Dexcom, Inc.

Philometron, Inc.

Naresh Chandra Bhavaraju

Apurv Ullas Kamath

Peter C Simpson

Stephen J Vanslyke

Hari Hampapuram

Arturo Garcia

Anna Leigh Davis

Leif N Bowman

Katherine Yerre Koehler

Andrew Attila Pal

Eli Reihman

Lauren Hruby Jepson

Aarthi Mahalingam

Laura J Dunn

Rian W Draeger

Nicholas Polytaridis

Philip Thomas Pupa

Thomas Hall

Scott M Belliveau

Phil Mayou

Aditya Sagar Mandapaka

Thomas Arnold Peyser

Daiting Rong

Gary A Morris

Claudio Cobelli

Daniel Smith

Michael Robert Mensinger

Benjamin Elrod West

Jeff Jackson

Eric Cohen

Michael J Estes

Jorge Valdes

Sebastian Böhm

John Michael Gray

Jennifer Blackwell

David Price

Jack Pryor

Robert James Boock

Brian Christopher Smith

Sebastian Bohm

Matthew D Wightlin

Jason M Halac

Mark Dervaes

Esteban Cabrera, Jr

Paul D Kramer

Douglas William Burnette

Richard Chien Yang

David A Keller

Paul Noble-Campbell

Alexandra Elena Constantin

Jonathan Hughes

Tomas C Walker

Nathaniel David Heintzman

Subrai Girish Pai

Matthew T Vogel

Giovanni Sparacino

Andrea Facchinetti

Chiara Zecchin

Christopher Robert Hannemann

Atiim Joseph Wiley

Anna Claire Harley-Trochimczyk

Andrea Jean Jackson

Alexandra Lynn Carlton

Anna Leigh Rack-Gomer

Rita M Castillo

Gary Brian Gable

Samuel Jere Marsh

Ashley Anne Rindfleisch

Sofie Wells Schunk

Disha B Sheth

Rui Ma

Matthew Lawrence Johnson

Patrick Wile McBride

Daniel Justin Wiedeback

Ted Tang Lee

Carl Frederick Edman

Becky L Clark

Jason Mitchell

David DeRenzy

David I-Chun Lee

Thomas Stuart Hamilton

Basab Dattaray

Sumitaka Mikami

Chris W Dring

Zebediah L McDaniel

Vincent Peter Crabtree

Alexander Leroy Teeter

Indrawati Gauba

Kenneth San Vicente

Andrew Michael Reinhardt

Minglian Shi

Stephen Alan Reichert

Michael Levozier Moore

James Stephen Amidei

Philip Mansiel Pellouchoud

Darrel Dean Drinan

Liang Wang

Lucas Bohnett

Katherine Yerre Grubstein

Kamuran Turksoy

Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

Machine learning in an artificial pancreas

Systems and method are described for determining if a decision support recommendation is to be presented to a user for treatment of a diabetic state, including receiving a plurality of input data items impacting a diabetic state of a user of continuous glucose monitor, the input data items serving as input data to a process for determining a decision support recommendation; assigning a reliability level to each of the input data items; calculating a reliability metric based on the reliability levels assigned to each of the input data items; determining a decision support recommendation based on the process and the input data and presenting the decision support recommendation to the user on a user interface only if the reliability metric exceeds a threshold.

Safety tools for decision support recommendations made to users of continuous glucose monitoring systems

Systems and methods described provide dynamic and intelligent ways to change the required level of user interaction during use of a monitoring device. The systems and methods generally relate to real time switching between a first or initial mode of user interaction and a second or new mode of user interaction. In some cases, the switching will be automatic and transparent to the user, and in other cases user notification may occur. The mode switching generally affects the user's interaction with the device, and not just internal processing. The mode switching may relate to calibration modes, data transmission modes, control modes, or the like.

System and method for mode switching

Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.

System and method for providing alerts optimized for a user

Systems and methods are provided to calibrate an analyte concentration sensor within a biological system, generally using only a signal from the analyte concentration sensor. For example, at a steady state, the analyte concentration value within the biological system is known, and the same may provide a source for calibration. Similar techniques may be employed with slow-moving averages. Variations are disclosed.

Transcutaneous analyte sensors and monitors, calibration thereof, and associated methods

Systems and methods are provided to determine a time to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a model or pattern. The time to deliver guidance may be calculated to be useful to a user in the management of a glucose concentration level.

System and method for providing personalized guidance to diabetes patients

Systems and methods that continuously adapt aspects of a continuous monitoring device based on collected information to provide an individually tailored configuration are described. The adaptations may include adapting the user interface, the alerting, the motivational messages, the training, and the like. Such adaptation can allow a patient to more readily identify and understand the information provided by/via the device.

Adaptive interface for continuous monitoring devices

A system, a method, and a computer program product for providing wearable continuous blood glucose monitoring. In some embodiments, there is provided a method that includes receiving, at a smartwatch, an alert representative of a glucose state of a host-patient coupled to a glucose sensor; detecting, at the smartwatch, a predetermined action indicative of a request to generate a glance view providing an indication of the glucose state of the host-patient; and presenting, at the smartwatch and in response to the detecting, the glance view providing the indication of the glucose state of the host-patient.

Wearable apparatus for continuous blood glucose monitoring

Systems and methods disclosed here provide ways to discriminate fault types encountered in analyte sensors and systems and further provide ways to process such discriminated faults responsively based on sensor data, clinical context information, and other data about the patient or patient's environment. The systems and methods thus employ clinical context in detecting and/or responding to errors or faults associated with an analyte sensor system, and discriminating the type of fault, and its root cause, particularly as fault dynamics can appear similar to the dynamics of physiological systems, emphasizing the importance of discriminating the fault and providing appropriate responsive processing. Thus, the disclosed systems and methods consider the context of the patient's health condition or state in determining how to respond to the fault.

Fault discrimination and responsive processing based on data and context

Methods and apparatus, including computer program products, are provided for processing analyte data. In some example implementations, a method may include generating glucose sensor data indicative of a host's glucose concentration using a glucose sensor; calculating a glycemic variability index (GVI) value based on the glucose sensor data; and providing output to a user responsive to the calculated glycemic variability index value. The GVI may be a ratio of a length of a line representative of the sensor data and an ideal length of the line. Related systems, methods, and articles of manufacture are also disclosed.

Systems and methods for managing glycemic variability

Systems and methods are disclosed which provide for a 'factory-calibrated' sensor. In doing so, the systems and methods include predictive prospective modeling of sensor behavior, and also include predictive modeling of physiology. With these two correction factors, a consistent determination of sensitivity can be achieved, thus achieving factory calibration.

System and method for factory calibration or reduced calibration of an indwelling sensor based on sensitivity profile and baseline model of sensors

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San Diego, CA, United States of America

Naresh Chandra Bhavaraju