The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 28, 2023

Filed:

Jan. 09, 2023
Applicant:

Express Scripts Strategic Development, Inc., St. Louis, MO (US);

Inventors:

Amit K. Bothra, Wildwood, MO (US);

Pritesh J. Shah, Paramus, NJ (US);

Christopher G. Lehmuth, St. Louis, MO (US);

Bradley D. Flynn, St. Louis, MO (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 80/00 (2018.01); G06F 17/18 (2006.01); G16H 40/20 (2018.01); G16H 20/10 (2018.01);
U.S. Cl.
CPC ...
G16H 80/00 (2018.01); G06F 17/18 (2013.01); G16H 20/10 (2018.01); G16H 40/20 (2018.01);
Abstract

A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with one of the principal components, selecting features of the training set most closely correlated with the principal components, performing a regression analysis on the selected features to determine a subset of the selected features that are most closely correlated with a model target, training a machine learning model with the subset, verifying the trained machine learning model with a verification set, and saving the verified trained machine learning model as the intervention model. The method includes determining an intervention expectation indicating a likelihood that the user will take action in response to an intervention being execute, determining a likelihood of a gap in care for the user, selecting and scheduling an intervention for execution based on the care gap likelihood and the intervention expectation.


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