Optum Services (ireland) Limited

Michael J McCarthy

Kieran O'Donoghue

Neill Michael Byrne

David Lewis Frankenfield

Mostafa Bayomi

Ahmed Selim

Vijay S Nori

Harutyun Shahumyan

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis using semi-structured input data. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis using semi-structured input data using at least one of techniques using inferred codified fields and temporally-arranged codified fields.

Machine learning techniques for generating disease prediction utilizing cross-temporal semi-structured input data

A method comprises: obtaining a current initial image generated by an image generator of an imaging device based on current input signals of sensors of the imaging device; and applying a transformation model to the current initial image to generate a current transformed image, wherein the transformation model is a machine-learning model that has been trained to generate transformed images that more closely resemble reference images generated by a reference image generator.

Enhanced calibration of imaging device output

There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing cross-temporal predictive data analysis. In one example, a method includes determining a time-adjusted encoding for each temporal unit of a group of temporal units, processing each time-adjusted encoding using a cross-temporal encoding machine learning model to generate a cross-temporal encoding of the group of temporal units, and performing one or more prediction-based actions based at least in part on the cross-temporal encoding.

Cross-temporal predictive data analysis

There is a need for more effective and efficient constrained-optimization-based operational load balancing. In one example, a method comprises determining constraint-satisfying operator-unit mapping arrangements that satisfy an operator unity constraint and an operator capacity constraint; for each constraint-satisfying operator-unit mapping arrangement, determining an arrangement utility measure; processing each arrangement utility measure using an optimization-based ensemble machine learning model that is configured to determine an optimal operator-unit mapping arrangement of the plurality of constraint-satisfying operator-unit mapping arrangements; and initiating the performance of one or more operational load balancing operations based on the optimal operator-unit mapping arrangement.

Ensemble machine learning framework for predictive operational load balancing

There is a need for solutions for more efficient predictive data analysis systems. This need can be addressed, for example, by a system configured to receive temporal inferences for a predictive task, where each temporal inference is associated with a temporal benchmark and the temporal benchmarks include a base temporal benchmark and supplemental temporal benchmarks; generate a cross-temporal prediction for the predictive task by applying one or more cross-temporal probabilistic updates to the base temporal inference, where each cross-temporal probabilistic update is associated with a supplemental temporal benchmark; and display the cross-temporal prediction using a cross-temporal prediction interface.

Predictive data analysis with cross-temporal probabilistic updates

There is a need for more effective and efficient constrained-optimization-based operational load balancing. In one example, a method comprises determining constraint-satisfying operator-unit mapping arrangements that satisfy an operator unity constraint and an operator capacity constraint; for each constraint-satisfying operator-unit mapping arrangement, determining an arrangement utility measure; processing each arrangement utility measure using an optimization-based ensemble machine learning model that is configured to determine an optimal operator-unit mapping arrangement of the plurality of constraint-satisfying operator-unit mapping arrangements; and performing one or more operational load balancing operations based on the optimal operator-unit mapping arrangement.

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Castleknock, Ireland

Michael J McCarthy