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US 12,009,100 B2Granted
Predictive monitoring of the glucose-insulin endocrine metabolic regulatory system
AssigneeUnitedHealth Group Incorporated
InventorsRachel Lauren Jennings, Jonathan Michael Rolfs, Alex Taub Bacon
Life of this patent
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Abstract
There is a need for more effective and efficient predictive data analysis, such as more effective and efficient data analysis solutions for performing predictive monitoring of the glucose-insulin endocrine metabolic regulatory system. Certain embodiments utilize systems, methods, and computer program products that perform predictive data analysis by utilizing at least one of glucose surge excursion detections, steady-state glucose-insulin machine learning models, and parameter space refinement machine learning models.
Claims20
A computer-implemented method comprising:
- receiving, by one or more processors, a model utility data object associated with a non-optimized machine learning model, wherein (i) the model utility data object maps a parameter value combination of a plurality of parameter value combinations defining a parameter space associated with the non-optimized machine learning model to a utility measurement of one or more utility measurements and (ii) the parameter value combination of the plurality of parameter value combinations comprises a parameter value for an optimizable parameter of one or more optimizable parameters associated with the non-optimized machine learning model;
- identifying, by the one or more processors and using a parameter space refinement machine learning model, a reduced parameter space from the model utility data object, wherein the reduced parameter space comprises a subrange of parameter values from the parameter space;
- and
- training, using an expediated local optimization technique, the non-optimized machine learning model to generate an optimized machine learning model configured to predict an inferred measurement from one or more recorded values by:
- determining an estimated optimum utility measurement from an intra-region utility measurement subset comprising a plurality of intra-region utility measurements for a plurality of parameter values within the reduced parameter space, and
- updating the non-optimized machine learning model based at least in part on the estimated optimum utility measurement.
3 more elements
A computing system comprising one or more processors and memory including a computer program code, the memory and the computer program code configured to, with the one or more processors, cause the computing system to:
- receive a model utility data object associated with a non-optimized machine learning model, wherein (i) the model utility data object maps a parameter value combination of a plurality of parameter value combinations defining a parameter space associated with the non-optimized machine learning model to a utility measurement of one or more utility measurements;
- and (ii) the parameter value combination of the plurality of parameter value combinations comprises a parameter value for an optimizable parameter of one or more optimizable parameters associated with the non-optimized machine learning model;
- identify, using a parameter space refinement machine learning model, a reduced parameter space from the model utility data object, wherein the reduced parameter space comprises a subrange of parameter values from the parameter space;
- and
- train, using an expediated local optimization technique, the non-optimized machine learning model to generate an optimized machine learning model configured to predict an inferred measurement from one or more recorded values by:
- determining an estimated optimum utility measurement from an intra-region utility measurement subset comprising a plurality of intra-region utility measurements for a plurality of parameter value within the reduced parameter space, and
- updating the non-optimized machine learning model based at least in part on the estimated optimum utility measurement.
4 more elements
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to:
- receive a model utility data object associated with a non-optimized machine learning model, wherein (i) the model utility data object maps a parameter value combination of a plurality of parameter value combinations defining a parameter space associated with the non-optimized machine learning model to a utility measurement of one or more utility measurements;
- and (ii) the parameter value combination of the plurality of parameter value combinations comprises a parameter value for an optimizable parameter of one or more optimizable parameters associated with the non-optimized machine learning model;
- identify, using a parameter space refinement machine learning model, a reduced parameter space from the model utility data object, wherein the reduced parameter space comprises a subrange of parameter values from the parameter space;
- and
- train, using an expediated local optimization technique, the non-optimized machine learning model to generate an optimized machine learning model configured to predict an inferred measurement from one or more recorded values by:
- determining an estimated optimum utility measurement from an intra-region utility measurement subset comprising a plurality of intra-region utility measurements for a plurality of parameter value within the reduced parameter space, and
- updating the non-optimized machine learning model based at least in part on the estimated optimum utility measurement.
4 more elements
Dependencies are inferred from the claim text.