System and method for diagnosing liver disease by using data of all levels of intestinal microorganisms

Abstract

The present invention relates to a system and a method for analyzing, by using an artificial intelligence model, intestinal microorganisms extracted from a patient's stool and automatically diagnosing alcoholic liver diseases on the basis of results of the analysis, the system comprising: a data receiving unit for receiving biological classification data of intestinal microorganisms included in the patient's stool; a data selection unit which, by using data of all levels of the biological classification data of the intestinal microorganisms received from the data receiving unit so as to output uniform data result values, selects the intestinal microorganisms having a high correlation with disease diagnosis for each level to configure data samples; a data correction unit for correcting the data samples by artificially generating the number of patients for each disease and intestinal microorganism data similar to the disease of the patient so as to resolve an imbalance of the data samples; and a disease diagnosis unit that configures learning data by using the corrected data samples for each level, learns the learning data to output a result value for each disease of the patient to be diagnosed, and outputs a final diagnosis result for the disease by summing the output result value for each disease by using a decision-making module.

Description

Liver disease diagnosis system and method using overall level data among intestinal microorganisms

[0001] The present invention relates to a system and method for diagnosing liver disease using an artificial intelligence model, and more particularly, to a system and method for automatically diagnosing alcoholic liver disease by using an artificial intelligence model to generate learning data for diagnosing alcoholic liver disease based on a microbiome and learning the generated learning data.

[0002] Humans are a super-organism, a mixture of human cells and genomes, as well as the cells and genetic characteristics of countless microorganisms that coexist with us [Nature 2007;449:804-810]. The human microbiome is the collective of all microbial communities naturally present in the human body. The human microbiota exists in various body parts, including the skin, oral cavity, teeth, genitals, respiratory tract, and gastrointestinal tract, but the gastrointestinal tract harbors the largest number and greatest diversity of microorganisms [Nature 2012;486:215-221].

[0003] The gastrointestinal tract harbors a diverse community of microorganisms, numbering between 4,000 and 10,000 species, of which approximately 1,565 are known to be associated with liver disease. The concentration and diversity of microbial communities (especially bacteria) within the gastrointestinal tract vary considerably by location, with the stomach having the lowest concentration, while the large intestine has the highest concentration.

[0004] Understanding the fundamental structure, dynamics, diversity, and stability of the human microbiome is largely due to large-scale research led by global microbiome consortia. Representative examples include the Human Microbiome Project (HMP), supported by the US National Institutes of Health (NIH), the Metagenomics of the human intestinal tract (Meta-HIT), supported by the European Commission, and the International Human Microbiome Consortium (IHMC), which aims to share data resources through human microbiome research.

[0005] Referring to research results from various institutions, it is known that various microorganisms within the human body influence all functions, including metabolic regulation, digestive function, and various diseases. They also influence genetic mutations due to environmental changes and the process of genetic transmission to the next generation. In particular, various metabolic and immune disorders related to allergies, rhinitis, atopy, obesity, enteritis, heart disease, and liver disease have been reported to be related to the microbiome.

[0006] What we can learn from these reports from various institutions is that the microbiome influences or is correlated with specific diseases. Therefore, if we can identify the microbiome that is related to or influences specific diseases, it will be able to contribute to disease diagnosis and prevention.

[0007] As conventional techniques for diagnosing liver disease, Patent Publication No. 10-2021-0061742 and Patent Publication No. 10-2019-0020581 are known.

[0008] Patent Publication No. 10-2021-0061742 discloses a biomarker composition for predicting the prognosis of chronic liver disease, comprising PODN (podocan), C7 and EPHA3 proteins or a gene encoding them as active ingredients.

[0009] In addition, Patent Publication No. 10-2019-0020581 discloses a composition for predicting or diagnosing liver disease, which comprises an agent for measuring the expression of a nucleic acid sequence encoding growth differentiation factor 15 (GDF15) protein or the activity of the GDF15 protein.

[0010] However, according to the prior art disclosed in Patent Publication No. 10-2021-0061742 and Patent Publication No. 10-2019-0020581, there is a problem that pain and side effects occur due to the use of invasive methods such as removing a portion of a patient's liver tissue to collect a sample for predicting or diagnosing liver disease.

[0011] Therefore, when building a disease diagnosis system using an artificial neural network, if the artificial neural network model is pre-trained using the microbiome (hereinafter referred to as “microorganisms”) that affect the disease, a system that can diagnose the disease more safely and conveniently can be built.

