CN108847282B - Expert experience reasoning system and method based on fuzzy reasoning - Google Patents
Expert experience reasoning system and method based on fuzzy reasoning Download PDFInfo
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Abstract
The invention discloses an expert experience reasoning system based on fuzzy reasoning, which comprises a symptom obtaining module, a symptom calculating module and a symptom calculating module, wherein the symptom obtaining module is used for obtaining symptom information of a user, and the symptom information comprises the name of a symptom and the severity information of the symptom; the fuzzification module is used for fuzzifying the severity information through a membership function; the expert experience rule base is used for storing a plurality of expert rules, wherein the rules are a certain disease, all symptoms of the disease and the severity of the symptoms; a fuzzy inference module: and the system is used for calculating the disease information corresponding to the symptom information of the user by utilizing fuzzy reasoning according to the fuzzified symptom information and the rules in the expert experience rule base. The invention also discloses an expert experience reasoning method based on fuzzy reasoning, which solves the problems of low disease judgment accuracy and low triage efficiency in the existing means.
Description
Technical Field
The invention relates to the technical field of intelligent reasoning, in particular to an expert experience reasoning system and method based on fuzzy reasoning.
Background
The decline of the body functions of the old people causes frequent diseases, and the adverse factors existing in the current medical mode increase the difficulty of the disease treatment of the old people. On the other hand, with the development and progress of society, people pay more and more attention to their health conditions, and go to a large hospital for a doctor no matter how sick or sick, which causes many three hospitals to be in an overload working state all the time. The patient does not judge the disease correctly, which causes the trouble of repeating queuing and seeking medical advice, and the doctor also carries out useless repeated labor, thereby dispersing the energy of the doctor and having low working efficiency. This situation further increases the difficulty of the elderly in seeing a doctor.
In order to solve the problems, most hospitals only set up a treatment guide table at present, a patient briefly describes symptoms to nurses, and the disease type is preliminarily judged by the subjective experience of the nurses; because of large workload, the inquiry is not careful or medical knowledge and experience are insufficient, the disease judgment accuracy is low, and the problems of repeated queuing and repeated diagnosis cannot be solved.
Disclosure of Invention
One of the purposes of the invention is to provide an expert experience reasoning system based on fuzzy reasoning to solve the problems of low disease judgment accuracy and low triage efficiency in the existing means.
The expert experience reasoning system based on fuzzy reasoning comprises,
the symptom obtaining module is used for obtaining symptom information of a user, and the symptom information comprises the name of a symptom and the severity information of the symptom;
the fuzzification module is used for fuzzifying the severity information through a membership function; the membership function is a Gaussian membership function;
the expert experience rule base is used for storing a plurality of expert rules, wherein the rules are a certain disease, all symptoms of the disease and the severity of the symptoms;
a fuzzy inference module: the system is used for calculating disease information corresponding to the symptom information of the user by fuzzy reasoning according to the fuzzified symptom information and rules in an expert experience rule base, wherein the disease information comprises the type of a disease and the probability of the disease of the user;
and selecting one or more diseases with the highest probability from the disease information as a reasoning result.
The system can accurately infer the occurrence probability of the disease by utilizing the fuzzified input and the expert experience rule base, the conclusion can meet the requirement of triage, and the use of the expert experience rule base avoids the low disease judgment accuracy rate caused by insufficient medical knowledge and experience; fuzzy reasoning is a mathematical method, belongs to the field of fuzzy mathematics, and has the principle that on the basis of the conclusion of a known rule and the rule, the obtained conditions are matched to obtain the closest conclusion in the known conclusion.
Further, the fuzzy inference module is used for calculating the closeness of the blurred symptom information and each rule in the expert experience rule base and the big data case rule base as the probability of the illness of the user, and selecting the disease name and the probability corresponding to one or more rules with the highest closeness as the inference result.
The closeness calculation is used for reasoning, can obtain the reasoning result and the probability of the reasoning result, and provides a quantitative standard for the comparison of the reasoning result, so that the most appropriate reasoning result can be selected by using a numerical value.
Further, the fuzzy inference module is further configured to select a rule with a closeness greater than a set threshold, select disease information corresponding to a rule with a highest closeness as an inference result, and if there is no rule with a closeness greater than the set threshold, not output any inference result.
And the correction module is used for correcting the closeness of the rule with the maximum closeness through a correction function to obtain the probability of occurrence of the disease information corresponding to the rule represented by the final correction result.
The obtained result is more consistent with the actual situation by improving the correction function.
Provides an expert experience reasoning method based on fuzzy reasoning to solve the problems of low disease judgment accuracy and low triage efficiency in the prior art.
