Abstract: Recommendations based on continuous glucose monitoring (CGM) are described. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a platform that provides a CGM system may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process. In implementations, a CGM platform includes a data analytics platform that obtains glucose measurements provided by a CGM system and also obtains additional data associated with a user. The data analytics platform processes these measurements and the additional data to predict a health indicator by using models. This prediction serves as a basis for generating a recommendation, such as a message recommending the user take action or adopt a behavior to mitigate a predicted negative health condition.

Abstract: Provided is a method for predicting future states based on time series data using feature engineering and/or hybrid machine learning models. The method may include receiving payment transaction data associated with a plurality of payment transactions, the plurality of payment transactions including a first subset of payment transactions associated with a first entity; determining a plurality of features based on the payment transaction data associated with the plurality of payment transactions; inputting the plurality of features into at least one machine learning model to provide at least one prediction of a net settlement position of the first entity; and communicating the at least one prediction of the net settlement position to a first entity system associated with the first entity. A system and computer program product are also disclosed.

Abstract: Certain aspects of the present disclosure relate to a method of configuring an application with one or more application features. The method comprises receiving a request to configure the application for use by a user. The method further comprises identifying an objective for the user and identifying classifying information associated with the user, the classifying information including at least one of the objective, interest, ability, demographic information, disease progression information, or medication regimen information of the user. The method further comprises selecting a group of users based on one or more similarities between the user and the group of users. The method further comprises identifying the one or more application features based on the objective of the user and a correlation of each of the plurality of application features with the objective. The method further comprises configuring the application with the one or more application features.

Abstract: Recommendations based on continuous glucose monitoring (CGM) are described. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a platform that provides a CGM system may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process. In implementations, a CGM platform includes a data analytics platform that obtains glucose measurements provided by a CGM system and also obtains additional data associated with a user. The data analytics platform processes these measurements and the additional data to predict a health indicator by using models. This prediction serves as a basis for generating a recommendation, such as a message recommending the user take action or adopt a behavior to mitigate a predicted negative health condition.

Abstract: Recommendations based on continuous glucose monitoring (CGM) are described. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a platform that provides a CGM system may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process. In implementations, a CGM platform includes a data analytics platform that obtains glucose measurements provided by a CGM system and also obtains additional data associated with a user. The data analytics platform processes these measurements and the additional data to predict a health indicator by using models. This prediction serves as a basis for generating a recommendation, such as a message recommending the user take action or adopt a behavior to mitigate a predicted negative health condition.

Abstract: Methods and devices for verifying that proper visual stimulation is applied to the visual prostheses are described. In one of the methods, a visual stimulation system implanted on a subject is simulated externally. An external testing device is also discussed.

Abstract: Methods and devices for verifying that proper visual stimulation is applied to the visual prostheses are described. In one of the methods, a visual stimulation system implanted on a subject is simulated externally. An external testing device is also discussed.

Abstract: Methods and devices for verifying that proper visual stimulation is applied to the visual prostheses are described. In one of the methods, a retinal stimulation system implanted on a subject is simulated externally. An external testing device is also discussed.

Abstract: Methods and devices for verifying that proper visual stimulation is applied to the visual prostheses are described. In one of the methods, a retinal stimulation system implanted on a subject is simulated externally. An external testing device is also discussed.

Abstract: Methods and devices for verifying that proper visual stimulation is applied to the visual prostheses are described. In one of the methods, a retinal stimulation system implanted on a subject is simulated externally. An external testing device is also discussed.

Abstract: Methods and devices for verifying that proper visual stimulation is applied to the visual prostheses are described. In one of the methods, a retinal stimulation system implanted on a subject is simulated externally. An external testing device is also discussed.