- Acton MA, US Ashutosh ZADE - San Diego CA, US Yibin ZHENG - Hartland WI, US Jason O'CONNOR - Acton MA, US
International Classification:
A61M 5/172 G16H 20/17 G16H 20/13 A61B 5/145
Abstract:
The insulin to carbohydrate ratio (ICR) and the correction factor for a user of a medicament delivery device may be automatically adjusted. The automatic adjustments may tailor the values to the user's actual insulin needs. Various factors may be examined to determine how to adjust the ICR and the correction factor. The identified factors are weighed with the processor to decide whether to increase or decrease the insulin to carbohydrate ratio or the correction factor. The insulin to carbohydrate ratio or the correction factor for the user are adjusted based on the weights of the identified factors. In addition or in the alternative, automatic adjustments of user-requested insulin boluses may be made to requested dosages and timing of deliveries of the insulin boluses. In some instances, the exemplary embodiments may deliver a percentage of the insulin bolus dosage initially and deliver the remaining percentage after a delay to reduce the risk of hypoglycemia for the user.
Dual Hormone Delivery System For Reducing Impending Hypoglycemia And/Or Hyperglycemia Risk
- Acton MA, US Ashutosh ZADE - San Diego CA, US Jason O'CONNOR - Acton MA, US Trang LY - Concord MA, US Yibin ZHENG - Hartland WI, US Connor GULLIFER - Brighton MA, US Kyle GROVER - Watertown MA, US
Assignee:
INSULET CORPORATION - Acton MA
International Classification:
A61M 5/172 G16H 20/17
Abstract:
The exemplary embodiments attempt to identify impending hypoglycemia and/or hyperglycemia and take measures to prevent the hypoglycemia or hyperglycemia. Exemplary embodiments may provide a drug delivery system for delivering insulin and glucagon as needed by a user of the drug delivery system. The drug delivery system may deploy a control system that controls the automated delivery of insulin and glucagon to a patient by the drug delivery system. The control system seeks among other goals to avoid the user experiencing hypoglycemia or hyperglycemia. The control system may employ a clinical decision support algorithm as is described below to control delivery of insulin and glucagon to reduce the risk of hypoglycemia or hyperglycemia and to provide alerts to the user when needed. The control system assesses whether the drug delivery system can respond enough to avoid hypoglycemia or hyperglycemia and generates alerts when manual action is needed to avoid hypoglycemia or hyperglycemia.
Insulin Delivery Using Actual User Insulin Delivery Amounts To Accurately Predict Quantity And Timing Of Insulin Reservoir Refills
- Acton MA, US Ashutosh ZADE - San Diego CA, US Joon Bok LEE - Acton MA, US Jason O'CONNOR - Acton MA, US Yibin ZHENG - Hartland WI, US
Assignee:
INSULET CORPORATION - Acton MA
International Classification:
A61M 5/172
Abstract:
Exemplary embodiments may provide an improved approach to automated insulin delivery by more accurately estimating the total daily insulin (TDI) of a user. As a result, less insulin is wasted by the delivery system, and the estimate of TDI more closely matches a user's actual daily insulin needs. Hence, the user need not refill the insulin reservoir excessively or need not fret unnecessarily about running out of insulin prematurely. The estimate relies on the history of actual automated insulin deliveries and thus reflects the actual insulin delivered to the user more accurately than conventional approaches.
Use Of Fuzzy Logic In Predicting User Behavior Affecting Blood Glucose Concentration In A Closed Loop Control System Of An Automated Insulin Delivery Device
In an automated insulin delivery device, fuzzy logic may be applied to the responding to the possibility of a user taking additional action that may affect the blood glucose concentration. Fuzzy sets may be defined for empirically derived different likelihoods of the user taking such additional action based on correlated factors. A membership function may be provided for each fuzzy set. The membership function may provide a probability of membership in the set based on a parameter. Each fuzzy set may have a response that is reflective of the likelihood of additional user action associated with the fuzzy set. The responses by the AID device to each of these cases may reflect the probability of each such case occurring as evidenced by empirical data.
Trimark Associates Inc. Jan 2017 - May 2018
Associate Si Engineer
Protech Design and Manufacturing Solutions Jan 2016 - Oct 2016
Junior Design Engineer
University of California, San Diego Mar 2013 - Dec 2015
Lead Set-Up Assistant
Macom Jun 2015 - Sep 2015
Applications Engineering Intern
Education:
Uc San Diego 2011 - 2015
Bachelor of Applied Science, Bachelors, Physics, Engineering
Highlands Christian School
Skills:
Autocad Agilent Ads Matlab Data Analysis Research C Html Css Javascript Java Microsoft Office Powerpoint Microsoft Excel Microsoft Word Leadership Rf Design Customer Service Public Speaking Cascading Style Sheets Microsoft Powerpoint
Interests:
Social Services Children Economic Empowerment Civil Rights and Social Action Education Environment Poverty Alleviation Science and Technology Disaster and Humanitarian Relief Human Rights Animal Welfare Arts and Culture Health