FIELD OF THE DISCLOSURE

The present disclosure relates to a method for generating a diagnosis model capable of diagnosing multi-cancer according to stratification-related values of patients by using biomarker group-related value information, a method for diagnosing multi-cancer by using the diagnosis model and a device using the same; and more particularly, to the method for (i) stratifying the information on the patients that affects diagnosis of cancer and (ii) generating the diagnosis model capable of diagnosing cancer of the patients for each of strata, the method for diagnosing multi-cancer by using the diagnosis model and the device using the same.

BACKGROUND OF THE DISCLOSURE

Tumor metastasis represents that some portion of a tumor is detached from a part of a body of patient and moves to other parts of the body via blood, which is an important cause of cancer-related death. A general way of determining tumor status is a biopsy which detaches and examines a part of a tissue in an early stage of metastasis. It is, however, not easy to determine a part of the body from which the tissue is removed. As an alternative way, a liquid biopsy attracting attention in recent years can detect tumor cells in a biological sample such as blood, urine, etc. derived from the body of patient. According to the liquid biopsy, a cancer in the early stage can be detected and diagnosed and additionally the progression of cancer and its corresponding cure can be monitored.

A biomarker is a measurable indicator of some biological state or condition. Biomarkers are often measured and evaluated using blood, urine, or soft tissues to examine normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. The biomarkers can detect changes in the body by using proteins, nucleic acids, and metabolites contained in biological samples.

However, since there is a limit in diagnosing cancers with a biomarker, a cancer diagnosing method using complex biomarkers with an improvement in diagnostic sensitivity and specificity is currently used in this field.

However, in case of predicting a certain cancer by using such complex biomarkers, a specific biomarker included in the complex biomarkers may not only represent an indicator for the certain cancer but also represent another indicator for another cancer. Thus, the certain cancer may be wrongly predicted by using the complex biomarkers.

As an example, it may indicate the adenocarcinoma as a result of prediction by using biomarker group-related value information derived from the complex biomarkers, although a patient's cancer is actually squamous cell carcinoma.

Also, in predicting cancers by using the biomarker group-related value information of the complex biomarkers, there is a possibility of acquiring a different result according to two or more judge.

Thus, various methodologies have been suggested to classify cancers by using the biomarker group-related value information of the complex biomarkers.

Particularly, variables, such as gender, age, etc., which have large influence on physiological features of patients have to be considered in training of a diagnosis model using the biomarker group-related value information.

For this, recently, a method of (i) stratifying data of the patients by using information of the patients and (ii) training different classifying model for each of strata by using the information of the patient for each of the strata.

However, in a stage of training classification model for each of the strata, the method is intuitive in that the method use only stratified data, there is a problem in that reliability is not secured due to large variance of estimated model because the number of pieces of the stratified data is small in case the number of the strata is large.

Accordingly, an applicant of the present disclosure provides a diagnosis model capable of diagnosing cancers of patients, by using the biomarker group-related value information, for each of the strata without a degradation of the reliability caused by increasing of the variance.

SUMMARY OF THE DISCLOSURE

It is an object of the present disclosure to solve all the aforementioned problems.

It is still another object of the present disclosure to provide a diagnosis model capable of diagnosing cancers of patients for each of strata in which each corresponding variance is minimized by using biomarker group-related value information.

It is still yet another object of the present disclosure to recognize types of cancers for each of strata of patients accurately by using the biomarker group-related value information for diagnosing multi-cancer.

It is still yet another object of the present disclosure to recognize types of cancers according to the strata of patients accurately by statistical determination method using the biomarker group-related value information for diagnosing multi-cancer.

It is still yet another object of present the disclosure to improve a reliability of determination of cancer according to the strata of patients by the statistical determination method using the biomarker group-related value information for diagnosing multi-cancer.

In order to accomplish objects above and characteristic effects to be described later of the present disclosure, distinctive configurations of the present disclosure are described as follows.

