Optum, Inc.

Optum Services (ireland) Limited

Matthew R Versaggi

Gregory J Boss

Paul J Godden

Ninad D Sathaye

Thomas R Gilbertson

Vicente Ruben Del Pino Ruiz

Nithya Sundararajan

Aaron C Wacker

Michael S Oneil

Sameer Gotkhindikar

Bryan W Stearns

Sarah Jean Scott

Systems and methods for routing data using an artificial intelligence (AI) model are disclosed. The method includes receiving a data request associated with one or more data gaps, determining, by an AI model, a plurality of ranking values for a plurality of candidate data sources respectively based on one or more attributes, each of the plurality of ranking values indicative of a likelihood of filling the one or more data gaps associated with the data request; and routing, over a network, the data request to a first candidate data source of the plurality of candidate data sources based on a first ranking value of the plurality of ranking values; and blocking routing of the data request over the network to a second candidate data source of the plurality of candidate data sources based on a second ranking value of the plurality of ranking values.

System and method for using an artificial intelligence (AI) model to route data

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

There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing/executing demographic-aware federated machine learning. In one example, a method includes receiving local machine learning model data objects from model data object provider agents; for each inference-profile pair that is associated with a corresponding inference identifier and a corresponding model profile, generating a global machine learning model data object based at least in part on a related local model subset of the local machine learning model data objects for the inference-profile pair; and providing, based at least in part on each global machine learning model data object, a demographic-aware predictive data analysis application programming interface (API), wherein the demographic-aware predictive data analysis API is accessible by the model data object provider agents.

Demographic-aware federated machine learning

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Plymouth, MN, United States of America

Matthew R Versaggi