Abstract: Systems and methods for statistically-representative sample data generation are disclosed. For example, a sample-data generator and/or a data discriminator may be received by a system, which may utilize the sample-data generator to generate sample data. The data discriminator may be utilized to train the sample-data generator until the data discriminator cannot discriminate between data received from the sample-data generator and data received by a database associated with the system. The trained sample-data generator may be sent to other systems, which may generate and utilize, such as for prediction model training, statistically-representative sample data generated by the trained sample-data generator.

Abstract: Systems and methods for models utilizing siloed data are disclosed. For example, data stored with and/or available to one or more systems may be siloed such that it may not be aggregated and/or shared with other systems. The presently-disclosed systems and methods utilize predictive layers and models to allow each system to predict outcomes using its own data and then models are shared between systems to allow each associated system to gain the benefits of the data of other systems without aggregating such data or otherwise sharing the data.

Abstract: Systems and methods for data unfolding are disclosed. For example, it may be desirable or necessary to increase a data set, such as for increasing accuracy of one or more predictive models. Data set proliferation without introducing unnecessary bias may be important for increasing such accuracy. Described herein are system and methods that allow for data set proliferation by generating records based on whether an event occurred with respect to an entity during multiple time intervals. A record may be generated for each time interval and the associated data may be unfolded and disassociated, at least partly, from other records related to the entity. Those records may then be used for data analytics and/or predictive model generation, for example.

Abstract: Systems and methods for models utilizing siloed data are disclosed. For example, data stored with and/or available to one or more systems may be siloed such that it may not be aggregated and/or shared with other systems. The presently-disclosed systems and methods utilize predictive layers and models to allow each system to predict outcomes using its own data and then models are shared between systems to allow each associated system to gain the benefits of the data of other systems without aggregating such data or otherwise sharing the data.

Abstract: Techniques and architectures for clustering health care providers based on data regarding services that have previously been provided by health care providers are described herein. For example, the techniques and architectures may receive data from devices of health care providers regarding services that have been provided by the health care providers to patients. The techniques and architectures may perform various machine learning techniques, including clustering, to categorize health care providers into multiple clusters based on the services that were provided. Health care providers that lie within a cluster may be associated with a category of the cluster. While health care providers that lie outside a cluster, and have designated themselves as being associated with or are otherwise associated with a practice area that is associated with the cluster, may be flagged as potentially practicing outside an area that is designated by or otherwise associated with the health care providers.

Abstract: Systems and methods for models utilizing siloed data are disclosed. For example, data stored with and/or available to one or more systems may be siloed such that it may not be aggregated and/or shared with other systems. The presently-disclosed systems and methods generate and utilize predictive layers and models to allow each system to predict outcomes using its own data and then models are shared between systems to allow each associated system to gain the benefits of the data of other systems without aggregating such data or otherwise sharing the data.

Abstract: Systems and methods for models utilizing siloed data are disclosed. For example, data stored with and/or available to one or more systems may be siloed such that it may not be aggregated and/or shared with other systems. The presently-disclosed systems and methods generate and utilize predictive layers and models to allow each system to predict outcomes using its own data and then models are shared between systems to allow each associated system to gain the benefits of the data of other systems without aggregating such data or otherwise sharing the data.

Abstract: Systems and methods for statistically-representative sample data generation are disclosed. For example, a sample-data generator and/or a data discriminator may be received by a system, which may utilize the sample-data generator to generate sample data. The data discriminator may be utilized to train the sample-data generator until the data discriminator cannot discriminate between data received from the sample-data generator and data received by a database associated with the system. The trained sample-data generator may be sent to other systems, which may generate and utilize, such as for prediction model training, statistically-representative sample data generated by the trained sample-data generator.

Abstract: Techniques and architectures for clustering health care providers based on data regarding services that have previously been provided by health care providers are described herein. For example, the techniques and architectures may receive data from devices of health care providers regarding services that have been provided by the health care providers to patients. The techniques and architectures may perform various machine learning techniques, including clustering, to categorize health care providers into multiple clusters based on the services that were provided. Health care providers that lie within a cluster may be associated with a category of the cluster. While health care providers that lie outside a cluster, and have designated themselves as being associated with or are otherwise associated with a practice area that is associated with the cluster, may be flagged as potentially practicing outside an area that is designated by or otherwise associated with the health care providers.

Abstract: Systems and methods for models utilizing siloed data are disclosed. For example, data stored with and/or available to one or more systems may be siloed such that it may not be aggregated and/or shared with other systems. The presently-disclosed systems and methods generate and utilize predictive layers and models to allow each system to predict outcomes using its own data and then models are shared between systems to allow each associated system to gain the benefits of the data of other systems without aggregating such data or otherwise sharing the data.

Abstract: Systems and methods for models utilizing siloed data are disclosed. For example, data stored with and/or available to one or more systems may be siloed such that it may not be aggregated and/or shared with other systems. The presently-disclosed systems and methods generate and utilize predictive layers and models to allow each system to predict outcomes using its own data and then models are shared between systems to allow each associated system to gain the benefits of the data of other systems without aggregating such data or otherwise sharing the data.

Abstract: Systems and methods for data unfolding are disclosed. For example, it may be desirable or necessary to increase a data set, such as for increasing accuracy of one or more predictive models. Data set proliferation without introducing unnecessary bias may be important for increasing such accuracy. Described herein are system and methods that allow for data set proliferation by generating records based on whether an event occurred with respect to an entity during multiple time intervals. A record may be generated for each time interval and the associated data may be unfolded and disassociated, at least partly, from other records related to the entity. Those records may then be used for data analytics and/or predictive model generation, for example.