[0012] [Patent Document]

[0013] Republic of Korea Patent Publication No. 10-2021-0061742 (Publication date: May 28, 2021, Title: Biomarker composition for predicting the prognosis of chronic liver disease)

[0014] Republic of Korea Patent Publication No. 10-2019-0020581 (Published on March 4, 2019, Title: Composition for predicting or diagnosing liver disease and method for predicting or diagnosing liver disease using the same)

[0015] The technical task of the present invention is to provide a liver disease diagnosis system that extracts intestinal microorganisms correlated with alcoholic liver disease from the stool of a patient and enables an artificial intelligence model to make a rapid and accurate diagnosis.

[0016] Furthermore, another technical task of the present invention is to provide a liver disease diagnosis method that selects intestinal microorganisms that have a high correlation with disease diagnosis, and generates disease diagnosis data that can automatically diagnose alcoholic liver disease by an artificial intelligence model by artificially generating data to resolve data imbalance.

[0017] In order to solve such technical problems, the present invention provides a liver disease diagnosis system using data of the entire level of intestinal microorganisms, including a data receiving unit that receives biological classification data of intestinal microorganisms contained in the stool of a patient, a data selection unit that selects intestinal microorganisms having a high correlation with disease diagnosis for each level using data of the entire level among the biological classification data of intestinal microorganisms received from the data receiving unit to form a data sample in order to output a uniform data result value, a data correction unit that corrects the data sample by artificially generating data of intestinal microorganisms similar to the number of patients for each disease and the disease of the corresponding patient in order to resolve an imbalance in the data sample, and a disease diagnosis unit that constructs learning data using the corrected data sample for each level, learns the learning data, outputs a disease-specific result value of a patient to be diagnosed, and adds up the output disease-specific result values ​​using a decision-making module to output a final diagnosis result for the disease.

[0018] The above biological classification data is characterized by including the types and expression levels of the intestinal microorganisms contained in the stool of the patient.

[0019] The above data selection unit selects the intestinal microorganisms of each level using the first artificial intelligence model, and repeats this process at least twice to select the intestinal microorganisms that have a high correlation with disease diagnosis.

[0020] The above disease diagnosis unit is characterized in that it diagnoses the disease of the patient to be diagnosed using a second artificial intelligence model that has learned learning data that can infer the disease diagnosis result.

[0021] The above disease is characterized by including at least one of normal, alcoholic fatty liver, alcoholic hepatitis, and alcoholic cirrhosis.

[0022] The above data correction unit is characterized in that it corrects the data sample by artificially generating the number of patients for each disease and intestinal microorganism data similar to the disease of the corresponding patient using upsampling.

[0023] The above decision-making module is characterized by using a soft voting technique to add up the disease-specific result values ​​generated for each level, averaging them, and outputting the final diagnosis result value for the highest result value.

[0024] Meanwhile, a liver disease diagnosis method using data on the overall level of intestinal microorganisms according to another embodiment of the present invention is as follows:

[0025] It is characterized by including a step of receiving bio-classification data of intestinal microorganisms contained in the stool of a patient to be diagnosed, a step of configuring diagnostic data for each level using data of all levels among the received bio-classification data of intestinal microorganisms, a step of analyzing the diagnostic data for each level using a second artificial intelligence model that has learned the learning data, and a step of generating disease-specific result values ​​generated by the analysis, and a step of summing the disease-specific result values ​​using a decision-making module to select the disease with the highest result value as the final diagnosis result value, and outputting the final diagnosis result of the patient to be diagnosed.

[0026] The above diagnostic data is characterized in that it is composed of overall level data having a high correlation to disease diagnosis among the species, genus, family, order, class, and phylum level data of the received intestinal microorganisms in order to output uniform data results.

[0027] The method for generating the final diagnosis result value is characterized in that the disease-specific result values ​​output for each level are added up using the decision-making module, and the average is performed to output the disease with the highest result value as the final diagnosis result value.

[0028] The final diagnosis result of the above-mentioned patient is characterized by outputting at least one of normal, alcoholic fatty liver, alcoholic hepatitis, and alcoholic cirrhosis.

[0029] Specific details of other embodiments are included in the detailed description and drawings.

