CN114093509A - Information processing method, device and system - Google Patents
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Abstract
The invention discloses an information processing method, system and system, wherein information is extracted according to materials provided by a user, the information is arranged into a structured data list, an incidence relation of each disease which commonly appears is obtained through Bayesian structure learning according to a historical user data set and an information database, a Bayesian network structure is constructed, the front and back-sequence diseases which belong to the same type are aggregated in the Bayesian network to obtain corresponding disease classification, and a corresponding strategy scheme is formed according to an expert knowledge graph to output an analysis result to the user. The invention extracts information of materials such as user medical records and the like based on Bayesian structure learning and carries out structured processing, obtains the incidence relation of each disease by combining a historical data set and an information database, and forms a corresponding reference strategy scheme by combining an expert experience knowledge map, thereby becoming important reference information for assisting judgment of doctors and saving processing time.
Description
The application is a divisional application of Chinese patent application with the application date of 2021, 7 and 14, and the application number of CN20210792815.8, namely a Bayesian structure learning-based information classification method, device and system.
Technical Field
The present invention relates to the field of artificial intelligence technologies, and in particular, to an information processing method, apparatus, and system.
Background
Classification analysis of diseases is an essential step in many scenes, and classification of diseases at present mainly depends on manual judgment, and classification is disordered due to irregular professional abilities of professionals, for example, classification of a certain disease may be due to insufficient medical expertise, such as many causes of cancer, doctors may have different expressions for thyroid cancer when providing diagnosis, such as thyroid tumor, thyroid malignant tumor, papillary thyroid tumor and the like, which all correspond to certain specific periods or specific types of thyroid cancer, but if professional literacy is insufficient, classification errors are easily caused, and the classification difficulty is more obvious in psychological diseases, so that reasonable classification of diseases needs to be assisted in a data-driven manner to improve auxiliary reference reliability.
Disclosure of Invention
Aiming at the defects, the technical problem to be solved by the invention is how to solve the data analysis and classification problem of sleep disorder by using scientific and technological means.
In view of the above-mentioned drawbacks, it is an object of the present invention to provide an information processing method, system, and electronic device, computer storage medium, and program product.
According to one aspect of the embodiments of the present specification, an information processing method based on bayesian structure learning is provided, which includes extracting information according to materials provided by a user, sorting the information into a structured data list, obtaining an incidence relation of each disease through bayesian structure learning according to a historical user data set and an information database, constructing a bayesian network structure, aggregating front and back-order diseases belonging to the same type in the bayesian network to obtain a corresponding disease classification, and forming a corresponding strategy scheme according to an expert knowledge graph to output an analysis result to the user.
In some embodiments, the bayesian network structure has a plurality of nodes, the nodes corresponding to random variables and the edges corresponding to dependencies or correlations of the random variables.
In some embodiments, the edges include directed edges and undirected edges.
In some embodiments, the node includes a stochastic factor or cause that causes sleep disturbance.
In some embodiments, directed edges represent unidirectional dependencies, and undirected edges represent dependent dependencies.
In some embodiments, structure learning finds the optimal network structure given a static sliced sample of a network and each node, thereby accounting for causal relationships between nodes with the conditional independence of bayesian networks.
According to an aspect of an embodiment of the present specification, there is provided an information processing method based on bayesian structure learning, applied to an internet medical platform, including:
receiving a user request, carrying out structuring processing and extracting information according to materials provided by a user to form a structured data list, obtaining an incidence relation of user diseases through Bayesian structure learning according to a historical database and the information of the user, constructing a Bayesian network structure, aggregating front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, forming a corresponding strategy scheme according to an expert experience knowledge graph, and outputting an analysis result to the user.
In some embodiments, the internet medical platform performs bayesian structure learning according to the collected historical data, learns the probability of the occurrence of disease combinations in a disease list, constructs a bayesian network structure, finds out an optimal network structure, and explains the causal relationship between nodes by using conditional independence of the bayesian network.
In some embodiments, greedy search is used to find the optimal solution, avoiding falling into local optimality.
