Data processing and analysis methods, devices, and systems

Abstract

The present invention discloses a data processing and analysis method, system and device. By constructing a domain knowledge graph, extracting subgraphs using the graph, constructing structural prior knowledge, and finally performing attribution analysis using a trained Bayesian network, it can effectively utilize expert experience, reduce the requirement of the model for the sample size, improve the performance of the model, accelerate the model training speed, and obtain effective analysis results by combining expert experience with model training in the case of lack of big data on mental diseases or sleep disorders.

Description

[0001] This application is a divisional application of a Chinese invention patent application with an application date of August 12, 2021, an application number of CN202110921652.9, and an invention title of "Method, Apparatus, and System for Attribution Analysis of Sleep Disorders Based on Knowledge Graph". Technical Field

[0002] The present invention relates to the field of artificial intelligence, and particularly to a data processing and analysis method, apparatus, and system. Background Art

[0003] With the accelerating pace of modern society and increasing life pressure, mental health has increasingly become the focus of attention of working people. Diseases caused by mental or psychological factors such as depression and neurasthenia are increasingly bothering many people. Establishing a model through artificial intelligence algorithms for the examination results of patients by means of big data can assist doctors or medical workers to provide more scientific judgments. How to effectively utilize these data and provide better decisions is a scientific problem that urgently needs to be solved. As the factors affecting disease judgment are constantly increasing and the changes in indicators have become normal, how to discover the potential factors promoting the growth of indicators is becoming a difficult problem. Summary of the Invention

[0004] Aiming at the above defects, the technical problem to be solved by the present invention is how to make better use of the big data of various factors affecting mental or sleep disorders and the increasing dimensions for more scientific judgments.

[0005] Aiming at the above defects, the purpose of the present invention is to provide a data processing and analysis method, system, electronic device, computer storage medium, and program product.

[0006] According to one aspect of the embodiments of the present specification, a data processing and analysis method is provided for the server side. Through an algorithm model, entity extraction is performed on the collected data, knowledge is stored using a graph database, and a knowledge graph is constructed in combination with the experience of business experts.

[0007] According to the knowledge graph and input information, relevant subgraphs are extracted, a structural prior is constructed, and in combination with training samples, a Bayesian network model is learned and established.

[0008] The learned and established Bayesian network model is used for attribution analysis.

[0009] Preferably, the algorithm model includes natural language processing, deep learning, and knowledge graph technology.

[0010] Preferably, entity extraction includes relation extraction, event extraction, entity disambiguation, knowledge fusion, and knowledge processing.

[0011] Preferably, the structural prior is constructed by extracting the subgraph structure, directly constructing the Bayesian network structure parameter distribution, and then combining the samples to jointly learn the Bayesian network structure.

[0012] Preferably, a structural prior is constructed to count the frequency of variables in the sample and the frequency between variables, and the average frequency of the variables and the average frequency between variables are calculated. According to the subgraph structure, the parent node is used as the tail node and the child node is used as the head node. The frequency and the average frequency are used to obtain the probability distribution between nodes. According to the subgraph structure, the probability distribution between nodes is repeatedly constructed, and the obtained subgraph probability distribution is used as a structural prior parameter. The Bayesian network structure is obtained in combination with sample learning.

[0013] Preferably, the structure prior is constructed by adding a penalty factor to the scoring function so that the prior structure is integrated into the posterior structure.

[0014] The present invention provides a data processing and analysis method, which is applied to an Internet medical platform, collects medical diagnosis and examination data input by users, extracts entities from the data through an algorithm model, uses a graph database to store knowledge, and constructs a knowledge graph in combination with the experience of doctors and experts;

[0015] According to the knowledge graph and input information, relevant subgraphs are extracted, structural priors are constructed, and combined with training samples, a Bayesian network model is learned and established;

[0016] The Bayesian network model established through learning is used for attribution analysis to form analysis results.

[0017] Preferably, the medical diagnosis and examination data includes text data and image data.

[0018] Preferably, entity extraction includes extraction of relationships between sleep disorders and medical diagnosis and examination data, extraction of user behavior events, expert experience entity disambiguation, disease and symptom knowledge graph knowledge fusion and knowledge processing.

