Insulin Optimization System with Noise-Adjusted Exit Criteria
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Solution Overview
Problem
Current insulin titration methods often use a single threshold value for all patients, failing to account for individual variations in biomarker noise, leading to increased risks of adverse events such as hyperglycemia and hypoglycemia due to system noise and protocol noise.
Innovation Solution
A structured approach that calculates a probability distribution function, hazard function, and risk value for biomarker data to optimize insulin dosage by minimizing the risk of adverse events, allowing for personalized exit criteria based on individual patient data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single threshold value is used for all patients in insulin titration, then the titration algorithm is simple and easy to implement, but it fails to account for individual variations in biomarker noise leading to increased risk of adverse events
Solution Approach 1:
The patent applies local quality by personalizing the exit criterion for each patient based on their individual biomarker noise characteristics. Instead of using a uniform threshold for all patients, the system calculates patient-specific noise levels from historical data and adjusts the exit criterion accordingly, allowing each patient to receive tailored titration parameters that account for their unique physiological variability
Solution Approach 2:
The patent implements preliminary action by calculating and storing each patient's biomarker noise level before initiating insulin titration. The system pre-processes historical biomarker data to determine individual noise characteristics, which are then used to set personalized exit criteria before the titration process begins, enabling proactive risk management rather than reactive adjustments
2Reliability
If the exit criterion is adjusted to account for system noise, then the risk of adverse events is reduced, but the calculation and implementation become more complex
Solution Approach 1:
The patent applies self-service by enabling the system to automatically calculate biomarker noise levels and determine personalized exit criteria without requiring manual clinical intervention. The algorithm autonomously processes historical data, computes variability metrics, and generates patient-specific titration parameters, reducing the burden on healthcare providers while improving safety
Solution Approach 2:
The patent implements feedback by continuously monitoring biomarker readings during insulin titration and comparing them against the personalized exit criterion. The system uses this feedback to determine when titration should be paused or adjusted, creating a closed-loop control mechanism that adapts to the patient's real-time physiological state and reduces adverse event risk
3Measurement precision
If personalized exit criteria are implemented based on individual patient noise levels, then glycemic control and safety are improved, but the data processing and analysis requirements increase
Solution Approach 1:
The patent applies partial action by focusing the personalized analysis on the specific biomarker noise relevant to insulin titration decisions, rather than processing all possible patient data. The system calculates only the necessary variability metrics from historical readings to establish exit criteria, avoiding unnecessary computational overhead while maintaining precision in glycemic control
Data Source
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AI summary
Embodiments of a testing method for optimizing a therapy to a diabetic patient comprise collecting at least one sampling set of biomarker data, computing a probability distribution function, a hazard function, a risk function, and a risk value for the sampling set of biomarker data wherein, wherein the probability distribution function is calculated to approximate the probability distribution of the biomarker data, the hazard function is a function which yields higher hazard values for biomarker readings in the sampling set indicative of higher risk of complications, the risk function is the product of the probability distribution function and the hazard function, and the risk value is calculated by the integral of the risk function, minimizing the risk value by adjusting the diabetic patient's therapy, and exiting the testing method when the risk value for at least one sampling set is minimized to an optimal risk level.