“Diabetes Onset And Progression Prediction Using A Computerized Model” in Patent Application Approval Process (USPTO 20230035564): Patent Application

2023 FEB 22 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity Daily News -- A patent application by the inventors Chiguluri, Vinay (Louisville, KY, US); Dong, Yanting (Lexington, KY, US); Fan, Jing (Louisville, KY, US); Gopal, Vipin (Louisville, KY, US), filed on October 10, 2022, was made available online on February 2, 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: “The world health organization estimates that the incidence of diabetes in the United States will almost double during the time period of 2000-2030. The Centers for Disease Control and Prevention estimated that in 2010 there were 26 million people in the United States that had diabetes, with greater than 25 percent of that number being undiagnosed. The National Diabetes Information Clearinghouse has estimated that diabetes costs in the United States are $132 billion a year.

“As noted above, it has been estimated that greater than 25 percent of those with diabetes in the United States are unaware of their condition. Patients who are unaware of their diabetes are at greater risk for a worsening of the disease or other health conditions and complications that arise as the result of the failure to treat their undetected diabetes. As with many other types of diseases, the symptoms of diabetes may vary along a continuum from minor to severe. In addition to greater health risks as the result of failure to treat their diabetic condition, a worsening of a patient’s condition may markedly increase their cost of care. Treatment cost data indicates that a patient with high severity diabetes may have costs that are eight times as much as a patient with low severity symptoms. There are three types of diabetes: type I; type II; and gestational diabetes. As its name suggests, gestational diabetes is a complication of pregnancy and not suffered by the population at large. Type I diabetes is genetic in origin, non-preventable, but fortunately accounts for only 5% of diabetes. The more prevalent type of diabetes is type II. Type II diabetes is preventable or at least controllable through the implementation of a healthy lifestyle and medication. Therefore, approximately 95% of diabetes instances may be prevented or controlled by lifestyle changes and medication. Additionally, without treatment, diabetes can progress in severity to the point that a buildup of glucose in the patient’s bloodstream may result in such complications as cardiovascular disease, vision loss, kidney failure, and even amputation of limbs. However, to treat or prevent progression of the disease, a patient must be aware of his or her diabetic condition. Therefore, prediction may be extremely beneficial to help care providers identify those persons who may have a high risk of developing diabetes. Further, identification of those who currently have diabetes who may be at risk of worsening symptoms is key to help those patients suffering from diabetes effectively manage their condition to avoid or minimize disease progression and the resulting negative health impacts.

“Caregivers and insurance providers also may have an interest in detecting a patient’s diabetic or pre-diabetic condition. In addition to detection, caregivers and insurance providers may have an interest in predicting the likelihood that a patient currently exhibiting symptoms of the disease will progress to worsening levels of diabetes symptoms. As noted above, the cost to treat a patient’s diabetic condition increases dramatically as that patient progresses from less severe to more severe diabetes symptoms. Therefore, a prediction of the likelihood that a segment of population may be at greater risk of developing or suffering a progression of an existing disease condition may be used by caregivers and insurance providers to identify patients with higher levels of risk and proactively initiate monitoring and the provision of appropriate care.

“More aggressive monitoring may help to detect the onset of diabetes while increased levels of care may prevent that onset. For persons who already have diabetes symptoms, increased levels of care may prevent the disease from progressing to more severe stages. In either case, in addition to helping persons avoid diabetes entirely or minimize the progression of symptoms, monitoring that results in earlier detection or proactive care may have the additional benefit of reducing the cost of providing care or health insurance to such a person.

“What is needed is a computerized system and method for identifying segments of a non-diabetic population that are most likely to develop diabetes over an identified period of time. Also needed is a computerized system and method for identifying those segments of a diabetic population that are likely to experience a progression in the severity of their diabetes and related complications.

“Such a system and method may use a severity index to both identify the severity of a diabetic condition and predict the likelihood of disease progression. In embodiments of the invention, input data for use by a predictive model may be collected from a population group. An example of such a group may be persons who are provided coverage by a health insurance provider. In an embodiment of the invention, input data may comprise insurance claims, lab test results, participation in health improvement programs, the output of medical and insurance claim data analysis systems, Medicare data, survey data, population demographics and other population characterizing data. This data may be processed to optimize and transform the various data components into analyzable population data. After optimization, data may be further processed to segment the data into population segments with common data characteristics and detail levels. Predictive models may then be applied to each segment to predict diabetes occurrence and progression risk for population members who are suffering from diabetes at the time of analysis. Once such predications have been performed, actions such as testing, treatment, or counseling may be implemented to reduce the predicted occurrences and slow the progression of the disease in those population members which exhibit symptoms.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “The world health organization estimates that the incidence of diabetes in the United States will almost double during the time period of 2000-2030. The Centers for Disease Control and Prevention estimated that in 2010 there were 26 million people in the United States that had diabetes, with greater than 25 percent of that number being undiagnosed. The National Diabetes Information Clearinghouse has estimated that diabetes costs in the United States are $132 billion a year.