[0019] Preferably, the extracted subgraph uses a graph neural network model to predict the relationship between nodes in the graph and mine more causal relationships.

[0020] Preferably, the method combines expert experience to construct a knowledge graph of related business fields, extracts relevant subgraphs based on images, uses a graph neural network model on the subgraphs to predict the relationship between nodes, mines more causal relationships, constructs a priori distribution of Bayesian network structures, and combines samples to learn the Bayesian network.

[0021] The present invention provides a data processing and analysis system, including a server, a client and an Internet medical platform.

[0022] The user submits medical diagnosis and examination data through the client,

[0023] The Internet medical platform collects medical diagnosis and examination data input by users. The server side extracts entities from the data through an algorithm model, stores the knowledge using a graph database, and constructs a knowledge graph by combining the experience of doctors and experts.

[0024] According to the knowledge graph and the input information, relevant subgraphs are extracted, a structural prior is constructed, and combined with training samples to learn and establish a Bayesian network model.

[0025] Use the established Bayesian network model for attribution analysis to form an analysis result.

[0026] Preferably, entity extraction includes the extraction of the relationship between sleep disorders and medical diagnosis and examination data, the extraction of user behavior events, the disambiguation of expert experience entities, the knowledge fusion and knowledge processing of disease and symptom knowledge graphs.

[0027] Preferably, to construct a structural prior, the frequency of variables and the frequency between variables in the sample are counted, the average frequency of variables and the average frequency between variables are calculated. According to the subgraph structure, the parent node is used as the tail node and the child node is used as the head node, and the probability distribution between nodes is obtained using the frequency and average frequency. According to the subgraph structure, the probability distribution between nodes is repeatedly constructed, and the obtained subgraph probability distribution is used as the structural prior parameter, and combined with the sample to learn the Bayesian network structure.

[0028] The present invention provides a computer-readable storage medium, on which a computer program / instructions are stored, characterized in that when the computer program / instructions are executed by a processor, the steps of the above method are implemented.

[0029] The present invention provides a computer program product, including a computer program / instructions, characterized in that when the computer program / instructions are executed by a processor, the steps of the above method are implemented.

[0030] The present invention provides an electronic device, including:

[0031] A processor; and

[0032] A memory configured to store computer-executable instructions, and when the executable instructions are executed, the processor is caused to perform the following operations:

[0033] Through an algorithm model, perform entity extraction on the collected data, store the knowledge using a graph database, and construct a knowledge graph by combining the experience of business experts;

[0034] According to the knowledge graph and the input information, extract relevant subgraphs, construct a structural prior, and combine with training samples to learn and establish a Bayesian network model;

[0035] Use the established Bayesian network model for attribution analysis.

[0036] The present invention provides an electronic device, comprising:

[0037] a processor; and

[0038] a memory configured to store computer-executable instructions, which when executed cause the processor to perform the following operations:

[0039] Collect medical diagnostic examination data input by a user, perform entity extraction on the data through an algorithm model, store the knowledge using a graph database, and construct a knowledge graph by combining the experience of doctor experts;

[0040] Extract relevant subgraphs according to the knowledge graph and input information, construct a structural prior, and learn to establish a Bayesian network model in combination with training samples;

[0041] Perform attribution analysis using the learned Bayesian network model to form an analysis result.

[0042] The present invention can effectively utilize expert experience, reduce the requirement of the model for the sample size, improve the performance of the model, accelerate the model training speed, and obtain an effective analysis result by combining expert experience with model training in the case of lack of big data on mental diseases or sleep disorders. Brief Description of the Drawings

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0044] Figure 1 Shows a schematic framework diagram of an embodiment of the data processing and analysis method of the present invention;

[0045] Figure 2 Shows a schematic framework diagram of another embodiment of the data processing and analysis method of the present invention;

[0046] Figure 3 Shows a schematic flow diagram of an embodiment of the data processing and analysis method of the present invention;

[0047] Figure 4 Shows a schematic flow diagram of another embodiment of the data processing and analysis method of the present invention;

[0048] Figure 5 Shows a schematic flow diagram of another embodiment of the data processing and analysis method of the present invention. Detailed Embodiments

[0049] The features and exemplary embodiments of various aspects of the present invention will be described in detail below. To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be implemented without some of these specific details. The following description of the embodiments is only provided to provide a better understanding of the present invention by showing examples of the present invention.

