Dexcom, Inc.

Mark Derdzinski

Joost Herman Van Der Linden

Subrai Girish Pai

Andrea Jean Jackson

Andrew Scott Parker

Maritza S Powell

Jessica S Larrabee

Lauren Hruby Jepson

Giada Acciaroli

Robert Dowd

In implementations of systems for determining a similarity of sequences of glucose values, a computing device implements a similarity system to receive input data describing a sequence of user glucose values measured by a continuous glucose monitoring (CGM) system. The similarity system computes similarity scores for a plurality of sequences of glucose values by comparing each glucose values included in the sequence of user glucose values with ever glucose value included in each sequence of the plurality of sequences. A particular sequence of glucose values that is associated with a highest similarity score is identified. The similarity system determines an externality associated with the particular sequence. The similarity system generates an indication of the externality for display in a user interface.

Systems for determining similarity of sequences of glucose values

Glucose measurement and glucose-impacting event prediction using a stack of machine learning models is described. A CGM platform includes stacked machine learning models, such that an output generated by one of the machine learning models can be provided as input to another one of the machine learning models. The multiple machine learning models include at least one model trained to generate a glucose measurement prediction and another model trained to generate an event prediction, for an upcoming time interval. Each of the stacked machine learning models is configured to generate its respective output when provided as input at least one of glucose measurements provided by a CGM system worn by the user or additional data describing user behavior or other aspects that impact a person's glucose in the future. Predictions may then be output, such as via communication and/or display of a notification about the corresponding prediction.

Glucose measurement predictions using stacked machine learning models

Glucose prediction using machine learning (ML) and time series glucose measurements is described. Given the number of people that wear glucose monitoring devices and because some wearable glucose monitoring devices can produce measurements continuously, a platform providing such devices may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process and covers a robust number of state spaces unlikely to be covered without the enormous amount of data. In implementations, a glucose monitoring platform includes an ML model trained using historical time series glucose measurements of a user population. The ML model predicts upcoming glucose measurements for a particular user by receiving a time series of glucose measurements up to a time and determining the upcoming glucose measurements of the particular user for an interval subsequent to the time based on patterns learned from the historical time series glucose measurements.

Glucose prediction using machine learning and time series glucose measurements

Certain aspects of the present disclosure relate to methods and systems for optimized delivery of communications including content to users of a software application. The method also includes obtaining, by a customer engagement platform (CEP), a set of cohort selection criteria for identifying a user cohort to deliver the content; identifying, by a data analytics platform (DAP), the user cohort to communicate with in accordance with the set of cohort selection criteria; identifying, by the DAP, one or more communication configurations for communicating with one or more sub-groups within the user cohort; and to each user of the user cohort, transmitting one or more communications based on the content and a corresponding communication configuration for a sub-group that may include the corresponding user; and measuring engagement outcomes associated with usage of the corresponding one or more communication configurations in communication with each of the sub-groups.

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Pittsburgh, PA, United States of America

Mark Derdzinski