A cause-effect relationship determination method

Abstract

The application relates to a cause-effect relationship judgment method, which comprises the following steps: constructing a Bayesian network by using a construction module; establishing a historical original literature database by using a cause-effect library; feeding back the results of expert argumentation to the cause-effect library; and performing deep learning on the results of the expert argumentation to correct the cause-effect library.

Description

[0001] The application is a divisional application of application No. 201910034540.4, with the application date of January 15, 2019, the application type of invention, and the application name of a cause-effect relationship determination method based on a Bayesian network. TECHNICAL FIELD

[0002] The application belongs to the technical field of cause-effect relationship determination, and relates to a cause-effect relationship determination method. BACKGROUND

[0003] Cause-effect relationship determination refers to reasoning step by step to obtain the key reason and the root cause hidden in an unexpected event or an unexpected effect, and revealing the complex cause-effect relationship.

[0004] For example, a Chinese patent with the publication number CN109063253A discloses an aviation power supply system reliability modeling method based on a Bayesian network, which includes establishing an input-output relationship description table for each element of the aviation power supply system; constructing a corresponding Bayesian network node for each row of data of N sub-tables; determining the parent nodes of each node of the constructed Bayesian network; constructing the same-named parent nodes for the same-named Bayesian network nodes of the element C in step 2, determining the conditional probability distribution of each node on the basis of the determined state of each node of the constructed Bayesian network; determining the target node for the power busbar, and calculating the power supply reliability of the aviation power supply system; determining the corresponding target node for each power busbar, and calculating the power supply reliability of the aviation power supply system. The application improves the reliability calculation efficiency of the aviation power supply system. The method only involves a certain event of one object.

[0005] For example, a Chinese patent with the publication number CN105468703A discloses a cause-effect relationship determination method, which includes the following steps: initializing a cause-effect relationship knowledge base, the cause-effect relationship knowledge base including the abnormal phenomenon of a type of object, the reason causing the abnormal phenomenon, and the cause-effect relationship between the abnormal phenomenon and the reason; selecting the abnormal phenomenon of the current known state from the abnormal phenomenon list, forming a new cause-effect relationship knowledge base according to the cause-effect relationship in the cause-effect relationship knowledge base, and recording the traced reason; and outputting the traced reason as the result information.

[0006] In the prior art, the cause-effect relationship determination is for an abnormal phenomenon or an unexpected effect of one entity object, but cannot be used for cause-effect relationship determination when at least two objects have at least one specific event. In addition, the cause-effect relationship determination method only involves qualitative determination. Therefore, how to determine the cause of the specific event when at least two objects have at least one specific event, and how to determine the cause of the specific event, need a system or a method to solve.

[0007] The prior art patent document with the publication number CN107563596A proposes a causal relationship determination system based on a Bayesian network, which determines the causal relationship between variables through conditional independence test, and the specific implementation method is as follows: one variable Xi in the top layer or the bottom layer is selected, and another node variable Xj connected with the node of Xi variable through a non-directed edge EAij in the middle layer is selected, the conditional independence between the variables Xi and Xj is tested, if there is another variable Xk, the variables Xi and Xj are conditionally independent given the variable Xk, then the non-directed edge EAij between the variables Xi and Xj is deleted, otherwise, the non-directed edge EAij is retained; the process is repeated until all variables in the top layer and the bottom layer have passed the conditional independence test; for those non-directed edges still retained and connected with the variables in the top layer or the bottom layer, the direction is that the top layer variable points to the middle layer variable or the middle layer variable points to the bottom layer variable; the nodes that have established directed edges are selected for conditional independence test, the direction of the non-directed edge between two nodes is determined according to the causal discovery rule, the causal discovery rule is repeatedly applied until all existing non-directed edges pass the conditional independence test and are marked with a certain or possible direction; for the edges between variables that still cannot determine the direction after the above steps, the edges remain unchanged.

[0008] The present application determines the causal relationship through expert argumentation after first encountering an unknown causal relationship, feeds the result of the expert argumentation to the causal library, and the causal library learns the result of the expert argumentation in depth to correct the causal library. When the causal relationship is encountered again, the present application can determine the causal relationship based on the corrected causal library without expert authentication. The above prior art does not set a technical solution for correcting the causal library, and an unknown relationship is not determined by the above prior art, so any event with the unknown relationship cannot be determined by the above prior art when the causal relationship is determined. SUMMARY

[0009] In view of the deficiencies of the prior art, the present application provides a causal relationship determination system based on a Bayesian network, comprising: a construction module for constructing a Bayesian network; a determination module for generating and outputting a causal relationship spectrum based on the requested Bayesian network; a causal library for establishing a historical original literature library; in the case that at least one non-specific event occurs in at least two objects, the construction module analyzes the self factors of the objects causing the non-specific event to construct a factor set and construct an undirected graph structure constraint based on the factor set; and the construction module is capable of correcting the undirected graph structure constraint based on the causal library to establish the Bayesian network; the determination module calculates a causal index of the self factors causing the non-specific event based on the Bayesian network and outputs a causal relationship spectrum of the non-specific event based on the causal index, so as to determine the key reason of the non-specific event.

[0010] According to a preferred embodiment, the construction module defines at least one field based on the non-specific event, and retrieves at least one exclusive factor related to the non-specific event in the field based on the causal library; in the case that the exclusive factor does not belong to the self factors, the construction module prompts the third party to perform evidence finding; in the case that the third party confirms that the exclusive factor objectively exists, the construction module supplements the exclusive factor to the undirected graph structure constraint and further corrects the undirected graph structure constraint.

[0011] According to a preferred embodiment, the construction module retrieves the relationship between the factor pair L m and L n fails, the construction module prompts the third party to perform expert argumentation on the relationship between the factor pair L m and L n , and feeds back the result of the expert argumentation to the causal library, which performs deep learning on the result of the expert argumentation to correct the causal library; in the case that the causal library performs deep learning on the result of the expert argumentation, the construction module corrects the relationship between the factor pair L m and L n based on the causal library according to the factor pair L m and L n .