[0050] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, elements defined by the statement "comprising..." do not preclude the existence of additional identical elements in the process, method, article or device comprising the said elements.

[0051] As Figure 1 shown, a data processing and analysis method provided by an embodiment of this specification is for the server side. Through an algorithm model, entity extraction is performed on the collected data, and a graph database is used to store knowledge, and a knowledge graph is constructed in combination with the experience of business experts;

[0052] According to the knowledge graph and input information, relevant subgraphs are extracted, a structural prior is constructed, and in combination with training samples, a Bayesian network model is learned and established;

[0053] The learned and established Bayesian network model is used for attribution analysis.

[0054] Causal judgment is a process of drawing conclusions about causal relationships based on the conditions under which an effect occurs.

[0055] The knowledge graph describes concepts, entities and the relationships between them in the objective world in a structured form, expresses the information of Internet medical care in a form closer to the human cognitive world, and has the ability to better organize, manage and understand the massive information in Internet medical big data.

[0056] Graph neural networks are typified by the GNN network structure model. MI is the mutual information between two random variables in probability and information theory. The mutual information (MI) measures the degree of mutual dependence between two variables.

[0057] CMI is conditional mutual information, which measures the degree of direct or indirect non-linear dependence between three variables X, Y, and Z, where X and Y are under the condition of Z.

[0058] PMI is partial mutual information, which is basically the same as CMI in principle. The difference is that X and Y have partial independence rather than complete conditional independence under the condition of Z.

[0059] In some embodiments, the algorithm model includes natural language processing, deep learning, and knowledge graph technologies.

[0060] In some embodiments, entity extraction includes relation extraction, event extraction, entity disambiguation, knowledge fusion, and knowledge processing.

[0061] In some embodiments, the construction of structural priors directly constructs the parameter distribution of the Bayesian network structure by extracting subgraph structures, and then jointly learns the Bayesian network structure in combination with samples.

[0062] As Figure 2 shown, a data processing and analysis method provided by an embodiment of this specification uses an algorithm model to perform entity extraction on the collected data, stores the knowledge using a graph database, and constructs a knowledge graph in combination with the experience of business experts;

[0063] According to the knowledge graph and input information, relevant subgraphs are extracted, structural priors are constructed, and combined with training samples to learn and establish a Bayesian network model;

[0064] Use the learned Bayesian network model for attribution analysis;

[0065] In the process of learning the Bayesian network structure, according to the relevant theories of information theory, the linear and non-linear causal relationships between variables are better measured. In this embodiment, partial mutual information (PMI) is added to accurately measure the linear and non-linear causal relationships between variables.

[0066] Let X, Y, and Z be three random variables. According to the relevant knowledge of information theory, the definitions of mutual information and conditional mutual information are as follows:

[0067]

[0068]

[0069] In the formula, the definition of partial mutual information is as follows:

[0070]

[0071] Among them,

[0072]

[0073]

[0074] By adding the above-mentioned partial mutual information (PMI), the serious overestimation and underestimation problems of MI and CMI in measuring linear and non-linear causal relationships between variables can be solved.

[0075] As Figure 3 shown, an embodiment of the present specification provides a data processing and analysis method, including:

[0076] S101. Extract entities from the collected data through an algorithm model;

[0077] S102. Store knowledge using a graph database;

[0078] S103. Construct a knowledge graph by combining the experience of business experts;

[0079] S104. Extract relevant subgraphs according to the knowledge graph and input information, and construct a structural prior;

[0080] S105. Combine training samples to learn and establish a Bayesian network model;

[0081] S106. Perform attribution analysis using the learned Bayesian network model.

