Optum Services (ireland) Limited

Neill Michael Byrne

Kieran O'Donoghue

Michael J McCarthy

David Lewis Frankenfield

Mostafa Bayomi

Michael J McCARTHY

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 perform 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 using a cross-temporal encoding machine learning model, such as a cross-temporal encoding machine learning model that is generated by using a target intervention classification machine learning model to map outputs of the cross-temporal encoding machine learning model to historical target intervention labels, thus enabling supervised training of the cross-temporal encoding machine learning without the need for ground-truth data corresponding to the output of the cross-temporal encoding machine learning model.

Cross-temporal encoding machine learning models

Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include generating a plurality of encoded input data objects associated with a measurement device; generating, using at least a bidirectional Recurrent Neural Networks (RNN) machine learning model, a predictive performance data object associated with the measurement device and a plurality of predictive weight data objects associated with the predictive performance data object, and performing one or more prediction-based actions based at least in part on the predictive performance data object or the plurality of predictive weight data objects.

Methods, apparatuses and computer program products for predicting measurement device performance

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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Dublin, Ireland

Neill Michael Byrne