A comprehensive health evaluation method and system based on multiple indicators of body composition metabolism

Abstract

The present invention discloses a comprehensive health evaluation method based on multiple indicators of body component metabolism, including: establishing an integrated model of a basic classification algorithm and a medical knowledge map library; if the body component metabolism indicators are sufficient and the disease classification labels of the medical knowledge map library can fully cover clinical diseases, the integrated model based on the basic classification algorithm predicts the user's disease classification and health status evaluation; conversely, after adaptively adding variables of fixed indicators and user basic information indicators and automatically updating disease classification labels, predict the user's disease classification and health status evaluation; for the fixed indicators adaptively added variables and the corresponding user basic information indicators and the automatically updated disease classification labels, collect corresponding newly added clinical user information, add non-existent entity nodes and relationships, and thus form an update to the medical knowledge map library. Also disclosed are a system, an electronic device, and a computer-readable storage medium.

Description

Technical Field

[0001] The present invention relates to the technical field of body component detection and evaluation, and in particular to a comprehensive health evaluation method and system based on multiple indicators of body component metabolism. Background Art

[0002] In existing technologies, characterization of body components is accomplished through classification tasks based on six body fluid indicators and basic features. However, due to the small number of indicators and the fact that rare diseases are not covered by clinical data, existing classification models may have false positives or omissions. In addition, existing classification models are based on ensemble algorithms or support vector machines (SVMs), which are designed for fixed features and specific labels. In actual work, adding features and label types requires a great deal of business knowledge and relevant experience to determine more suitable features and label types. Furthermore, data is collected based on the needs of professionals, and the model is gradually iterated and optimized to continuously improve. This requires a large amount of work and places extremely high demands on the professional quality of personnel.

[0003] Therefore, there is a need to develop new evaluation methods and systems for multi-index detection of body component metabolism. Summary of the Invention

[0004] The purpose of the present invention is to provide a comprehensive health evaluation method and system based on multiple indicators of body component metabolism, which is used for comprehensive evaluation of diseases based on multiple indicators of body fluids tested by reagents and basic information characteristics of users. In view of the situation that model personnel are unable to obtain effective indicators in the short term due to lack of business knowledge or experience, it is obtained that in the prediction of fixed indicator models, indicators can be adaptively added, and the types of iterative labels can be automatically updated, so as to achieve the technical effect of continuous optimization and continuous improvement of the accuracy of the model; at the same time, it solves the situation that the model personnel are misreporting or missing diseases due to lack of business knowledge or experience. Specifically, through the similarity and threshold setting operations of the medical knowledge graph library, relevant indicators can be obtained in a relatively short time and label optimization and completion operations can be performed. At the same time, the clinical data collected by the indicators can continuously optimize the medical knowledge graph library, forming a dynamic update comprehensive evaluation algorithm and database automatic update function.

[0005] The first aspect of the present invention is to provide a comprehensive health evaluation method based on multiple indicators of body composition metabolism, comprising:

[0006] S1, establishing an integrated model of a basic classification algorithm and a medical knowledge graph library; the medical knowledge graph library includes multiple disease classification labels and corresponding network structures, and the network structure includes indicator nodes and disease nodes;

[0007] S2, if the body component metabolism indicators are sufficient and the disease classification labels of the medical knowledge atlas library can fully cover clinical diseases, predict the user's disease classification and health status evaluation based on the integrated model of the basic classification algorithm; otherwise, execute S3;

[0008] S3, if the body component metabolism indicators are insufficient and / or the disease classification labels of the medical knowledge atlas library cannot fully cover clinical diseases, after adaptively adding variables of the fixed indicators and user basic information indicators and automatically updating the disease classification labels, predict the user's disease classification and health status evaluation based on all the fixed indicators and user basic information indicators after the adaptive addition of the variables and the automatically updated disease classification labels;

[0009] S4, for the fixed indicators adaptively added to the variables and the corresponding user basic information indicators and the automatically updated disease classification labels, collect the corresponding newly added clinical user information, and after analyzing the corresponding newly added clinical user information, determine the entity nodes and relationships that do not exist in the current medical knowledge graph library, and add the non-existent entity nodes and relationships to form an update to the medical knowledge graph library.