[0012] According to a preferred embodiment, the construction module establishes a data set D based on the self factors, divides the self factors into a plurality of factor sets L according to the data set D, forms factor pairs L m and L n in a pairwise manner, and performs deep learning on the data set D and the factor pairs L m and Ln is numbered; the constructing module retrieves the relationship between factor pair L m and L n , and corrects the relationship between factor pair L m and L n , and assigns the relationship confidence value between L m → L n , the relationship confidence value between L n → L m , and the relationship confidence value between L n ⊥ L m , thereby constructing the undirected graph structure constraint.

[0013] According to a preferred embodiment, the constructing module constructs a Bayesian network evaluation function based on the data set D and the factor pair set L according to the Bayesian rule, and the evaluation function is used to iteratively generate a Bayesian network with the highest evaluation index from several candidate Bayesian networks based on the undirected graph structure constraint with the constructing module enabling a heuristic search algorithm, wherein the constructing module first constructs an initial first candidate Bayesian network based on the undirected graph structure constraint and evaluates it using the Bayesian network evaluation function to obtain a first evaluation index; then, the constructing module enables the heuristic search algorithm to locally modify the second candidate Bayesian network based on the undirected graph structure constraint, and again uses the Bayesian network evaluation function to evaluate the second candidate Bayesian network to obtain a second evaluation index; the constructing module can obtain at least two candidate Bayesian networks and corresponding evaluation indexes in an iterative loop manner based on the heuristic search algorithm; the constructing module outputs a final Bayesian network with the optimal evaluation index as the output causal relationship spectrum of the non-specific event when it obtains at least two candidate Bayesian networks and at least two evaluation indexes.

[0014] According to a preferred embodiment, the determining module calculates the causal index between each factor pair L m and L n based on the final Bayesian network and the Pearl principle, thereby outputting the causal relationship spectrum, wherein for factor L m , the node set is obtained by traversing the nodes connected to factor L m based on the undirected graph structure constraint; and the correlation between each node and factor L m is calculated in sequence, and the node with the largest correlation is selected to make an independence assumption, and the node independent of L m under the given data set D is deleted to improve the determination efficiency of the determining module; factor L n is independent of factor Lm The independence between them is measured by mutual information:

[0015]

[0016] When the mutual information exceeds a threshold of mutual information, then factor L n has a correlation, not much independence; when the mutual information does not exceed the threshold of mutual information, then factor L m has no correlation, is independent. n has a correlation, not much independence; when the mutual information does not exceed the threshold of mutual information, then factor L m has no correlation, is independent.

[0017] According to a preferred embodiment, the causal library is established in the following way: the causal library is constructed by obtaining a plurality of relevant documents containing a plurality of historical attributes in the technical field and classifying them to form a plurality of document units, so as to construct the original document library, so as to mine the relationship confidence value between the attributes by data pattern mining; wherein the document layer in the causal library counts the frequency of words / phrases in each document, obtains the joint occurrence probability of the words / phrases according to the independence assumption; the document layer calculates the correlation strength of the words / phrases; the document layer constructs the correlation reduction coordinates of the documents, and classifies the relevant documents in the form of an iterative algorithm based on the classification function constructed by the correlation reduction coordinates and the correlation strength of all the relevant documents to form a plurality of document units.

[0018] According to a preferred embodiment, when the data layer in the causal library obtains the document units, the data layer obtains the data set in the form of pairing two historical attributes; the data layer extracts the relationship between two historical attributes in each relevant document in the form of syntactic analysis of natural language processing, so as to establish the relationship knowledge base of the two historical attributes, the relationship between the two historical attributes includes positive relationship, negative relationship and vertical relationship; and the data layer searches for the documents containing the two historical attributes in the document units based on the relationship knowledge table to obtain the relationship confidence value of the two historical attributes in a fusion manner to establish the relationship confidence value base of the two historical attributes, the relationship between the two historical attributes includes positive relationship confidence value, negative relationship confidence value and vertical relationship confidence value; thereby, the data layer constructs the historical data set based on the relationship knowledge base and the relationship confidence value base established by pairing all the historical attributes in pairs.

[0019] According to a preferred embodiment, the application further discloses a method for determining the cause-effect relationship based on the Bayesian network, which comprises: a constructing module for constructing the Bayesian network; a determining module for generating and outputting the cause-effect relationship spectrum based on the Bayesian network; a cause-effect library for establishing a historical original document library; in the case that at least one non-specific event occurs in at least two objects, the constructing module analyzes the self factors of the objects causing the non-specific event to construct a factor set and construct a constraint of the undirected graph structure based on the factor set; and the constructing module is capable of modifying the constraint of the undirected graph structure based on the cause-effect library to establish the Bayesian network; the determining module calculates the cause-effect index of the self factors causing the non-specific event based on the Bayesian network and outputs the cause-effect relationship spectrum of the non-specific event based on the cause-effect index, so as to determine the key reason of the non-specific event.

[0020] According to a preferred embodiment, the method further comprises: the constructing module defines at least one field based on the non-specific event and retrieves at least one exclusive factor related to the non-specific event in the field based on the cause-effect library; in the case that the exclusive factor does not belong to the self factors, the constructing module prompts the third party to find the evidence; in the case that the third party confirms that the exclusive factor is objectively existing, the constructing module supplements the exclusive factor to the constraint of the undirected graph structure and further modifies the constraint of the undirected graph structure.