The expert experience reasoning method based on fuzzy reasoning comprises the following steps:
a symptom acquisition step: acquiring symptom information of a user, wherein the symptom information comprises names of symptoms and severity information of the symptoms;
fuzzy reasoning steps: fuzzifying the severity information through a membership function, obtaining disease information corresponding to the symptom information of the user by utilizing fuzzy reasoning according to the fuzzified symptom information and an existing expert experience rule base, wherein the disease information comprises the types of diseases and the probability of the diseases suffered by the user, and selecting one or more diseases with the highest probability from the disease information as a reasoning result;
the expert experience rules library contains a plurality of rules which are rules for a disease and all symptoms and severity of symptoms of the disease.
The method can accurately infer the diseases suffered by the user by utilizing the fuzzified input and the expert experience rule base, the conclusion can meet the requirement of triage, and the use of the expert experience rule base avoids the low disease judgment accuracy rate caused by insufficient medical knowledge and experience; fuzzy reasoning is a mathematical method, belongs to the field of fuzzy mathematics, and has the principle that on the basis of the conclusion of a known rule and the rule, the obtained conditions are matched to obtain the closest conclusion in the known conclusion.
Further, in the fuzzy inference step, the closeness degree of each rule in the expert experience rule base is calculated as the probability of the user suffering from the disease by calculating the fuzzified severity information and symptom information, and the disease information corresponding to one or more rules with the highest closeness degree is selected as the inference result.
The closeness calculation is used for reasoning, can obtain the reasoning result and the probability of the reasoning result, and provides a quantitative standard for the comparison of the reasoning result, so that the most appropriate reasoning result can be selected by using a numerical value.
Further, in the fuzzy inference step, a rule with the closeness degree larger than a set threshold value is selected, disease information corresponding to one or more rules with the highest closeness degree is searched as an inference result, and if the closeness degree is larger than the set threshold value, no inference result is output.
The result with too low closeness is prevented from being output, the probability that the result is inaccurate is high due to the too low closeness, and the user can be misled.
And further, a correction step of performing phase correction on the obtained closeness by using a correction function to obtain the probability of occurrence of the disease information corresponding to the rule represented by the final correction result.
The obtained result is more consistent with the actual situation by improving the correction function.
Drawings
FIG. 1 is a schematic block diagram of an expert experience reasoning system based on fuzzy reasoning according to an embodiment of the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
example 1
The expert experience reasoning system based on fuzzy reasoning in the embodiment comprises:
the symptom obtaining module is used for obtaining symptom information of a user, and the symptom information comprises the name of a symptom and the severity information of the symptom;
the fuzzification module is used for fuzzifying the severity information through a membership function;
the expert experience rule base is used for storing a plurality of expert rules, wherein the rules are a certain disease, all symptoms of the disease and the severity of the symptoms;
a fuzzy inference module: the system is used for calculating disease information corresponding to the symptom information of the user by fuzzy reasoning according to the fuzzified symptom information and rules in an expert experience rule base, wherein the disease information comprises the type of a disease and the probability of the disease of the user; one or more diseases with the highest probability are selected from the disease information as an inference result;
in the module, the closeness of the symptom information and each rule in the expert experience rule base is calculated to be used as the probability of the disease corresponding to each rule;
and the correction module is used for correcting the obtained closeness through the correction function to obtain the probability of disease occurrence corresponding to the rules represented by the final correction result.
Expert experience rule base
The knowledge embodied by the expert experience rule base through the database in the embodiment mainly comprises diseases, symptoms and occurrence probability corresponding to each symptom of the diseases, and the database is divided into a symptom base, a disease base and a rule base.
Each disease and all its symptoms and severity of symptoms constitute a rule of an expert system, and the rule base in this embodiment is filled in by a medical professional in advance. The following three tables are examples of a symptom library, a disease library, and a rule library, respectively, used in the expert system.
TABLE 1 symptom Bank
TABLE 2 disease library
TABLE 3 rule base
The numbers in the symptom library are listed in table 3, and the numbers in the disease library are listed; in order to make the system better perform disease diagnosis, each symptom is classified into a plurality of grades from low to high according to the judgment criteria of disease medical diagnosis, each grade is represented by a fuzzy quantifier, and for convenience, the fuzzy quantifier used for representing the severity is marked by numbers in a table, namely, 0 is particularly serious, 1 is serious, 2 is serious, 3 is more serious, 4 is more dotted, 5 is slight, 6 is less, 7 is less, the fuzzy quantifier is particularly serious, 0 is 0, 1 is more serious, 2 is serious, 3 is more serious, 4 is more general, 5 is slightly, 6 is less, 7 is less, the symptom and the severity thereof form a regular condition part, and the disease is a regular conclusion part.