[0030] According to the technical problem solving means described above, the liver disease diagnosis system and method using data on the entire level of intestinal microorganisms according to the embodiment of the present invention has the following advantages because intestinal microorganisms extracted from a patient's stool can be used as a data sample for an artificial intelligence model.

[0031] First, a liver disease diagnosis system and method that utilizes data at the overall level of intestinal microorganisms can build a system that can automatically diagnose alcoholic fatty liver disease, alcoholic hepatitis, and alcoholic cirrhosis simply by collecting stool from a patient requiring diagnosis.

[0032] Second, the liver disease diagnosis system and method using the entire level of data among the intestinal microorganisms can prevent the risk of overfitting and excessive memory usage during artificial intelligence model learning by selecting intestinal microorganisms that have a high correlation with disease diagnosis, and can facilitate model interpretation by users by optimizing the number of data to the level necessary for model interpretation.

[0033] Third, the liver disease diagnosis system and method that utilizes data at the overall level among the intestinal microorganisms can resolve the data imbalance by artificially generating data for the insufficient number of disease samples, since the number of samples from patients with each disease is insufficient compared to normal patients.

[0034] Fourth, the liver disease diagnosis system and method using the data of the entire level of intestinal microorganisms can predict results that are not biased toward a specific level because the final diagnosis result value is output by combining the result values ​​of each level obtained by analyzing the entire level (species, genus, family, order, class, and phylum) data extracted from the patient's stool.

[0035] Figure 1 illustrates a liver disease diagnosis system using data at the overall level of intestinal microorganisms according to an embodiment of the present invention.

[0036] Figure 2 illustrates the process of diagnosing liver disease using data at the overall level of intestinal microorganisms.

[0037] Figure 3 illustrates the basic process of a liver disease diagnosis system.

[0038] Figure 4 illustrates a full level data process using the full level of biological classification data.

[0039] The following detailed description of the present invention refers to the accompanying drawings, which illustrate specific embodiments in which the present invention may be practiced, in order to clarify the objects, techniques, solutions, and advantages of the present invention. These embodiments are described in sufficient detail to enable those skilled in the art to practice the present invention. Furthermore, throughout the detailed description and claims, the word "comprise" and variations thereof are not intended to exclude other technical features, additions, components, or steps. Other objects, advantages, and features of the present invention will become apparent to those skilled in the art, in part from this description, and in part from practice of the present invention. The examples and drawings below are provided by way of illustration and are not intended to limit the present invention. Moreover, the present invention encompasses all possible combinations of the embodiments set forth herein. It should be understood that the various embodiments of the present invention, while different from one another, are not necessarily mutually exclusive. It should also be understood that the positions or arrangements of individual components within each disclosed embodiment may be varied without departing from the spirit and scope of the present invention. Accordingly, the detailed description set forth below is not intended to be limiting, and the scope of the present invention is defined solely by the appended claims, along with the full scope equivalent to which such claims are entitled, if properly described. Furthermore, unless otherwise indicated herein or clearly contradicted by context, items referred to in the singular encompass the plural unless the context otherwise requires. Furthermore, in describing the present invention, if a detailed description of a related known structure or function is determined to obscure the gist of the present invention, the detailed description will be omitted.

[0040] Figure 1 illustrates a liver disease diagnosis system using data at the overall level of intestinal microorganisms according to an embodiment of the present invention, and Figure 2 illustrates a liver disease diagnosis process using data at the overall level of intestinal microorganisms.

[0041] Before describing FIGS. 1 and 2, the liver disease diagnosis system and method utilizing intestinal microbiome data at the entire level according to an embodiment of the present invention can diagnose alcoholic liver disease using intestinal microbiome data extracted from a patient's stool through Next Generation Sequencing (NGS) testing. For reference, NGS testing extracts data on the biological classification of intestinal microbiomes by decoding and analyzing the genome of a patient's stool.

[0042] Here, biological classification means organizing organisms into species, genus, family, order, class, and phylum in order based on their similarities and biological forms. Hereinafter, data extracted from patient stool through NGS testing is referred to as biological classification data.

[0043] In addition, although the embodiment of the present invention describes a system and method that can automatically diagnose alcoholic liver disease, it is not limited thereto, and a liver disease diagnosis system that uses data on the entire level of intestinal microorganisms can automatically diagnose liver diseases caused by various factors such as alcohol, drugs, viruses, and eating habits.

