Closed-Loop Blood Sugar Control for Automated Insulin Delivery

Automatically calculating insulin doses for diabetes management without user input of factors like correction factors and insulin/carb ratios. The method involves two techniques: 1) during online operation (with glucose measurements) it generates personalized insulin doses based on glucose trends, and 2) during offline operation (without glucose measurements) it uses information from previous online periods to calculate insulin doses. This allows regulating blood sugar without manual inputs once the system learns the user's insulin requirements.

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Automated method for maintaining blood glucose homeostasis. The method includes generating a glucose target that approaches a desired steady state setpoint from a current glucose value over a prediction horizon, determining an insulin delivery pattern that is more similar to the desired glucose trajectory than any other predicted glucose trajectory, and comparing the desired glucose trajectory against each predicted glucose trajectory and selecting a predicted glucose trajectory that is more similar to the desired glucose trajectory than any other predicted glucose trajectory.

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An artificial pancreas system that can improve glycemic control without requiring user input for meals and exercise. The system uses a modified internal model controller (IMC) that automatically adjusts insulin delivery based on food and exercise detection. The IMC eliminates the need for user announcements by generating an insulin correction signal without readjusting the main controller. It also suggests rescue carbohydrates to mitigate hypoglycemia. The IMC has parameters adjusted through optimization to achieve good postprandial control.

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Improving glycemic control in people with type 1 diabetes using closed-loop insulin delivery systems like artificial pancreases. The method involves automatically adjusting insulin delivery when a meal is detected but not reported, to prevent hyperglycemia. It also modifies insulin basal rates based on predicted blood glucose levels and detects changes in glucose trends. This helps compensate for uninformed meals and unannounced carb intake. The system uses a machine learning model to predict glucose levels and calculate insulin doses, with adaptive control strengths based on recent glucose trends. It also has a meal priming system that delivers extra insulin when a meal is detected but not reported. This prevents large glucose spikes from unannounced meals. The goal is to provide improved glycemic control in closed-loop insulin delivery systems that can handle un

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Adaptive glucose level control system for insulin pumps that optimizes insulin dosing based on historical data. The system adapts basal insulin rates, prediction models, and total daily doses over time based on feedback from actual glucose levels. It learns how much insulin is needed during certain time periods and adjusts future doses accordingly. This allows the pump to adapt to a patient's changing insulin needs over time without constant user input.

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Adaptive meal bolus calculation for closed-loop insulin pumps to improve glucose control in people with type 1 diabetes. The adaptation uses case-based reasoning and run-to-run control to automatically adjust meal insulin requirements based on previous meals and insulin responses. This avoids the need for manual bolus adjustments and addresses the high variability of insulin requirements in type 1 diabetes. The adaptive bolus calculator communicates with the closed-loop controller to coordinate insulin delivery.

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A method for optimizing insulin dosage in diabetes patients to improve glycemic control and reduce hypoglycemic events. The method involves tracking patient blood glucose levels over time, identifying when and why measurements were taken, and determining if insulin dosage adjustments are needed to bring the patient closer to their desired glucose range without increasing hypoglycemia. The adjustments may involve changing insulin distribution rather than the total dose.

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Improving the accuracy of predictive control for closed-loop insulin delivery in artificial pancreas systems. The system uses a physiological model to predict future glucose levels based on insulin, meals, and heart rate. It calculates the optimal insulin dose to avoid hyper/hypoglycemia. The model parameters are calibrated periodically to account for variations. The cost of predicted glucose levels is minimized to find the optimal dose. The maximum insulin dose is limited by a function of patient sensitivity, which can be affected by factors like heart rate.

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Reducing the risk of hypoglycemia in insulin pump users by considering the rate of change in blood glucose levels. The system calculates an initial insulin bolus dose based on the current glucose level. It then calculates a revised dose taking into account the rate of change in glucose levels over time. A function is applied to compare the two doses and determine a final insulin value. This final value is used to set the actual insulin bolus delivered to the user. The idea is to account for the delay between when the glucose level is measured and when the user takes the insulin, and to adjust the dose accordingly to avoid overshooting and causing hypoglycemia.

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CGM-based automated basal insulin titration system for Type 2 diabetes patients to improve insulin dosing accuracy and safety. The system uses historical CGM data, basal insulin doses, hypoglycemia reports, and past recommendations to generate adjusted insulin doses. It creates personalized dose-response models from CGM metrics like glucose percentiles and estimated fasting levels. Regularization biases early fit towards safe doses. CGM variability guards against overdosing. Checks ensure safe dosing like coherence with estimated BG levels and reductions after severe hypo.