[0021] The advantage of the application mainly lies in that the constraint of the undirected graph of the Bayesian network between the factors of at least one non-specific event occurring between at least two objects is constructed, the constraint of the undirected graph is modified based on the document knowledge base and the expert knowledge base, and then the Bayesian network thereof is constructed based on the constraint of the undirected graph by using the heuristic search algorithm. The structure learning of the Bayesian network is mainly used for revealing the qualitative relationship between the variables and revealing the quantitative relationship. However, there are many difficulties in constructing the Bayesian network from the data angle. The correctness of the construction of the Bayesian network can be increased by relying on the non-specific event between the objects and the historical event thereof. Finally, the cause-effect analysis reasoning is realized based on the constructed Bayesian network. In summary, the key problem to be solved by the application is to determine the cause-effect relationship spectrum of the non-specific event and determine the key reason by fusing the object knowledge to construct a reasonable Bayesian network and the cause-effect analysis reasoning. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 is a flowchart of a preferred embodiment of the determining method provided by the application; and

[0023] Figure 2 is a preferred module schematic diagram of the determining system provided by the application.

[0024] LIST OF REFERENCE NUMERALS

[0025] 1: construction module 2: determination module

[0026] 3: causal library DETAILED DESCRIPTION

[0027] The application will be described in detail below with reference to the accompanying drawings. Figure 1 And 2 will be described in detail.

[0028] In the description of the application, the terms "first", "second", "third" and "fourth" are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third" and "fourth" can explicitly or implicitly include one or more of the features. In the description of the application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0029] Example 1

[0030] The present embodiment provides a causal relationship determination system based on Bayesian network, which aims to output the causal relationship spectrum when at least one non-specific event occurs between at least two objects. The advantage of the present application mainly lies in constructing the undirected graph constraint of the Bayesian network between the factors of at least one non-specific event occurring between at least two objects, and correcting the undirected graph constraint based on the literature knowledge base and the expert knowledge base, and then constructing the Bayesian network based on the undirected graph constraint using a heuristic search algorithm. The structure learning of the Bayesian network is mainly used to reveal the qualitative relationship between variables, and at the same time to reveal the quantitative relationship. However, there are many difficulties in constructing the Bayesian network from the data alone. Relying on the non-specific events and their historical events between objects can increase the correctness of the construction of the Bayesian network. Finally, based on the constructed Bayesian network, causal analysis reasoning is realized. In summary, the fusion of object knowledge to construct a reasonable Bayesian network and causal analysis reasoning to determine the causal relationship spectrum of non-specific events and to determine the key reasons are the key problems to be solved by the present application.

[0031] Specifically, the system includes a construction module 1, a decision module 2, and a causal database 3. Construction module 1 is used to construct a Bayesian network. Decision module 2 is used to generate and output a causal relationship spectrum based on the requested Bayesian network. Causal database 3 is used to establish a historical original literature database. For at least two objects in at least one non-specific event, construction module 1 analyzes the intrinsic factors of the objects causing the non-specific event to construct a factor set and builds an undirected graph structure constraint based on the factor set. Furthermore, construction module 1 can modify the undirected graph structure constraint based on causal database 3 to establish a Bayesian network. Decision module 2 calculates the causal indices of the non-specific events caused by the intrinsic factors based on the Bayesian network and outputs the causal relationship spectrum of the non-specific events based on the causal indices, thereby determining the key causes of the non-specific events. In this invention, non-specific events can be real events, such as a collision between two vehicles. Non-specific events can also be events anticipated by researchers, such as a spacecraft docking failure.

[0032] Preferably, the construction module 1 defines at least one domain based on non-specific events, and retrieves at least one other factor related to the non-specific events in the domain based on the causal database 3. If the other factor is not a self-factor, the construction module 1 prompts a third party to verify the evidence; if the third party confirms the objective existence of the other factor, the construction module 1 adds the other factor to the undirected graph structure constraint to further modify the undirected graph structure constraint.

[0033] Preferably, module 1 constructs factor pairs L based on causal library 3. m and L n If the retrieval of the relationship between factors fails, Module 1 prompts a third party to check the relationship between factors and L. m and L n The relationships between factors are verified by experts, and the results of the expert verification are fed back to causal database 3. Causal database 3 then uses deep learning to refine its causal database based on the expert verification results. With causal database 3 using deep learning to refine its causal database based on the expert verification results, module 1 constructs modules based on causal database 3 to classify L according to factors. m and L n Numbering of factors to L m and L n The relationship between them needs to be corrected.

[0034] Preferably, module 1 establishes a dataset D based on its own factors, and divides its own factors into several factor sets L according to dataset D, forming factor pairs L by pairing them up in a pairwise manner. m and L n And for dataset D and factors on L m and L n Numbering is performed. Module 1, based on the causal library 3, assigns factor pairs to L based on their numbering. m and Ln the relationship between L m and L n the relationship between L m → L n the relationship between L n → L m the relationship between L n ⊥ L m the relationship between L

[0035] Preferably, according to the Bayes rule, the constructing module 1 constructs a Bayes network evaluation function based on the data set D and the factor pair set L, and the evaluation function is used to iteratively generate a Bayes network with the highest evaluation index from several candidate Bayes networks based on the undirected graph structure constraint and in the case that the constructing module 1 enables the heuristic search algorithm. Preferably, the constructing module 1 firstly constructs an initial first candidate Bayes network based on the undirected graph structure constraint and evaluates it using the Bayes network evaluation function to obtain a first evaluation index; then, the constructing module 1 enables the heuristic search algorithm to locally modify a second candidate Bayes network based on the undirected graph structure constraint, and again uses the Bayes network evaluation function to evaluate the second candidate Bayes network to obtain a second evaluation index; the constructing module 1 can obtain at least two candidate Bayes networks and corresponding evaluation indexes in an iterative loop manner based on the heuristic search algorithm. In the case that the constructing module 1 obtains at least two candidate Bayes networks and at least two evaluation indexes, it will output a final Bayes network with the optimal evaluation index as the output causal relationship spectrum of the non-specific event.

