PREDICTION OF MEAL AND/OR EXERCISE EVENTS BASED ON PERSISTENT RESIDUALS

    公开(公告)号:US20240307618A1

    公开(公告)日:2024-09-19

    申请号:US18675900

    申请日:2024-05-28

    CPC classification number: A61M5/1723 A61M5/14244 A61M2205/52 A61M2230/201

    Abstract: Exemplary embodiments provide an approach to predicting meal and/or exercise events for an insulin delivery system that otherwise does not otherwise identify such events. The insulin delivery system may use a model of glucose insulin interactions that projects estimated future glucose values based on the history of glucose values and insulin deliveries for the user. The predictions of meal events and/or exercise events may be based on residuals between actual glucose values and predicted glucose values. The exemplary embodiments may calculate a rate of change of the residuals over a period of time and compare the rate of change to thresholds to determine whether there likely has been a meal event or an exercise event. The insulin delivery system may then take measures to account for the meal or exercise by the user.

    ONBOARDING AND TOTAL DAILY INSULIN ADAPTIVITY

    公开(公告)号:US20240185980A1

    公开(公告)日:2024-06-06

    申请号:US18440058

    申请日:2024-02-13

    Abstract: Disclosed are a device, a computer-readable medium, and techniques that provide an onboarding process and an adaptivity process for a drug delivery device. A processor executing an onboarding process determines whether a history of delivered insulin to a user meets certain sufficiency requirements. The onboarding process enables a processor to cause the drug delivery device to administer doses of insulin to a user according to an initial total daily insulin dose calculation that is determined based on the sufficiency of the insulin delivery history. The initial total daily insulin may be adapted according to the adaptivity process as new insulin delivery is collected. The insulin delivery history, when sufficient, may be used to set total daily insulin dosages that enable automated insulin delivery upon replacement of a drug delivery device. The adaptivity process may be implemented to modify an initial insulin delivery doses to provide adapted insulin delivery doses.

    INFORMING A USER OF ANTICIPATED INSULIN DELIVERY ACTIONS AND PROVIDING RECOMMENDED USER ACTIONS

    公开(公告)号:US20240009392A1

    公开(公告)日:2024-01-11

    申请号:US18350223

    申请日:2023-07-11

    CPC classification number: A61M5/1723 A61M5/14244 A61M2202/0486

    Abstract: Exemplary embodiments may determine anticipated basal insulin delivery action to a user from an insulin delivery device over a future time window. Indications of the anticipated basal insulin delivery action over the future time window may be output to the user. The exemplary embodiments may determine the anticipated basal insulin delivery action over the future time window based on a rate of change (ROC) of glucose level of the user by the insulin delivery device, a most recent (“current”) glucose level for the user and insulin on board (IOB) for the user. The exemplary embodiments may also determine whether the user is likely to experience a undesired high glucose level (e.g., hyperglycemia) and/or an undesired low glucose level (e.g., hypoglycemia) during the future time window. The exemplary embodiments may output recommendations based on the projected glucose levels of the user over the future time window.

    CUSTOMIZATION OF A GLUCOSE PREDICTION MODEL FOR A USER IN AN AUTOMATED INSULIN DELIVERY (AID) DEVICE

    公开(公告)号:US20230372613A1

    公开(公告)日:2023-11-23

    申请号:US18199476

    申请日:2023-05-19

    CPC classification number: A61M5/1723 A61M5/14244 A61M2205/52

    Abstract: The exemplary embodiments may employ a glucose prediction model (GPM) that is tailored to a user to account for insulin sensitivity or insulin insensitivity. The exemplary embodiments may predict future glucose levels based on past glucose levels for the user. Specifically, the GPM in exemplary embodiments may predict the future glucose level of the user as a weighted sum of most recent glucose level readings from the user. The exemplary embodiments may employ linear regression analysis to determine the values of the weights. These weights customize the GPM of the user based on the user's most recent glucose level history. Due to the customization, the GPM may more accurately predict future glucose levels of the user. As a result, the AID may exhibit better glucose level control for the user. The GPM of the exemplary embodiments may be updated on an ongoing basis.

    ADJUSTING MEDICAMENT DELIVERY PARAMETERS IN AN OPEN LOOP MEDICAMENT DELIVERY MODE

    公开(公告)号:US20230285671A1

    公开(公告)日:2023-09-14

    申请号:US18119062

    申请日:2023-03-08

    CPC classification number: A61M5/1723 G16H20/17 A61M2230/201

    Abstract: The exemplary embodiments provide an automated approach for adjusting the medicament delivery rate to the user when operating in an open loop manner (“open mode”). The approach relies upon an insulin delivery history to the user make adjustments to the medicament delivery rate in the open mode. In particular, the exemplary embodiments may look at the medicament delivery history while the medicament delivery device is operating in a closed loop manner (“closed mode”) to determine how to adjust the open mode medicament delivery rate. It is presumed that in closed mode, the control system of the medicament device has gained knowledge over time about how to control the medicament delivery rate to produce good treatment outcomes for the user. The exemplary embodiments leverage this knowledge to adjust the open mode medicament delivery rates. Medicament bolus deliveries in open mode may also be adjusted in like fashion.

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