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:
Jan. 24, 2023

Filed:

Sep. 30, 2010
Applicants:

John S. Eberhardt, Iii, Jeffersonton, VA (US);

Philip M. Kalina, Reston, VA (US);

Todd A. Radano, Jeffersonton, VA (US);

Inventors:

John S. Eberhardt, III, Jeffersonton, VA (US);

Philip M. Kalina, Reston, VA (US);

Todd A. Radano, Jeffersonton, VA (US);

Assignee:

DECISIONQ CORPORATION, Washington, DC (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 40/00 (2012.01); G06Q 10/10 (2012.01); G06Q 40/08 (2012.01); G16H 50/50 (2018.01); G16H 50/20 (2018.01); G16Z 99/00 (2019.01);
U.S. Cl.
CPC ...
G06Q 10/10 (2013.01); G06Q 40/08 (2013.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G16Z 99/00 (2019.02);
Abstract

According to one aspect of the invention, health insurance claim data for a first group of individuals is obtained to generate a training corpus, including a training set of claim data and a holdout set of claim data. The first group of individuals represents enrollees of one or more first health insurance plans and the health insurance claim data represents historic insurance claim information for each individual in the first group. A Bayesian belief network (BBN) model is created by training a BBN network based on the training set of claim data using predetermined machine learning algorithms. The BBN model is validated using the holdout set of claim data. The BBN model, when having been successfully validated, is configured to identify at least one of individuals with risk for a disorder and individuals with risk who are most likely to benefit from intervention and treatment for the disorder.


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