Information processing method, apparatus and system

Abstract

The application discloses an information processing method, system and system, extracts information according to materials provided by a user, arranges into a structured data list, obtains a correlation relationship of common occurrence of each disease through Bayesian structure learning according to a historical user data set and an information database, constructs a Bayesian network structure, aggregates antecedent and posterior diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, and outputs an analysis result to the user according to a corresponding strategy scheme formed according to expert experience knowledge graph. The application carries out information extraction on user medical records and other materials based on Bayesian structure learning, carries out structured processing, obtains a correlation relationship of each disease in combination with a historical data set and an information database, forms a corresponding reference strategy scheme in combination with an expert experience knowledge graph, can become important reference information for a doctor to assist in judgment, and saves processing time.

Description

[0001] This application is a divisional application of the Chinese Invention Patent Application with the application number CN20210792815.8, the application date of July 14, 2021, and the invention name of "Information Classification Method, Device and System Based on Bayesian Structure Learning". TECHNICAL FIELD

[0002] The present application relates to the field of artificial intelligence, in particular to an information processing method, device and system. BACKGROUND

[0003] Disease classification analysis is essential in many scenarios. Current disease classification mainly relies on manual judgment. The professional abilities of professionals are uneven, leading to confusion in classification. For example, the classification of a certain disease may be incorrect due to insufficient medical expertise. There are many causes of cancer, and doctors may use different terms when issuing a diagnosis, such as thyroid tumor, thyroid malignant tumor, and thyroid papillary malignant tumor. These expressions correspond to certain specific periods or specific types of thyroid cancer. However, if the professional knowledge is not sufficient, it may lead to classification errors. This classification difficulty is more pronounced in psychological diseases. Therefore, it is necessary to use data-driven methods to assist in reasonable disease classification to improve the reliability of auxiliary reference. SUMMARY

[0004] To solve the above-mentioned defects, the technical problem to be solved by the present application is how to use scientific and technological means to solve the data analysis and classification problem of sleep disorders.

[0005] The purpose of the present application is to provide an information processing method, system, electronic device, computer storage medium and program product.

[0006] According to an aspect of an embodiment of the present application, an information processing method based on Bayesian structure learning is provided. Information is extracted from user-provided materials and organized into a structured data list. The association between each disease is obtained through Bayesian structure learning based on historical user data sets and information databases. A Bayesian network structure is constructed. Diseases of the same type are aggregated in the Bayesian network to obtain corresponding disease classification. According to the expert experience knowledge graph, a corresponding strategy scheme is formed to output the analysis result to the user.

[0007] In some embodiments, the Bayesian network structure has a plurality of nodes, and the nodes correspond to random variables. The edges correspond to the dependencies or correlations of the random variables.

[0008] In some embodiments, the edges include directed edges and undirected edges.

[0009] In some embodiments, the nodes include random factors or causes that induce sleep disorders.

[0010] In some embodiments, a directed edge represents a unidirectional dependency, and an undirected edge represents a related dependency.

[0011] In some embodiments, structure learning is given a network and a static slice sample of each node, and finds the optimal network structure, so as to explain the causal relationship between nodes by the conditional independence of the Bayesian network.

[0012] According to an aspect of embodiments of the present specification, there is provided an information processing method based on Bayesian structure learning applied to an Internet medical platform, comprising:

[0013] Upon receiving a user request, structured processing is performed and information is extracted according to the materials provided by the user to form a structured data list, and the correlation of the co-occurrence of diseases of the user is obtained by Bayesian structure learning according to the historical database and the information of the user, a Bayesian network structure is constructed, the diseases before and after the same type are aggregated in the Bayesian network to obtain the corresponding disease classification, and the corresponding strategy scheme is formed according to the expert experience knowledge graph to output the analysis result to the user.

[0014] In some embodiments, the Internet medical platform performs Bayesian structure learning according to the collected historical data, learns the probability of the co-occurrence of the disease combination in the disease list, constructs a Bayesian network structure, finds the optimal network structure, and explains the causal relationship between nodes by the conditional independence of the Bayesian network.

[0015] In some embodiments, a greedy search is used to find the optimal solution to avoid falling into a local optimum.