[0010] Preferably, the entity items of the medical knowledge graph library include: types of diseases and complications, clinical symptoms, body component metabolic indicators, microbiome, body parts, population, treatment methods and regions; wherein, body component metabolic indicators are fixed indicators, and microbiome, body parts, population, treatment methods and regions are user basic information indicators;

[0011] Preferably, the relational items of the medical knowledge graph library include: symptoms corresponding to diseases and complications, examination items corresponding to body component metabolic indicators, and the involved microbial communities.

[0012] Preferably, the process of establishing the integrated model of the classification algorithm based on S1 includes:

[0013] S11, determining a training data set, wherein the training data set includes a fixed number of training features and a fixed number of labels;

[0014] S12, inputting the data in the training data set into a classification ensemble algorithm for training, thereby establishing an initial ensemble model of the classification algorithm;

[0015] S13, verifying the accuracy and recall of the initial integrated model of the classification algorithm. When the accuracy and recall meet predetermined requirements, an integrated model of the classification algorithm based on the obtained integrated model is obtained; wherein the recall is related to the false alarm rate and the missed alarm rate.

[0016] Preferably, the indicator nodes and disease nodes of the medical knowledge graph library are adaptively added to the training features and labels of the training data set.

[0017] Preferably, the S2 includes:

[0018] S21, obtaining fixed indicators and user basic information indicators; wherein the fixed indicators include multiple indicators of body fluid detection;

[0019] S22, predicting the user's disease classification and health status evaluation based on the fixed indicators and the user's basic information indicators.

[0020] Preferably, the S3 includes:

[0021] S31, obtain initial disease classification based on the integrated model of basic classification algorithms and current body component metabolic indicators;

[0022] S32, respectively calculating the similarity between a plurality of disease classification labels in the medical knowledge graph library and the initial disease classification, and determining all disease classification labels and corresponding network structures whose similarity meets a first preset condition;

[0023] S33, parallelly calculating the first-degree and second-degree relationships of the network structures corresponding to all disease classification labels whose similarities meet the first preset condition;

[0024] S34, screening indicators and disease categories with a correlation degree higher than a second threshold from the first-degree and second-degree relationships, and performing adaptive addition of variables to the fixed indicators and user basic information indicators and automatic updating of the disease classification labels;

[0025] S35, predicting the user's disease classification and health status evaluation based on all fixed indicators and user basic information indicators after the variables are adaptively added and the automatically updated disease classification labels.

[0026] The second aspect of the present invention is to provide a comprehensive health evaluation system based on multiple indicators of body composition metabolism, which is used to implement the method of the first aspect, including:

[0027] A model and knowledge graph library establishment module (101) is used to establish an integrated model of a basic classification algorithm and a medical knowledge graph library; the medical knowledge graph library includes multiple disease classification labels and corresponding network structures, and the network structure includes indicator nodes and disease nodes;

[0028] The first prediction and evaluation module (102) is used to predict the disease classification and health status evaluation of the user based on the integrated model of the basic classification algorithm when the body component metabolism indicators are sufficient and the disease classification labels of the medical knowledge atlas library can fully cover clinical diseases;

[0029] The second prediction and evaluation module (103) is used to predict the user's disease classification and health status evaluation based on all the fixed indicators and user basic information indicators after the variables are adaptively added and the automatically updated disease classification labels after the variables are adaptively added, in case the body component metabolism indicators are insufficient and / or the disease classification labels of the medical knowledge atlas library cannot fully cover clinical diseases;

[0030] The knowledge graph update module (104) collects the corresponding newly added clinical user information for the fixed indicators adaptively added to the variables and the corresponding user basic information indicators and the automatically updated disease classification labels, analyzes the corresponding newly added clinical user information, determines the entity nodes and relationships that do not exist in the current medical knowledge graph library, and adds the non-existent entity nodes and relationships to form an update to the medical knowledge graph library.

