Researchers Submit Patent Application, “Method And Apparatus For Visualizing Health Status Information By Using Health Space Model”, for Approval (USPTO 20230030787): Patent Application

2023 FEB 20 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Washington, D.C., NewsRx journalists report that a patent application by the inventors KIM, Yun Soo (Suwon-si, KR); KWON, O Ran (Seoul, KR); LEE, Chan Hee (Seoul, KR); LEE, Eun Ok (Seoul, KR); PARK, Cheol Gyun (Seoul, KR); PARK, Tae Sung (Seoul, KR), filed on August 3, 2022, was made available online on February 2, 2023.

No assignee for this patent application has been made.

News editors obtained the following quote from the background information supplied by the inventors: “

“The present disclosure relates to an apparatus and a method for visualizing health status information using a health space model.

“Recently, interest in health information of each individual has increased, and interest in technology for collecting health status information and processing and visualizing the information by using a statistical technology has increased.

“According to the conventionally known statistical model-based health status visualization technology (Bouwman, Jildau, et al. “Visualization and identification of health space, based on personalized molecular phenotype and treatment response to relevant underlying biological processes.” BMC medical genomics 5.1 (2012): 1.), an individual’s health status may be expressed as a vector of a two-dimensional space or a three-dimensional space. Therefore, by using this, a change in health status of a group and a current health status of an individual may be objectively expressed. The methodology used in this study is called a health space, and the health space may be designed in various statistical models. Therefore, in order to provide the most effective health space, it is an important issue to select one of various statistical models well.

“On the other hand, as a technology develops, biometric information that may be collected from each individual has become very diverse, and a health status of each individual may be analyzed in various aspects by using the various biometric information collected in this way. However, it is difficult for an individual to understand all such biometric information, and thus, it is very important to integrate/summarize biometric information indicating various health conditions and deliver the biometric information to the individual. In order to solve the problem, various machine learning methods have been developed, and the present inventor has developed a methodology that may accurately and quantitatively measure an individual’s health status by using an advanced statistical methodology.

“In the present disclosure, a health space model is constructed by using a deep learning model considering ordinal data, and through this, a health status of each individual is more accurately expressed compared to other technologies.

“An example of related art includes Korea Patent Publication No. 10-2254481 (Title of the invention: METHOD FOR ESTIMATING MENTAL HEALTH AND PROVIDING SOLUTION FOR MENTAL HEALTH BY LEARNING PSYCHOLOGICAL DATA AND PHYSICAL DATA BASED ON MACHINE LEARNING AND MENTAL HEALTH ESTIMATING DEVICE USING THE SAME)”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “In order to solve the problems of the related art described above, the present disclosure provides a visualization apparatus and a visualization method that may visualize health status information of each individual by using an ordinal regression deep neural network model.

“However, the technical objects to be achieved by the present embodiments are not limited to the technical object described above, and there may be other technical tasks.

“According to one aspect of the present disclosure, an apparatus for visualizing a health status information of each individual by using a health space model includes a memory storing a health status information visualization program, and a processor configured to execute the health status information visualization program stored in the memory. The health status information visualization program inputs multidimensional data on the health status of each individual to the health space model to visually display a position of each individual in a two-dimensional health space, the health space model includes a first ordinal regression deep neural network model for outputting a first health status value based on multidimensional data of a first group and a second ordinal regression deep neural network model for outputting a second health status value based on multidimensional data of a second group, and the health status information visualization program displays the health status information of each individual in a two-dimensional health space by causing the first health status value to correspond to a first axis and the second health status value to correspond to a second axis.

“According to another aspect of the present disclosure, a method of visualizing health status information by using an apparatus for visualizing health status information includes receiving multi-dimensional data on a health status of a target person, inputting the received multidimensional data on the health status to a health space model, outputting a first health status value as an output for multidimensional data of a first group by a first ordinal regression deep neural network model included in the health space model, and a second health status value as an output for multidimensional data of a second group by a second ordinal regression deep neural network model, and displaying the first health status value corresponding to a first axis and the second health status value corresponding to a second axis.”

The claims supplied by the inventors are:

“1. An apparatus for visualizing a health status information of each individual by using a health space model, the apparatus comprising: a memory storing a health status information visualization program; and a processor configured to execute the health status information visualization program stored in the memory, wherein the health status information visualization program inputs multidimensional data on the health status of each individual to the health space model to visually display a position of each individual in a two-dimensional health space, the health space model includes a first ordinal regression deep neural network model for outputting a first health status value based on multidimensional data of a first group and a second ordinal regression deep neural network model for outputting a second health status value based on multidimensional data of a second group, and the health status information visualization program displays the health status information of each individual in a two-dimensional health space by causing the first health status value to correspond to a first axis and the second health status value to correspond to a second axis.