In some embodiments, the specific process of finding the optimal solution includes:
s1, selecting an initial structure;
s2, when the local optimum is not reached or the maximum step number is not reached, all the possible structures are changed;
s3, updating the score of the corresponding node, and if the score is increased, taking the adjacent structure as the alternative structure of the next step;
s4, if the alternative structure is empty, selecting the structure with the most score increase from all the alternative structures as the current structure, otherwise, judging that the local optimum is achieved, and returning to the current structure;
s5, executing S1-S4 until all structures are traversed.
According to another aspect of embodiments of the present specification, there is provided a method of processing information for a patient based on bayesian structure learning, including:
the method comprises the steps that a user provides materials of symptoms, medical history and examination information, a third-party internet medical platform conducts structuralization processing on the materials, extracts the information and arranges the information into a structuralization pathological data list, an incidence relation where one or more diseases commonly appear is obtained through Bayesian structure learning according to user historical data and a related disease database established by the third-party internet medical platform, a Bayesian network structure is constructed, front-back-order diseases belonging to the same type are aggregated in the Bayesian network to obtain corresponding disease classification, and a corresponding strategy scheme is formed according to expert experience knowledge maps to output analysis results to the user.
In some embodiments, a third party internet medical platform structures the material by OCR and/or image recognition and/or semantic analysis to obtain data in a standard format.
In some embodiments, the bayesian network structure has a plurality of nodes, the nodes corresponding to random variables.
According to another aspect of embodiments of the present specification, there is provided an information processing system based on bayesian structure learning, including: a user terminal and an internet medical platform, wherein,
the user terminal is used for providing materials for users, including but not limited to providing pictures, documents and standardized inspection reports;
the internet medical platform conducts structuralization processing on the materials and extracts information to arrange the information into a structuralized pathological data list, and according to user historical data and a related disease database established by a third-party internet medical platform, an incidence relation of one or more commonly occurring diseases is obtained through Bayesian structure learning, and a Bayesian network structure is constructed;
the Internet medical platform aggregates the front-rear-order diseases belonging to the same type in the Bayesian network to obtain corresponding disease classifications, forms a corresponding strategy scheme according to the expert experience knowledge graph, and outputs an analysis result to a user through the user side.
In some embodiments, the third-party internet medical platform performs structured processing on the material through the background OCR, image recognition and semantic analysis functional module to obtain data in a standard format.
According to another aspect of embodiments of the present specification, there is provided an electronic apparatus including:
a processor; and
a memory arranged to store computer-executable instructions that, when executed, cause the processor to:
receiving a user request, carrying out structuring processing and extracting information according to materials provided by a user to form a structured data list, obtaining an incidence relation of user diseases through Bayesian structure learning according to a historical database and the information of the user, constructing a Bayesian network structure, aggregating front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, forming a corresponding strategy scheme according to an expert experience knowledge graph, and outputting an analysis result to the user.
According to another aspect of embodiments herein, there is provided a computer readable storage medium having stored thereon a computer program/instructions which when executed by a processor implement the steps of: the method comprises the steps of extracting information according to materials provided by a user, arranging the information into a structured data list, obtaining the incidence relation of common occurrence of each disease through Bayesian structure learning according to a historical user data set and an information database, constructing a Bayesian network structure, aggregating the front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, and forming a corresponding strategy scheme according to an expert experience knowledge graph to output an analysis result to the user.
According to another aspect of embodiments herein there is provided a computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the steps of: the method comprises the steps of extracting information according to materials provided by a user, arranging the information into a structured data list, obtaining the incidence relation of common occurrence of each disease through Bayesian structure learning according to a historical user data set and an information database, constructing a Bayesian network structure, aggregating the front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, and forming a corresponding strategy scheme according to an expert experience knowledge graph to output an analysis result to the user.
According to another aspect of embodiments of the present specification, there is provided an electronic apparatus including:
a processor; and
a memory arranged to store computer-executable instructions that, when executed, cause the processor to:
the method comprises the steps of extracting information according to materials provided by a user, arranging the information into a structured data list, obtaining the incidence relation of common occurrence of each disease through Bayesian structure learning according to a historical user data set and an information database, constructing a Bayesian network structure, aggregating the front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, and forming a corresponding strategy scheme according to an expert experience knowledge graph to output an analysis result to the user.
