Machine learning techniques using model deficiency data objects for tensor-based graph processing models

Patent metadata

Field	Value
Publication number	US-2024013064-A1
Application number	US-202217811229-A
Country	US
Kind code	A1
Filing date	Jul 7, 2022
Priority date	Jul 7, 2022
Publication date	Jan 11, 2024
Grant date	—

Abstract

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a model deficiency data object for a tensor-based graph processing machine learning model. Certain embodiments of the present invention utilize systems, methods, and computer program products that generate a model deficiency data object for a tensor-based graph processing machine learning model using holistic graph links generated by utilizing a graph representation machine learning model.

First claim

1 . A computer-implemented method for generating a model deficiency data object for a tensor-based graph processing machine learning model that is associated with a risk category, the computer-implemented method comprising:
identifying, using one or more processors, a positive input set that is associated with the risk category, wherein:
the positive input set comprises a plurality of prediction input data objects that are associated with an affirmative label for the risk category, and
each prediction input data object in the positive input set is associated with: (i) a prediction input feature set, (ii) a plurality of risk tensors each generated based at least in part on a categorical subset of the prediction input feature set for the prediction input data object that is associated with an input category of a plurality of input categories, and (iii) a plurality of tensor-based graph representations generated based at least in part on the plurality of risk tensors for the prediction input data object;
identifying, using the one or more processors, a tensor-based graph representation set for the positive input set, wherein: (i) the tensor-based graph representation set comprises, for each prediction input data object in the positive input set, the plurality of tensor-based graph representations for the prediction input data object, and (ii) the tensor-based graph representation set describes a group of tensor-based graph links; generating, using the one or more processors and a graph representation machine learning model, and based at least in part on each prediction input feature set, a group of holistic graph links; generating, using the one or more processors and based at least in part on the group of tensor-based graph links and the group of holistic graph links, the model deficiency data object; and performing, using the one or more processors, one or more prediction-based actions based at least in part on the model deficiency data object.

2 . The computer-implemented method of claim 1 , wherein, for a given prediction input data object, the tensor-based graph processing machine learning model is configured to:
for each tensor-based graph representation that is associated with the given prediction input data object, generate a tensor-based graph feature embedding that is associated with a respective input category of the risk tensor that is used to generate the tensor-based graph representation, and generate an inferred hybrid risk score for the given prediction input data object based at least in part on each tensor-based graph feature embedding that is associated with the given prediction input data object.

3 . The computer-implemented method of claim 2 , wherein:
the tensor-based graph processing machine learning model comprises a plurality of graph-based machine learning models each associated with a respective input category and an ensemble machine learning model, and generating the inferred hybrid risk score for the given prediction input data object comprises: (i) for each input category, generating, using the graph-based machine learning model and based at least in part on the tensor-based graph feature embedding for the input category, a categorical tensor-based graph feature embedding, and (ii) generating, using the ensemble model and based at least in part on each categorical tensor-based graph feature embedding, the inferred hybrid risk score.

4 . The computer-implemented method of claim 2 , wherein:
for each prior prediction input data object of a plurality of prediction input data objects, the plurality of tensor-based graph feature embeddings for the prior prediction input data object are generated by the tensor-based graph processing machine learning model to generate the inferred hybrid risk score for the prior prediction input data object, a hybrid risk score generation machine learning model is generated using one or more genetic programming operations, and the one or more genetic programming operations are configured to, for each prior prediction input data object, relate the plurality of tensor-based graph feature embeddings for the prior prediction input data object to the inferred hybrid risk score for the prior prediction input data object.

5 . The computer-implemented method of claim 4 , wherein the one or more genetic programming operations comprise one or more symbolic regression operations that are configured to generate one or more refined regressor variables for the hybrid risk score generation machine learning model and one or more refined input variables for the hybrid risk score generation machine learning model.

6 . The computer-implemented method of claim 1 , wherein generating the model deficiency data object comprises:
generating one or more deficiency graph links that are in the group of holistic graph links but are not in the group of tensor-based graph links; and generating the model deficiency data object based at least in part on the one or more deficiency graph links.

7 . The computer-implemented method of claim 6 , wherein the model deficiency data object comprises a selected subset of the one or more deficiency graph links that is generated based at least in part on: (i) an immutability score for each deficiency graph link, (ii) an actionability score for each deficiency graph link, and (iii) a prevalence score for each deficiency graph link.

8 . An apparatus for generating a model deficiency data object for a tensor-based graph processing machine learning model that is associated with a risk category, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least:
identify a positive input set that is associated with the risk category, wherein:
the positive input set comprises a plurality of prediction input data objects that are associated with an affirmative label for the risk category, and
each prediction input data object in the positive input set is associated with: (i) a prediction input feature set, (ii) a plurality of risk tensors each generated based at least in part on a categorical subset of the prediction input feature set for the prediction input data object that is associated with an input category of a plurality of input categories, and (iii) a plurality of tensor-based graph representations generated based at least in part on the plurality of risk tensors for the prediction input data object;
identify a tensor-based graph representation set for the positive input set, wherein: (i) the tensor-based graph representation set comprises, for each prediction input data object in the positive input set, the plurality of tensor-based graph representations for the prediction input data object, and (ii) the tensor-based graph representation set describes a group of tensor-based graph links; generate, using a graph representation machine learning model, and based at least in part on each prediction input feature set, a group of holistic graph links; generate, based at least in part on the group of tensor-based graph links and the group of holistic graph links, the model deficiency data object; and perform one or more prediction-based actions based at least in part on the model deficiency data object.

9 . The apparatus of claim 8 , wherein, for a given prediction input data object, the tensor-based graph processing machine learning model is configured to:
for each tensor-based graph representation that is associated with the given prediction input data object, generate a tensor-based graph feature embedding that is associated with a respective input category of the risk tensor that is used to generate the t

Assignees

• Optum Inc

Inventors

• Godden Paul J
• Nystrom Erik A
• Boss Gregory J

Classifications

• G06N3/126 Primary

  Evolutionary algorithms, e.g. genetic algorithms or genetic programming · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/045

  Combinations of networks · CPC title