[0016] In some embodiments, the specific process of finding the optimal solution includes:

[0017] S1, selecting an initial structure;

[0018] S2, when a local optimum is not reached or a maximum number of steps is not reached, changing all possible structures;

[0019] S3, updating the score of the corresponding node, if the score increases, the adjacent structure is selected as the candidate structure for the next step;

[0020] S4, if the candidate structure is empty, selecting the structure with the largest score increase among all candidate structures as the current structure, otherwise judging that a local optimum has been reached, and returning the current structure;

[0021] S5, performing S1-S4 until all structures are traversed.

[0022] According to another aspect of embodiments of the present specification, there is provided a disease and cause information processing method based on Bayesian structure learning, comprising:

[0023] The user provides materials of symptoms, medical history and examination information, the third-party Internet medical platform processes the materials and extracts information to form a structured pathology data list, according to the user's historical data and the related disease database established by the third-party Internet medical platform, obtains the correlation of one or more diseases through Bayesian structure learning, constructs a Bayesian network structure, aggregates the antecedent and consequent diseases belonging to the same type in the Bayesian network to obtain the corresponding disease classification, and forms a corresponding strategy scheme according to the expert experience knowledge graph to output the analysis result to the user.

[0024] In some embodiments, the third-party Internet medical platform processes the materials through OCR and / or image recognition and / or semantic analysis to obtain data in a standard format.

[0025] In some embodiments, the Bayesian network structure has a plurality of nodes, and the nodes correspond to random variables.

[0026] According to another aspect of the embodiments of the present specification, an information processing system based on Bayesian structure learning is provided, comprising: a user end and an Internet medical platform, wherein,

[0027] The user end is used for the user to provide materials, including but not limited to providing pictures, documents and standardized examination reports;

[0028] The Internet medical platform processes the materials and extracts information to form a structured pathology data list, according to the user's historical data and the related disease database established by the third-party Internet medical platform, obtains the correlation of one or more diseases through Bayesian structure learning, and constructs a Bayesian network structure.

[0029] The Internet medical platform aggregates the antecedent and consequent diseases belonging to the same type in the Bayesian network to obtain the corresponding disease classification, forms a corresponding strategy scheme according to the expert experience knowledge graph, and outputs the analysis result to the user through the user end.

[0030] In some embodiments, the third-party Internet medical platform processes the materials through the background OCR, image recognition and semantic analysis function modules to obtain data in a standard format.

[0031] According to another aspect of the embodiments of the present specification, an electronic device is provided, comprising:

[0032] a processor; and

[0033] a memory configured to store computer-executable instructions that, when executed, cause the processor to perform the following operations:

[0034] Upon receiving the user request, information is extracted from the material provided by the user and structured, forming a structured data list, and a correlation between co-occurrence of diseases is obtained through Bayesian structure learning based on historical database and information of the user, a Bayesian network structure is constructed, diseases of the same type are aggregated in the Bayesian network to obtain corresponding disease classification, and an analysis result is output to the user according to a corresponding strategy scheme formed based on an expert experience knowledge graph.

[0035] According to another aspect of the embodiments of the present specification, a computer readable storage medium having stored thereon a computer program / instruction which, when executed by a processor, implements the following steps: extracting information from material provided by a user, arranging into a structured data list, obtaining a correlation between co-occurrence of each disease through Bayesian structure learning based on a historical user data set and an information database, constructing a Bayesian network structure, aggregating diseases of the same type in the Bayesian network to obtain corresponding disease classification, and outputting an analysis result to the user according to a corresponding strategy scheme formed based on an expert experience knowledge graph.

[0036] According to another aspect of the embodiments of the present specification, a computer program product comprising a computer program / instruction, characterized in that the computer program / instruction, when executed by a processor, implements the following steps: extracting information from material provided by a user, arranging into a structured data list, obtaining a correlation between co-occurrence of each disease through Bayesian structure learning based on a historical user data set and an information database, constructing a Bayesian network structure, aggregating diseases of the same type in the Bayesian network to obtain corresponding disease classification, and outputting an analysis result to the user according to a corresponding strategy scheme formed based on an expert experience knowledge graph.

[0037] According to another aspect of the embodiments of the present specification, an electronic device is provided, comprising:

[0038] a processor; and

[0039] a memory configured to store computer executable instructions which, when executed, cause the processor to perform the following operations:

[0040] extracting information from material provided by a user, arranging into a structured data list, obtaining a correlation between co-occurrence of each disease through Bayesian structure learning based on a historical user data set and an information database, constructing a Bayesian network structure, aggregating diseases of the same type in the Bayesian network to obtain corresponding disease classification, and outputting an analysis result to the user according to a corresponding strategy scheme formed based on an expert experience knowledge graph.