[0082] In a specific example, the structural prior is constructed by counting the frequencies of variables and the frequencies between variables in the sample, calculating the average frequencies of variables and the average frequencies between variables, taking the parent node as the tail node and the child node as the head node according to the subgraph structure, obtaining the probability distribution between nodes using the frequencies and average frequencies, repeating the construction of the probability distribution between nodes according to the subgraph structure, taking the obtained subgraph probability distribution as the structural prior parameter, and combining with the sample to learn the Bayesian network structure.

[0083] In some embodiments, a penalty factor is added to the scoring function when constructing the structural prior, so that the prior structure is integrated into the posterior structure.

[0084] As Figure 4 shown, an embodiment of the present invention provides a data processing and analysis method, which is applied to an Internet medical platform and includes:

[0085] S201. Collect medical diagnosis and examination data input by users;

[0086] S202. Extract entities from the data through an algorithm model;

[0087] S203. Store knowledge using a graph database and construct a knowledge graph by combining the experience of doctor experts;

[0088] S204. Extract relevant sub - graphs according to the knowledge graph and input information, and construct a structural prior;

[0089] S205. Combine the training samples and learn to establish a Bayesian network model;

[0090] S206. Use the established Bayesian network model for attribution analysis to form an analysis result.

[0091] In an embodiment of the present invention, the Internet medical platform can be for psychotherapy or sleep disorders, etc.

[0092] In some embodiments, the Internet medical platform performs preliminary modeling based on the historically collected data, and models the big data of different disease types, different populations, and different detection schemes by establishing a model.

[0093] In some specific examples, the medical diagnosis and examination data includes text data and picture data.

[0094] In some specific examples, entity extraction includes the extraction of the relationship between sleep disorders and medical diagnosis and examination data, the extraction of user behavior events, the disambiguation of expert experience entities, the knowledge fusion and knowledge processing of disease and symptom knowledge graphs.

[0095] In some specific examples, the sub - graph extraction uses a graph neural network model to predict the relationships between nodes in the graph and mine more causal relationships.

[0096] In some specific examples, the method combines expert experience, constructs a knowledge graph of the relevant business field, extracts relevant sub - graphs based on pictures, uses a graph neural network model on the sub - graph to predict the relationships between nodes, mines more causal relationships, constructs a prior distribution of the Bayesian network structure, and combines the samples to learn the Bayesian network.

[0097] An embodiment of a data processing and analysis method provided by the present invention is applied to an Internet medical platform, and includes:

[0098] S301. Collect the sleep disorder physical examination data input by the user; the physical examination data includes blood data, blood pressure data, urine routine data, electrocardiogram, B - ultrasound, brain CT and other data;

[0099] S302. Through technologies such as NLP, deep learning, and knowledge graphs, perform entity extraction on the data; entity extraction includes the extraction of the relationship between sleep disorders and physical examination data, the extraction of user behavior events such as staying up late, caffeine intake, work pressure, excessive exercise, etc., the disambiguation of doctor experience entities, the knowledge fusion and knowledge processing of disease and symptom knowledge graphs;

[0100] S303. Store knowledge using technologies such as graph databases and the RDF Resource Description Framework, and construct a knowledge graph in combination with the experience of doctor experts; the knowledge graph includes an overall data graph of different factors, different physical examination data indicators, and corresponding sleep disorders and mental illnesses.

[0101] S304. Extract relevant subgraphs according to the knowledge graph and the input information, and construct a structural prior.

[0102] S305. Combine the training samples and learn to establish a Bayesian network model.

[0103] S306. Use the learned Bayesian network model to perform attribution analysis to form a sleep disorder analysis result.

[0104] In some embodiments, to construct the structural prior, count the frequencies of variables (such as electrocardiogram, B-ultrasound, brain CT data, or event variables such as staying up late, caffeine intake, work pressure, excessive exercise, etc.) and the frequencies between variables in the big data samples of different patient users, calculate the average frequencies of the variables and the average frequencies between variables. According to the subgraph structure, use the parent node as the tail node and the child node as the head node, and obtain the probability distribution between nodes using the frequencies and average frequencies. According to the subgraph structure, repeatedly construct the probability distribution between nodes, and use the obtained subgraph probability distribution as the structural prior parameter, and combine with the samples to learn the Bayesian network structure.