In accordance with one aspect of the present disclosure, there is provided a method for generating a diagnosis model capable of diagnosing multi-cancer according to stratification-related values of patients by using biomarker group-related value information including steps of: (a) a diagnosis model generating device, in response to acquiring n pieces of training data including (i) the stratification-related values corresponding to K stratification-related variables, (ii) the biomarker group-related value information and (iii) its corresponding Ground Truth cancer information for each of the patients, setting initial prediction values of a base classification model capable of classifying cancers for each of the stratification-related values by using the biomarker group-related value information, wherein the stratification-related values are generated by at least two stratification-related variables among the K stratification-related variables and wherein n and K are integers of 1 or more; and (b) the diagnosis model generating device generating a first decision tree to a T-th decision tree by using its corresponding previous classification model, wherein the previous classification model has been updated by its corresponding previous iteration via a first iteration to a T-th iteration, and wherein the previous classification model represents the base classification model at the first iteration, to thereby generate the diagnosis model, wherein, during a t-th iteration at the step of (b) wherein t is an integer of 1 or more and T or less, the diagnosis model generating device performs processes of: (i) inputting the n pieces of training data into a (t-1)-th classification model which is the previous classification model at the t-th iteration and instructing the (t-1)-th classification model to output (t-1)-th cancer-predicting values which are results of classifying cancers for each of the n pieces of training data, (ii) generating each of total residuals corresponding to the n pieces of training data by referring to the Ground Truth cancer information for each of the patients and to the (t-1)-th cancer-predicting values, (iii) generating a t-th decision tree that maximizes a correlation with the total residuals and (iv) updating the (t-1)-th classification model by using the t-th decision tree, to thereby generate a t-th classification model.

As one example, wherein the base classification model includes (i) a first classification model, which is a total classification model, capable of classifying cancers by using all pieces of the biomarker group-related value information, (ii) a second classification model, which is a stratification-related variable classification model, capable of classifying cancers by using the biomarker group-related value information for each of the K stratification-related variables and (iii) a third classification model, which is a stratification-related value classification model, capable of classifying cancers by using the biomarker group-related value information for each of the stratification-related values.

As one example, wherein, at the step of (b), the diagnosis model generating device (i) makes a (t-1)-th total decision tree branch off from a (t-1)-th total classification model by using the total residuals corresponding to the n pieces of training data and a preset first normalization parameter such that a total correlation generated by referring to the total residuals and the first normalization parameter is maximized, (ii) makes (t-1)-th stratification-related variable decision trees branch off from a (t-1)-th stratification-related variable classification model by using the total residuals and a preset second normalization parameter such that each of stratification-related variable correlations generated by referring to stratification-related variable residuals corresponding to each of the stratification-related variables and the second normalization parameter is maximized, (iii) makes (t-1)-th stratification-related value decision trees branch off from a (t-1)-th stratification-related value classification model by using each of the total residuals corresponding to the n pieces of training data and a preset third normalization parameter such that each of stratification-related value correlations generated by referring to stratification-related value residuals corresponding to each of the stratification-related values and the third normalization parameter is maximized and (iv) generate a certain decision tree, that has the largest correlation among the (t-1)-th total decision tree, the (t-1)-th stratification-related variable decision trees and the (t-1)-th stratification-related value decision trees, as the t-th decision tree.

As one example, wherein, at the step of (b), the diagnosis model generating device updates the (t-1)-th classification model by applying at least one preset weight whose range is greater than zero and less than one to the t-th decision tree.

As one example, wherein the diagnosis model generating device applies the weight to a t-th decision tree classification model corresponding to the t-th decision tree and generates the t-th classification model by taking a sum of (i) the t-th decision tree classification model to which the weight has been applied and (ii) the (t-1)-th classification model.

As one example, wherein, at the step of (b), the diagnosis model generating device (i) generates losses by using the Ground Truth cancer information and the (t-1)-th cancer-predicting values and (ii) generates the total residuals by differentiating the losses.