[0041] According to another aspect of embodiments of the present specification, a computer-readable storage medium having stored thereon computer programs / instructions, which, when executed by a processor, implement the following steps:

[0042] Upon receiving a user request, structured processing is performed on the materials provided by the user and information is extracted to form a structured data list, the correlation of co-occurrence of diseases of the user is obtained through Bayesian structure learning based on the historical database and information of the user, a Bayesian network structure is constructed, diseases belonging to the same type are aggregated in the Bayesian network to obtain corresponding disease classification, and the analysis result is output to the user according to the corresponding strategy scheme formed based on the expert experience knowledge graph.

[0043] According to another aspect of embodiments of the present specification, a computer program product is provided, including computer programs / instructions, which, when executed by a processor, implement the following steps:

[0044] Upon receiving a user request, structured processing is performed on the materials provided by the user and information is extracted to form a structured data list, the correlation of co-occurrence of diseases of the user is obtained through Bayesian structure learning based on the historical database and information of the user, a Bayesian network structure is constructed, diseases belonging to the same type are aggregated in the Bayesian network to obtain corresponding disease classification, and the analysis result is output to the user according to the corresponding strategy scheme formed based on the expert experience knowledge graph.

[0045] According to another aspect of embodiments of the present specification, an electronic device is provided, including:

[0046] a processor; and

[0047] a memory configured to store computer-executable instructions that, when executed, cause the processor to perform the following operations:

[0048] Upon receiving a user request, structured processing is performed on the materials provided by the user and information is extracted to form a structured data list, the correlation of co-occurrence of diseases of the user is obtained through Bayesian structure learning based on the historical database and information of the user, a Bayesian network structure is constructed, diseases belonging to the same type are aggregated in the Bayesian network to obtain corresponding disease classification, and the analysis result is output to the user according to the corresponding strategy scheme formed based on the expert experience knowledge graph.

[0049] The present application extracts information from user medical records and the like and performs structured processing based on Bayesian structure learning, obtains the correlation of each disease in combination with a historical data set and an information database, forms a corresponding reference strategy scheme in combination with an expert experience knowledge graph, and can become important reference information for doctor-assisted judgment, thereby saving processing time. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly introduced as follows, and other drawings can be obtained by those of ordinary skill in the art without any creative effort on the premise that the drawings are not creative.

[0051] Figure 1 An embodiment structure schematic diagram of the information processing method based on Bayesian structure learning of the present application is shown;

[0052] Figure 2 Another embodiment structure schematic diagram of the information processing method based on Bayesian structure learning of the present application is shown;

[0053] Figure 3 Another embodiment structure schematic diagram of the information processing method based on Bayesian structure learning of the present application is shown;

[0054] Figure 4 An embodiment architecture schematic diagram of the information processing system based on Bayesian structure learning of the present application is shown. DETAILED DESCRIPTION

[0055] The features and exemplary embodiments of various aspects of the present application will be described in detail below, in order to make the purposes, technical solutions and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present application, and are not configured to limit the present application. The present application can be implemented without some of these specific details by those skilled in the art. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0056] It should be noted that, in this paper, the relationship 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 such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the elements defined by the statement "include" do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

[0057] As Figure 1As shown in the figure, one embodiment of this specification provides an information processing method based on Bayesian structure learning. Information is extracted from user-provided materials and organized into a structured data list. Based on historical user datasets and an information database, Bayesian structure learning is used to obtain the common associations of each disease. A Bayesian network structure is constructed, and sequential diseases belonging to the same type are aggregated in the Bayesian network to obtain corresponding disease classifications. Based on expert experience knowledge graphs, corresponding strategy schemes are formed and the analysis results are output to the user.

[0058] In some embodiments, the Bayesian network structure has multiple nodes, where nodes correspond to random variables and edges correspond to dependencies or correlations of random variables.

[0059] In some embodiments, edges include directed edges and undirected edges.

[0060] In a specific example, nodes include random factors or triggers that cause sleep disorders.

[0061] In some embodiments, directed edges represent unidirectional dependencies, and undirected edges represent related dependencies.

[0062] In some embodiments, structural learning, given a network and static slice samples of each node, seeks the optimal network structure to explain the causal relationships between nodes using the conditional independence of Bayesian networks.