[0031] A third aspect of the present invention is to provide an electronic device, comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method described in the first aspect.

[0032] A fourth aspect of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the method described in the first aspect.

[0033] Beneficial effects of the method and system of the present invention:

[0034] The false positives and missed negatives in the prediction results of the integrated model are analyzed, and structures that are extremely similar to the feature and label network structures are found in the medical knowledge graph library. The disease and indicator nodes in the network results are added to the model features, which reduces the time for model personnel to find important features to optimize the model when they lack business knowledge and experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A flow chart of a comprehensive health evaluation method based on multiple indicators of body composition metabolism according to an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of a comprehensive health evaluation method based on multiple indicators of body composition metabolism according to an embodiment of the present invention;

[0038] Figure 3 A flowchart for establishing an integrated model of a basic classification algorithm provided in an embodiment of the present invention;

[0039] Figure 4 A flowchart of a method for predicting a user's disease classification and health status assessment using an integrated model based on the basic classification algorithm, provided in accordance with an embodiment of the present invention, when in vivo metabolic indicators are sufficient and the disease classification labels of the medical knowledge graph library fully cover clinical diseases;

[0040] Figure 5 A flowchart for predicting a user's disease classification and health status evaluation based on all the fixed indicators and user basic information indicators after the adaptive addition of variables and the automatically updated disease classification labels, and the automatically updated disease classification labels, is provided according to an embodiment of the present invention for situations where in vivo component metabolism indicators are insufficient and / or the disease classification labels of the medical knowledge graph library cannot fully cover clinical diseases.

[0041] Figure 6 This is a diagram showing the architecture of a comprehensive health evaluation system based on multiple indicators of body composition metabolism according to an embodiment of the present invention;

[0042] Figure 7 A schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0044] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0045] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances. Example 1

[0046] like Figure 1-2 As shown, this embodiment provides a comprehensive health evaluation method based on multiple indicators of body composition metabolism, including:

[0047] S1, establishing an integrated model of a basic classification algorithm and a medical knowledge graph library; the medical knowledge graph library includes multiple disease classification labels and corresponding network structures, and the network structure includes indicator nodes and disease nodes;

[0048] As a preferred embodiment, the entity items of the medical knowledge graph library include: types of diseases and complications, clinical symptoms, body component metabolic indicators, microbiome, body parts, population, treatment methods and regions; among them, body component metabolic indicators are fixed indicators, and microbiome, body parts, population, treatment methods and regions are user basic information indicators;

[0049] As a preferred embodiment, the relational items of the medical knowledge graph library include: symptoms corresponding to diseases and complications, examination items corresponding to metabolic indicators of body components, and the microbial communities involved.

[0050] like Figure 3 As shown, as a preferred embodiment, the process of establishing the integrated model of the classification algorithm based on S1 includes:

[0051] S11, determining a training data set, wherein the training data set includes a fixed number of training features and a fixed number of labels;

[0052] In this embodiment, the training features are set to 6 fixed indicators (x1, x2, x3, x4, x5 and x6); and 6 user basic information indicators (baseinfo1, baseinfo2, baseinfo3, baseinfo4, baseinfo5 and baseinfo6); and the labels are set to 8, namely disease1, disease2, disease3, disease4, disease5, disease6, disease7 and disease8.

[0053] As a preferred embodiment, the indicator nodes and disease nodes of the medical knowledge graph library are adaptively added to the training features and labels of the training data set.

[0054] S12, inputting the data in the training data set into the classification integration algorithm for training, thereby establishing an initial integration model of the classification algorithm; the classification integration algorithm adopted is, for example, xgboost. Of course, those skilled in the art should know that other appropriate classification integration algorithms can also be adopted, all of which are within the scope of protection of the present invention.

