Closed-Loop Blood Sugar Control via Artificial Pancreas

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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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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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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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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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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Intelligent insulin management system that adapts insulin infusion rates based on glucose trends and meal intake to prevent hypoglycemia and hyperglycemia. The system uses a glucometer to measure blood glucose levels at intervals. It calculates insulin infusion rates using glucose readings and adaptive multipliers based on glucose trends. If glucose drops rapidly, it shortens the measurement interval. If glucose stability is good, it increases the interval. This prevents over-infusion during fast drops and under-infusion during slow drops. It also coordinates meal boluses with intravenous infusions. The system can transition to subcutaneous insulin delivery if glucose stability is sustained.

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Method and system for optimizing insulin therapy in diabetes by dynamically adjusting basal insulin profiles based on retrospective analysis of glucose levels. The system involves monitoring glucose over a period, determining patterns in the data, and recommending basal profile modifications to improve insulin therapy based on those patterns. The patient can then choose to accept the recommended modification. This allows tailoring basal profiles based on individual patient responses rather than static presets.

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Automated insulin delivery system for people with diabetes that provides a lower and narrower target glucose range during normal activity to improve glycemic control without increasing hypoglycemia risk. The system lets users select between a standard and alternate normal mode with tighter glucose ranges. It calculates and delivers insulin doses based on CGM data according to the chosen mode. The alternate mode has a lower and narrower range but prevents automatic correction boluses when glucose is high and falling rapidly to reduce hypo risk.

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Automated blood glucose regulation system for diabetes management that improves performance and reduces risks of hyper/hypoglycemia when users forget to declare meals. The system has a method to automatically detect and estimate missed meals. If a missed meal is detected, it estimates the likely meal size and time based on historical probabilities. If the missed meal probability is low, it falls back to a non-meal-specific regulation. If the user confirms a missed meal, it uses the estimated values. This prevents long hyperglycemia periods from non-specific regulation if meals are omitted.

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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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