[0063] Another embodiment of this specification provides an information processing method based on Bayesian structure learning, applied to an internet healthcare platform, including:

[0064] Upon receiving a user request, the system performs structured processing and extracts information from the materials provided by the user to form a structured data list. Based on historical databases and user information, it learns the co-occurrence relationships of user diseases through Bayesian structure learning, constructs a Bayesian network structure, aggregates sequential diseases of the same type in the Bayesian network to obtain the corresponding disease classification, and formulates corresponding strategy plans based on expert experience knowledge graphs to output analysis results to the user.

[0065] In some embodiments, the internet healthcare platform performs Bayesian structure learning based on collected historical data, learns the probability of disease combinations appearing together in the disease list, constructs a Bayesian network structure, finds the optimal network structure, and uses the conditional independence of the Bayesian network to explain the causal relationships between nodes.

[0066] In a specific example, greedy search is used to find the optimal solution and avoid getting trapped in local optima.

[0067] like Figure 2 As shown, in a specific example, the process of finding the optimal solution includes:

[0068] S1, selecting an initial structure;

[0069] S2, modifying all possible structures when local optimum is not reached or the maximum number of steps is not reached;

[0070] S3, updating the score of the corresponding node, if the score increases, the adjacent structure is selected as the candidate structure for the next step;

[0071] S4, if the candidate structure is empty, the structure with the largest score increase is selected as the current structure among all candidate structures, otherwise it is judged that the local optimum has been reached, and the current structure is returned;

[0072] S5, performing S1-S4 until all structures are traversed.

[0073] As Figure 3 shown, another embodiment of the present specification provides an information processing method based on Bayesian structure learning, comprising:

[0074] The user provides the materials of symptoms, medical history and examination information, the third-party internet medical platform structures the materials and extracts the information to form a structured pathology data list, according to the user's historical data and the related disease database established by the third-party internet medical platform, obtains one or more associated relationships of common diseases through Bayesian structure learning, constructs a Bayesian network structure, aggregates the pre and post diseases belonging to the same type in the Bayesian network to obtain the corresponding disease classification, and forms a corresponding strategy scheme according to the expert experience knowledge graph to output the analysis result to the user.

[0075] In some embodiments, the third-party internet medical platform structures the materials through OCR and / or image recognition and / or semantic analysis to obtain data in standard format.

[0076] In some embodiments, the Bayesian network structure has a plurality of nodes, and the nodes correspond to random variables.

[0077] As Figure 4 shown, another embodiment of the present specification provides an information processing system based on Bayesian structure learning, comprising: a user end and an internet medical platform, wherein,

[0078] The user end is used for the user to provide materials, including but not limited to providing pictures, documents and standardized examination reports;

[0079] The Internet medical platform structures the materials and extracts information to form a structured pathological data list, obtains one or more associated relationships of common diseases through Bayesian structure learning according to user historical data and a related disease database established by a third-party Internet medical platform, and constructs a Bayesian network structure.

[0080] The Internet medical platform aggregates the pre- and post-sequenced diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, forms a corresponding strategy scheme according to an expert experience knowledge graph, and outputs an analysis result to the user through the user terminal.

[0081] In some embodiments, the third-party Internet medical platform structures the materials through an OCR, image recognition, and semantic analysis function module in the background to obtain data in a standard format.

[0082] In a specific example, the Internet medical platform can be a comprehensive Internet health medical platform, such as Ali Health, Jingdong Health, can be a medical health inquiry platform specializing in medical health, such as Chunyu Doctor, DXY, and can also be a specialized medical platform, such as a professional mobile medical platform Good Mood Mobile Medical Platform focusing on the central nervous system.

[0083] According to another aspect of the embodiments, a computer readable storage medium is also provided, which stores a computer program / instruction, and the computer program / instruction is executed by a processor to implement the following steps:

[0084] According to the materials provided by the user, information is extracted and arranged into a structured data list, the associated relationship of each disease is obtained through Bayesian structure learning according to a historical user data set and an information database, a Bayesian network structure is constructed, the pre- and post-sequenced diseases belonging to the same type are aggregated in the Bayesian network to obtain corresponding disease classification, a corresponding strategy scheme is formed according to an expert experience knowledge graph, and an analysis result is output to the user.