[0036] Preferably, the determining module 2 calculates the causal index between each factor pair L m and L n based on the final Bayes network and the Pearl principle, thereby outputting the causal relationship spectrum. Wherein, for the factor L m , the node set connected with the factor L m is obtained based on the undirected graph structure constraint in a traversal manner. And the correlation between each node and the factor L m is calculated in sequence, and the node with the largest correlation is selected to make the independence assumption, and the node independent of L m under the given data set D is deleted to improve the determination efficiency of the determining module 2. The independence between the factor L n and the factor L m is measured by mutual information:

[0037]

[0038] When the mutual information exceeds the threshold value of mutual information, the factor Ln with factor L m with correlation, not quite independent; in mutual information does not exceed the threshold of mutual information, then factor L n with factor L m without correlation, independent.

[0039] Preferably, the causal library 3 is established in the following way: the causal library 3 forms a number of literature units based on a number of relevant literature containing a plurality of historical attributes obtained and classified in the technical field to construct a raw literature library, so as to mine the relationship between the attributes by data pattern mining Relationship confidence value; wherein, the literature layer in the causal library 3 counts the frequency of words / phrases in each literature, obtains the joint occurrence probability of words / phrases according to the independence assumption; the literature layer calculates the correlation strength of words / phrases; the literature layer constructs the correlation reduction coordinates of the literature, and classifies the relevant literature in the form of iterative algorithm according to the classification function constructed based on the correlation reduction coordinates and the correlation strength of all relevant literature to form a number of literature units.

[0040] Preferably, in the case of obtaining the literature unit in the data layer of the causal library 3, the data layer obtains the data set in the form of two historical attribute pairs; the data layer extracts the relationship between the two historical attributes in each relevant literature in the form of syntactic analysis of natural language processing, so as to establish the relationship knowledge base of the two historical attributes, the relationship between the two historical attributes includes positive relationship, negative relationship and vertical relationship; and the data layer searches for the literature containing two historical attributes in the literature unit based on the relationship knowledge table to obtain the relationship confidence value of the two historical attributes in a fusion manner to establish the relationship confidence value library of the two historical attributes, the relationship between the two historical attributes includes positive relationship confidence value, negative relationship confidence value and vertical relationship confidence value; thereby, the data layer constructs the historical data set based on the relationship knowledge base and the relationship confidence value library established in the form of two-by-two pairing between all historical attributes.

[0041] Embodiment 2

[0042] The embodiment discloses a causal relationship determination method based on Bayesian network, which is used for responsibility determination in medical disputes. In the case of not causing conflicts or contradictions, the whole and / or part of the preferred embodiments of other embodiments can be supplemented as the embodiment. Preferably, the method can be realized by the method of the application and / or other alternative modules.

[0043] Medical trouble refers to the behavior of patients who are employed by medical disputes, together with patient families, to seriously hinder medical order, expand the situation, and cause negative impact on the hospital in various ways to exert pressure on the hospital and make profits. The direct consequence of medical trouble is to cause a large number of direct or indirect losses of medical staff in China, and to have very serious and adverse effects, which seriously affect the development of medical undertakings in China. When a medical dispute occurs, it is difficult to classify the causes of the medical dispute. Medical dispute refers to the dispute occurring in medical health, prevention and health care, medical beauty and other medical enterprises or institutions with legal qualifications. At present, medical disputes in China are particularly difficult to handle. The reason is that medical disputes are usually caused by medical errors and negligence. Medical negligence is the error of medical staff in the process of diagnosis and nursing. Medical error refers to the error of medical staff in medical activities such as diagnosis and treatment. These errors often lead to patient dissatisfaction or harm to patients, thus causing medical disputes. In addition to medical errors and negligence, sometimes the medical party has no negligence and errors in medical activities, and only due to the patient's unilateral dissatisfaction, disputes can also be caused. This kind of dispute can be caused by the patient's lack of basic medical knowledge, misunderstanding of correct medical treatment, natural change of disease and inevitable complications, and medical accidents, or it can be caused by the patient's unreasonable accusation. Some people also call it medical tort dispute, that is, the dispute between the provider and the recipient of medical services on whether the medical behavior and its consequences are tort and the responsibility for tort. Therefore, in order to give the doctor a comfortable and healthy working environment and a fair and just explanation to the patient, the causes of the medical dispute need to be presented in a transparent way, and transparency and fairness are achieved.

[0044] Therefore, the present embodiment provides a method for determining causality based on a Bayesian network, which aims to assist the judicial department in solving medical disputes. In the judicial practice of medical disputes, the adoption of the evidence or factors claimed by both parties is mostly from a legal perspective, and the relationship between the evidence or factors is mostly evaluated from a qualitative perspective, which is one of the reasons for the entanglement of the medical and patient parties and one of the reasons for the confusion of the judicial personnel in adjudication or judgment. In the case of entanglement of the medical and patient parties, both parties will question and even appeal the adjudication or judgment, occupying too many legal resources. In the era of advocating the value pursuit of "fairness and justice", solving the contradiction between the medical and patient parties through the way of "data speaking" to give the doctor a comfortable and easy working environment for saving lives, give the patient or his family a convincing explanation, and provide the judicial department with a scientific reference file, which is the important value of the present invention.

[0045] Specifically, the method mainly includes:

[0046] S1: constructing module 1: constructing a Bayesian network. Specifically, the constructing module 1 constructs an undirected graph structure constraint based on the factors claimed by the medical party and the patient party. And in the case of third party intervention, the constructing module 1 corrects the undirected graph structure constraint based on the causal library 3 to establish a Bayesian network.

[0047] S2: determining module 2: outputting a causal relationship spectrum based on the Bayesian network. The determining module 2 calculates the causal index between each factor pair based on the Bayesian network and outputs the causal relationship spectrum of the medical dispute based on the causal index, so that the causal relationship spectrum takes into account the factors claimed by both the medical party and the patient party and considers scientificity, which can effectively prevent the escalation of disputes between the medical party and the patient party and provide data support for the third party to make a ruling or judgment.

