Patents by Inventor Philip M. Kalina

Philip M. Kalina has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • APPLICATION OF BAYESIAN NETWORKS TO PATIENT SCREENING AND TREATMENT
    Publication number: 20230142594
    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.
    Type: Application
    Filed: December 29, 2022
    Publication date: May 11, 2023
    Inventors: John S. EBERHARDT, III, Philip M. Kalina, Todd A. Radano
  • Application of bayesian networks to patient screening and treatment
    Patent number: 11562323
    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.
    Type: Grant
    Filed: September 30, 2010
    Date of Patent: January 24, 2023
    Assignee: DECISIONQ CORPORATION
    Inventors: John S. Eberhardt, III, Philip M. Kalina, Todd A. Radano
  • APPLICATION OF BAYESIAN NETWORKS TO PATIENT SCREENING AND TREATMENT
    Publication number: 20110082712
    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.
    Type: Application
    Filed: September 30, 2010
    Publication date: April 7, 2011
    Applicant: DecisionQ Corporation
    Inventors: John S. Eberhardt, III, Philip M. Kalina, Todd A. Radano