In accordance with another aspect of the present invention, there is provided a method for generating a diagnosis model capable of diagnosing multi-cancer according to stratification-related values of patients by using biomarker group-related value information including steps of: (a) on the condition that (1) a diagnosis model generating device, in response to acquiring n pieces of training data including (i) the stratification-related values corresponding to K stratification-related variables, (ii) the biomarker group-related value information and (iii) its corresponding Ground Truth cancer information for each of the patients, has set initial prediction values of a base classification model capable of classifying cancers for each of the stratification-related values by using the biomarker group-related value information, wherein the stratification-related values are generated by at least two stratification-related variables among the K stratification-related variables and wherein n and K are integers of 1 or more; and (2) the diagnosis model generating device has generated a first decision tree to a T-th decision tree by using its corresponding previous classification model, wherein the previous classification model has been updated by its corresponding previous iteration via a first iteration to a T-th iteration, and wherein the previous classification model represents the base classification model at the first iteration, to thereby generate the diagnosis model, wherein, during a t-th iteration at the (2) wherein t is an integer of 1 or more and T or less, the diagnosis model generating device has performed processes of: (i) inputting the n pieces of training data into a (t-1)-th classification model which is the previous classification model at the t-th iteration and instructing the (t-1)-th classification model to output (t-1)-th cancer-predicting values which are results of classifying cancers for each of the n pieces of training data, (ii) generating each of total residuals corresponding to the n pieces of training data by referring to the Ground Truth cancer information for each of the patients and to the (t-1)-th cancer-predicting values, (iii) generating a t-th decision tree that maximizes a correlation with the total residuals and (iv) updating the (t-1)-th classification model by using the t-th decision tree, to thereby generate a t-th classification model, a multi-cancer diagnosis device acquiring certain stratification-related values and certain biomarker group-related value information of a certain patient; and (b) the multi-cancer diagnosis device (i) inputting the certain stratification-related values and the certain biomarker group-related value information into the diagnosis model and (ii) instructing the diagnosis model to output certain cancer information which is a result of classifying cancers based on the certain biomarker group-related value information via a certain classification model corresponding to the certain stratification-related values of the certain patient.

As one example, wherein the base classification model includes (i) a first classification model, which is a total classification model, classifying cancers by using all pieces of the biomarker group-related value information, (ii) a second classification model, which is a stratification-related variable classification model, capable of classifying cancers by using the biomarker group-related value information for each of the K stratification-related variables and (iii) a third classification model, which is a stratification-related value classification model, capable of classifying cancers by using the biomarker group-related value information for each of the stratification-related values.

As one example, wherein, at the step of (a), by the diagnosis model generating device, (i) a (t-1)-th total decision tree has been made to branch off from a (t-1)-th total classification model by using the total residuals corresponding to the n pieces of training data and a preset first normalization parameter such that a total correlation generated by referring to the total residuals and the first normalization parameter is maximized, (ii) (t-1)-th stratification-related variable decision trees have been made to branch off from a (t-1)-th stratification-related variable classification model by using the total residuals and a preset second normalization parameter such that each of stratification-related variable correlations generated by referring to stratification-related variable residuals corresponding to each of the stratification-related variables and the second normalization parameter is maximized, (iii) (t-1)-th stratification-related value decision trees have been made to branch off from a (t-1)-th stratification-related value classification model by using each of the total residuals corresponding to the n pieces of training data and a preset third normalization parameter such that each of stratification-related value correlations generated by referring to stratification-related value residuals and the third normalization parameter is maximized and (iv) a certain decision tree has been generated such that the certain decision tree has the largest correlation among the (t-1)-th total decision tree, the (t-1)-th stratification-related variable decision trees and the (t-1)-th stratification-related value decision trees, as the t-th decision tree.

As one example, wherein, at the step of (a), by the diagnosis model generating device, the (t-1)-th classification model has been updated by applying at least one preset weight whose range is greater than zero and less than one to the t-th decision tree.

