and user interface. Each module handles specific aspects of the therapy, allowing the overall complex system to be managed through modular components that can be independently optimized and maintained.
Solution Approach 2:
The system introduces an intelligent software intermediary layer that processes complex patient data, historical patterns, and contextual information to generate simplified control decisions. This software mediator translates complex multi-variable analysis into straightforward pump control commands, shielding the physical hardware from the full complexity of the decision-making process.
3Measurement precision
If real-time glucose monitoring and continuous adjustment are implemented, then glucose control precision is improved, but energy consumption and measurement frequency requirements increase
Solution Approach 1:
The system employs periodic glucose measurements at strategically determined intervals rather than truly continuous monitoring. The measurement frequency is dynamically adjusted based on glucose stability, recent changes, and predicted future states, allowing the system to maintain control precision while reducing unnecessary sensor activation and energy consumption during stable periods.
Data Source
AI summary
Infusion devices and related medical devices, patient data management systems, and methods are provided for monitoring a physiological condition of a patient. An exemplary method of monitoring a physiological condition of a patient involves obtaining current measurement data for the physiological condition of the patient provided by a sensing arrangement, obtaining a user input indicative of one or more future events associated with the patient, and in response to the user input, determining a prediction of the physiological condition of the patient in the future based at least in part on the current measurement data and the one or more future events using one or more prediction models associated with the patient, and displaying a graphical representation of the prediction on a display device.