[0105] According to an embodiment of another aspect, a data processing and analysis system is further provided, including a server side, a client side, and an Internet medical platform.

[0106] The user submits medical diagnosis and examination data through the client side.

[0107] The Internet medical platform collects the medical diagnosis and examination data input by the user. The server side performs entity extraction on the data through an algorithm model, stores knowledge using a graph database, and constructs a knowledge graph in combination with the experience of doctor experts.

[0108] Extract relevant subgraphs according to the knowledge graph and the input information, construct a structural prior, combine with the training samples, and learn to establish a Bayesian network model.

[0109] Use the learned Bayesian network model to perform attribution analysis to form an analysis result.

[0110] In some embodiments, entity extraction includes the extraction of the relationship between sleep disorders and medical diagnosis and examination data, the extraction of user behavior events, the disambiguation of expert experience entities, the knowledge fusion and knowledge processing of disease and symptom knowledge graphs.

[0111] According to an embodiment of another aspect, there is also provided a computer-readable storage medium, on which a computer program / instructions are stored, and when the computer program / instructions are executed by a processor, the following steps are implemented:

[0112] Through an algorithm model, entity extraction is performed on the collected data, the knowledge is stored using a graph database, and a knowledge graph is constructed by combining the experience of business experts;

[0113] According to the knowledge graph and the input information, relevant subgraphs are extracted, a structural prior is constructed, and combined with training samples, a Bayesian network model is learned and established;

[0114] Use the learned and established Bayesian network model for attribution analysis.

[0115] According to an embodiment of another aspect, there is also provided a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the following steps are implemented:

[0116] Through an algorithm model, entity extraction is performed on the collected data, the knowledge is stored using a graph database, and a knowledge graph is constructed by combining the experience of business experts;

[0117] According to the knowledge graph and the input information, relevant subgraphs are extracted, a structural prior is constructed, and combined with training samples, a Bayesian network model is learned and established;

[0118] Use the learned and established Bayesian network model for attribution analysis.

[0119] According to another aspect of the embodiments of the present specification, there is also provided an electronic device, including:

[0120] A processor; and

[0121] A memory configured to store computer-executable instructions, and when the executable instructions are executed, the processor performs the following operations:

[0122] Through an algorithm model, entity extraction is performed on the collected data, the knowledge is stored using a graph database, and a knowledge graph is constructed by combining the experience of business experts;

[0123] According to the knowledge graph and the input information, relevant subgraphs are extracted, a structural prior is constructed, and combined with training samples, a Bayesian network model is learned and established;

[0124] Use the learned and established Bayesian network model for attribution analysis.

[0125] According to another aspect of the embodiments of the present specification, there is also provided a computer-readable storage medium, on which a computer program / instructions are stored, and when the computer program / instructions are executed by a processor, the following steps are implemented:

[0126] Collect the medical diagnostic examination data input by the user, perform entity extraction on the data through an algorithm model, store the knowledge using a graph database, and construct a knowledge graph in combination with the experience of doctors and experts;

[0127] Extract relevant subgraphs according to the knowledge graph and the input information, construct a structural prior, and combine with training samples to learn and establish a Bayesian network model;

[0128] Use the established Bayesian network model for attribution analysis to form an analysis result.

[0129] According to another aspect of the embodiments of the present specification, there is provided a computer program product, including a computer program / instructions, which when executed by a processor, implement the following steps:

[0130] Collect the medical diagnostic examination data input by the user, perform entity extraction on the data through an algorithm model, store the knowledge using a graph database, and construct a knowledge graph in combination with the experience of doctors and experts;

[0131] Extract relevant subgraphs according to the knowledge graph and the input information, construct a structural prior, and combine with training samples to learn and establish a Bayesian network model;

[0132] Use the established Bayesian network model for attribution analysis to form an analysis result.

