Abstract: Methods and systems are disclosed for treating a person with diabetes by basal rate adjustment of insulin from a therapy delivery device based on risk associated with a glucose state of the person with diabetes. A method may include determining a current risk metric associated with a detected glucose state. The method may include determining a current risk metric associated with the detected glucose state based on a weighted average of cumulative hazard values of return paths generated from a glucose state distribution around a detected glucose state. The method may include calculating an adjustment to a basal rate of a therapy delivery device based on the current risk metric associated with the detected glucose state and a reference risk metric associated with a reference glucose level.

Abstract: A patient diabetes monitoring system with an efficient unsupervised daily monitoring profile clustering algorithm, a method, and a computer product thereof are disclosed. The system may include a physiological data input device or sensor which receives a plurality of physiological measurements to generate a dataset, a memory which stores a clustering algorithm, and a processor. The clustering algorithm when executed by the processor, causes the processor to automatically pre-process the dataset to control an amount of bias/aggressiveness from the collected unsupervised daily monitoring profiles, thereby generating a pre-processed dataset, build a similarity matrix from the pre-processed dataset, and output an optimum number of similarity clusters found by the processor from the similarity matrix.

Abstract: Methods and systems are disclosed for treating a person with diabetes by basal rate adjustment of insulin from a therapy delivery device based on risk associated with a glucose state of the person with diabetes. A method may include determining a current risk metric associated with a detected glucose state. The method may include determining a current risk metric associated with the detected glucose state based on a weighted average of cumulative hazard values of return paths generated from a glucose state distribution around a detected glucose state. The method may include calculating an adjustment to a basal rate of a therapy delivery device based on the current risk metric associated with the detected glucose state and a reference risk metric associated with a reference glucose level.

Abstract: Methods and systems are disclosed for determining a basal rate adjustment of insulin in a continuous glucose monitoring system of a person with diabetes. A method may include receiving, by at least one computing device, a signal representative of at least one glucose measurement and detecting, by the at least one computing device, a glucose state of the person based on the signal, the detected glucose state including a glucose level of the person and a rate of change of the glucose level. The method may also include calculating, by the at least one computing device, an adjustment to a basal rate of a therapy delivery device based on a control-to-range algorithm and at least one failsafe constraint to account for changes in the insulin sensitivity of the person with diabetes or inaccurate glucose measurement.

Abstract: A method and system of determining a basal rate adjustment of insulin in a continuous glucose monitoring system of a person with diabetes is provided. The method includes receiving, by at least one computing device, a signal representative of at least one glucose measurement; detecting, by the at least one computing device, a glucose state of the person based on the signal, the detected glucose state including a glucose level of the person and a rate of change of the glucose level; determining, by the at least one computing device, a current risk metric, the current risk metric indicating a risk of at least one of a hypoglycemic condition and a hyperglycemic condition of the person; and calculating, by the at least one computing device, an adjustment to a basal rate of a therapy delivery device based on the current risk metric and a control-to-range algorithm comprising at least one aggressiveness parameter.

Abstract: Methods and systems are disclosed for determining a basal rate adjustment of insulin based on risk associated with a glucose state of a person with diabetes. A method may include detecting a glucose state of the person based on a received glucose measurement signal and determining a current risk metric associated with the detected glucose state. The method may include determining a current risk metric associated with the detected glucose state based on a weighted average of cumulative hazard values of return paths generated from a glucose state distribution around a detected glucose state. The method may include calculating an adjustment to a basal rate of a therapy delivery device based on the current risk metric associated with the detected glucose state and a reference risk metric associated with a reference glucose level.

Abstract: Methods and systems are disclosed for determining a basal rate adjustment of insulin based on risk associated with a glucose state of a person with diabetes. A method may include detecting a glucose state of the person based on a received glucose measurement signal and determining a current risk metric associated with the detected glucose state. The method may include determining a current risk metric associated with the detected glucose state based on a weighted average of cumulative hazard values of return paths generated from a glucose state distribution around a detected glucose state. The method may include calculating an adjustment to a basal rate of a therapy delivery device based on the current risk metric associated with the detected glucose state and a reference risk metric associated with a reference glucose level.

Abstract: Methods and systems are disclosed for determining a basal rate adjustment of insulin in a continuous glucose monitoring system of a person with diabetes. A method may include receiving, by at least one computing device, a signal representative of at least one glucose measurement and detecting, by the at least one computing device, a glucose state of the person based on the signal, the detected glucose state including a glucose level of the person and a rate of change of the glucose level. The method may also include calculating, by the at least one computing device, an adjustment to a basal rate of a therapy delivery device based on a control-to-range algorithm and at least one failsafe constraint to account for changes in the insulin sensitivity of the person with diabetes or inaccurate glucose measurement.

Abstract: A method and system of determining a basal rate adjustment of insulin in a continuous glucose monitoring system of a person with diabetes is provided. The method includes receiving, by at least one computing device, a signal representative of at least one glucose measurement; detecting, by the at least one computing device, a glucose state of the person based on the signal, the detected glucose state including a glucose level of the person and a rate of change of the glucose level; determining, by the at least one computing device, a current risk metric, the current risk metric indicating a risk of at least one of a hypoglycemic condition and a hyperglycemic condition of the person; and calculating, by the at least one computing device, an adjustment to a basal rate of a therapy delivery device based on the current risk metric and a control-to-range algorithm comprising at least one aggressiveness parameter.

Abstract: A patient diabetes monitoring system with an efficient unsupervised daily monitoring profile clustering algorithm, a method, and a computer product thereof are disclosed. The system may include a physiological data input device or sensor which receives a plurality of physiological measurements to generate a dataset, a memory which stores a clustering algorithm, and a processor. The clustering algorithm when executed by the processor, causes the processor to automatically pre-process the dataset to control an amount of bias/aggressiveness from the collected unsupervised daily monitoring profiles, thereby generating a pre-processed dataset, build a similarity matrix from the pre-processed dataset, and output an optimum number of similarity clusters found by the processor from the similarity matrix.