“Application Of Bayesian Networks To Patient Screening And Treatment” in Patent Application Approval Process (USPTO 20230142594): Patent Application

2023 MAY 25 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventors EBERHARDT, III, John S. (Jeffersonton, VA, US); Kalina, Philip M. (Reston, VA, US); Radano, Todd A. (Jeffersonton, VA, US), filed on December 29, 2022, was made available online on May 11, 2023, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application has not been assigned to a company or institution.

The following quote was obtained by the news editors from the background information supplied by the inventors: “A Bayesian belief network (BBN) is a directed graph and an associated set of probability tables. The graph consists of nodes and arcs. The nodes represent variables, input data for which can be discrete or continuous; however the BBN must segment continuous data into parameterized ranges. The arcs represent causal or influential relationships between variables. More specifically, a BBN is a probabilistic graphical model that represents a set of random variables and their conditional independencies. It is a way of describing complex, probabilistic reasoning.

“Machine learning is a field of computer science that uses intelligent algorithms to allow a computer to mimic the process of human learning. The machine learning algorithm allows the computer to learn information structure dynamically from the data that resides in the data warehouse. The machine learning algorithms automatically detect and promote significant relationships between variables, without the need for human interaction. This allows for the processing of vast amounts of complex data quickly and easily.

“The machine learning models can be scored in different ways: Minimum Description Length (MDL), also known as the Bayesian Information Criterion (BIC), as well as Bayesian Scoring (BDe). Minimum Description Length scoring provides a measure of the quality of a model. It trades off between goodness of fit and model complexity (parsimonious scoring). Goodness of fit is measured as the likelihood of the data given the model. Model complexity equals the amount of information required to store the model, subject to an inflator/deflator set by the user. The BBN networks and/or machine learning models have not been previously utilized in policy decision making processes of insurance plans or in selection of enrollees for disease management or care interventions.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “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 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 classifier using the training set of claim data using predetermined machine learning algorithms. K-fold cross-validation may also be used during the model training process to assess model robustness and feature selection. The BBN model is finally validated using the holdout set of claim data. A successfully validated BBN model can then be used to identify individual-specific risk of disorder and individual-specific likelihood of benefit from intervention and successful treatment for the disorder.

“According to another aspect of the invention, a first set of claim data is received from a client, where the first set of claim data is associated with a first group of individuals representing enrollees of a first health insurance plan. A screening operation is performed using at least one screening Bayesian belief network (BBN) model based on the first set of claim data to identify a subset of individuals in the first group having risk characteristics associated with a disorder. Cost estimation is performed on the subset of individuals using at least one cost BBN model to produce enrollee specific cost estimates. These BBN models can consist of at least one screening BBN model and at least one cost BBN model that were trained using a predetermined machine learning algorithm based on a second, separate set of claim data associated with a second group of individuals of a second health insurance plan. The second set of claim data represents historic insurance claim information for each individual in the second group. The enrollee-specific estimates of risk and cost as developed using a BBN model trained on the second set are then transmitted to the client.

“Other features of the present invention will be apparent from the accompanying drawings and from the detailed description which follows.”

The claims supplied by the inventors are:

“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.

“2. The method of claim 1, wherein the BBN model comprises at least one screening model to identify a subset of the individuals in the first group with potential risk characteristics of the disorder and at least one cost model to calculate a cost estimate to produce an enrollee-specific cost estimate.

“3. The method of claim 2, wherein creating a BBN model comprises: building a list of a plurality of BBN model candidates based on the training set of claim data according to a plurality of categories using the predetermined machine learning algorithm; scoring each of the BBN model candidates in the list using a predetermined scoring method; and selecting the BBN model from the list of BBN model candidates, the selected BBN model having the highest score among the BBN model candidates to be validated.

“4. The method of claim 3, wherein the predetermined scoring method comprises a minimum description length (MDL) or Bayesian Information Criterion (BIC) compatible method.

“5. The method of claim 3, wherein building a list of BBN model candidates comprises: calculating distributions of discrete states for variables of the BBN network; performing a preliminary modeling operation on the variables to identify appropriate machine learning parameters, optionally generating a first BBN model; performing a global modeling operation to set the appropriate machine learning parameters and to observe global data structures, optionally generating a second BBN model; performing a naive modeling operation to identify a contribution of each of the variables, optionally generating a third BBN model; performing a focused modeling operation on a subset of the variables identified in at least one of the preliminary modeling operation, the global modeling operation, and the naive model operation, optionally generating a fourth BBN model; using k-fold cross-validation to assist in attribute selection; and scoring, using the predetermined scoring method, all BBN models to select the best BBN model.