According to another aspect of embodiments of the present specification, there is provided a computer readable storage medium having stored thereon a computer program/instructions, characterized in that the computer program/instructions, when executed by a processor, implement the steps of:
receiving a user request, carrying out structuring processing and extracting information according to materials provided by a user to form a structured data list, obtaining an incidence relation of user diseases through Bayesian structure learning according to a historical database and the information of the user, constructing a Bayesian network structure, aggregating front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, forming a corresponding strategy scheme according to an expert experience knowledge graph, and outputting an analysis result to the user.
According to another aspect of embodiments herein there is provided a computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the steps of:
receiving a user request, carrying out structuring processing and extracting information according to materials provided by a user to form a structured data list, obtaining an incidence relation of user diseases through Bayesian structure learning according to a historical database and the information of the user, constructing a Bayesian network structure, aggregating front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, forming a corresponding strategy scheme according to an expert experience knowledge graph, and outputting an analysis result to the user.
According to another aspect of embodiments of the present specification, there is provided an electronic apparatus including:
a processor; and
a memory arranged to store computer-executable instructions that, when executed, cause the processor to:
receiving a user request, carrying out structuring processing and extracting information according to materials provided by a user to form a structured data list, obtaining an incidence relation of user diseases through Bayesian structure learning according to a historical database and the information of the user, constructing a Bayesian network structure, aggregating front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, forming a corresponding strategy scheme according to an expert experience knowledge graph, and outputting an analysis result to the user.
The invention extracts information of materials such as user medical records and the like based on Bayesian structure learning and carries out structured processing, obtains the incidence relation of each disease by combining a historical data set and an information database, and forms a corresponding reference strategy scheme by combining an expert experience knowledge map, thereby becoming important reference information for assisting judgment of doctors and saving processing time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic structural diagram illustrating an embodiment of an information processing method based on Bayesian structure learning according to the present invention;
FIG. 2 is a schematic structural diagram of another embodiment of the information processing method based on Bayesian structure learning according to the invention;
FIG. 3 is a schematic structural diagram of another embodiment of the information processing method based on Bayesian structure learning according to the invention;
fig. 4 shows an architecture diagram of an embodiment of the information processing system based on bayesian structure learning.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
As shown in fig. 1, an embodiment of the present specification provides an information processing method based on bayesian structure learning, which extracts information according to materials provided by a user, arranges the information into a structured data list, obtains an association relationship where each disease commonly appears through bayesian structure learning according to a historical user data set and an information database, constructs a bayesian network structure, aggregates front-back-order diseases belonging to the same type in the bayesian network to obtain a corresponding disease classification, and forms a corresponding policy scheme according to an expert knowledge graph to output an analysis result to the user.
In some embodiments, the bayesian network structure has a plurality of nodes, the nodes corresponding to random variables and the edges corresponding to dependencies or correlations of the random variables.
In some embodiments, the edges include directed edges and undirected edges.
In one particular example, a node includes a stochastic factor or cause that causes sleep disturbance.
In some embodiments, directed edges represent unidirectional dependencies, and undirected edges represent dependent dependencies.
In some embodiments, structure learning finds the optimal network structure given a static sliced sample of a network and each node, thereby accounting for causal relationships between nodes with the conditional independence of bayesian networks.
Another embodiment of the present disclosure provides an information processing method based on bayesian structure learning, applied to an internet medical platform, including:
receiving a user request, carrying out structuring processing and extracting information according to materials provided by a user to form a structured data list, obtaining an incidence relation of user diseases through Bayesian structure learning according to a historical database and the information of the user, constructing a Bayesian network structure, aggregating front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, forming a corresponding strategy scheme according to an expert experience knowledge graph, and outputting an analysis result to the user.
