Abstract:
Processor-implemented methods of controlling an insulin infusion device for a user are provided here. A first method obtains a current insulin on board (IOB) value that estimates active insulin in the user, and compensates a calculated insulin infusion rate in response to the obtained IOB value. A second method supervises the operation of a glucose sensor by obtaining and processing insulin-delivered data and glucose sensor data for the user. An alert is generated if the second method determines that a current glucose sensor value has deviated from a predicted sensor glucose value by at least a threshold amount.
Abstract:
Disclosed herein are techniques related to determining medical parameters. In some embodiments, the techniques involve: obtaining a metric of insulin dosage for a patient; and determining at least one carbohydrate-to-insulin ratio for the patient based at least in part on a model and using the obtained metric of insulin dosage for the patient, wherein parameters of the model were determined based on data associated with a population of patients.
Abstract:
Disclosed herein are techniques related to configurable target values. The techniques may involve identifying a target glucose value obtained based on user input. Additionally, the techniques may involve determining a fluid delivery command based on a difference between a measured glucose value and the target glucose value. The techniques may further involve causing fluid delivery by a fluid infusion device in accordance with the fluid delivery command to regulate the measured glucose value to the target glucose value.
Abstract:
Technologies are provided for classifying detected events as labeled event combinations. Input data for different combinations of detected events are received at a server system, and each combination of detected events is processed at the server system. Processing can include classifying that combination of detected events at a classifier to map that combination of detected events to a probability of an increased insulin delivery demand or a probability of a decreased insulin delivery demand, and labeling each classified combination of detected events with a label to generate a corresponding labeled event combination that has the probability of the increased insulin delivery demand or the probability of the decreased insulin delivery demand. Each labeled event combination can be stored in storage as a database of labeled event combinations, and selected ones of the labeled event combinations can be processed at a client application to generate an output result.
Abstract:
Techniques disclosed herein relate to operating a fluid delivery device in a personalized manner based at least in part on historical data of a patient. In some embodiments, the techniques involve determining a predicted physiological condition of a patient in response to a future activity of the patient, based at least in part on historical data corresponding to the future activity for the patient; determining, based at least in part on the predicted physiological condition of the patient, an adjustment to fluid delivery to the patient by a medical device to prospectively account for the future activity; and operating the medical device to deliver a fluid to the patient in accordance with the adjustment.
Abstract:
Techniques related to temporary setpoint values are disclosed. The techniques may involve causing operation of a fluid delivery device in a closed-loop mode for automatically delivering fluid based on a difference between a first setpoint value and an analyte concentration value during operation of the fluid delivery device in the closed-loop mode. Additionally, the techniques may involve obtaining a second setpoint value. The second setpoint value may be a temporary setpoint value to be used for a period of time to regulate fluid delivery, and the second setpoint value may be greater than the first setpoint value. The techniques may further involve causing operation of the fluid delivery device for automatically reducing fluid delivery for the period of time based on the second setpoint value.
Abstract:
Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device capable of delivering fluid to a patient involves obtaining, by a control system associated with the infusion device, user input indicating an activity by the patient, obtaining historical data for the patient corresponding to the activity, determining a probable patient response corresponding to the activity based at least in part on the historical data for the patient, determining an adjustment for delivering the fluid by the infusion device based at least in part on the probable patient response, and operating the infusion device to deliver the fluid to the patient in accordance with the adjustment.
Abstract:
The present disclosure relates to default carbohydrate consumption counts based on population carbohydrate consumption models. Aspects of the present disclosure relate to receiving, from a device, at least one characteristic of a person; accessing a population carbohydrate consumption model which relates the at least one characteristic with carbohydrate intake for a population; determining, based on applying the population carbohydrate consumption model to the at least one characteristic, at least one of: a preliminary carbohydrate count for the person or a default carbohydrate consumption count for the person; and causing delivery of insulin to the person based on communicating, to the device, at least one of: the preliminary carbohydrate count for the person or the default carbohydrate consumption count for the person.
Abstract:
A medical device system and related method of automatically adjusting parameters of an insulin delivery controller of an insulin infusion device are disclosed. The methodology obtains therapy-related data associated with operation of the insulin infusion device for a number of days in the past, including sensor glucose data associated with glucose levels of the user, and meal data associated with identified meals. The obtained therapy-related data is processed to generate a suitable pharmacokinetic/pharmacodynamic (PK/PD) model of the user, wherein the PK/PD model fits at least some of the sensor glucose data obtained for the user. The PK/PD model can be used to calculate at least one adjusted parameter of the insulin delivery controller, based on additional therapy-related data associated with further operation of the insulin infusion device. The insulin delivery controller can be instructed or controlled to adjust its settings in accordance with the model-calculated parameters.
Abstract:
A processor-implemented method includes obtaining measurement data indicative of a physiological condition measured by a sensing arrangement located at a site on a body, determining a lag associated with the sensing arrangement based on a relationship between the measurement data and reference data, and identifying, based on the lag, the site at which the sensing arrangement is located from a plurality of sites on the body.