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Systems and methods for creating and selecting models for predicting medical conditions

  • US 10,672,509 B2
  • Filed: 10/13/2017
  • Issued: 06/02/2020
  • Est. Priority Date: 06/26/2013
  • Status: Active Grant
First Claim
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1. A computer-implemented method for selecting one or more models for predicting medical conditions, the method comprising:

  • receiving, using a processor, initial data related to a user, the initial data including blood glucose levels of the user, wherein receiving the initial data including blood glucose levels of the user includes receiving at least some initial data, wirelessly over a server, from a continuous glucose monitoring (CGM) device, and includes receiving at least some initial data that is self-monitored blood glucose data;

    processing, using the processor and one or more machine learning algorithms, the initial data to identify patterns between the blood glucose levels of the user, and other data related to the user'"'"'s medical condition;

    assigning values to an unmeasured data category for which no measured data is received by the processor, and processing the unmeasured data category, using the processor and the one or more machine algorithms, to identify one or more patterns between the values of the unmeasured data category, and blood glucose levels of the user and the other data,creating a library of models containing a plurality of models based on the identified patterns;

    after creating the library of models, receiving additional data related to the user, the additional data including blood glucose levels of the user, wherein receiving additional data including blood glucose levels of the user includes receiving at least some additional data, wirelessly over a server, from a continuous glucose monitoring (CGM) device, and includes receiving at least some additional data that is self-monitored blood glucose data;

    extracting metadata from at least some of the received additional data, wherein extracting metadata from at least some of the received additional data comprises extracting metadata indicating whether a given stream of data relating to blood glucose levels is continuous glucose monitor data or self-monitored blood glucose data;

    selecting the one or more models from the library of models based on the extracted metadata, wherein selecting the one or more models from the library of models based on the extracted metadata comprises selecting a first type of model when the extracted metadata indicates that the blood glucose levels are continuous glucose monitor data, selecting a second type of model when the extracted metadata indicates that the blood glucose levels are self-monitored blood glucose data, and selecting the second type of model when the extracted metadata indicates that the blood glucose levels include both continuous glucose monitor data and self-monitored blood glucose data;

    applying the selected one or models; and

    generating a notification when the application of the selected one or more model indicates an intervention is necessary.

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