[0085] According to another aspect of the embodiments, a computer program product is also provided, which includes a computer program / instruction, and the computer program / instruction is executed by a processor to implement the following steps:

[0086] According to the materials provided by the user, information is extracted and arranged into a structured data list, the associated relationship of each disease is obtained through Bayesian structure learning according to a historical user data set and an information database, a Bayesian network structure is constructed, the pre- and post-sequenced diseases belonging to the same type are aggregated in the Bayesian network to obtain corresponding disease classification, a corresponding strategy scheme is formed according to an expert experience knowledge graph, and an analysis result is output to the user.

[0087] According to another aspect of the embodiments of the present specification, an electronic device is also provided, which includes:

[0088] a processor; and

[0089] a memory configured to store computer-executable instructions that, when executed, cause the processor to perform the following operations:

[0090] According to the user-provided material extraction information, a structured data list is arranged, the correlation of the common occurrence of each disease is obtained through Bayesian structure learning according to a historical user data set and an information database, a Bayesian network structure is constructed, diseases belonging to the same type are aggregated in the Bayesian network to obtain the corresponding disease classification, a corresponding strategy scheme is formed according to an expert experience knowledge graph, and an analysis result is output to the user.

[0091] According to another aspect of the embodiments of the present specification, a computer-readable storage medium having a computer program / instruction stored thereon is further provided, and the computer program / instruction is executed by a processor to implement the following steps:

[0092] Upon receiving a user request, structured processing is performed on the user-provided material to extract information, a structured data list is formed, the correlation of the common occurrence of the user's diseases is obtained through Bayesian structure learning according to a historical database and the user's information, a Bayesian network structure is constructed, diseases belonging to the same type are aggregated in the Bayesian network to obtain the corresponding disease classification, a corresponding strategy scheme is formed according to an expert experience knowledge graph, and an analysis result is output to the user.

[0093] According to another aspect of the embodiments of the present specification, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the following steps:

[0094] Upon receiving a user request, structured processing is performed on the user-provided material to extract information, a structured data list is formed, the correlation of the common occurrence of the user's diseases is obtained through Bayesian structure learning according to a historical database and the user's information, a Bayesian network structure is constructed, diseases belonging to the same type are aggregated in the Bayesian network to obtain the corresponding disease classification, a corresponding strategy scheme is formed according to an expert experience knowledge graph, and an analysis result is output to the user.

[0095] According to another aspect of the embodiments of the present specification, an electronic device is provided, including:

[0096] a processor; and

[0097] a memory configured to store computer-executable instructions that, when executed, cause the processor to perform the following operations:

[0098] Upon receiving the user request, the user-provided materials are structured and information is extracted to form a structured data list, the correlation of co-occurrence of diseases of the user is obtained through Bayesian structure learning based on the historical database and information of the user, the Bayesian network structure is constructed, the diseases before and after the same type are aggregated in the Bayesian network to obtain the corresponding disease classification, and the analysis result is output to the user according to the corresponding strategy scheme formed based on the expert experience knowledge graph.

[0099] In some embodiments, the internet medical platform performs Bayesian structure learning according to the collected historical data, learns the probability of co-occurrence of disease combinations in the disease list, constructs a Bayesian network structure, and finds the optimal network structure, and uses the conditional independence of the Bayesian network to explain the causal relationship between nodes.

[0100] The greedy search is used to find the optimal solution to avoid falling into a local optimum.

[0101] In some embodiments, the specific process of finding the optimal solution includes:

[0102] S1, selecting an initial structure;

[0103] S2, changing all possible structures when a local optimum is not reached or a maximum step number is not reached;

[0104] S3, updating the score of the corresponding node, if the score increases, the adjacent structure is selected as the candidate structure for the next step;

[0105] S4, if the candidate structure is empty, the structure with the largest score increase is selected as the current structure from all candidate structures, otherwise it is judged that the local optimum has been reached, and the current structure is returned;

[0106] S5, performing S1-S4 until all structures are traversed.

[0107] The third-party internet medical platform performs structured processing on the materials through OCR and / or image recognition and / or semantic analysis to obtain data in a standard format.

[0108] In some embodiments, the Bayesian network structure has a plurality of nodes, and the nodes correspond to random variables.

[0109] The present application extracts information from user medical records and the like and performs structured processing based on Bayesian structure learning, obtains the correlation of each disease in combination with a historical data set and an information database, and forms a corresponding reference strategy scheme in combination with an expert experience knowledge graph, which can become important reference information for doctors to assist in judgment and save processing time.