In some embodiments, the internet medical platform performs bayesian structure learning according to the collected historical data, learns the probability of the occurrence of disease combinations in a disease list, constructs a bayesian network structure, finds out an optimal network structure, and explains the causal relationship between nodes by using conditional independence of the bayesian network.
In a specific example, a greedy search is adopted to find an optimal solution, so that the situation that the optimal solution is locally optimal is avoided.
As shown in fig. 2, in a specific example, a specific process for finding the optimal solution includes:
s1, selecting an initial structure;
s2, when the local optimum is not reached or the maximum step number is not reached, all the possible structures are changed;
s3, updating the score of the corresponding node, and if the score is increased, taking the adjacent structure as the alternative structure of the next step;
s4, if the alternative structure is empty, selecting the structure with the most score increase from all the alternative structures as the current structure, otherwise, judging that the local optimum is achieved, and returning to the current structure;
s5, executing S1-S4 until all structures are traversed.
As shown in fig. 3, another embodiment of the present specification provides an information processing method based on bayesian structure learning, including:
the method comprises the steps that a user provides materials of symptoms, medical history and examination information, a third-party internet medical platform conducts structuralization processing on the materials, extracts the information and arranges the information into a structuralization pathological data list, an incidence relation where one or more diseases commonly appear is obtained through Bayesian structure learning according to user historical data and a related disease database established by the third-party internet medical platform, a Bayesian network structure is constructed, front-back-order diseases belonging to the same type are aggregated in the Bayesian network to obtain corresponding disease classification, and a corresponding strategy scheme is formed according to expert experience knowledge maps to output analysis results to the user.
In some embodiments, a third party internet medical platform structures the material by OCR and/or image recognition and/or semantic analysis to obtain data in a standard format.
In some embodiments, the bayesian network structure has a plurality of nodes, the nodes corresponding to random variables.
As shown in fig. 4, another embodiment of the present specification provides an information processing system based on bayesian structure learning, including: a user terminal and an internet medical platform, wherein,
the user terminal is used for providing materials for users, including but not limited to providing pictures, documents and standardized inspection reports;
the internet medical platform conducts structuralization processing on the materials and extracts information to arrange the information into a structuralized pathological data list, and according to user historical data and a related disease database established by a third-party internet medical platform, an incidence relation of one or more commonly occurring diseases is obtained through Bayesian structure learning, and a Bayesian network structure is constructed;
the Internet medical platform aggregates the front-rear-order diseases belonging to the same type in the Bayesian network to obtain corresponding disease classifications, forms a corresponding strategy scheme according to the expert experience knowledge graph, and outputs an analysis result to a user through the user side.
In some embodiments, the third-party internet medical platform performs structured processing on the material through the background OCR, image recognition and semantic analysis functional module to obtain data in a standard format.
In a specific example, the internet medical platform may be a comprehensive internet health medical platform, such as ali health and jingdong health, a platform dedicated to medical health inquiry, such as spring rain doctors, syringus garden, etc., or a specialized medical platform, such as a professional mobile medical platform focused on the central nervous field.
According to an embodiment of another aspect, there is also provided a computer readable storage medium having stored thereon a computer program/instructions which when executed by a processor implement the steps of:
the method comprises the steps of extracting information according to materials provided by a user, arranging the information into a structured data list, obtaining the incidence relation of common occurrence of each disease through Bayesian structure learning according to a historical user data set and an information database, constructing a Bayesian network structure, aggregating the front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, and forming a corresponding strategy scheme according to an expert experience knowledge graph to output an analysis result to the user.
According to an embodiment of another aspect, there is also provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of:
the method comprises the steps of extracting information according to materials provided by a user, arranging the information into a structured data list, obtaining the incidence relation of common occurrence of each disease through Bayesian structure learning according to a historical user data set and an information database, constructing a Bayesian network structure, aggregating the front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, and forming a corresponding strategy scheme according to an expert experience knowledge graph to output an analysis result to the user.
