Abstract: Methods, systems, apparatuses, and computer program products for discovering fraudulent account usage are disclosed. In an example, a database is queried to identify a list of accounts and transaction categories. A first table is generated based on the transaction categories. A second table is generated by populating the first table based on a response to querying the database for a first set of historical transactions over a first period of time. A third table is generated by populating the first table based on a response to querying the database for a second set of historical transactions over a second period of time. A measure of similarity for each of the accounts is generated based on the second table and the third table. For each account with a corresponding measure of similarity below a threshold, the account is identified as a fraudulent account candidate.

Abstract: Systems, methods, and devices are described for classifying and answering medical inquiries based on machine-generated data resources and machine learning models. A CCDA document including clinical information and observations of a patient are received from a requestor and utilized to generate a FHIR model instance specific to the CCDA document. Question text having medical inquires for the patient, and received with the CCDA document, is processed by a machine learning model to determine question categories for the medical inquiries which are utilized to map the inquires to objects of the model instance using another machine learning model. The model instance is queried based on the mapping to return values associated with the inquiries. The values are transmitted back to the requestor.

Abstract: The present invention relates to a method and system for generating client preference recommendations in a high performance computing regime. Accordingly, one embodiment of the present invention comprises: providing a sparse ratings matrix, forming a plurality of data structures representing the sparse ratings matrix, forming a runtime recommendation model from the plurality of data structures, determining a recommendation from the runtime recommendation model in response to a request from a user, and providing the recommendation to the user.

Abstract: The present invention relates to a method and system for generating client preference recommendations in a high performance computing regime. Accordingly, one embodiment of the present invention comprises: providing a sparse ratings matrix, forming a plurality of data structures representing the sparse ratings matrix, forming a runtime recommendation model from the plurality of data structures, determining a recommendation from the runtime recommendation model in response to a request from a user, and providing the recommendation to the user.

Abstract: The present invention relates to a method and system for generating client preference recommendations in a high performance computing regime. Accordingly, one embodiment of the present invention comprises: providing a sparse ratings matrix, forming a plurality of data structures representing the sparse ratings matrix, forming a runtime recommendation model from the plurality of data structures, determining a recommendation from the runtime recommendation model in response to a request from a user, and providing the recommendation to the user.