“As noted above, it has been estimated that greater than 25 percent of those with diabetes in the United States are unaware of their condition. Patients who are unaware of their diabetes are at greater risk for a worsening of the disease or other health conditions and complications that arise as the result of the failure to treat their undetected diabetes. As with many other types of diseases, the symptoms of diabetes may vary along a continuum from minor to severe. In addition to greater health risks as the result of failure to treat their diabetic condition, a worsening of a patient’s condition may markedly increase their cost of care. Treatment cost data indicates that a patient with high severity diabetes may have costs that are eight times as much as a patient with low severity symptoms. There are three types of diabetes: type I; type II; and gestational diabetes. As its name suggests, gestational diabetes is a complication of pregnancy and not suffered by the population at large. Type I diabetes is genetic in origin, non-preventable, but fortunately accounts for only 5% of diabetes. The more prevalent type of diabetes is type II. Type II diabetes is preventable or at least controllable through the implementation of a healthy lifestyle and medication. Therefore, approximately 95% of diabetes instances may be prevented or controlled by lifestyle changes and medication. Additionally, without treatment, diabetes can progress in severity to the point that a buildup of glucose in the patient’s bloodstream may result in such complications as cardiovascular disease, vision loss, kidney failure, and even amputation of limbs. However, to treat or prevent progression of the disease, a patient must be aware of his or her diabetic condition. Therefore, prediction may be extremely beneficial to help care providers identify those persons who may have a high risk of developing diabetes. Further, identification of those who currently have diabetes who may be at risk of worsening symptoms is key to help those patients suffering from diabetes effectively manage their condition to avoid or minimize disease progression and the resulting negative health impacts.

“Caregivers and insurance providers also may have an interest in detecting a patient’s diabetic or pre-diabetic condition. In addition to detection, caregivers and insurance providers may have an interest in predicting the likelihood that a patient currently exhibiting symptoms of the disease will progress to worsening levels of diabetes symptoms. As noted above, the cost to treat a patient’s diabetic condition increases dramatically as that patient progresses from less severe to more severe diabetes symptoms. Therefore, a prediction of the likelihood that a segment of population may be at greater risk of developing or suffering a progression of an existing disease condition may be used by caregivers and insurance providers to identify patients with higher levels of risk and proactively initiate monitoring and the provision of appropriate care.

“More aggressive monitoring may help to detect the onset of diabetes while increased levels of care may prevent that onset. For persons who already have diabetes symptoms, increased levels of care may prevent the disease from progressing to more severe stages. In either case, in addition to helping persons avoid diabetes entirely or minimize the progression of symptoms, monitoring that results in earlier detection or proactive care may have the additional benefit of reducing the cost of providing care or health insurance to such a person.

“What is needed is a computerized system and method for identifying segments of a non-diabetic population that are most likely to develop diabetes over an identified period of time. Also needed is a computerized system and method for identifying those segments of a diabetic population that are likely to experience a progression in the severity of their diabetes and related complications.

“Such a system and method may use a severity index to both identify the severity of a diabetic condition and predict the likelihood of disease progression. In embodiments of the invention, input data for use by a predictive model may be collected from a population group. An example of such a group may be persons who are provided coverage by a health insurance provider. In an embodiment of the invention, input data may comprise insurance claims, lab test results, participation in health improvement programs, the output of medical and insurance claim data analysis systems, Medicare data, survey data, population demographics and other population characterizing data. This data may be processed to optimize and transform the various data components into analyzable population data. After optimization, data may be further processed to segment the data into population segments with common data characteristics and detail levels. Predictive models may then be applied to each segment to predict diabetes occurrence and progression risk for population members who are suffering from diabetes at the time of analysis. Once such predications have been performed, actions such as testing, treatment, or counseling may be implemented to reduce the predicted occurrences and slow the progression of the disease in those population members which exhibit symptoms.”

The claims supplied by the inventors are:

“1. A system for predicting the onset of diabetes in a population using population segment specific modeling, said system comprising: databases comprising medical data for a plurality of members of a population; one or more non-transitory electronic storage devices comprising software instructions, which when executed, configure the one or more processors to: retrieve said medical data for the members of the population from the databases; perform a feature extraction subroutine on said retrieved medical data to extract characteristics for said members of the population; segment the population into a plurality of segments by at least some of the extracted characteristics; apply a different model to each of the plurality of segments to score each of said members; generate a scored member list for electronic display comprising identifying information for each of the members provided in association with the score for each of the members; and cause electronic display of said scored member list.

“2. The system of claim 1 wherein: said medical data comprises clinical data, risk data, and demographic data for each member.

“3. The system of claim 2 wherein: said medical data comprises health risk alerts, membership information, survey information, consumer information, health program information, CMS data, medical claims, pharmaceutical claims, and lab and test result information.

“4. The system of claim 2 wherein: said clinical data comprises claim counts, drug class counts, physician visit counts, and test costs; said risk data comprises obesity, smoking, prescription risk score, and global risk; and said demographic data comprises race, education level, and active month information.

“5. The system of claim 1 wherein: the extracted features comprise a demographic profile, clinical profile, behavior profile, medication profile, and disease progression profile.

