Optum Services (Ireland) Limited

Peter Cogan

Conor Breen

Lorcan B MacManus

Riccardo Mattivi

Dong Fang

Lorcan B Mac Manus

David Belton

Venkata Krishnan Mittinamalli Thandapani

Anthony R O'Neill

Mark Tonge

Kashyap Krishnamurthy

Lorcan B Macmanus

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.

Hybrid graph-based prediction machine learning frameworks

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.

Categorical input machine learning models

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.

Global graph-based classification techniques for large data prediction domain

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.

Intelligent automatic orchestration of machine-learning based processing pipeline

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. In accordance with one embodiment, a method is provided that includes: identifying a document corpus comprising the multi-section document and other multi-section documents; for each section of the document that is associated with a section type identifier: identifying a section batch that comprises common-type sections across the document corpus; and processing the section batch using the machine learning model to generate per-type section clusters for the section type identifier that comprise an inferred per-type section cluster for the current section; generating the inferred document representation based at least in part on each inferred per-type section cluster for a section of the document; and performing a prediction-based action based at least in part on the representation.

Explainable unsupervised vector representation of multi-section documents

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.

Machine learning techniques for eligibility prediction determinations

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.

Predictive data analytics with automatic feature extraction

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.

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Dublin, Ireland

Peter Cogan