Meanwhile, the disease is also classified by severity, and the disease severity information is stored in a disease membership library as shown in Table 4. Therefore, in the more complete conclusion base and the rule base, the disease and the rule thereof are further subdivided according to the severity degree, that is, a plurality of numbers correspond to the same disease name, but the severity degrees of the numbers in the disease membership degree base are different, and in the embodiment, the severity degree of the disease and the severity degree of the symptom adopt the same fuzzy quantifier.
TABLE 4 membership database for diseases
| Disorders of the disease | y1 | y2 | y3 | y4 | y5 | y6 | y7 | y8 | y9 | y10 | y11 | y12 | y13 |
| Degree of membership | 2 | 3 | 1 | 0 | 3 | 3 | 1 | 2 | 3 | 4 | 2 | 1 | 2 |
| y14 | y15 | y16 | y17 | y18 | y19 | y20 | y21 | y22 | y23 | y24 | y25 | y26 | |
| 4 | 3 | 0 | 2 | 3 | 4 | 4 | 1 | 1 | 2 | 2 | 3 | 3 |
Thus, for example, "if there is a more severe cough and a general degree of expectoration, then the patient has severe bronchial asthma. "such empirical knowledge can be expressed as rule 1 in the rule base (first line in table 3 corresponding to the number y 1), which is premised on" there is a relatively severe cough and expectoration in general "and the conclusion is" there is severe bronchial asthma ".
Fuzzification
The fuzzification process mainly converts the definition value into a membership function of a fuzzy set. Because the severity is a fuzzy concept, a fuzzy quantifier is adopted, and the fuzziness of the fuzzy quantifier needs to be represented by a membership function; in order to make the system better perform disease diagnosis and to be closer to the actual situation, the membership function of each fuzzy quantifier in this embodiment adopts a normal distribution (between 0 and 1) as shown in table 4. For computer processing, the membership function of the fuzzy quantifier is subjected to sampling discretization to obtain a membership vector (hereinafter referred to as membership) with the length of 6 in the table 6.
TABLE 5 membership functions corresponding to each fuzzy quantifier
TABLE 6 membership vector after discretization of each fuzzy quantity word value
Fuzzy inference
The fuzzy inference combines the results together through the inference of various control rules to generate a set of 'fuzzy inference output', and the embodiment adopts a proximity inference algorithm, and the specific process is as follows:
first, the closeness calculation formula of the membership degree a and the membership degree B used in this embodiment is:
secondly, the pattern matching is to calculate the closeness of the symptoms of the patient with the symptoms corresponding to the diseases in the rule base.
The patient provides a symptom set X, the closeness of the patient symptoms Xi and the symptoms Xi of a rule R1 in a rule base is calculated by using a closeness formula, after the closeness of i symptoms is obtained, the i closeness is summed and then divided by i, and the obtained result is that the patient symptom set X and the rule R are1I.e. the probability of the patient suffering from the disease R1. Sequentially calculating the symptom set X of the patient and other rules (R) in the rule base2,R3,R4… …) to ultimately arrive at the overall probability that the patient has the disease in the rule base.
And finally, judging whether to excite a certain rule according to a threshold value sigma of the closeness determined in advance, namely whether to bring the rule into a set of fuzzy inference output.
For example: suppose the user has symptoms of relatively severe abdominal pain, slight abdominal distension, slight nausea and somewhat vomiting. The threshold σ is 0.9, as can be seen from table 6: "none" ([ 1,0,0,0, 0], "more severe" ([ 0,0.03,0.11,0.83,0.83,0.11 ]).
And rule has R1And (3) comparison:
let r be the symptom severity membership in the rule, t be the symptom severity membership in the resulting symptom set;
calculate the first symptom: since the symptom "hot" is absent in both rule R1 and the resulting symptom, it is not
SM1(r1，t1)＝1
The second symptom is calculated:
in the same way, the rule R is obtained1And (4) calculating the closeness of all symptoms. For convenience of holding SM1(r1，t1) Denoted S1, SM1(r2，t2) The results are shown in Table 7, with S2 being noted, and so on.
TABLE 7 input symptoms and rules R1Is obtained by matching
| S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 | S9 | S10 | S11 | S12 |
| 1 | 0.38 | 0.38 | 1 | 1 | 1 | 1 | 0.38 | 1 | 0.78 | 1 | 1 |
| S13 | S14 | S15 | S16 | S17 | S18 | S19 | S20 | S21 | S22 | S23 | |
| 1 | 1 | 1 | 0.68 | 0.78 | 1 | 1 | 1 | 1 | 1 | 1 |
The result of the above table is summed and averaged to obtain 0.88, i.e. the value after matching with the first rule Y1 is 0.88, and the result of comparison with all the rules is obtained in the same way, as shown in table 8.
