Automated Insulin Dosing System Improvements

CGM-based automated basal insulin titration for Type 2 Diabetes patients uses historical CGM data, basal insulin doses, hypoglycemia reports, and past recommendations to generate adjusted insulin doses that minimize hypoglycemia risk and avoid overdosing. The method involves estimating personalized dose-response models from CGM metrics, regularizing fits in the early days, incorporating glucose variability, and ensuring safe dose changes. By leveraging CGM's comprehensive view of glucose levels, this approach aims to improve insulin titration compared to fingerstick-based manual titration, reducing the risk of overdosing and minimizing hypoglycemia.

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An automated insulin dosing system for diabetes patients using subcutaneous insulin pumps operates via a central server that receives blood glucose data from glucose meters. The server calculates optimal insulin dosages based on aggregated glucose trends and transmits them to the patient's insulin pump. This system enables personalized insulin dosing based on historical glucose patterns rather than relying solely on current measurements.

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Apparatus to optimize insulin dosage regimen for diabetes patients between clinic visits. The device analyzes patient blood glucose data and adjusts insulin dosages to maintain future levels within a range. It uses algorithms to determine how much to vary long-acting, meal, and correction insulin based on past glucose trends. The device stores the initial insulin regimen set by the doctor, and then continually evaluates if adjustments are needed based on new glucose readings. It can also suggest immediate insulin corrections for high glucose readings.

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Customizing basal insulin delivery in automated insulin pumps to better regulate blood glucose levels for users. The system adaptively adjusts the basal insulin rate based on actual total daily insulin (TDI) needs rather than assuming 50%. It calculates the average actual TDI over a period and then updates the basal rate to match a fraction of that average. This provides more personalized basal delivery compared to the fixed 50% assumption.

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Adjusting insulin therapy in a blood glucose management device to optimize basal insulin rates. The device allows users to easily test and optimize their basal insulin rates. It prompts the user to start a basal rate test, where insulin is delivered at a specified rate during a certain time period. The device monitors blood glucose during the test and calculates if too much or too little insulin was delivered to meet the target. It recommends basal rate adjustments that anticipate under/over-delivery to compensate for lag times. This helps users find the optimal basal rates for their needs.

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Intelligent medicine administering system that calculates insulin doses to optimize diabetes management. The system uses a pen device and companion app to calculate doses that account for factors like glucose trends, recent carbs, and insulin on board. It aims to provide more accurate dosing recommendations by compensating for factors beyond just current glucose levels. The app also optimizes finger stick vs CGM scans for BG control.

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Optimizing insulin dosing for diabetes patients using a system that leverages patient input, sensors, and correction factors to determine and adjust insulin doses. The system receives a blood glucose status input from a patient device, calculates an initial insulin dose, and optimizes it based on correction factors. This optimized dose is then facilitated to the patient's insulin pump or pen. The system can also measure impedance in the patient's tissue to proactively control insulin delivery.

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Automatic insulin delivery system for diabetes management that responds to meal announcements to estimate and deliver insulin without requiring user input of carbohydrate intake. The system estimates meal-related insulin needs based on factors like recent insulin delivery, current glucose, and meal announcement time. It adapts insulin delivery based on glucose trends after meals. This allows automated insulin dosing without user carb entry for meal compensation.

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Closed-loop insulin infusion systems using orthogonally redundant glucose sensors for improved accuracy and reliability. The system has two glucose sensors, one optical and one electrochemical, to provide orthogonal redundancy. An algorithm combines the sensor data to improve accuracy and reliability. If one sensor fails, the other can provide glucose values. The sensors have features like distributed electrodes and membrane barriers to reduce drift and fouling. The system uses on-demand calibration rather than frequent fingersticks.

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Detecting missed bolus insulin injections and meal sizes to improve diabetes treatment. It uses machine learning to analyze blood glucose, insulin, and carb intake data to generate probabilities of meals and missed boluses. This allows for detecting missed injections and estimating meal sizes. The detection can trigger alerts, adjustments to insulin dosing, and recommendations.

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Modifying closed-loop insulin delivery algorithms to prevent glucose oscillations during eating periods when a meal announcement is received. Instead of just increasing the basal rate, the insulin on board (IOB) target is increased. This is done by activating a pre-meal scale that modifies the inner IOB loop of the closed-loop algorithm. This scale increases the IOB target when a meal announcement is received. This allows the system to account for the unknown carb intake and prevent glucose spikes/crashes.

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Medical device and method for determining insulin doses for diabetes management with improved glycemic control and safety features. The device has functions to modify initial dose settings, calculate stepwise dose adjustments, and terminate dose increases based on glycemic events. It protects against unauthorized changes and provides alerts when doses are not appropriate. This aims to prevent overdosing or underdosing by adaptive dose progression and termination triggered by events like hypoglycemia.

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Optimizing insulin delivery for new diabetes patients using automatic insulin delivery systems. The technique involves setting an initial total daily insulin dose based on user weight and reducing factors, then adjusting it over time based on HbA1c levels. It allows customizing insulin needs for newly diagnosed diabetics without historical data. The system calculates an initial adjusted total daily insulin factor based on user weight and reduction factors. It checks if this exceeds a maximum algorithm threshold. If so, it sets a lower total daily insulin dose. It then monitors glucose levels over time and adjusts the insulin dose based on HbA1c levels. This allows tailoring insulin delivery for new diabetics during the honeymoon phase when insulin needs fluctuate rapidly.

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Autonomous glucose control system that allows users to manually input insulin doses in addition to the system's automated dosing. The system generates a control signal based on real-time glucose levels to regulate insulin delivery. It also provides an interface for users to enter dose amounts. The system attempts to deliver the user-specified dose and incorporates it into subsequent autonomous dosing calculations. This allows users to manually input meal insulin doses while the system handles bolus and basal insulin. The user-entered dose bounds are enforced to prevent overdosing.

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Method for determining insulin bolus doses for diabetes management using continuous glucose monitoring (CGM) data. The method involves calculating insulin boluses based on glucose level trends derived from CGM data instead of relying solely on meal carbohydrates. The glucose trends are used to estimate patient-specific factors like carbohydrate-to-insulin ratio (CIR) and insulin sensitivity factor (ISF) that are normally adjusted manually. The CGM data provides more accurate and dynamic estimates of these factors compared to manual adjustment. The method can be implemented in insulin pumps, meters, or remotes to automatically calculate personalized boluses based on CGM trends.

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Optimizing diabetes management by dynamically adjusting insulin dosage regimens to improve glycemic control without increasing hypoglycemic events. The method involves analyzing a patient's historical blood glucose levels to determine the optimal insulin distribution between basal and mealtime doses. By iteratively adjusting the regimen based on trends in the patient's data, it aims to find the balance point where glucose levels are lowest without excessive hypoglycemia.

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Treating diabetes by recognizing that insulin sensitivity varies over time and using that knowledge to optimize insulin dosing. The method involves measuring interstitial fluid glucose and insulin delivery to estimate insulin sensitivity at specific times. Factors like meal absorption, physical activity, and circadian rhythm are considered. The estimated insulin sensitivity is then used to calculate personalized insulin doses that account for the patient's current condition.

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