[0133] According to another aspect of the embodiments of the present specification, there is provided an electronic device, including:

[0134] A processor; and

[0135] A memory configured to store computer-executable instructions, and the executable instructions, when executed, cause the processor to perform the following operations:

[0136] Collect the medical diagnostic examination data input by the user, perform entity extraction on the data through an algorithm model, store the knowledge using a graph database, and construct a knowledge graph in combination with the experience of doctors and experts;

[0137] Extract relevant subgraphs according to the knowledge graph and the input information, construct a structural prior, and combine with training samples to learn and establish a Bayesian network model;

[0138] Use the established Bayesian network model for attribution analysis to form an analysis result.

[0139] The data processing and analysis method, system and device of the present invention construct a domain knowledge graph, extract subgraphs using the graph, construct structural prior knowledge, and finally perform attribution analysis using the trained Bayesian network, which can effectively utilize expert experience, reduce the requirement of the model for the sample size, improve the performance of the model, accelerate the model training speed, and combine expert experience with model training to obtain effective analysis results in the absence of big data on mental diseases or sleep disorders.

[0140] For the convenience of description, when describing the above device, it is divided into various units according to functions and described separately. Of course, when implementing the present application, the functions of each unit can be implemented in one or more software and / or hardware.

[0141] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, system, or computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0142] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in one Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0143] The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0144] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction means that implements the function specified in one or more of the processes Figure 1 or blocks Figure 1 specified in one or more of the processes and / or blocks.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in one or more of the processes Figure 1 or blocks Figure 1 specified in one or more of the processes and / or blocks.

[0146] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0147] The memory may include non-permanent memory in the computer-readable medium, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0148] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storing information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0149] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0150] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and reference can be made to the relevant part of the method embodiment for the relevant content.

[0151] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A data processing and analysis method for the server side, which collects the data input by users, extracts data relationships, extracts user behavior events, disambiguates expert experience entities, fuses and processes knowledge in the professional knowledge graph through an algorithm model, stores the knowledge using a graph database and RDF resource description framework technology, and constructs a knowledge graph in combination with doctors' expert experience; the knowledge graph includes an overall data graph of different factors, different physical examination data indicators, and corresponding sleep disorders and mental diseases; according to the knowledge graph and the input information, relevant subgraphs are extracted, the structure prior is constructed to count the frequencies of variables in the sample and the frequencies between variables, the average frequencies of variables and the average frequencies between variables are calculated, according to the subgraph structure, the parent node is used as the tail node and the child node is used as the head node, the probability distribution between nodes is obtained using the frequencies and average frequencies, the probability distribution between nodes is repeatedly constructed, and the obtained subgraph probability distribution is used as the structure prior parameter, and the Bayesian network structure is obtained by combining sample learning. The construction structure prior adds a penalty factor to the scoring function, enabling the prior structure to be integrated into the posterior structure; Combined with training samples, a Bayesian network model is learned and established. During the learning process of the Bayesian network structure, partial mutual information is added to accurately measure the linear and non-linear causal relationships between variables; Attribution analysis is performed using the learned and established Bayesian network model, and the analysis result is formed by performing attribution analysis using the learned and established Bayesian network model.

2. The data processing and analysis method according to claim 1, wherein the algorithm model includes natural language processing, deep learning, and knowledge graph technology.

3. The data processing and analysis method according to claim 1, wherein the construction structure prior directly constructs the parameter distribution of the Bayesian network structure by extracting subgraph structures, and then jointly learns the Bayesian network structure in combination with the samples.

4. The data processing and analysis method according to claim 1, wherein the method combines expert experience to construct a knowledge graph of the relevant business domain, extracts relevant subgraphs based on pictures, predicts the relationships between nodes on the subgraphs using a graph neural network model, mines more causal relationships, constructs the prior distribution of the Bayesian network structure, and learns the Bayesian network in combination with the samples.