[0055] S13, verifying the accuracy and recall of the initial integrated model of the classification algorithm. When the accuracy and recall meet predetermined requirements, an integrated model of the classification algorithm based on the obtained integrated model is obtained; wherein the recall is related to the false alarm rate and the missed alarm rate.

[0056] S2, if the body component metabolism indicators are sufficient and the disease classification labels of the medical knowledge atlas library can fully cover clinical diseases, predict the user's disease classification and health status evaluation based on the integrated model of the basic classification algorithm; otherwise, execute S3;

[0057] like Figure 4 As shown, as a preferred embodiment, the S2 includes:

[0058] S21, obtaining fixed indicators and user basic information indicators; wherein the fixed indicators include multiple indicators of body fluid detection; in this embodiment, there are six indicators, for example, six urine detection indicators in a medical weight loss program. Of course, those skilled in the art can also add or replace other detection indicators, all within the scope of protection of the present invention;

[0059] S22, predicting the user's disease classification and health status evaluation based on the fixed indicators and the user's basic information indicators.

[0060] S3, if the body component metabolism indicators are insufficient and / or the disease classification labels of the medical knowledge atlas library cannot fully cover clinical diseases, after adaptively adding variables of the fixed indicators and user basic information indicators and automatically updating the disease classification labels, predict the user's disease classification and health status evaluation based on all the fixed indicators and user basic information indicators after the adaptive addition of the variables and the automatically updated disease classification labels;

[0061] like Figure 5 As shown, as a preferred embodiment, the S3 includes:

[0062] S31, obtain initial disease classification based on the integrated model of basic classification algorithms and current body component metabolic indicators;

[0063] S32, respectively calculating the similarity between a plurality of disease classification labels in the medical knowledge graph library and the initial disease classification, and determining all disease classification labels and corresponding network structures whose similarity meets a first preset condition;

[0064] As a preferred embodiment, the first preset condition is that the similarity is greater than a first threshold; in this embodiment, the first threshold is set to 0.7. Of course, those skilled in the art will set different thresholds according to the number of indicators added; for a large number of added indicators, the first threshold can be set to be less than 0.7, otherwise it can be greater than 0.7.

[0065] S33, parallelly calculating the first-degree and second-degree relationships of the network structures corresponding to all disease classification labels whose similarities meet the first preset condition.

[0066] The second-degree relationship refers to the relationship between the branches of the network structure discovered between one end of the network structure and the other end of the network structure through the intermediate structure as a bridge.

[0067] GraphX leverages Spark to represent graphs as RDDs, distributed datasets that can be loaded into memory. Compared to MapReduce's sequential data processing, Spark performs most operations in memory due to its inherent random access nature, making it more suitable for graph processing. GraphX also handles end-to-end graph iteration faster than Giraph, so we use GraphX for first- and second-degree relationship mining and recommendation.

[0068] S34: Indicators and disease categories with correlations above a second threshold are selected from the first-degree and second-degree relationships to adaptively add variables to the fixed indicators and user basic information indicators and automatically update the disease classification labels. This enables automatic addition and optimization of features and labels, thereby continuously reducing the false positive and false negative rates of the integrated model of the classification algorithm and continuously optimizing the model.

[0069] S35, predicting the user's disease classification and health status evaluation based on all fixed indicators and user basic information indicators after the variables are adaptively added and the automatically updated disease classification labels.

[0070] S4: For the adaptively added fixed indicators and corresponding user basic information indicators of the variables, as well as the automatically updated disease classification labels, the corresponding newly added clinical user information is collected and analyzed to determine entity nodes and relationships that do not currently exist in the medical knowledge graph. These entity nodes and relationships are then added to update the medical knowledge graph. This step enables the integrated model of the classification algorithm and the medical knowledge graph to interact and form a closed loop, thereby continuously improving the corresponding models and knowledge graphs of both, further reducing false positives and false negatives. Example 2

[0071] like Figure 6 As shown, this embodiment provides a comprehensive health evaluation system based on multiple indicators of body composition metabolism, which is used to implement the method of embodiment 1, including:

[0072] Model and knowledge graph library establishment module 101 is used to establish an integrated model of the basic classification algorithm and a medical knowledge graph library; the medical knowledge graph library includes multiple disease classification labels and corresponding network structures, and the network structure includes indicator nodes and disease nodes;

[0073] The first prediction and evaluation module 102 is used to predict the user's disease classification and health status evaluation based on the integrated model of the basic classification algorithm when the body component metabolism indicators are sufficient and the disease classification labels of the medical knowledge atlas library can fully cover clinical diseases;

[0074] The second prediction and evaluation module 103 is configured to, in the event that the body component metabolism indicators are insufficient and / or the disease classification labels of the medical knowledge graph library cannot fully cover clinical diseases, adaptively add variables to the fixed indicators and user basic information indicators and automatically update the disease classification labels, and then predict the user's disease classification and health status evaluation based on all the fixed indicators and user basic information indicators after the adaptively added variables and the automatically updated disease classification labels;

[0075] The knowledge graph update module 104 collects the corresponding newly added clinical user information for the fixed indicators adaptively added to the variables and the corresponding user basic information indicators and the automatically updated disease classification labels, and analyzes the corresponding newly added clinical user information to determine the entity nodes and relationships that do not exist in the current medical knowledge graph library, and adds the non-existent entity nodes and relationships to form an update to the medical knowledge graph library.

[0076] Application examples:

[0077] This embodiment can be used for multi-indicator diagnosis, combining basic user information to provide a comprehensive evaluation. For example, in a sarcopenia risk assessment, six urine metabolic indicators (L-lactic acid, creatine, creatinine, urea, β-hydroxybutyrate, and L-glutamate) are tested. Using these six indicators along with basic information such as the user's age, gender, height, weight, liver and kidney function, skeletal muscle mass, and strength, the XGBOOST model can be used to determine the user's disease type.

[0078] For user features that are incorrectly predicted by the model, the network similarity algorithm is used to find the network structure with the highest similarity from the JYR_KG graph. The disease nodes and indicator nodes in the network structure are then filled into the XGBOOST model, and the XGBOOST model is optimized by collecting data.

[0079] The present invention also provides a memory storing a plurality of instructions, wherein the instructions are used to implement the method as in the first embodiment.

[0080] like Figure 7 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301, the memory 302 stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute the method as in embodiment 1.

[0081] Through the above description of the embodiments, those skilled in the art will clearly understand that the above embodiments can be implemented via software or by utilizing software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solutions of the above embodiments can be embodied in the form of a software product. This software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) and includes a number of instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various embodiments of the present invention.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive health evaluation method based on multiple indicators of body composition metabolism, characterized by: include: S1, establishing an integrated model of a basic classification algorithm and a medical knowledge graph library; the medical knowledge graph library includes multiple disease classification labels and corresponding network structures, and the network structure includes indicator nodes and disease nodes; S2, if the body component metabolism indicators are sufficient and the disease classification labels of the medical knowledge atlas library can fully cover clinical diseases, predict the user's disease classification and health status evaluation based on the integrated model of the basic classification algorithm; otherwise, execute S3; S3: If the body component metabolism indicators are insufficient and / or the disease classification labels of the medical knowledge graph library cannot fully cover clinical diseases, adaptively add variables of the body component metabolism indicators and user basic information indicators and automatically update the disease classification labels, and then predict the user's disease classification and health status evaluation based on all the body component metabolism indicators and user basic information indicators after the adaptively added variables and the automatically updated disease classification labels; S3 includes: S31, obtain initial disease classification based on the integrated model of basic classification algorithms and current body component metabolic indicators; S32, respectively calculating the similarity between a plurality of disease classification labels in the medical knowledge graph library and the initial disease classification, and determining all disease classification labels and corresponding network structures whose similarity meets a first preset condition; S33, parallelly calculating the first-degree and second-degree relationships of the network structures corresponding to all disease classification labels whose similarities meet the first preset condition; S34, screening indicators and disease categories with a correlation degree higher than a second threshold from the first-degree and second-degree relationships, adaptively adding variables to the body component metabolism indicators and user basic information indicators, and automatically updating the disease classification labels; S35, predicting the user's disease classification and health status evaluation based on all body component metabolic indicators and user basic information indicators after the variables are adaptively added and the automatically updated disease classification labels; S4, for the body component metabolic indicators and corresponding user basic information indicators and automatically updated disease classification labels that are adaptively added to the variables, collect the corresponding newly added clinical user information, and after analyzing the corresponding newly added clinical user information, determine the entity nodes and relationships that do not exist in the current medical knowledge graph library, and add the non-existent entity nodes and relationships to form an update to the medical knowledge graph library.