[0048] Preferably, step S1 includes the following specific steps:

[0049] S11: establishing a data set D=(D1, D2……D i ) according to the factors claimed by the medical party and the patient party. L=(L1, L2……L n ) is a specific factor pair of a certain group of attributes. For example, the factors claimed by the medical party include patient illness time, hospitalization time, illness degree and illness type. Then the data set can be established with the time attribute and the disease attribute, i.e. D=(time attribute, disease attribute). And the specific factor pair L corresponds to the specific attributes of the illness time and the hospitalization time of the time factor, and is numbered.

[0050] S12: constructing an undirected graph structure constraint based on the relationship between the factors claimed by the medical party and the patient party. The relationship between each specific factor pair is determined by searching the relationship between the factors claimed by the medical party and the patient party, and the factor pairs are numbered. Preferably, the relationship between the factor pairs includes positive relationship, negative relationship and vertical relationship, i.e. attribute L m influences attribute L n , denoted as L m →L n . The relationship between attribute L m and attribute L n may be inverse relationship, i.e. attribute L n influences attribute L m , denoted as L n →L m . The relationship between attribute L m and attribute L n may be vertical relationship, i.e. attribute L m and attribute L n , mutually independent L n ⊥L mFor example, the patient believes that the drug component causes the cerebral palsy to aggravate, and the drug component affects the cerebral palsy to aggravate, so the drug component and the cerebral palsy to aggravate constitute a positive relationship, which is recorded as drug component→cerebral palsy to aggravate. Of course, it can also be considered that the cerebral palsy to aggravate and the drug component constitute a negative relationship, which is also recorded as drug component→cerebral palsy to aggravate.

[0051] S13: modifying the undirected graph structure constraint based on the causal library. In the causal library, the factor pair number is searched, and the relationship between L m and L n is modified, and the relationship confidence value of L m → L n , the relationship confidence value of L n → L m and the relationship confidence value of L n ⊥ L m are assigned. For example, the patient believes that the drug component causes the cerebral palsy to aggravate, but the search in the causal library finds that the drug component does not cause the cerebral palsy to aggravate, so the drug component→cerebral palsy to aggravate is modified to drug component⊥cerebral palsy to aggravate. For example, the drug component is L1, and the cerebral palsy to aggravate is L2, so the number of drug component→cerebral palsy to aggravate is recorded as 12.

[0052] Preferably, the construction module 1 defines at least one request field based on the factors claimed by the medical party and the patient party, and searches for at least one other factor related to the factors claimed by the medical party and the patient party based on the request field. In the case that the at least one factor is not in the factors claimed by the medical party and the patient party, the construction module 1 prompts the third party to find evidence. In the case that the third party determines that the at least one factor exists objectively, the construction module 1 supplements the at least one factor to the undirected graph structure constraint and further modifies the undirected graph structure constraint. For example, a newborn death event occurs, and the factors claimed by the medical party and the patient party include amniotic embolism, hypoxia and multiple births, so the construction module 1 defines the request field of women giving birth based on these factors, and then searches for other factors existing in the accident in the corresponding women giving birth field in the causal library 3, such as searching for thin uterine wall. However, the thin uterine wall does not appear in the factors claimed by the medical party and the patient party, so the construction module 1 prompts the third party to find out whether the puerpera has a thin uterine wall. If the condition exists objectively, the construction module 1 supplements the factor of thin uterine wall to further modify the undirected graph structure constraint. That is, in step S13, there is another possibility that at least one factor not claimed by either party is an important factor affecting the result, so the construction module 1 prompts the third party to find out the at least one factor. If the at least one factor exists objectively, the at least one factor needs to be added to the undirected graph structure constraint for modification, so as to increase the reliability and scientificity of the result and improve the fair judgment or ruling of the third party, which shows the rigor, fairness and responsibility of the third party.

[0053] Preferably, the relationship between some factor pair or some factor pairs and its relationship confidence value can not exist in the cause-effect library 3. In order to guarantee the factors claimed by both parties can be supported. That is, the building module 1 prompts the third party to conduct expert argumentation on the relationship between the factor pair L m and L n if the building module 1 fails to search the relationship between the factor pair L m and L n based on the cause-effect library 3. For example, the patient party claims that the head size of the newborn is not the cause of the death of the newborn. The relationship between the head size of the newborn and the death of the newborn does not exist in the cause-effect library 3. Then the building module 1 prompts the third party to conduct expert argumentation. And the result of the expert argumentation is fed back to the cause-effect library 3, and the cause-effect library 3 learns the result of the expert argumentation in depth to correct the cause-effect library 3. In the case that the cause-effect library 3 learns the result of the expert argumentation in depth, the building module 1 corrects the relationship between the factor pair L m and L n based on the cause-effect library 3 according to the factor pair L m and L n number.

[0054] Preferably, the building module 1 builds a Bayesian network evaluation function based on the cause-effect library 3, the data set D and the factor pair set L:

[0055] logP(G, D, K L ) = logP(G) + logP(D|G) + logP(K L |G)

[0056] Preferably, the building module 1 builds a requested Bayesian network based on the Bayesian network evaluation function and the undirected graph structure constraint. In the formula, G is a Bayesian grid, whose value includes a directed acyclic graph in which a specific factor pair of a certain group of attributes L = (L1, L2, …, L n ) is combined as a node. Wherein, P(G) is a prior distribution. According to existing knowledge, the maximum value of logP(G) + logP(D|G) is equivalent to logP(G|D). logP(G|D) can be calculated according to the Bayesian information criterion BIC score. In the formula,

[0057]

[0058] Wherein, if any edge in the structure G is represented as L m → L n , then KL(L m → L n) is the relational confidence value. The summation in the formula is the summation of the document knowledge confidences of the positive relations corresponding to all the directed edges in the structure G. For a given dataset D, for any factor pair L m and L n , a Bayesian network is constructed based on the Bayesian network evaluation function and the undirected graph structure constraint. After determining the undirected graph structure constraint of the Bayesian network, a heuristic search algorithm such as the K2 algorithm can be performed to seek the network structure with the optimal score function. The general steps are: starting from an initial model, at each step of the search, first locally modify the current model using a search operator to obtain a series of candidate models, then calculate the score of each candidate model, and compare the optimal candidate model with the current model. If the score of the optimal candidate model is large, it is used as the next current model and the search continues; otherwise, the search is stopped and the current model is returned. According to the Bayesian principle, the candidate model with the largest score is the Bayesian network. Preferably, the Bayesian network evaluation function is constructed according to the established Bayesian network and the Bayesian rule. The Bayesian network evaluation function can be constructed according to classical heuristic structure learning algorithms, such as the K2 algorithm, the Max-Min Parents and Children algorithm, and the Markov Chain Monte Carlo search, etc.