“6. The system of claim 5 wherein: said demographic profile comprises age, gender, race and socio-economic status; said clinical profile comprises chronic conditions, mental health conditions, hospitalizations, and medication; said behavior profile comprises health program participations; and said medication profile comprises adherence to various medications, including diabetes, heart failure, coronary artery disease.

“7. The system of claim 1 wherein: the characteristics comprise date of membership to the population such that the segmentation is performed between new members and existing members; the characteristics comprise line of business associated with the member such that the segmentation is performed between members associated with different lines of business; and the characteristics comprise data availability of certain types of said medical data such that the segmentation is performed between members associated with different types of data.

“8. The system of claim 1 wherein: said databases comprise at least one publicly accessible database and at least one privately accessible database associated with a health insurance provider; and said one or more non-transitory electronic storage devices comprise additional software instructions, which when executed, configure the one or more processors to pre-process the data received from each of said databases, said pre-processing comprising summarizing, standardizing, and filtering said data received from each of said databases which increases homogeneity of said data.

“9. The system of claim 1 wherein: said one or more non-transitory electronic storage devices comprise additional software instructions, which when executed, configure the one or more processors to: apply a plurality of different models to each of the segments of the population; apply each of the plurality of different models to a test set of data; determine which of said plurality of different models provides a highest level of accuracy relative to the test set of data using holdout data; and select the highest accuracy one of the plurality of different models for each of the segments of the population.

“10. The system of claim 1 wherein: said one or more non-transitory electronic storage devices comprise additional software instructions, which when executed, configure the one or more processors to: use a plurality of different ones of the characteristics to segment the population; use a plurality of different ones of the characteristics to segment a test set of data; determine which of said plurality of different characteristics provides a highest level of accuracy relative to the test set of data; and select the highest accuracy one of the plurality of different characteristics to segment the population.

“11. The system of claim 1 wherein: each of said different models comprise at least one of: a neural network, logistic regression, and decision tree.

“12. The system of claim 11 wherein: at least one of said different models comprise an ensemble model.

“13. The system of claim 1 wherein: said one or more non-transitory electronic storage devices comprise additional software instructions, which when executed, configure the one or more processors to generate a diabetes complication score for each of said members of said population; said medical data comprises standardized codes for various diagnoses; said diabetes complication score is generated by applying a weight to each of said standardized codes associated with any one of: cardiovascular, cerebrovascular, metabolic, nephropathy, neuropathy, peripheral vascular disease, and retinopathy conditions; categorize each of said members of said population into a high, medium, or low complications risk category based on said diabetes complication score; and generate a graphical display with said categorized members.

“14. The system of claim 13 wherein: said weight comprises a one or a two for each of said conditions, except for neuropathy which is weighted a one; each of said members having said diabetes complication score ranging from 0-3 are assigned into said low complications risk category; each of said members having said diabetes complication score ranging from 4-7 are assigned into said medium complications risk category; and each of said members having said diabetes complication score ranging from 8-13 are assigned into said high complications risk category.

“15. The system of claim 13 wherein: said one or more non-transitory electronic storage devices comprise additional software instructions, which when executed, configure the one or more processors to automatically assign at least one intervention to at least each of said members categorized into said high complications risk category, assign at least one different intervention to at least each of said members categorized into said medium complications risk category, and assign at least one different intervention to at least each of said members categorized into said low complications risk category.

“16. The system of claim 15 wherein: said one or more non-transitory electronic storage devices comprise additional software instructions, which when executed, configure the one or more processors to automatically schedule a visit by a healthcare provider to at least each of said members categorized into said high complications risk category, and automatically schedule a call by a healthcare provider to at least each of said members categorized into said medium complications risk category

“17. A system for predicting the progression of diabetes complications in a population using population segment specific modeling, said system comprising: databases comprising medical data for a plurality of members of a population, said medical data comprising standardizes codes for various diagnoses; one or more non-transitory electronic storage devices comprising software instructions, which when executed, configure the one or more processors to: retrieve said medical data for the members of the population from the databases; perform a feature extraction subroutine on said retrieved medical data to extract characteristics for said members of the population; segment the population into a plurality of segments by at least some of the characteristics; apply a different model to each of the plurality of segments to score each of said members; generate a scored member list comprising identifying information for each of the members provided in association with the score for each of the members; generate a diabetes complication score for each of said members by applying a weight to each of said standardized codes associated with any one of: cardiovascular, cerebrovascular, metabolic, nephropathy, neuropathy, peripheral vascular disease, and retinopathy conditions; and categorize each of said members within said scored member list into a high, medium, or low complications risk category based on said diabetes complication score; electronically display the scored member list in a manner indicating categorization of each of said members.

“18. The system of claim 17 wherein: said one or more non-transitory electronic storage devices comprise additional software instructions, which when executed, configure the one or more processors to generate a category progression risk score for each of said members by applying a progression risk model to said medical data.”

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

URL and more information on this patent application, see: Chiguluri, Vinay; Dong, Yanting; Fan, Jing; Gopal, Vipin. Diabetes Onset And Progression Prediction Using A Computerized Model. U.S. Patent Application Number 20230035564, filed October 10, 2022 and posted February 2, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230035564)&db=US-PGPUB&type=ids

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