Abstract: Various embodiments of the present disclosure provide data storage, processing, and prediction techniques for providing predictive insights within large data prediction domains. The techniques may include generating, using a plurality of source tables for a prediction domain, a global graph for the prediction domain. The techniques may include generating, using a graph-based machine learning model, a plurality of node-level weights for the plurality of graph nodes based on a plurality of node attributes corresponding to the plurality of graph nodes. The techniques may include generating, using the graph-based machine learning model, a plurality of semantic-level weights for the plurality of weighted edges based on a designated predictive task for the global graph. The techniques may include generating plurality of graph node embeddings and initiating the performance of the designated predictive task based on the plurality of graph node embeddings.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating an inferred document representation for a multi-section document using a machine learning model.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by generating a predicted eligibility score for a predictive entity using a cross-feature-type eligibility prediction machine learning framework.

Abstract: There is a need for solutions that predictive data analytics with improved training efficiency and/or accuracy. This need can be addressed by, for example, processing an original feature entry to generate a group of low-order feature values, including performing a first number of iterations of a feature engineering transformation to generate a group of engineered feature values and determining the group of low-ordered feature values based on a number of feature values from the group of engineered feature values; processing the original feature entry to generate a group of high-order feature values; merging the group of low-order feature values and the group of high-order feature values to generate a processed feature entry corresponding to the original feature entry; and providing the processed feature entry as an input to a prediction unit.

Abstract: There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.

Abstract: There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.

Abstract: There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.

Abstract: There is a need for more accurate and more efficient predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a method includes generating, by a processor, utilizing a risk determination machine learning model and based at least in part on one or more hidden features of the first predictive entity, the predicted risk measure, and performing one or more prediction-based actions based at least in part on the predicted risk measure.

Abstract: Various embodiments of the present invention disclose techniques for optimizing a plurality of machine-learning based models for an end-to-end investigative process. The techniques include generating a profile for an input data object associated with an investigative process. A first machine-learning based model is used to select a predictive data analysis sub-routine for processing the profile. A second machine-learning based model is used to determine a predictive entity for performing an investigative process for the profile. An investigative outcome is received from the predictive entity. A historical optimization data object is augmented with the investigative outcome, the profile, the predictive data analysis sub-routine, or the predictive entity and the first machine-learning based model and the second machine-learning based model are trained using the historical optimization object.

Abstract: Various embodiments of the present invention disclose techniques for orchestrating a complex data processing scheme for an investigative process using a machine-learning based orchestration model that is trained to optimize the use of computing resources based at least in part on a feedback loop. An input data object associated with an investigative process can be selected for investigation; a predictive data analysis sub-routine for processing the input data object can be intelligently selected from a plurality of predictive data analysis sub-routines by the machine-learning based orchestration model; and a processing orchestration action can be initiated based at least in part on an investigative score output by the predictive data analysis sub-routine. The processing orchestration action can include closing the input data object, continuing to process the input data object with additional predictive data analysis sub-routines, or passing the input data object to a predictive entity for further processing.

Abstract: Systems, methods, and apparatuses for detecting and identifying anomalous data in an input data set are provided.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by generating a predicted eligibility score for a predictive entity using a cross-feature-type eligibility prediction machine learning framework.

Abstract: Various embodiments of the present invention disclose techniques for determining a graph-based prediction based at least in part on a cross-entity relationship graph data object and using a hybrid graph-based processing machine learning framework. In some embodiments, the hybrid graph-based prediction machine learning framework is configured to generate the graph-based prediction based at least in part on a comprehensive representation of the cross-entity relationship graph data object that is generated based at least in part on output data of a graph convolutional neural machine learning model and an image-based graph convolutional neural network machine learning model.

Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations using at least one of query-compliant hash databases, segmentation-based hashing models, and hash segmentation models.

Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations using at least one of query-compliant hash databases, segmentation-based hashing models, and hash segmentation models.

Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations using at least one of query-compliant hash databases, segmentation-based hashing models, and hash segmentation models.

Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations using at least one of query-compliant hash databases, segmentation-based hashing models, and hash segmentation models.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for data prioritization. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform prospective prioritization target using at least one of a historical triggering event data, trained prospective prediction machine learning model, and predictive input channels.

Abstract: A method includes obtaining a series of more than two Boolean events, each respective Boolean event having either a first value indicating that reimbursement for a respective overpayment of a service was timely received or a second value indicating reimbursement was not timely received. The method includes determining an actual number of switch events in the series of Boolean events and determining an expected number of switch events. The method includes determining a confidence interval for the expected number of switch events, and determining a score based on the actual number of switch events, the expected number of switch events, and the confidence interval. The score is indicative of a fault in a process generating the series of Boolean events. The method includes generating an alert based on the score being greater than or equal to a threshold score.

Abstract: Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for generating, training, and utilizing a digital signal processor (DSP) to evaluate graph data that may include irregular grid graph data. An example DSP that may be generated, trained, and used may include a set of hidden layers, wherein each hidden layer of the set of hidden layers comprises a set of heterogeneous kernels (HKs), and wherein each HK of the set of HKs includes a corresponding set of filters selected from the constructed set of filters and associated with one or more initial Laplacian operators and corresponding initial filter parameters.