[0110] For the sake of description, the above-described apparatus is described with various units in function to describe the embodiment. Of course, the units can be implemented by one or more software and / or hardware in the embodiment of the present application.

[0111] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code.

[0112] The present application is described with reference to the flowchart and / or block diagram of the method, apparatus (system) and computer program product according to the embodiments of the present application. It will be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing unit or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0113] The present application can be described in the general context of computer- executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media including memory storage devices.

[0114] 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 function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0115] These computer program instructions can also be loaded into 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 such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0116] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0117] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), flash memory, or a combination of non-volatile memories in different types. The memory is an example of computer readable storage media.

[0118] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as 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 programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to computing devices. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0119] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to encompass a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0120] ​​The various embodiments in the specification are described in progressive manner, and the same or similar parts among the various embodiments can be mutually referred to, and each embodiment focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0121] The above only describes the embodiments of the present application and is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. An information processing method characterized by comprising: The user provides symptom, medical history and examination information materials, the third party internet medical platform carries out structured processing to the materials through the background OCR, image recognition and semantic analysis function module, obtains the data of standard format, aggregates the corresponding sleep disorder classification in the Bayesian network according to the same type of antecedent and posterior diseases, forms the corresponding strategy scheme according to the expert experience knowledge graph and outputs the analysis result to the user through the user end, structure learning is given a network and the static slice sample of each node, the probability that the sleep disorder combination in the sleep disorder list appears together is learned, the Bayesian network structure is constructed, and the optimal network structure is found out, so that the conditional independence of the Bayesian network is used to explain the causal relationship between nodes;The Bayesian network structure has multiple nodes, nodes correspond to random variables, edges correspond to the dependence or correlation relationship of random variables, nodes include random factors or causes that cause sleep disorders, an initial structure is selected, when the local optimum is not reached or the maximum step number is not reached, all possible structures are changed, the score of the corresponding node is updated, if the score increases, the adjacent structure is selected as the next candidate structure, if the candidate structure is empty, in all candidate structures, the structure with the largest score increase is selected as the current structure, otherwise it is judged that the local optimum has been reached, the current structure is returned, and all structures are iterated and traversed to find the optimal solution, so that falling into local optimum is avoided.

2. The information processing method of claim 1, wherein the edges include directed edges and undirected edges.

3. The information processing method of claim 2, wherein the directed edges represent one-way dependence, and the undirected edges represent correlation dependence.

4. An information processing system comprising: A user end and a third party internet medical platform, wherein, The user end is used for the user to provide symptom, medical history and examination information materials; The third party internet medical platform carries out structured processing to the materials through the background OCR, image recognition and semantic analysis function module, obtains the data of standard format, aggregates the corresponding sleep disorder classification in the Bayesian network according to the same type of antecedent and posterior diseases, forms the corresponding strategy scheme according to the expert experience knowledge graph and outputs the analysis result to the user through the user end. The structure learning is given a network and a static slice sample of each node, learns the probability of the sleep disorder combination in the sleep disorder list appearing together, constructs a Bayesian network structure, finds out the optimal network structure, and thus explains the causal relationship between nodes by using the conditional independence of the Bayesian network; the Bayesian network structure has multiple nodes, the nodes correspond to random variables, the edges correspond to the dependence or correlation relationship of the random variables, the nodes include random factors or causes that cause sleep disorders, an initial structure is selected, when the local optimum is not reached or the maximum number of steps is not reached, all possible structures are changed, the score of the corresponding node is updated, if the score increases, the adjacent structure is taken as the candidate structure of the next step, if the candidate structure is empty, in all candidate structures, the structure with the largest score increase is selected as the current structure, otherwise it is judged that the local optimum has been reached, the current structure is returned, and all structures are iterated and traversed to find the optimal solution, so as to avoid falling into the local optimum.

5. The system of claim 4, the third-party internet medical platform structures the materials and extracts information into a structured pathology data list, obtains the correlation relationship of the sleep disorders or psychological diseases appearing together according to the user historical data and the related disease database established by the third-party internet medical platform, constructs a Bayesian network structure, aggregates the front and rear sequence sleep disorders or psychological diseases belonging to the same type in the Bayesian network to obtain the corresponding result classification, and outputs the analysis result to the user according to the corresponding strategy scheme formed by the expert experience knowledge graph.

6. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the steps of the method of any one of claims 1-3.

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