Predictive data analysis techniques using a hierarchical risk prediction machine learning framework

Patent metadata

Field	Value
Publication number	US-2023360199-A1
Application number	US-202217662157-A
Country	US
Kind code	A1
Filing date	May 5, 2022
Priority date	May 5, 2022
Publication date	Nov 9, 2023
Grant date	—

Abstract

Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations using a hierarchical risk prediction machine learning framework that comprises an initially-deployed risk prediction machine learning model, a dynamically-deployed risk prediction machine learning model, and a risk aggregation machine learning model. In some embodiments, the dynamically-deployed risk prediction machine learning model is deployed when a dynamic deployment training entry count of one or more dynamic deployment training entries satisfies a dynamic deployment training entry count threshold.

First claim

1 . A computer-implemented method for generating a predicted risk score for an input feature data object, the computer-implemented method comprising:
generating, by one or more processors and based at least in part on the input feature data object, an input deidentified three-dimensional model; generating, by the one or more processors, based at least in part on the input deidentified three-dimensional model, and using a dynamically-deployed risk prediction machine learning model, the predicted risk score, wherein:
the dynamically-deployed risk prediction machine learning model is deployed when a dynamic deployment training entry count of one or more dynamic deployment training entries satisfies a dynamic deployment training entry count threshold,
the dynamically-deployed risk prediction machine learning model is trained using the one or more dynamic deployment training entries,
each dynamic deployment training entry comprises a corresponding training entry deidentified three-dimensional model that is associated with a corresponding training entry feature data object and a corresponding dynamic deployment ground-truth indicator,
each corresponding dynamic deployment ground-truth indicator of a corresponding dynamic deployment training entry is determined based at least in part on a corresponding recommendation validation indicator for a corresponding predicted recommendation that is generated based at least in part on an initial risk score, and
each initial risk score is generated by an initially-deployed risk prediction machine learning model based at least in part on an identifiable feature data object for the corresponding dynamic deployment training entry; and
performing, by the one or more processors, one or more prediction-based actions based at least in part on the predicted risk score.

2 . The computer-implemented method of claim 1 , wherein:
prior to the deployment of the dynamically-deployed risk prediction machine learning model, predicted risk scores for input feature data objects are generated based at least in part on corresponding initial risk scores generated by the initially-deployed risk prediction machine learning model.

3 . The computer-implemented method of claim 1 , wherein each identifiable feature data object comprises image/video sensor data associated with the corresponding dynamic deployment training entry.

4 . The computer-implemented method of claim 1 , wherein the deidentified three-dimensional model comprises a three-dimensional movement profile and one or more three-dimensional body-related characteristics.

5 . The computer-implemented method of claim 4 , wherein the one or more three-dimensional body-related characteristics comprise at least one of holistic characteristics, head and face characteristics, posture, muscle tone, and arm and hand characteristics.

6 . The computer-implemented method of claim 1 , wherein inputs to the dynamically-deployed risk prediction machine learning model further comprise one or more contextual features associated with an operational context of the deidentified three-dimensional model.

7 . The computer-implemented method of claim 1 , wherein the comprises a convolutional neural network and an image-based classification machine learning component.

8 . An apparatus for generating a predicted risk score for an input feature data object, 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:
generate, based at least in part on the input feature data object, an input deidentified three-dimensional model; generate, based at least in part on the input deidentified three-dimensional model, and using a dynamically-deployed risk prediction machine learning model, the predicted risk score, wherein:
the dynamically-deployed risk prediction machine learning model is deployed when a dynamic deployment training entry count of one or more dynamic deployment training entries satisfies a dynamic deployment training entry count threshold,
the dynamically-deployed risk prediction machine learning model is trained using the one or more dynamic deployment training entries,
each dynamic deployment training entry comprises a corresponding training entry deidentified three-dimensional model that is associated with a corresponding training entry feature data object and a corresponding dynamic deployment ground-truth indicator,
each corresponding dynamic deployment ground-truth indicator of a corresponding dynamic deployment training entry is determined based at least in part on a corresponding recommendation validation indicator for a corresponding predicted recommendation that is generated based at least in part on an initial risk score, and
each initial risk score is generated by an initially-deployed risk prediction machine learning model based at least in part on an identifiable feature data object for the corresponding dynamic deployment training entry; and
perform one or more prediction-based actions based at least in part on the predicted risk score.

9 . The apparatus of claim 8 , wherein:
prior to the deployment of the dynamically-deployed risk prediction machine learning model, predicted risk scores for input feature data objects are generated based at least in part on corresponding initial risk scores generated by the initially-deployed risk prediction machine learning model.

10 . The apparatus of claim 8 , wherein each identifiable feature data object comprises image/video sensor data associated with the corresponding dynamic deployment training entry.

11 . The apparatus of claim 8 , wherein the deidentified three-dimensional model comprises a three-dimensional movement profile and one or more three-dimensional body-related characteristics.

12 . The apparatus of claim 11 , wherein the one or more three-dimensional body-related characteristics comprise at least one of holistic characteristics, head and face characteristics, posture, muscle tone, and arm and hand characteristics.

13 . The apparatus of claim 8 , wherein inputs to the dynamically-deployed risk prediction machine learning model further comprise one or more contextual features associated with an operational context of the deidentified three-dimensional model.

14 . The apparatus of claim 8 , wherein the comprises a convolutional neural network and an image-based classification machine learning component.

15 . A computer program product for generating a predicted risk score for an input feature data object, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:
generate, based at least in part on the input feature data object, an input deidentified three-dimensional model; generate, based at least in part on the input deidentified three-dimensional model, and using a dynamically-deployed risk prediction machine learning model, the predicted risk score, wherein:
the dynamically-deployed risk prediction machine learning model is deployed when a dynamic deployment training entry count of one or more dynamic deployment training entries satisfies a dynamic deployment training entry count threshold,
the dynamically-deployed risk prediction machine learning model is trained using the one or more dynamic deployment training entries,
each dynamic deployment training entry comprises a corresponding training entry deidentified three-dimensional model that is

Assignees

• Optum Services Ireland Ltd

Inventors

• Godden Paul J
• Majerus Melanie
• Selim Ahmed
• Mendelsohn Nancy Joan
• Satterwhite Erin A
• Boss Gregory J

Classifications

• G06N3/096

  Transfer learning · CPC title

• G06N3/098

  Distributed learning, e.g. federated learning · CPC title

• G06T7/0012 Primary

  Biomedical image inspection · CPC title

• G06T17/00

  Three-dimensional [3D] modelling for computer graphics · CPC title

• A61B5/0077 Primary

  Devices for viewing the surface of the body, e.g. camera, magnifying lens · CPC title