According to another aspect of embodiments of the present specification, there is also provided an electronic apparatus including:
a processor; and
a memory arranged to store computer-executable instructions that, when executed, cause the processor to:
the method comprises the steps of extracting information according to materials provided by a user, arranging the information into a structured data list, obtaining the incidence relation of common occurrence of each disease through Bayesian structure learning according to a historical user data set and an information database, constructing a Bayesian network structure, aggregating the front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, and forming a corresponding strategy scheme according to an expert experience knowledge graph to output an analysis result to the user.
According to another aspect of embodiments herein, there is also provided a computer readable storage medium having stored thereon a computer program/instructions which, when executed by a processor, implement the steps of:
receiving a user request, carrying out structuring processing and extracting information according to materials provided by a user to form a structured data list, obtaining an incidence relation of user diseases through Bayesian structure learning according to a historical database and the information of the user, constructing a Bayesian network structure, aggregating front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, forming a corresponding strategy scheme according to an expert experience knowledge graph, and outputting an analysis result to the user.
According to another aspect of embodiments herein there is provided a computer program product comprising computer programs/instructions which when executed by a processor implement the steps of:
receiving a user request, carrying out structuring processing and extracting information according to materials provided by a user to form a structured data list, obtaining an incidence relation of user diseases through Bayesian structure learning according to a historical database and the information of the user, constructing a Bayesian network structure, aggregating front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, forming a corresponding strategy scheme according to an expert experience knowledge graph, and outputting an analysis result to the user.
According to another aspect of embodiments of the present specification, there is provided an electronic apparatus including:
a processor; and
a memory arranged to store computer-executable instructions that, when executed, cause the processor to:
receiving a user request, carrying out structuring processing and extracting information according to materials provided by a user to form a structured data list, obtaining an incidence relation of user diseases through Bayesian structure learning according to a historical database and the information of the user, constructing a Bayesian network structure, aggregating front and back-sequence diseases belonging to the same type in the Bayesian network to obtain corresponding disease classification, forming a corresponding strategy scheme according to an expert experience knowledge graph, and outputting an analysis result to the user.
In some embodiments, the internet medical platform performs bayesian structure learning according to the collected historical data, learns the probability of the occurrence of disease combinations in a disease list, constructs a bayesian network structure, finds out an optimal network structure, and explains the causal relationship between nodes by using conditional independence of the bayesian network.
And (3) searching an optimal solution by adopting greedy search, and avoiding falling into local optimization.
In some embodiments, the specific process of finding the optimal solution includes:
s1, selecting an initial structure;
s2, when the local optimum is not reached or the maximum step number is not reached, all the possible structures are changed;
s3, updating the score of the corresponding node, and if the score is increased, taking the adjacent structure as the alternative structure of the next step;
s4, if the alternative structure is empty, selecting the structure with the most score increase from all the alternative structures as the current structure, otherwise, judging that the local optimum is achieved, and returning to the current structure;
s5, executing S1-S4 until all structures are traversed.
And the third-party internet medical platform carries out structured processing on the material through OCR and/or image recognition and/or semantic analysis to obtain data in a standard format.
In some embodiments, the bayesian network structure has a plurality of nodes, the nodes corresponding to random variables.
The invention extracts information of materials such as user medical records and the like based on Bayesian structure learning and carries out structured processing, obtains the incidence relation of each disease by combining a historical data set and an information database, and forms a corresponding reference strategy scheme by combining an expert experience knowledge map, thereby becoming important reference information for assisting judgment of doctors and saving processing time.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, 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 flow or flows and/or block diagram block or blocks.
The application may 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 application may 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 may be located in both local and remote computer storage media including memory storage devices.
These computer program instructions may 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 instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may 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 such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, 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 technology, 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 medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. An information processing method, extracting information according to materials provided by users, arranging into a structured data list, obtaining the incidence relation of each result type through Bayesian structure learning according to a historical user data set and an information database, constructing a Bayesian network structure, aggregating the front and back sequence result types belonging to the same type in the Bayesian network to obtain corresponding result classification, forming a corresponding strategy scheme according to an expert experience knowledge graph, and outputting an analysis result to the users; selecting an initial structure, modifying all possible structures when the local optimum is not reached or the maximum step number is not reached, updating scores of corresponding nodes, if the scores are increased, taking an adjacent structure as a next alternative structure, if the alternative structures are empty, selecting the structure with the most increased scores from all the alternative structures as a current structure, otherwise, judging that the local optimum is reached, returning the current structure, continuously and circularly iterating all the structures to search for an optimal solution, and avoiding falling into the local optimum.