[0044] Referring to FIGS. 1 and 2, the liver disease diagnosis system (100) includes a data receiving unit (110), a data selection unit (120), a data correction unit (130), a disease diagnosis unit (140), a data storage unit (150), a data output unit (160), and a control unit (170).

[0045] The data receiving unit (110) can receive biological classification data of intestinal microorganisms contained in the patient's stool.

[0046] Biological taxonomy data may include data (Data1, Data2, Data3, ... Datan) obtained by extracting the types and expression levels of intestinal microorganisms from the patient's stool through NGS testing.

[0047] According to an embodiment of the present invention, intestinal microorganisms contained in the stool of a patient include, but are not limited to, at least one biological classification among species, genus, family, order, class, and phylum, and may include at least one biological classification among species, genus, family, order, class, phylum, kingdom, and domain.

[0048] The data selection unit (120) uses data of all levels (species, genus, family, order, class, phylum) among the biological classification data of intestinal microorganisms received from the data receiving unit (110) to output uniform data results, and the first artificial intelligence model can select intestinal microorganisms of all levels of data that have a high correlation with disease diagnosis at each level among the all levels of data to form data samples (Data6, Data9, Data10, Data13, ... Datan).

[0049] According to an embodiment of the present invention, the first artificial intelligence model may be a machine learning model, and the first artificial intelligence model may use various machine learning models such as Logistic Regression, Support Vector Machine, Naive Bayes, Decision Tree, k Nearest Neighbors, and Random Forest.

[0050] The data selection unit (120) can select intestinal microorganisms with a high correlation to disease diagnosis by repeating the selection process of intestinal microorganisms with a high correlation to disease diagnosis at least twice among all level data using the first artificial intelligence model.

[0051] According to an embodiment of the present invention, as can be seen in FIG. 2, the data selection unit (120) can configure a data sample by using the results of the first selection (Data1, Data6, Data8, Data9,... Datan) from the biological classification data (Data1, Data2, Data3, Data4,... Datan) and the results of the second selection (Data6, Data9, Data10, Data13,... Datan).

[0052] In this way, if the step of selecting gut microbes is repeated at least twice, the risk of overfitting and excessive memory usage can be prevented during AI model training, and the amount of data can be reduced to a level optimized for model interpretation, making it easier for users to interpret the model.

[0053] The data correction unit (130) can correct the data sample by artificially generating the number of patients with each disease and intestinal microorganism data similar to the disease of the corresponding patient to resolve the imbalance of the data sample.

[0054] The data correction unit (130) can correct unbalanced data by using an upsampling technique when artificially generating data on the number of patients with each disease and intestinal microorganisms similar to the disease of the corresponding patient.

[0055] According to an embodiment of the present invention, the UPsampling technique is a method of increasing the number of data by expressing that more data is collected than actually collected, but not actually measured.

[0056] For example, when the number of patients by disease is 100 normal, 80 fatty liver, 50 hepatitis, and 70 cirrhosis, the data correction unit (130) artificially creates the number of patients by disease as 100 normal, 100 fatty liver, 100 hepatitis, and 100 cirrhosis to maintain balance.

[0057] For reference, data imbalances can occur because it is more difficult to collect samples from patients with diseases than from normal patients. To address this, the data correction unit (130) artificially generates similar data for each disease being diagnosed.

[0058] In addition, the data correction unit (130) may generate data for insufficient diseases to be diagnosed based on the disease with the largest number of patients, or may artificially reduce or generate data for diseases to be diagnosed so that the data is the same as the average by averaging the total number of patients.

[0059] The disease diagnosis unit (140) learns learning data composed of data samples corrected by the data correction unit (130) and outputs a disease-specific result value for each level, and combines the output result values ​​using the decision-making module (141) to output the final diagnosis result for the disease of the patient to be diagnosed.

[0060] The disease diagnosis unit (140) can diagnose the disease of the patient to be diagnosed using a second artificial intelligence model that has learned learning data that can infer the disease diagnosis results.

[0061] Here, the disease of the patient to be diagnosed includes at least one of normal, alcoholic fatty liver, alcoholic hepatitis, and alcoholic cirrhosis.

[0062] The decision-making module (141) of the disease diagnosis unit (140) can add up the result values ​​generated for each disease at each level using a soft voting technique, average them, and output the final diagnosis result for the disease with the highest result value.

[0063] For reference, soft voting is a method that adds up all decision probabilities, averages them, and selects the value with the highest probability as the final diagnosis result.