“2. The apparatus of claim 1, wherein the health space model further includes a third ordinal regression deep neural network model configured to output a third health status value based on multidimensional data of a third group, and the health space model visually displays the position of each individual in a three-dimensional space based on the first to third health status values.

“3. The apparatus of claim 1, wherein the multidimensional data of the first group includes age, gender smoking status, white blood cell count, and glutamic pyruvic transaminase (GPT) data of a person, which are used to measure oxidative stress, the first ordinal regression deep neural network model outputs the first health status value indicating oxidative stress of the person, the multidimensional data of the second group includes gender, body mass index (BMI), triglyceride level, high-density lipoprotein cholesterol index, and blood sugar level data of the person, which are used to measure metabolic stress, and the second ordinal regression deep neural network model outputs the second health status value indicating metabolic stress of the person.

“4. The apparatus of claim 1, wherein the first ordinal regression deep neural network model includes a deep neural network configured to be trained based on the multidimensional data of the first group for each individual and label values indicating the health status of each individual, and a classifier configured to divide the health status of each individual into k pieces (k is a plural natural number) based on the first health status value converted into a scalar value by multiplying an output of the deep neural network by a vector indicating a sharing coefficient, the classifier classifies health statuses according to an ordinal regression analysis technique, the second ordinal regression deep neural network model includes a deep neural network configured to be trained based on the multidimensional data of the second group for each individual and the label values indicating the health status of each individual, and a classifier configured to divide the health status of each individual into k pieces (k is a plural natural number) based on the second health status value converted into a scalar value by multiplying an output of the deep neural network by a vector indicating a sharing coefficient, and the classifier classifies health statuses according to the order regression analysis technique.

“5. The apparatus of claim 4, wherein the classifier divides the health status into the k pieces based on k-1 values obtained by adding k-1 different intercept values to the values converted into the scalar value.

“6. A method of visualizing health status information by using an apparatus for visualizing health status information, the method comprising: receiving multi-dimensional data on a health status of a target person; inputting the received multidimensional data on the health status to a health space model; outputting a first health status value as an output for multidimensional data of a first group by a first ordinal regression deep neural network model included in the health space model, and a second health status value as an output for multidimensional data of a second group by a second ordinal regression deep neural network model; and displaying the first health status value corresponding to a first axis and the second health status value corresponding to a second axis.

“7. The method of claim 6, wherein the outputting further includes outputting a third health status value as an output for multidimensional data of a third group by a third ordinal regression deep neural network model included in the health space model, and the displaying includes displaying the third health status value corresponding to a third axis.

“8. The method of claim 6, wherein the multidimensional data of the first group includes age, gender, smoking status, white blood cell count, and glutamic pyruvictransaminase (GPT) data of a person, which are used to measure oxidative stress, the first ordinal regression deep neural network model outputs the first health status value indicating the oxidative stress of the person, the multidimensional data of the second group includes gender, body mass index (BMI), triglyceride level, high-density lipoprotein cholesterol index, and blood sugar level data of the person, which are used to measure metabolic stress, and the second ordinal regression deep neural network model outputs the second health status value indicating the metabolic stress of the person.

“9. The method of claim 6, wherein the first ordinal regression deep neural network model includes a deep neural network configured to be trained based on the multidimensional data of the first group for each individual and label values indicating the health status of each individual, and a classifier configured to divide the health status of each individual into k pieces (k is a plural natural number) based on the first health status value converted into a scalar value by multiplying an output of the deep neural network by a vector indicating a sharing coefficient, the classifier classifies health statuses according to an ordinal regression analysis technique, the second ordinal regression deep neural network model includes a deep neural network configured to be trained based on the multidimensional data of the second group for each individual and the label values indicating the health status of each individual, and a classifier configured to divide the health status of each individual into k pieces (k is a plural natural number) based on the second health status value converted into a scalar value by multiplying an output of the deep neural network by a vector indicating a sharing coefficient, and the classifier classifies health statuses according to the order regression analysis technique.

“10. The method of claim 9, wherein the classifier divides the health status into the k pieces based on k-1 values obtained by adding k-1 different intercept values to the values converted into the scalar value.”

For additional information on this patent application, see: KIM, Yun Soo; KWON, O Ran; LEE, Chan Hee; LEE, Eun Ok; PARK, Cheol Gyun; PARK, Tae Sung. Method And Apparatus For Visualizing Health Status Information By Using Health Space Model. U.S. Patent Application Number 20230030787, filed August 3, 2022 and posted February 2, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230030787)&db=US-PGPUB&type=ids

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