In accordance with still another aspect of the present invention, there is provided a diagnosis model generating device capable of diagnosing multi-cancer according to stratification-related values of patients by using biomarker group-related value information including: one or more memories that stores instructions; and at least one processor configured to execute the instructions to perform processes of (I) in response to acquiring n pieces of training data including (i) the stratification-related values corresponding to K stratification-related variables, (ii) the biomarker group-related value information and (iii) its corresponding Ground Truth cancer information for each of the patients, setting initial prediction values of a base classification model capable of classifying cancers for each of the stratification-related values by using the biomarker group-related value information, wherein the stratification-related values are generated by at least two stratification-related variables among the K stratification-related variables and wherein n and K are integers of 1 or more; and (II) generating a first decision tree to a T-th decision tree by using its corresponding previous classification model, wherein the previous classification model has been updated by its corresponding previous iteration via a first iteration to a T-th iteration, and wherein the previous classification model represents the base classification model at the first iteration, to thereby generate the diagnosis model, wherein, during a t-th iteration at the process of (II) wherein t is an integer of 1 or more and T or less, the processor performs processes of: (i) inputting the n pieces of training data into a (t-1)-th classification model which is the previous classification model at the t-th iteration and instructing the (t-1)-th classification model to output (t-1)-th cancer-predicting values which are results of classifying cancers for each of the n pieces of training data, (ii) generating each of total residuals corresponding to the n pieces of training data by referring to the Ground Truth cancer information for each of the patients and to the (t-1)-th cancer-predicting values, (iii) generating a t-th decision tree that maximizes a correlation with the total residuals and (iv) updating the (t-1)-th classification model by using the t-th decision tree, to thereby generate a t-th classification model.

As one example, the base classification model includes (i) a first classification model, which is a total classification model, capable of classifying cancers by using all pieces of the biomarker group-related value information, (ii) a second classification model, which is a stratification-related variable classification model, capable of classifying cancers by using the biomarker group-related value information for each of the K stratification-related variables and (iii) a third classification model, which is a stratification-related value classification model, capable of classifying cancers by using the biomarker group-related value information for each of the stratification-related values.

As one example, at the process of (II), the processor (i) makes a (t-1)-th total decision tree branch off from a (t-1)-th total classification model by using the total residuals corresponding to the n pieces of training data and a preset first normalization parameter such that a total correlation generated by referring to the total residuals and the first normalization parameter is maximized, (ii) makes (t-1)-th stratification-related variable decision trees branch off from a (t-1)-th stratification-related variable classification model by using the total residuals and a preset second normalization parameter such that each of stratification-related variable correlations generated by referring to stratification-related variable residuals corresponding to each of the stratification-related variables and the second normalization parameter is maximized, (iii) makes (t-1)-th stratification-related value decision trees branch off from a (t-1)-th stratification-related value classification model by using each of the total residuals corresponding to the n pieces of training data and a preset third normalization parameter such that each of stratification-related value correlations generated by referring to stratification-related value residuals corresponding to each of the stratification-related values and the third normalization parameter is maximized and (iv) generate a certain decision tree, that has the largest correlation among the (t-1)-th total decision tree, the (t-1)-th stratification-related variable decision trees and the (t-1)-th stratification-related value decision trees, as the t-th decision tree.

As one example, at the process of (II), the processor updates the (t-1)-th classification model by applying at least one preset weight whose range is greater than zero and less than one to the t-th decision tree.

As one example ,the processor applies the weight to a t-th decision tree classification model corresponding to the t-th decision tree and generates the t-th classification model by taking a sum of (i) the t-th decision tree classification model to which the weight has been applied and (ii) the (t-1)-th classification model.

As one example, at the process of (II), the processor (i) generates losses by using the Ground Truth cancer information and the (t-1)-th cancer-predicting values and (ii) generates the total residuals by differentiating the losses.