[0059] Preferably, the determining module 2 calculates the causal index between each factor pair based on the requested Bayesian network and the Pearl principle, thereby outputting the causal relationship spectrum. The determining module 2 calculates the causal index between attributes based on data pattern mining, thereby being able to determine whether the attributes constitute a complication or a comorbidity according to the causal index. When the causal index is calculated, the determining module 2 calculates the causal index between attributes based on the Pearl principle and the Bayesian network structure. Pearl needs to calculate E(Y|do(X)) by intervening X to implement the X event when exploring whether event X is the cause of event Y, i.e. the average change of event Y under the intervention of X is greater than the significance level, then X is considered to be the cause of Y.

[0060] When the determining module 2 calculates the causal index between attributes based on data pattern mining, due to the huge amount of literature, the Bayesian grid is huge, therefore, the backdoor criterion is used to calculate the causal index. The backdoor criterion refers to that the Bayesian grid G is a directed acyclic graph, (L m , L n ) is a pair of nodes of G, and the node Z set is the backdoor of (L m , L n ), wherein all nodes in Z are not descendants of Z and Z blocks all connections L m to L m pointing to L n . Therefore, the causal relationship between the factor pair Lm and Ln can be inferred by the backdoor principle.

[0061] To be able to determine the module 2 by independence test to simplify the undirected graph constraints without affecting the causal relationship between factors. For example, the independence test can use the chi-square independence test. In the present invention, the independence test can also be used as follows: for the factor L m , by tabulating form based on the constructed undirected graph to obtain the node set consisting of L m connected nodes. And each node and the factor L m between the correlation is calculated, and the largest correlation from the node is selected to make the independence assumption, delete the node under the given request subset D i independent of L m . In the present invention, the entropy is used to measure the uncertainty of the factor pair set L m . Given the factor L m , the uncertainty of the factor L n can be measured by conditional entropy as follows:

[0062]

[0063] The degree of correlation between the factor L n and L m can be measured by mutual information:

[0064]

[0065] If the mutual information exceeds the threshold of mutual information, it is considered that L n and L m have correlation. If the mutual information does not exceed the threshold of mutual information, it is considered that L n and L m have no correlation.

[0066] Preferably, the causal library 3 is established as follows: the causal library 3 is based on the obtained a large number of related literature containing various historical attributes in the technical field and classifies them to form a number of literature units to construct the original literature library, to mine the relationship between historical attributes by data pattern. Among them, the literature layer of the causal library 3 counts the frequency of words / phrase in each literature, and obtains the joint occurrence probability of words / phrase according to the independence assumption. The literature layer calculates the correlation strength of words / phrase; the literature layer constructs the correlation reduction coordinates of the literature, and classifies the related literature according to the iterative algorithm form based on the correlation reduction coordinates and the correlation strength of all related literature to form a number of literature units.

[0067] Preferably, the document layer is based on numerous relevant documents containing various historical attributes. The document layer categorizes these relevant documents into several document units to construct the original document database. These relevant documents include medical records, research reports, conference proceedings, journal articles, books, academic papers, and patents. Given such a large volume of documents, they need to be classified using specific methods. Document classification aims to effectively observe the relationships between historical attributes and reduce the system load. For example, they can be classified according to digestive diseases, cardiovascular diseases, and neurological diseases. They can also be classified according to academic fields, such as rehabilitation medicine and psychology. However, given the large volume of documents, accurate and efficient classification directly affects the differentiation of complications and comorbidities. Preferably, document classification can employ Bayesian methods, SVM methods, and k-NN methods.

[0068] Preferably, the relevant literature classification is performed as follows: the frequency of words / phrases in each literature is counted at the literature level, and the joint occurrence probability of words / phrases is obtained according to the independence assumption. For example, the joint occurrence probability distribution of a specific literature can be calculated using the Naive Bayes method.

[0069] Preferably, the document layer calculates the correlation strength of words / phrases. Calculating the correlation strength reflects the relevance of words / phrases, which is suitable for document classification. Preferably, in classification, N is defined as the set of document samples, and V is the set of document types. i It is a subset of the i-th document type. W is the set of words / phrases. i It is a subset of the i-th word / phrase. In V i Contains S j There are n samples, where the reduced coordinates T of the p-th sample are... p It is an n-dimensional array:

[0070]

[0071] Where, k i The number of occurrences of the i-th word in (i = 1, 2, 3, ..., n) Normalization coefficient.

[0072] In V i The correlation vector is all V i The average of the reduced coordinates of the mid-sample associations reflects the strength of the associations between words / phrases in the literature.

[0073]

[0074] Preferably, the document layer obtains the associated reduced coordinates of the documents, and classifies the associated documents in the form of an iterative algorithm based on the classification function constructed by the associated reduced coordinates of all the associated documents to form a plurality of document unit bodies. Preferably, the associated reduced coordinates of any document are:

[0075]

[0076] wherein q i is the number of occurrences of the i-th word in the document. In the classification, the distance between the document to be classified and the support points (b1, b2, …, b i ) of each category of documents V n is recorded as:

[0077]

[0078] According to the relevance intensity, a document classification function is constructed:

[0079]

[0080] wherein γ i is related to the relevance intensity.

[0081] Preferably, the iterative algorithm can adopt a minimization iterative algorithm, a minimum optimization iterative algorithm, and an expectation maximum iterative algorithm. Preferably, the classification function can perform deep learning based on the sample size of the associated documents, thereby enhancing the accuracy of the document layer.

