Optum Services (ireland) Limited

Optum, Inc.

Unitedhealth Group Incorporated

Paul J Godden

Gregory J Boss

Mark Gregory Megerian

Jon Kevin Muse

Ahmed Selim

Michael Bridges

Ninad D Sathaye

Art Swanson

Matthew R Versaggi

Vicente Ruben Del Pino Ruiz

Sarah McCandless

Darrel Naidoo

Peter M Wahl

There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of genetic variant data. In one example, embodiments comprise receiving an input feature, generating one or more image representations of the input feature, generating a tensor representation of the one or more image representations, generating a plurality of positional encoding maps, generating an image-based prediction based at least in part on the image representation, and performing one or more prediction-based actions based at least in part on the image-based prediction.

Predictive data analysis using image representations of genomic data

Methods, apparatuses, systems, computing devices, computing entities, and/or the like are provided. An example method may include selecting at least one client profile data object from a plurality of client profile data objects; retrieving at least one initial transcriptome data object and at least one subsequent transcriptome data object associated with the at least one client profile data object; generating at least one dynamic multigraph data object based at least in part on the at least one initial transcriptome data object, the at least one subsequent transcriptome data object, and at least one clinical event data object; training a temporal graph network based at least in part on the at least one dynamic multigraph data object to generate a risk window prediction data object; and performing at least one data operation based at least in part on the risk window prediction data object.

Method, apparatus and computer program product for providing a multi-omics framework for estimating temporal disease trajectories

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing health-related predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform health-related predictive data analysis by generating an optimal predictor set for a gene regulatory network using a quantum logic circuit that comprises one or more quantum logic subcircuits for each quantum processing unit that is associated with a quantum subcircuit and is configured to perform a conjunctive phase logic operation performed on each ancilla bit of a quantum subcircuit.

Quantum computing techniques for determining gene predictors in gene regulatory networks

Systems and methods are configured to perform machine-learning-based predictive behavioral response. In various embodiments, one or more behavioral monitoring data objects are identified and processed using a behavioral pattern prediction machine learning model to generate a behavioral pattern prediction model. The behavioral pattern prediction model is processed using a risk generation machine learning model to generate a risk model, wherein: (i) the risk generation machine learning model is generated based at least in part by one or more risk factors, and (ii) the risk model comprises a per-risk factor score for each risk factor of the one or more risk factors. The risk model is processed using an adjustment generation machine learning model to generate an adjustment model and one or more prediction-based actions are performed based on the adjustment model.

Machine-learning-based predictive behavioral monitoring

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive prioritization. Certain embodiments utilize systems, methods, and computer program products that perform predictive prioritization using a combination of supervised machine learning models and unsupervised machine learning models that are in turn used to generate target features for a resultant prioritization machine learning model.

Machine learning techniques for predictive prioritization

Methods, apparatus, systems, computing devices, computing entities, and/or the like for generating medical research reports automatically collect data from a plurality of separate health data storage systems, standardize the received data to support at least a requested report type, apply one or more machine-learning quality control check to identify potentially inaccurate data included within the received data, and to generate the requested report based at least in part on the standardized, refined data. Moreover, one or more recommended additional reports supported by the refined data set is identified and recommended to a user based at least in part on user attributes and reports initially requested.

Automated computing platform for aggregating data from a plurality of inconsistently configured data sources to enable generation of reporting recommendations

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London, United Kingdom

Paul J Godden