[0064] A detailed description of the disease diagnosis unit (140) will be described later in FIGS. 3 and 4.

[0065] According to an embodiment of the present invention, the second artificial intelligence model may be a Boosting-based machine learning model specialized for tabular data, but is not limited thereto, and the second artificial intelligence model may use various boosting-based algorithms such as AdaBoost, GBM (Gradient Boost Machine), XGBoost (Extreme Gradient Boosting), and Light GBM (Light Gradient Boost Machine).

[0066] Additionally, the second artificial intelligence model using a boosting-based algorithm uses a learning method that weights the error data of the iteration to learn the next model and a learning method that trains the data on multiple models and then aggregates the results.

[0067] According to an embodiment of the present invention, the data selection unit (120) uses a first artificial intelligence model, and the disease diagnosis unit (140) uses a second artificial intelligence model, but the first artificial intelligence model and the second artificial intelligence model use different models.

[0068] The data storage unit (150) can store data samples corrected by the data correction unit (130) and diagnosis results generated from the disease diagnosis unit (140).

[0069] The disease diagnosis unit (140) can analyze the entire level data extracted from the stool of the patient to be diagnosed and use the data stored in the data storage unit (150) to make a diagnosis.

[0070] The data output unit (160) can output the disease of the patient to be diagnosed predicted from the disease diagnosis unit (140) to an output device such as a monitor and provide it to the user.

[0071] According to an embodiment of the present invention, the data output unit (160) can provide the user with the numerical values ​​and expression levels of alcoholic liver disease and intestinal microorganisms output as prediction results.

[0072] The control unit (170) generally corresponds to a server of a computer system.

[0073] The control unit (170) performs overall control of the data receiving unit (110), data selection unit (120), data correction unit (130), disease diagnosis unit (140), data storage unit (150), and data output unit (160).

[0074] Figure 3 illustrates the basic process of a liver disease diagnosis system.

[0075] Referring to FIG. 3, the liver disease diagnosis system can diagnose a disease of a patient to be diagnosed by analyzing the patient's biological classification data as follows.

[0076] A step is performed in which the data receiving unit (110) receives the biological classification data of intestinal microorganisms contained in the patient's stool (S100).

[0077] Using the biological classification data of intestinal microorganisms received from the data receiving unit (110), the first artificial intelligence model of the data selection unit (120) selects intestinal microorganisms with a high correlation to disease diagnosis and proceeds with the step of composing a data sample (S110).

[0078] At this time, the data selection unit (120) repeats the process of selecting intestinal microorganisms with a high correlation to disease diagnosis at least twice using the first artificial intelligence model to select the intestinal microorganisms with a high correlation to disease diagnosis and proceeds with the step of composing a data sample (S120).

[0079] To resolve the imbalance of the data sample, the data correction unit (130) performs a step of correcting the data sample by artificially generating the number of patients for each disease and intestinal microorganism data similar to the disease of the corresponding patient (S130).

[0080] Learning data capable of inferring disease diagnosis results is constructed using data samples corrected from the data correction unit (130), and the second artificial intelligence model of the disease diagnosis unit (140) learns the learning data for disease diagnosis (S140).

[0081] The second artificial intelligence model of the disease diagnosis unit (140) diagnoses the disease of the patient to be diagnosed using the learning data in the following manner (S150).

[0082] The data receiving unit (110) proceeds with the step of receiving biological classification data of intestinal microorganisms contained in the stool of the patient to be diagnosed.

[0083] The disease diagnosis unit (140) proceeds with the step of constructing diagnostic data using the received intestinal microorganism classification data.

[0084] The disease diagnosis unit (140) analyzes the diagnostic data using the second artificial intelligence model that learned the learning data, and outputs the disease with the highest result value among the disease-specific result values ​​generated through the analysis as the diagnosis result of the patient to be diagnosed.

[0085] Figure 4 illustrates a full level data process using the full level of biological classification data.

[0086] Referring to Figure 4, the entire level data process can output the final diagnosis result value by summing the result values ​​of each level by analyzing the entire levels of Species, Genus, Family, Order, Class, and Phylum in parallel in the biological classification data.

[0087] The overall level data process proceeds with a step of receiving bio-classification data of intestinal microorganisms contained in the stool of at least one patient from a data receiving unit (110) (S200).