2. The information processing method according to claim 1, wherein the type of the result is a sleep disorder or a psychological disease.
3. The information processing method according to claim 1 or 2, the bayesian network structure having a plurality of nodes, a node corresponding to a random variable, an edge corresponding to a dependency or correlation of the random variable.
4. The information processing method according to claim 2 or 3, the edge comprising a directed edge and an undirected edge.
5. The information processing method according to claim 2, the node including a random factor or an incentive that causes a sleep disorder.
6. The information processing method according to claim 3, wherein the directed edges represent unidirectional dependencies, and undirected edges represent dependent dependencies.
7. The information processing method according to claim 5, wherein the structure learning is performed for a static sliced sample given a network and each node, and an optimal network structure is found, so that causal relationships between nodes are explained by conditional independence of a Bayesian network.
8. An information processing system comprising: a user terminal and an internet medical platform, wherein,
the user side is used for providing materials for users;
the internet medical platform conducts structuralization processing on the materials and extracts information to arrange the information into a structuralized pathological data list, and according to user historical data and a related disease database established by a third-party internet medical platform, an incidence relation of one or more commonly occurring diseases is obtained through Bayesian structure learning, and a Bayesian network structure is constructed;
the Internet medical platform aggregates the front-rear-order diseases belonging to the same type in the Bayesian network to obtain corresponding disease classifications, forms a corresponding strategy scheme according to the expert experience knowledge graph, and outputs an analysis result to a user through the user side.
9. The system of claim 8, wherein the third-party internet medical platform obtains data in a standard format by performing structured processing on the material through a background OCR, image recognition and semantic analysis functional module.
10. A computer-readable storage medium, on which a computer program/instructions is stored, characterized in that the computer program/instructions, when executed by a processor, performs the steps of the method according to any of the claims 1-7.
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| CN115331805A (en) * | 2022-07-04 | 2022-11-11 | 深圳市镜象科技有限公司 | Mental disease diagnosis method based on natural language processing and computer equipment |
| CN115985491A (en) * | 2023-03-21 | 2023-04-18 | 安徽通灵仿生科技有限公司 | Medical information processing system of intervention type ventricular catheter pump |
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| US20150356272A1 (en) * | 2014-06-10 | 2015-12-10 | Taipei Medical University | Prescription analysis system and method for applying probabilistic model based on medical big data |
| CN106844194B (en) * | 2016-12-21 | 2018-06-15 | 北京航空航天大学 | A kind of construction method of multi-level software fault diagnosis expert system |
| CN108461151B (en) * | 2017-12-15 | 2021-06-15 | 北京大学深圳研究生院 | Logic enhancement method and device of knowledge graph |
| EP3756189A4 (en) * | 2018-01-22 | 2021-09-29 | Cancer Commons | Platforms for conducting virtual trials |
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| CN110085325B (en) * | 2019-04-30 | 2021-06-01 | 王小岗 | Method and device for constructing knowledge graph about traditional Chinese medicine experience data |
| CN112133385A (en) * | 2020-09-14 | 2020-12-25 | 黄军彩 | Inquiry information acquisition and analysis system and acquisition and analysis method |
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| CN112885471B (en) * | 2021-03-12 | 2023-01-24 | 上海中医药大学附属岳阳中西医结合医院 | Psoriasis curative effect evaluation system based on Bayesian network maximum entropy self-learning extension set pair analysis |
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| CN115985491A (en) * | 2023-03-21 | 2023-04-18 | 安徽通灵仿生科技有限公司 | Medical information processing system of intervention type ventricular catheter pump |
| CN115985491B (en) * | 2023-03-21 | 2023-07-18 | 安徽通灵仿生科技有限公司 | Medical information processing system of interventional ventricular catheter pump |
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