2. A comprehensive health evaluation method based on multiple indicators of body composition metabolism according to claim 1, characterized in that: The entity items of the medical knowledge graph library include: types of diseases and complications, clinical symptoms, metabolic indicators of body components, microbiome, body parts, population, treatment methods and regions; among them, microbiome, body parts, population, treatment methods and regions are user basic information indicators.

3. A comprehensive health evaluation method based on multiple indicators of body composition metabolism according to claim 2, characterized in that: The relational items of the medical knowledge graph library include: symptoms corresponding to diseases and complications, examination items corresponding to body component metabolic indicators, and the involved microbial communities.

4. A comprehensive health evaluation method based on multiple indicators of body composition metabolism according to claim 3, characterized in that: The process of establishing the integrated model of the classification algorithm based on S1 includes: S11, determining a training data set, wherein the training data set includes a fixed number of training features and a fixed number of labels; S12, inputting the data in the training data set into a classification ensemble algorithm for training, thereby establishing an initial ensemble model of the classification algorithm; S13, verifying the accuracy and recall of the initial integrated model of the classification algorithm. When the accuracy and recall meet predetermined requirements, an integrated model of the classification algorithm based on the obtained integrated model is obtained; wherein the recall is related to the false alarm rate and the missed alarm rate.

5. A comprehensive health evaluation method based on multiple indicators of body composition metabolism according to claim 4, characterized in that: The indicator nodes and disease nodes of the medical knowledge graph library are adaptively added to the training features and labels of the training data set.

6. A comprehensive health evaluation method based on multiple indicators of body composition metabolism according to claim 5, characterized in that: The S2 includes: S21, obtaining body component metabolism indicators and user basic information indicators; wherein the body component metabolism indicators include multiple indicators of body fluid detection; S22, predicting the user's disease classification and health status evaluation based on the body component metabolism index and the user's basic information index.

7. A comprehensive health evaluation system based on multiple indicators of body composition metabolism, used to implement the method according to any one of claims 1 to 6, characterized in that: include: A model and knowledge graph library establishment module (101) is used to establish an integrated model of a basic classification algorithm and a medical knowledge graph library; the medical knowledge graph library includes multiple disease classification labels and corresponding network structures, and the network structure includes indicator nodes and disease nodes; The first prediction and evaluation module (102) is used to predict the disease classification and health status evaluation of the user based on the integrated model of the basic classification algorithm when the body component metabolism indicators are sufficient and the disease classification labels of the medical knowledge atlas library can fully cover clinical diseases; The second prediction and evaluation module (103) is used to predict the user's disease classification and health status evaluation based on all the body component metabolism indicators and user basic information indicators after the variables are adaptively added and the automatically updated disease classification labels when the body component metabolism indicators are insufficient and / or the disease classification labels of the medical knowledge atlas library cannot fully cover clinical diseases; The knowledge graph updating module (104) collects the corresponding newly added clinical user information for the body component metabolism indicators and corresponding user basic information indicators adaptively added to the variables and the automatically updated disease classification labels, analyzes the corresponding newly added clinical user information, determines the entity nodes and relationships that do not exist in the current medical knowledge graph library, and adds the non-existent entity nodes and relationships to form an update to the medical knowledge graph library.

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the method according to any one of claims 1 to 6.

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