5. The method according to claim 1, wherein the data input by the user includes text data and picture data.

6. A data processing and analysis method, applied to an Internet medical platform, collects sleep disorder physical examination data input by users, and through an algorithm model, extracts the relationship between sleep disorders and physical examination data, extracts user behavior events, eliminates doctor experience entity ambiguity, fuses and processes knowledge of disease and symptom knowledge graphs, stores the knowledge using a graph database and RDF resource description framework technology, and constructs a knowledge graph in combination with doctor expert experience; the knowledge graph includes an overall data graph of different factors, different physical examination data indicators, and corresponding sleep disorders and mental diseases; according to the knowledge graph and input information, relevant subgraphs are extracted, a structural prior is constructed to count the frequencies of variables in the sample and the frequencies between variables, the average frequency of variables and the average frequency between variables are calculated, according to the subgraph structure, the parent node is used as the tail node and the child node is used as the head node, the probability distribution between nodes is obtained using the frequency and average frequency, the probability distribution between nodes is repeatedly constructed, the obtained subgraph probability distribution is used as a structural prior parameter, and a Bayesian network structure is obtained by combining sample learning; The construction structure prior adds a penalty factor to the scoring function, enabling the prior structure to be integrated into the posterior structure; Combined with training samples, a Bayesian network model is learned and established. During the learning process of the Bayesian network structure, partial mutual information is added to accurately measure the linear and non-linear causal relationships between variables; Attribution analysis is performed using the learned and established Bayesian network model to form an analysis result.

7. A data processing and analysis system, including a server side, a client side, and an Internet medical platform, The user submits physical examination data through the client side, The Internet medical platform collects the physical examination data input by the user, extracts the relationships between diseases and physical examination data, extracts user behavior events, disambiguates doctor experience entities, fuses knowledge of the disease and symptom knowledge graph, and performs knowledge processing through an algorithm model. The knowledge is stored using a graph database and RDF resource description framework technology, and a knowledge graph is constructed in combination with doctor expert experience; the knowledge graph includes an overall data graph of different factors, different physical examination data indicators, and corresponding sleep disorders and mental diseases; According to the knowledge graph and the input information, extract relevant subgraphs, construct a structural prior, count the frequencies of variables in the sample and the frequencies between variables, calculate the average frequency of variables and the average frequency between variables. According to the subgraph structure, use the parent node as the tail node and the child node as the head node, obtain the probability distribution between nodes using the frequencies and average frequencies, repeat constructing the probability distribution between nodes, use the obtained subgraph probability distribution as the structural prior parameter, and combine with the sample learning to obtain the Bayesian network structure; The construction structure prior adds a penalty factor to the scoring function, enabling the prior structure to be integrated into the posterior structure; Combined with training samples, a Bayesian network model is learned and established. During the learning process of the Bayesian network structure, partial mutual information is added to accurately measure the linear and non-linear causal relationships between variables; Attribution analysis is performed using the learned and established Bayesian network model to form an analysis result.

8. A computer-readable storage medium, on which computer programs / instructions are stored, and when executed by a processor, implement the steps of the method according to any one of claims 1-5.

9. A computer program product, including computer programs / instructions, and when executed by a processor, implement the steps of the method according to any one of claims 1-5.

10. An electronic device, including: A processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to perform the following operations: Collect user-input data, perform data relationship extraction, user behavior event extraction, expert experience entity disambiguation, professional knowledge graph knowledge fusion, and knowledge processing on the data through an algorithm model, store the knowledge using a graph database and RDF (Resource Description Framework) technology, and construct a knowledge graph in combination with doctors' expert experience; the knowledge graph includes an overall data graph of different factors, different physical examination data indicators, and corresponding sleep disorders and mental illnesses; According to the knowledge graph and the input information, extract relevant subgraphs, construct a structural prior to statistically obtain the frequencies of variables and the frequencies between variables in the sample, calculate the average frequencies of variables and the average frequencies between variables, based on the subgraph structure, use the parent node as the tail node and the child node as the head node, obtain the probability distribution between nodes using the frequencies and average frequencies, repeatedly construct the probability distribution between nodes, use the obtained subgraph probability distribution as the structural prior parameter, and learn to obtain a Bayesian network structure in combination with sample learning; Construct a structural prior and add a penalty factor to the scoring function to fuse the prior structure into the posterior structure; In combination with the training samples, learn to establish a Bayesian network model, and during the Bayesian network structure learning process, add partial mutual information to accurately measure the linear and non-linear causal relationships between variables; Perform attribution analysis using the learned Bayesian network model to form an analysis result.

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