In accordance with still another aspect of the present invention, there is provided a multi-cancer diagnosis device capable of diagnosing multi-cancer according to stratification-related values of patients by using biomarker group-related value information including: one or more memories that stores instructions; and at least one processor configured to execute the instructions to perform processes of (I) on the condition that (1) a diagnosis model generating device, in response to acquiring n pieces of training data including (i) the stratification-related values corresponding to K stratification-related variables, (ii) the biomarker group-related value information and (iii) its corresponding Ground Truth cancer information for each of the patients, has set initial prediction values of a base classification model capable of classifying cancers for each of the stratification-related values by using the biomarker group-related value information, wherein the stratification-related values are generated by at least two stratification-related variables among the K stratification-related variables and wherein n and K are integers of 1 or more; and (2) the diagnosis model generating device has generated a first decision tree to a T-th decision tree by using its corresponding previous classification model, wherein the previous classification model has been updated by its corresponding previous iteration via a first iteration to a T-th iteration, and wherein the previous classification model represents the base classification model at the first iteration, to thereby generate the diagnosis model, wherein, during a t-th iteration at the (2) wherein t is an integer of 1 or more and T or less, the diagnosis model generating device has performed processes of: (i) inputting the n pieces of training data into a (t-1)-th classification model which is the previous classification model at the t-th iteration and instructing the (t-1)-th classification model to output (t-1)-th cancer-predicting values which are results of classifying cancers for each of the n pieces of training data, (ii) generating each of total residuals corresponding to the n pieces of training data by referring to the Ground Truth cancer information for each of the patients and to the (t-1)-th cancer-predicting values, (iii) generating a t-th decision tree that maximizes a correlation with the total residuals and (iv) updating the (t-1)-th classification model by using the t-th decision tree, to thereby generate a t-th classification model, acquiring certain stratification-related values and certain biomarker group-related value information of a certain patient; and (II) (i) inputting the certain stratification-related values and the certain biomarker group-related value information into the diagnosis model and (ii) instructing the diagnosis model to output certain cancer information which is a result of classifying cancers based on the certain biomarker group-related value information via a certain classification model corresponding to the certain stratification-related values of the certain patient.

As one example, the base classification model includes (i) a first classification model, which is a total classification model, classifying cancers by using all pieces of the biomarker group-related value information, (ii) a second classification model, which is a stratification-related variable classification model, capable of classifying cancers by using the biomarker group-related value information for each of the K stratification-related variables and (iii) a third classification model, which is a stratification-related value classification model, capable of classifying cancers by using the biomarker group-related value information for each of the stratification-related values.

As one example, at the process of (I), by the diagnosis model generating device, (i) a (t-1)-th total decision tree has been made to branch off from a (t-1)-th total classification model by using the total residuals corresponding to the n pieces of training data and a preset first normalization parameter such that a total correlation generated by referring to the total residuals and the first normalization parameter is maximized, (ii) (t-1)-th stratification-related variable decision trees have been made to branch off from a (t-1)-th stratification-related variable classification model by using the total residuals and a preset second normalization parameter such that each of stratification-related variable correlations generated by referring to stratification-related variable residuals corresponding to each of the stratification-related variables and the second normalization parameter is maximized, (iii) (t-1)-th stratification-related value decision trees have been made to branch off from a (t-1)-th stratification-related value classification model by using each of the total residuals corresponding to the n pieces of training data and a preset third normalization parameter such that each of stratification-related value correlations generated by referring to stratification-related value residuals and the third normalization parameter is maximized and (iv) a certain decision tree has been generated such that the certain decision tree has the largest correlation among the (t-1)-th total decision tree, the (t-1)-th stratification-related variable decision trees and the (t-1)-th stratification-related value decision trees, as the t-th decision tree.

As one example, at the process of (I), by the diagnosis model generating device, the (t-1)-th classification model has been updated by applying at least one preset weight whose range is greater than zero and less than one to the t-th decision tree.

