Loading patent…
AssigneeOptum Services (Ireland) Limited
InventorsMichael Bridges, Paul J. Godden
Estimating the term…
Various embodiments of the present invention describe techniques for generating a polygenic risk score generation machine learning framework that integrates an optimal genetic variant refinement model without requiring brute-force traversal of potential parameter spaces defined by various distinct genetic variant sets. In response, various embodiments of the present invention use holistic Bayesian sampling routines to efficiently generate Bayesian evidence numerical estimates for various genetic variant refinement models and select an optimal genetic variant refinement model accordingly. This enables enhancing the accuracy of polygenic risk score generation machine learning frameworks without resorting to computationally resource-intensive traversals of potential parameter spaces defined by various distinct genetic variant sets. In doing so, various embodiments of the present invention enhance the computational efficiency of generating a polygenic risk score generation machine learning framework that integrates an optimal genetic variant refinement model in contrast to computationally-inefficient techniques that require brute-force traversal of potential parameter spaces.
A computer-implemented method for generating a polygenic risk score for a target phenotype using a comparatively-refined polygenic risk score generation machine learning framework, the computer-implemented method comprising:
A system for generating a polygenic risk score for a target phenotype using a comparatively-refined polygenic risk score generation machine learning framework, the system comprising one or more processors and one or more non-transitory computer readable media storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
One or more non-transitory computer-readable storage media for generating a polygenic risk score for a target phenotype using a comparatively-refined polygenic risk score generation machine learning framework storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
Dependencies are inferred from the claim text.