Dynamic Patient-Specific Insulin Therapy Modeling
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Solution Overview
Problem
Current clinical approaches for managing diabetes, particularly in diabetic patients, fail to account for patient-specific factors such as physiological variability, metabolic differences, and the effects of stress, exercise, and meals, leading to suboptimal insulin delivery and glucose control.
Innovation Solution
A computerized system for developing patient-specific therapies using dynamic modeling of patient physiology, which includes physiological and metabolic models, and mathematical analysis to determine insulin delivery and dosage, integrating data from glucose measurements, patient activities, and other physiological parameters to provide personalized insulin therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional therapy approaches are used for glucose control, then basic glucose monitoring is provided, but patient-specific factors such as physiological variability, metabolic differences, and effects of stress, exercise, sickness, and meals are not accounted for
Solution Approach 1:
The system employs dynamic modeling of patient physiology that continuously adapts to changing conditions. The model updates insulin sensitivity, carbohydrate-to-insulin ratio, and other parameters in response to real-time glucose measurements, meal intake, physical activity, and stress indicators, allowing the therapy to dynamically adjust to patient-specific factors rather than using static protocols
Solution Approach 2:
The system changes key therapeutic parameters including insulin delivery rate, timing, and dosage based on patient-specific physiological states. By monitoring multiple parameters (glucose levels, meal composition, activity level, stress markers) and adjusting insulin parameters accordingly, the system adapts therapy to individual patient needs while managing complexity through automated parameter optimization
2Loss of information
If a series of measurements is used to understand system dynamics, then more comprehensive data is obtained, but it becomes difficult to translate data into actionable information
Solution Approach 1:
The system continuously monitors glucose measurements and other physiological parameters, compares them against the dynamic model predictions, and uses this feedback to adjust insulin delivery recommendations. This closed-loop feedback mechanism transforms raw measurement data into actionable therapy adjustments by automatically identifying deviations from expected physiological behavior and generating appropriate responses
Solution Approach 2:
The dynamic physiological model acts as an intermediary between raw measurement data and therapeutic decisions. The model processes multiple measurements (glucose, meals, activity, stress) and translates them into meaningful predictions about patient physiology, making the complex data actionable by providing interpretable recommendations for insulin delivery
3Ease of manufacture
If population-based approaches are used for drug dosing, then general guidelines are established, but patient-specific variability in pharmacokinetics and pharmacodynamics is not captured
Solution Approach 1:
The system performs preliminary characterization of patient-specific physiological parameters during an initial period when the patient uses the system. By measuring glucose responses to meals, exercise, and stress during this learning phase, the system builds a personalized dynamic model that captures individual variability before providing long-term therapy recommendations
Solution Approach 2:
The system enables patients to self-monitor glucose levels, report meal intake, activity, and stress conditions, and automatically generates personalized therapy recommendations. This self-service approach allows the system to capture patient-specific data without requiring manual input from healthcare providers, improving measurement precision while maintaining ease of use
Data Source
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AI summary
A system for developing patient-specific therapies based on dynamic modeling of patient-specific physiology and method thereof are disclosed. The system includes software modules configured to provide access via a computer to one or more data collection protocols defining at least a type of patient-specific data to be collected and a manner in which the patient-specific data is to be collected, and to information from which one or more patient-specific models, configured to simulate one or more aspects of the patient's physiology, is developed. Another software module of the system is configured to provide access via the computer to one or more software tools that apply patient-specific data, collected according to the one or more data collection protocols, to the one or more patient specific models to determine therefrom one or more patient-specific therapies.