[0082] Preferably, in the case where the data layer in the cause-effect library 3 obtains the document unit bodies, the data layer obtains the historical data set in the form of two historical attribute pairings. The data layer extracts the relationship between the two historical attributes in each associated document in a syntactic analysis manner of natural language processing, to establish a relationship knowledge base of the two historical attributes. The relationship between the two historical attributes includes a positive relationship, a reverse relationship, and a vertical relationship. Moreover, the data layer retrieves the documents containing the two histories in the document unit bodies based on the relationship knowledge table to obtain the relationship confidence values of the two historical attributes in a fusion manner, to establish a relationship confidence value base of the two historical attributes. The relationship between the two historical attributes includes a positive relationship confidence value, a reverse relationship confidence value, and a vertical relationship confidence value. Thus, the data layer constructs the historical data set based on the relationship knowledge base and the relationship confidence value base established by pairing all the histories in pairs.

[0083] Preferably, the data layer can obtain the main characteristic parameters based on the literature unit and construct the historical data set based on the main characteristic parameters. In order to reduce the interference of a large number of characteristic parameters formed by a large number of related literatures on the causal relationship between historical attributes and improve the utilization value of the original literature library. Preferably, in the case of obtaining the literature unit in the data layer, the data layer obtains the historical data set in the form of pairing two historical attributes. The data layer extracts the relationship between two historical attributes in each related literature in the form of syntactic analysis of natural language processing, so as to establish the relationship knowledge base of two historical attributes, and the relationship between two historical attributes includes positive relationship, reverse relationship and vertical relationship. And, the data layer searches for the literature containing two historical attributes in the literature unit based on the relationship knowledge table to obtain the relationship confidence value of two historical attributes in a fusion manner to establish the relationship confidence value library of two historical attributes, and the relationship between two historical attributes includes positive relationship confidence value, reverse relationship confidence value and vertical relationship confidence value. Thus, the data layer constructs the historical data set based on the relationship knowledge base and the relationship confidence value library established in the form of pairing two by two between all historical attributes. For example, in the related literature, a plurality of historical attributes such as historical attribute L1, historical attribute L2, historical attribute L3 and historical attribute L4 are obtained. According to the relationship of the above historical attributes, the relationship knowledge base of historical attribute L1 and historical attribute L3, the relationship knowledge base of historical attribute L2 and historical attribute L3 can be established, and so on. Then, in the unit literature body, the relationship confidence value library is constructed based on the above relationship knowledge base according to the content in different literatures. Preferably, the sum of the positive relationship confidence value, the reverse relationship confidence value and the vertical relationship confidence value is normalized. That is, in the unit literature body, all the literatures are queried, and the positive relationship confidence value, the reverse relationship confidence value and the vertical relationship confidence value are weighted according to the frequency. The data layer inputs the above relationship knowledge base and relationship confidence value library into the judgment module 2 to perform the next step.

[0084] Preferably, for journal literature, the positive relationship confidence value of L1→L2 can also be defined as follows:

[0085]

[0086] Wherein, C(Xi) is the credibility of literature Xi, the formula is: C(Xi)=(IFi+1)×(CIi+1), Xi represents the i th literature, IFi is the standardized impact factor of the journal where the literature Xi is located, CIi is the standardized citation amount. If there is no literature existing the relationship between L1 and L2, then KL(L1→L2)=0, KL(L2→L1)=0, KL(L1⊥L2)=1. Other types of literature can be defined in the same way, for example, medical records can be defined according to the authority of doctors. For conference articles, they can be defined according to the authority of the conference, etc.

[0087] Embodiment 3

[0088] This embodiment discloses a Bayesian network-based causal relationship determination system. The whole and / or part of the preferred embodiments of other embodiments can be supplemented as the supplement of this embodiment without causing conflicts or contradictions. Preferably, the method can be realized by the method of the application and / or other alternative modules.

[0089] This embodiment provides a Bayesian network-based causal relationship determination method. The method comprises: a constructing module 1 constructs a Bayesian network. A determining module 2 generates and outputs a causal relationship spectrum based on the Bayesian network. A causal database 3 establishes a historical original literature database. In the case that at least one non-specific event occurs in at least two objects, the constructing module 1 analyzes the self factors of the objects causing the non-specific event to construct a factor set and construct a non-directional graph structure constraint based on the factor set; and the constructing module 1 is capable of modifying the non-directional graph structure constraint based on the causal database 3 to establish the Bayesian network; the determining module 2 calculates a causal index of the self factors causing the non-specific event based on the Bayesian network and outputs a causal relationship spectrum of the non-specific event based on the causal index, so as to determine the key reason of the non-specific event.

[0090] Preferably, the constructing module 1 defines at least one field based on the non-specific event, and retrieves at least one exclusive factor related to the non-specific event in the field based on the causal database 3. In the case that the exclusive factor does not belong to the self factors, the constructing module 1 prompts a third party to find evidence. In the case that the third party confirms that the exclusive factor objectively exists, the constructing module 1 further modifies the non-directional graph structure constraint by adding the exclusive factor to the non-directional graph structure constraint.

[0091] Preferably, the constructing module 1 used in the application is a server with a search engine and an operation function. The determining module 2 is a data server with an operation function. The causal database 3 is a server with a search engine, an operation function and storage. The constructing module 1, the determining module 2 and the causal database 3 are connected by wired and wireless communication modes such as optical fiber, data line, Bluetooth, wifi and / or 4G.

[0092] It should be noted that the above specific embodiments are exemplary, and those skilled in the art can think of various solutions under the inspiration of the disclosure of the application, and these solutions also belong to the disclosed range of the application and fall within the protection scope of the application. Those skilled in the art should understand that the specification and drawings of the application are illustrative and do not constitute a limitation on the claims. The protection scope of the application is defined by the claims and their equivalents.