[0088] The basic process of the entire level data among the intestinal microorganism classification data received from the data receiving unit (110) is carried out as follows, but the basic process of the entire level data can be carried out in parallel.

[0089] According to an embodiment of the present invention, the method of performing the basic process by level can be performed in the same manner as the basic process of FIG. 3.

[0090] Therefore, in order to output uniform data results, the first artificial intelligence model of the data selection unit (120) uses the entire level data among the received intestinal microorganism biological classification data to select intestinal microorganisms with a high correlation to disease diagnosis by level, thereby proceeding with a step of composing a data sample.

[0091] At this time, the data selection unit (120) can repeat the process of selecting intestinal microorganisms by level at least twice using the first artificial intelligence model to select intestinal microorganisms that have a high correlation with disease diagnosis.

[0092] Next, in order to resolve the imbalance of the data sample, the data correction unit (130) performs a step of correcting the data sample by artificially generating the number of patients with each disease and intestinal microorganism data similar to the disease of the corresponding patient for each level.

[0093] The disease diagnosis unit (140) constructs learning data that can infer disease diagnosis results using data samples corrected for each level, and proceeds with a step in which the second artificial intelligence model of the disease diagnosis unit (140) learns the learning data for disease diagnosis.

[0094] Next, the data receiving unit (110) proceeds to a step of receiving biological classification data of intestinal microorganisms contained in the stool of the patient to be diagnosed.

[0095] The disease diagnosis unit (140) proceeds with the step of constructing diagnostic data using data at the entire level among the received intestinal microorganism classification data.

[0096] According to an embodiment of the present invention, diagnostic data may be composed of overall level data having a high correlation to disease diagnosis among the species, genus, family, order, class, and phylum level data of received intestinal microorganisms to output uniform data results.

[0097] The disease diagnosis unit (140) analyzes diagnostic data by level using a second artificial intelligence model that has learned the learning data, generates disease-specific result values ​​generated through the analysis, and completes the basic process that is performed in parallel for each level (S210).

[0098] The decision-making module (141) of the disease diagnosis unit (140) adds up the disease-specific result values ​​generated through the basic process for each level to generate a final diagnosis result value (S220).

[0099] A step is performed to output the disease with the highest average as the final diagnosis result of the patient to be diagnosed based on the final diagnosis result value (S230).

[0100] As the final diagnosis result of the patient diagnosed by the disease diagnosis unit (140), at least one of normal, alcoholic fatty liver, alcoholic hepatitis, and alcoholic cirrhosis can be output.

[0101] According to an embodiment of the present invention, the method for generating a final diagnosis result value adds all the disease-specific result values ​​output for each level using a decision-making module (141), averages them, and outputs the disease with the highest result value as the final diagnosis result value.

[0102] Details on how to generate the final diagnosis result values ​​will be provided later using the table below.

[0103] According to an embodiment of the present invention, as shown in the table below, the entire level data process provides disease-specific result values ​​for normal, alcoholic fatty liver, alcoholic hepatitis, and alcoholic cirrhosis for each level.

[0104] LevelNormalAlcoholicFattyLivesAlcoholicHepatitisAlcoholicCirrhosisTotalSpecies10%20%30%40%100%Genus5%20%30%45%100%Family15%20%30%35%100%Order5%20%40%35%100%Class15%30%20%35%100%Phylum10%10%30%50%100%FinalDiagnosisResultValue10%20%30%40%100%

[0105] Next, by adding the disease-specific result values ​​generated for each level and averaging them, a final diagnosis result value is generated for each disease, and among the final diagnosis result values, alcoholic cirrhosis, which accounts for the highest 40%, is output as the final diagnosis result, and the final diagnosis result can be provided to the user through the data output unit (160).

[0106] Here, the sum of normal, alcoholic fatty liver, alcoholic hepatitis, and alcoholic cirrhosis is 100%.

[0107] For reference, the liver disease diagnosis process using data at the entire level of intestinal microorganisms proceeds with the process at each level, and combines the result values ​​of each level through the decision-making module (141) to output the final diagnosis result value, so that an unbiased and uniform result can be predicted.

[0108] Although the present invention has been described with reference to the embodiments illustrated in the drawings, these are merely exemplary, and those skilled in the art will understand that various modifications and equivalent other embodiments are possible from the embodiments of the present invention. For example, the embodiments of the present invention have been described assuming a system for diagnosing liver disease using data on the overall level of intestinal microorganisms, but the present invention can be equally applied to all cases where disease can be diagnosed by analyzing biological classification data of intestinal microorganisms without any particular modification. Therefore, the true technical protection scope of the present invention should be defined solely by the appended claims.