“6. The method of claim 2, wherein validating the BBN model comprises: generating a receiver operating characteristic (ROC) curve based on an outcome of the BBN model operated on the holdout set of claim data; and calculating an area under the curve (AUC) based on the ROC curve, wherein the AUC is used to evaluate predictive correctness of the BBN model given the holdout set of claim data.

“7. The method of claim 2, further comprising: receiving a second set of claim data from a client, the second set of claim data being associated with a second group of individuals representing enrollees of one or more second health insurance plans; performing a screening operation using at least one screening BBN model based on the second set of claim data to identify a second subset of individuals in the second group having risk characteristics associated with the disorder; performing a cost estimation on the second subset of individuals using at least one cost BBN model to produce enrollee specific cost estimates; and transmitting the enrollee-specific disease risk and cost estimates to the client.

“8. The method of claim 7, further comprising retraining at least one screening BBN model and at least one cost BBN model based on the second set of claim data and the enrollee specific cost estimates for future usages.

“9. A computer-readable storage medium having computer instructions stored therein, which when executed by a computer, cause the computer to perform a 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 algorithms; 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.

“10. The computer-readable storage medium of claim 9, wherein the BBN model comprises at least one screening model to identify a subset of the individuals in the first group with potential risk characteristics of the disorder and at least one cost model to predict a cost estimate to produce patient specific cost estimate.

“11. The computer-readable storage medium of claim 10, wherein creating a BBN model comprises: building a list of a plurality of BBN model candidates based on the training set of claim data according to a plurality of categories using the predetermined machine learning algorithm; scoring each of the BBN model candidates in the list using a predetermined scoring method; and selecting the BBN model from the list of BBN model candidates, the selected BBN model having the highest score among the BBN model candidates to be validated.

“12. The computer-readable storage medium of claim 11, wherein the predetermined scoring method comprises a minimum description length (MDL) compatible method.

“13. The computer-readable storage medium of claim 11, wherein building a list of BBN model candidates comprises: calculating distributions of discrete states for variables of the BBN network; performing a preliminary modeling operation on the variables to identify appropriate machine learning parameters, optionally generating a first BBN model; performing a global modeling operation to set the appropriate machine learning parameters and to observe global data structures, optionally generating a second BBN model; performing a naive modeling operation to identify a contribution of each of the variables, optionally generating a third BBN model; performing a focused modeling operation on a subset of the variables identified in at least one of the preliminary modeling operation, the global modeling operation, and the naive model operation, optionally generating a fourth BBN model; using k-fold cross-validation to assist in attribute selection; and scoring, using the predetermined scoring method, all BBN models to select the best BBN model.

“14. The computer-readable storage medium of claim 10, wherein validating the BBN model comprises: generating a receiver operating characteristic (ROC) curve based on an outcome of the BBN model operated on the holdout set of claim data; and calculating an area under the curve (AUC) based on the ROC curve, wherein the AUC is used to evaluate predictive correctness of the BBN model given the holdout set of claim data.

“15. The computer-readable storage medium of claim 10, wherein the method further comprises: receiving a second set of claim data from a client, the second set of claim data being associated with a second group of individuals representing enrollees of one or more second health insurance plans; performing a screening operation using the at least one screening BBN model based on the second set of claim data to identify a second subset of individuals in the second group having risk characteristics associated with the disorder; performing a cost estimation on the second subset of individuals using the at least one cost BBN model to produce enrollee specific cost estimates; and transmitting the enrollee specific cost estimates to the client.

“16. The computer-readable storage medium of claim 15, wherein the method further comprises retraining the at least one screening BBN model and the at least one cost BBN model based on the second set of claim data and the enrollee specific cost estimates for future usages.”

There are additional claims. Please visit full patent to read further.

URL and more information on this patent application, see: EBERHARDT, III, John S.; Kalina, Philip M.; Radano, Todd A. Application Of Bayesian Networks To Patient Screening And Treatment. U.S. Patent Application Number 20230142594, filed December 29, 2022 and posted May 11, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230142594)&db=US-PGPUB&type=ids

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