Claims

1. A cause-effect relationship determining system, comprising at least: A constructing module (1) is configured to construct a Bayesian network, wherein in the case that at least one non-specific event occurs in at least two objects, the constructing module (1) establishes a data set D based on self factors of the objects, divides the self factors into a plurality of factor sets L according to the data set D, and forms factor pairs L in a pairwise manner m and L n , and numbers the data set D and the factor pairs L m and L n . a cause-effect library (3) for establishing a historical original literature library, characterized in that the building module (1) is configured to prompt a third party to conduct expert argumentation on the relationship between the factor pair L m and L n and feed the result of the expert argumentation back to the causal library (3), which conducts deep learning on the result of the expert argumentation to correct the causal library (3) in the case of failure to search for the relationship between the factor pair L m and L n based on the causal library (3). In case the causal library (3) learns the results of the expert argumentation in depth, the construction module (1) corrects the relationship between the factors to L m and L n based on the causal library (3) according to the factors. The construction module (1) can also correct the relationship between the factors to L m and L n based on the causal library (3) according to the factors.

2. A cause-effect relationship determining system, comprising at least: A constructing module (1) is configured to construct a Bayesian network, wherein in the case that at least one unspecified event occurs in at least two objects, the constructing module (1) establishes a data set D based on self factors of the objects, divides the self factors into a plurality of factor sets L according to the data set D, and forms factor pairs L in a pairwise manner m and L n , and numbers the data set D and the factor pairs L m and L n . a cause-effect library (3) for establishing a historical original literature library, characterized in that, the constructing module (1) analyzes the self factors of the object causing the non-specific event to construct a factor set and constructs an undirected graph structure constraint based on the factor set, and the constructing module (1) is capable of modifying the undirected graph structure constraint based on the cause-effect library (3) to establish the Bayesian network, the constructing module (1) is configured to define at least one field based on the non-specific event, and retrieve at least one exclusive factor related to the non-specific event in the field based on the cause-effect library (3); in the case that the exclusive factor does not belong to the self factors, the constructing module (1) prompts a third party to conduct evidence finding; in the case that the third party confirms that the exclusive factor objectively exists, the constructing module (1) supplements the exclusive factor to the undirected graph structure constraint to further modify the undirected graph structure constraint.

3. The causality determination system according to claim 1 or 2, characterized by, The construction module (1) searches for a relationship between the factor pair L m and L n and corrects the relationship between the factor pair L m and L n based on the search result, and assigns a relationship reliability value to the relationship between L m → L n , a relationship reliability value to the relationship between L n → L m , and a relationship reliability value to the relationship between L n ⊥ L m , thereby constructing a constraint of an undirected graph structure.

4. The causality determination system according to claim 3, characterized by, The cause-effect library (3) forms a plurality of literature unit bodies by classifying a plurality of related literatures containing a plurality of historical attributes based on the obtained literatures in a technical field to construct an original literature library, so as to mine the relationship confidence values between the attributes by data pattern.

5. The causality determination system according to claim 4, characterized by, The literature layer in the cause-effect library (3) counts the frequency of words / phrases in each literature, and obtains the joint occurrence probability of the words / phrases according to the independence assumption.

6. The causality determination system according to claim 5, characterized by, The literature layer calculates the correlation strength of the words / phrases; the literature layer constructs the correlation reduction coordinates of the literatures, and classifies the related literatures in the form of an iterative algorithm based on the correlation reduction coordinates and the correlation strength of all the related literatures to form a plurality of literature unit bodies.

7. The causality determination system according to claim 6, characterized by, The data layer of the cause-effect library (3) retrieves two historical literatures containing two historical attributes in the literature unit body based on the relationship knowledge table to obtain the relationship confidence values of the two historical attributes in a fusion manner to establish a relationship confidence value library of the two historical attributes, so that the data layer constructs a historical data set based on the relationship knowledge library and the relationship confidence value library established in a pairwise manner between all the histories.

8. The causality determination system according to claim 7, characterized by, In V i The association vector is the average of the reduced coordinates of the samples in V i This value reflects the strength of the association of the words / phrases in the document, i.e.:

9. A cause-effect relationship determination method characterized by comprising: comprising at least: constructing a Bayesian network using a constructing module (1) which, in the case of at least two subjects having at least one nonspecific event, establishes a data set D based on the subjects' own factors, divides the own factors into a plurality of factor sets L according to the data set D, and forms factor pairs L in a pairwise manner m and L n , and numbers the data set D and the factor pairs L m and L n . establishing a historical original literature library by using the cause-effect library (3); In case of failure of retrieving the relationship between the factor pair L m and L n , the building module (1) prompts a third party to perform expert consultation on the relationship between the factor pair L m and L n , and feeds the result of the expert consultation back to the causal library (3). deeply learning the results of expert argumentation to modify the cause-effect library (3); In case the causal library (3) learns the results of the expert argumentation in depth, the construction module (1) corrects the relationship between the factors on L m and L n based on the causal library (3) according to the factors. m and L n based on the causal library (3) according to the factors.

10. A cause-effect relationship determination method characterized by comprising: comprising at least: constructing a Bayesian network, establishing a data set D based on the self factors of the objects in the case that at least one non-specific event occurs to at least two objects, and dividing the self factors into a plurality of factor sets L according to the data set D and forming factor pairs L in a pairwise manner m and L n , and numbering the data set D and the factor pairs L m and L n ​ establishing a historical original literature library by using the cause-effect library; analyzing the self factors of the object causing the non-specific event to construct a factor set and constructing an undirected graph structure constraint based on the factor set, and modifying the undirected graph structure constraint based on the cause-effect library (3) to establish the Bayesian network; defining at least one field based on the non-specific event; retrieving at least one exclusive factor related to the non-specific event in the field based on the cause-effect library; in the case that the exclusive factor does not belong to the self factors, prompting a third party to conduct evidence finding; in the case that the third party confirms that the exclusive factor objectively exists, supplementing the exclusive factor to the undirected graph structure constraint to further modify the undirected graph structure constraint.

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