[0109] [Explanation of symbols]

[0110] 100: Liver disease diagnosis system using data from the entire intestinal microbiome

[0111] 110: Data receiving unit

[0112] 120: Data Selection Department

[0113] 130: Data Correction Department

[0114] 140: Disease Diagnosis Department

[0115] 141: Decision-making module

[0116] 150: Data storage

[0117] 160: Data output section

[0118] 170: Control unit

Claims

1. A data receiving unit for receiving data on the biological classification of intestinal microorganisms contained in the patient's stool; A data selection unit that selects intestinal microorganisms having a high correlation to disease diagnosis at each level by using data of the entire level of the intestinal microorganism classification data received from the data receiving unit to form a data sample in order to output uniform data results; A data correction unit that corrects the data sample by artificially generating the number of patients by disease and intestinal microorganism data similar to the disease of the corresponding patient to resolve the imbalance of the data sample; and A liver disease diagnosis system using data of all levels of intestinal microorganisms, characterized by including a disease diagnosis unit that configures learning data using the above-mentioned corrected data sample for each level, learns the learning data to output disease-specific result values ​​of a patient to be diagnosed, and outputs the final diagnosis result for the disease by adding the output disease-specific result values ​​using a decision-making module.

2. In paragraph 1, A liver disease diagnosis system using data at the overall level of intestinal microorganisms, characterized in that the above biological classification data includes the types and expression levels of intestinal microorganisms contained in the stool of the patient.

3. In paragraph 1, The above data selection unit selects the intestinal microorganisms of each level using the first artificial intelligence model, and repeats this process at least twice to select the intestinal microorganisms that have a high correlation with disease diagnosis. A liver disease diagnosis system using data at the entire level of intestinal microorganisms, characterized in that the disease diagnosis unit diagnoses the disease of the patient to be diagnosed using a second artificial intelligence model that has learned learning data capable of inferring the disease diagnosis result.

4. In paragraph 1, A liver disease diagnosis system using data on the overall level of intestinal microflora, characterized in that the above disease includes at least one of normal, alcoholic fatty liver, alcoholic hepatitis, and alcoholic cirrhosis.

5. In paragraph 1, A liver disease diagnosis system using data at the entire level of intestinal microorganisms, characterized in that the above data correction unit uses upsampling to artificially generate data on the number of patients by disease and intestinal microorganisms similar to the disease of the corresponding patient to correct the data sample.

6. In paragraph 1, The above decision-making module is a liver disease diagnosis system that uses data from all levels of intestinal microorganisms, characterized in that it adds up the disease-specific result values ​​generated for each level using a soft voting technique, averages them, and outputs the final diagnosis result value for the highest result value.

7. A step of receiving biological classification data of intestinal microorganisms contained in the stool of a patient to be diagnosed; A step of configuring diagnostic data for each level using data of the entire level among the received intestinal microorganism classification data; A step of analyzing the diagnostic data by level using a second artificial intelligence model that has learned the above learning data, and generating a disease-specific result value generated through the analysis; and A method for diagnosing liver disease using data at the entire level of intestinal microorganisms, characterized by including a step of adding up the result values ​​for each disease using a decision-making module, selecting the disease with the highest result value as the final diagnosis result value, and outputting it as the final diagnosis result of the patient to be diagnosed.

8. In paragraph 7, The above diagnostic data is, A liver disease diagnosis method using data at the entire level of intestinal microorganisms, characterized in that the data at the species, genus, family, order, class, and phylum levels of the received intestinal microorganisms are composed of data at the entire level that has a high correlation to disease diagnosis in order to output uniform data results.

9. In paragraph 7, The method for generating the final diagnosis result value above is: A liver disease diagnosis method using data of the entire level of intestinal microorganisms, characterized in that the disease-specific result values ​​output at each level are added up using the above decision-making module, and the average is performed to output the disease with the highest result value as the final diagnosis result value.

10. In paragraph 7, A liver disease diagnosis method using data on the entire level of intestinal microorganisms, characterized in that at least one of normal, alcoholic fatty liver, alcoholic hepatitis, and alcoholic cirrhosis is output as the final diagnosis result of the above-mentioned patient.

Images