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公开(公告)号:US20230346320A1
公开(公告)日:2023-11-02
申请号:US18219845
申请日:2023-07-10
Applicant: Dexcom, Inc.
Inventor: Stephen D. Patek , Stephen J. Vanslyke
CPC classification number: A61B5/7275 , G16H50/30 , A61B5/4833 , G16H10/60 , G16H20/60 , G16H70/20 , C07K14/62
Abstract: An amount of glycemic dysfunction associated with mis-timing (e.g., delay) of meal boluses based on replay analysis is determined. The amount of dysfunction of historical or estimated bolusing as compared to an optimally timed bolus based on the replay analysis is quantified and visualized. Inferences may be made about diabetes meal management regarding inputs from a patient.
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公开(公告)号:US20220240848A1
公开(公告)日:2022-08-04
申请号:US17592137
申请日:2022-02-03
Applicant: Dexcom, Inc.
Inventor: Stephen D. Patek
Abstract: Systems and methods are provided for managing hyperglycemia and hypoglycemia by reconciling incoming data to provide safe and reliable control to range using automatic bolus determination wherein the rate of insulin delivery is dependent on the level of hyperglycemic risk or hypoglycemic risk. Additionally, some implementations are directed to converting insulin delivery into a rate based on glycemic risk.
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23.
公开(公告)号:US20210151196A1
公开(公告)日:2021-05-20
申请号:US17096775
申请日:2020-11-12
Applicant: Dexcom, Inc.
Inventor: Stephen D. Patek
IPC: G16H50/50 , G16H50/20 , G06F16/23 , G16H20/17 , G16H10/20 , G16H50/30 , G16H70/20 , G16H10/60 , G16H50/70 , G16H20/60 , G16H40/67 , A61B5/00 , A61B5/145
Abstract: Systems and methods are provided for reconciling untrusted data of a subject using trusted data pertaining to the subject. Systems and methods are directed to evaluating differences in predicted data with respect to corresponding received data. Systems and methods estimate metabolic states from a combination of trusted and untrusted metabolic inputs, along with optionally using a personalized mathematical model with parameter optimization. Systems and methods provide for reconciled untrusted inputs with their measured impact of the glycemic signals that is consistent with a metabolic model. Estimation of future metabolic states for decision support and automated insulin dosing is enabled. Replay of scenarios with estimated or reconciled data is also provided.
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24.
公开(公告)号:US20210151141A1
公开(公告)日:2021-05-20
申请号:US17096763
申请日:2020-11-12
Applicant: Dexcom, Inc.
Inventor: Stephen D. Patek
Abstract: Systems and methods are provided for reconciling untrusted data of a subject using trusted data pertaining to the subject. Systems and methods are directed to evaluating differences in predicted data with respect to corresponding received data. Systems and methods estimate metabolic states from a combination of trusted and untrusted metabolic inputs, along with optionally using a personalized mathematical model with parameter optimization. Systems and methods provide for reconciled untrusted inputs with their measured impact of the glycemic signals that is consistent with a metabolic model. Estimation of future metabolic states for decision support and automated insulin dosing is enabled. Replay of scenarios with estimated or reconciled data is also provided.
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公开(公告)号:US20200205744A1
公开(公告)日:2020-07-02
申请号:US16721810
申请日:2019-12-19
Applicant: DexCom, Inc.
Inventor: Stephen D. Patek , Stephen J. Vanslyke
Abstract: An amount of glycemic dysfunction associated with mis-timing (e.g., delay) of meal boluses based on replay analysis is determined. The amount of dysfunction of historical or estimated bolusing as compared to an optimally timed bolus based on the replay analysis is quantified and visualized. Inferences may be made about diabetes meal management regarding inputs from a patient.
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公开(公告)号:US20200205742A1
公开(公告)日:2020-07-02
申请号:US16721679
申请日:2019-12-19
Applicant: DexCom, Inc.
Inventor: Stephen D. Patek , Stephen J. Vanslyke
Abstract: An amount of glycemic dysfunction associated with mis-timing (e.g., delay) of meal boluses based on replay analysis is determined. The amount of dysfunction of historical or estimated bolusing as compared to an optimally timed bolus based on the replay analysis is quantified and visualized. Inferences may be made about diabetes meal management regarding inputs from a patient.
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