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APPLICATION OF BAYESIAN NETWORKS TO PATIENT SCREENING AND TREATMENT

  • US 20110082712A1
  • Filed: 09/30/2010
  • Published: 04/07/2011
  • Est. Priority Date: 10/01/2009
  • Status: Active Grant
First Claim
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1. A computer-implemented method for evaluating enrollees of a health insurance plan, the method comprising:

  • obtaining health insurance claim data for a first group of individuals to generate a training corpus, including a training set of claim data and a holdout set of claim data, the first group of individuals representing enrollees of one or more first health insurance plans and the health insurance claim data representing historic insurance claim information for each individual in the first group;

    creating a Bayesian belief network (BBN) model by training a BBN network based on the training set of claim data using a predetermined machine learning algorithm; and

    validating the BBN model using the holdout set of claim data, wherein 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; and

    using the validated BBN model to develop enrollee-specific estimates of disease risk, enrollee-specific future estimates of utilization and cost, and enrollee-specific estimates of the change which would result from successful intervention and/or treatment.

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