Exemplary embodiments relate to a Health Care Fraud Waste and Abuse predictive analytics projects sharing network where analytic models can be shared and used directly with minimum changes. The shared/passed Models and Rules on the network are directly applied to datasets from different customers by mapping and creating useful results electronically within a healthcare claims space. A drag-and-drop graphical user interface simplifies the creation of models by associating one or more data sources with one or more pre-defined plug-and-play application graphically.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A healthcare fraud detection system comprising: a user interface; a core processing system coupled to the user interface, the core processing system also coupled to a database storage; and a data input providing healthcare data, the data input being user selectable from at least one data source, the data input being coupled to the core processing system; wherein the core processing system comprises a set of stored pre-defined plug-and-play applications configured to manipulate the data, and wherein the core processing system is configured to permit, via the user interface, drag-and-drop selection and interconnection of at least one data source and at least one pre-defined plug-and-play application by a user to produce a healthcare fraud detection model and to display, via the user interface, fraud analytics data produced by the healthcare fraud detection model. 2. The healthcare fraud detection system according to claim 1, wherein the user interface is a web-browser interface. 3. The healthcare fraud detection system according to claim 1, wherein core processing system comprises a deep learning engine configured to process the data. 4. The healthcare fraud detection system according to claim 3, wherein the deep learning engine is a machine learning engine. 5. The healthcare fraud detection system according to claim 3, wherein the deep learning engine is configured to automatically determine a set of performance metrics and a plurality of algorithms to use for the at least one data source and create therefrom an ensemble of models, where each component in the ensemble is a deep learning model focusing on a specific type of fraud. 6. The healthcare fraud detection system according to claim 1, wherein graphs and/or dashboards are reusable artifacts that are part of a template that can be integrated with data sources, filters and models to build a complete template. 7. The healthcare fraud detection system according to claim 3, wherein the deep learning engine is configured to detect medical claim fraud in real time, or substantially in real time, from a stream of medical claims. 8. The healthcare fraud detection system according to claim 1, wherein the core processing system allows the user to alter the display of the fraud analytics data. 9. The healthcare fraud detection system according to claim 1, wherein the core processing system allows sharing of the healthcare fraud detection model over a network. 10. The healthcare fraud detection system according to claim 1, wherein the set of stored pre-defined plug-and-play applications includes an analyzer operator. 11. The healthcare fraud detection system according to claim 10, wherein the analyzer operator is configured to extract meta-data from the at least one data source, perform data cleansing on a set of user-specified fields, select a set of default metrics for use in comparing performance of a plurality of fraud detection models, select a set of operators to be applied to the data, format the data for each selected operator, execute the selected operators, and determine a best model from the plurality of models based on the execution of the selected operators. 12. The healthcare fraud detection system according to claim 1, wherein the set of stored pre-defined plug-and-play applications includes at least one filter operator. 13. The healthcare fraud detection system according to claim 1, wherein the set of stored pre-defined plug-and-play applications includes at least one fraud detection operator. 14. The healthcare fraud detection system according to claim 1, wherein the set of stored pre-defined plug-and-play applications includes at least one visualization operator. 15. The healthcare fraud detection system according to claim 1, wherein the core processing system displays the least one data source and at least one pre-defined plug-and-play application as interconnected icons on the user interface. 16. The healthcare fraud detection system according to claim 1, wherein the core processing system allows the user to associate the at least one data source and the healthcare fraud detection model as a project. 17. The healthcare fraud detection system according to claim 16, wherein the core processing system allows sharing of the project over a network. 18. The healthcare fraud detection system according to claim 1, wherein the core processing system allows the user to export results from the healthcare fraud detection model to CSV. 19. The healthcare fraud detection system according to claim 1, further comprising: a distributed in-memory cache coupled to the core processing unit. 20. The healthcare fraud detection system according to claim 1, wherein the core processing system runs on a distributed computing cluster and utilizes a distributed file system.