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An insulin delivery system for people with diabetes that can automatically manage insulin during fasting periods like overnight sleep. The system calculates correction bolus doses based on blood glucose measurements during the fasting period, compensating for a determined insulin on board. Initially, the insulin on board is assumed based on a calculated correction dose before fasting started. This allows gradual lowering of insulin needs during fasting. The system uses a user's insulin sensitivity factor and other techniques to accurately calculate correction doses during fasting.

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Optimizing insulin delivery for diabetes patients using closed-loop data to adjust manual mode settings. The method involves analyzing closed-loop insulin delivery data to generate recommendations for adjusting manual mode settings like basal rates. This analysis is done using a computing device in communication with the patient's insulin pump. The recommendations are then communicated to the pump to automatically adjust manual mode settings during manual delivery. This allows leveraging closed-loop optimization to improve manual mode insulin delivery.

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A clinical decision support system for managing insulin dosing in hospital patients using subcutaneous insulin rather than IV infusion. The system calculates personalized insulin regimens for tube-fed patients based on their blood glucose measurements and other patient data. It selects an appropriate subcutaneous insulin treatment program from a collection of options. This allows a safer transition from IV insulin when patients are NPO or can't eat, by avoiding subcutaneous insulin injection risks if the patient decides not to eat. The system executes the selected subcutaneous insulin treatment program for the patient.

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Improving glucose control in diabetes by combining oral medications like SGLT2 inhibitors with automated insulin delivery systems. The approach involves using subtherapeutic doses of SGLT2 inhibitors like empagliflozin, dapagliflozin, or sotagliflozin in conjunction with insulin pumps to mitigate post-meal glucose spikes. The insulin pump adjusts basal rates and parameters based on CGM data to maintain target range. This helps prevent ketoacidosis risk from SGLT2 inhibitors. By adding low-dose oral meds, insulin delivery systems can improve daytime glycemic control beyond what insulin alone provides.

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Preemptively alerting a user during the operation of a fluid infusion device like an insulin pump to provide actionable alerts that avoid non-actionable alarms and allow timely intervention. The alerts are based on a homeostasis metric that predicts future glucose levels accounting for insulin on board, future insulin deliveries, and current glucose. This provides alerts for potential hypoglycemia or hyperglycemia before levels change. The alerts recommend actions like insulin adjustments or carb intake to avoid issues. The alerts are cleared when conditions change or higher priority alerts replace them.

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Method for adjusting insulin injections in diabetic patients using real-time blood sugar monitoring to optimize insulin dosing and prevent hyperglycemia. The method involves closed-loop control where an algorithm adjusts insulin infusion based on real-time blood sugar levels. It aims to maintain blood sugar within normal range by dynamically infusing insulin in response to real-time monitoring. The algorithm uses blood sugar data to determine the optimal infusion volumes and timings.

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Optimizing insulin delivery for new diabetes patients using an artificial pancreas system. The technique involves initially setting an adjusted total daily insulin factor based on user characteristics and comparing it to a maximum delivery threshold. If the factor exceeds the threshold, it is adjusted. Over time, blood glucose levels are monitored to determine glycated hemoglobin. The total daily insulin dose is then modified based on the glycated hemoglobin level. This adaptive approach provides personalized insulin delivery optimization for new diabetes patients using an artificial pancreas system.

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Closed-loop control system for managing glucose levels in people with diabetes using an insulin pump. The system uses a multi-model predictive controller that can adapt to rapidly changing glucose dynamics. It executes multiple models with varying parameters to predict future glucose levels. It selects the best model based on measured glucose data to determine optimal insulin doses. This allows the system to handle sudden glucose changes that regular controllers may struggle with.

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Intelligent control method for artificial pancreas systems that improves blood sugar management for diabetes patients. The method involves switching between basal rate insulin delivery and miniature bolus insulin delivery based on real-time glucose trends. If glucose is not rising, basal rate is used. If glucose is rising, miniature bolus delivers extra insulin in smaller doses over time to prevent spikes from foods with less carbs absorbed. This adaptive insulin delivery aims to maintain normal blood sugar levels.

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Artificial pancreas control method to improve safety and efficiency of closed-loop insulin delivery for diabetes management. The method involves using a hybrid approach that combines model predictive control (MPC) with safety checks. It judges the rate of blood sugar change and determines if it's below a threshold. If so, it stops insulin infusion until stability is restored. If the sugar is rising, it uses MPC to optimize insulin delivery. This hybrid approach balances MPC's accuracy with safety checks to prevent hypoglycemia and minimize MPC calculations.

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