TABLE 8 matching results of input symptoms with rule base
| R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | R11 | R12 | R13 |
| 0.88 | 0.87 | 0.87 | 0.84 | 0.89 | 0.87 | 0.85 | 0.88 | 0.94 | 0.94 | 0.90 | 0.94 | 0.89 |
| R14 | R15 | R16 | R17 | R18 | R19 | R20 | R21 | R22 | R23 | R24 | R25 | R26 |
| 0.90 | 0.93 | 0.91 | 0.95 | 0.98 | 0.88 | 0.86 | 0.92 | 0.90 | 0.90 | 0.87 | 0.88 | 0.91 |
By comparison with a threshold α of 0.9, when the result is greater than the threshold, the rule is activated; otherwise the rule is not activated and the results are shown in Table 9.
TABLE 9 results of comparing match results to threshold
| R9 | R10 | R11 | R12 | R14 | R15 | R16 | R17 | R18 | R21 | R22 | R23 | R26 |
| 0.94 | 0.94 | 0.90 | 0.94 | 0.90 | 0.93 | 0.91 | 0.95 | 0.98 | 0.92 | 0.90 | 0.90 | 0.91 |
Finally, the rule R is obtained through comparison18The value of (d) is a maximum value.
Correction module
For the result obtained by the proximity calculation, the correction function is usually used to improve, so that the result obtained by the system better conforms to the actual situation.
Using the membership degree of the severity degree of the disease corresponding to the selected rule to perform fuzzy number multiplication operation with the closeness degree; for example, the severity of R18 is "more severe", and according to table 6 "more severe" — [0,0.03,0.11,0.83,0.83,0.11], the vector is multiplied by the number of ambiguities to obtain the corrected closeness (0.98) as follows:
B*＝
(0∧0.98)∨(0.03∧0.98)∨(0.11∧0.98)∨(0.83∧0.98)∨(0.83∧0.98)∨(0.11∧0.98)
＝0.83
therefore, the probability of the patient having the disease corresponding to the selected rule is 83%.
System and workflow thereof
The system in this embodiment is connected to input and output devices, and the user's symptoms and the degree of the symptoms are input to the symptom obtaining module as input to the system, for example, if the user inputs a headache, the input is a symptom-headache and degree-extraordinary. The system carries out fuzzy reasoning according to the input symptoms and symptom degrees to obtain the possible diseases of the user and the probability of the diseases and puts the diseases into a temporary database, and the database is sorted according to the probability of the diseases from big to small. When the input symptom number reaches the upper limit (the symptom number threshold N, here temporarily set as 5), the system ends the inference, and outputs M diseases with the maximum probability greater than P according to the disease probability threshold P (i.e. the closeness threshold σ) (the disease output number M is here temporarily set as 3, for example, after fuzzy inference, 4 diseases with the maximum probability greater than P, and then 3 diseases with the maximum output probability).
Wherein, the whole inference algorithm based on the closeness comprises the following steps,
the first step is as follows: initialization
1) The size of the threshold sigma is determined.
2) Let i equal 1 and j equal 1. Wherein i is 1, 2, … n, j is 1, 2, …, m; n is the number of rules in the library and m is the number of symptoms acquired.
The second step is that: the pattern is matched with the pattern of the pattern,
the method comprises the steps of carrying out proximity calculation on symptoms appearing on a patient and symptoms corresponding to diseases in a rule base, and carrying out proximity calculation on user data and a certain rule;
the third step: selecting a rule;
the rule selection process comprises the following steps:
1) when the closeness of the symptoms of the patient and a certain rule in the expert experience rule base is more than or equal to sigma, the rule is excited, and if i is n and j is m, the step is carried out to 3), otherwise, the step is carried out to the second step; if the closeness is < σ, the rule is not activated, in which case if i is n and j is m, we go to 3), otherwise we go to the second step.
2) When only one rule is excited, the system outputs a conclusion part of the rule; if no rule is triggered, the system outputs 'no qualified disease, advise user to visit hospital'.
3) When a plurality of rules from the expert experience rule base are excited, the system automatically compares values meeting the results, one or more rules with the maximum proximity value are selected because the larger the proximity value is, the closer the rules are, and the conclusion part of the rules is output, wherein the conclusion comprises the name and the severity of the disease; if the proximity values of a plurality of rules are the same, the system outputs the conclusion parts of the rules at the same time.
The fourth step: calculating inference results
And correspondingly correcting the closeness of the selected rule by using the membership degree of the disease severity degree in the conclusion of the third step to obtain a final correction result.
The final output of the system includes whether the patient has the disease, what kind of disease, the severity of the disease and the probability of the disease, such as "ileus with disease is more severe 0.83".
Example 2
The present embodiment is different from embodiment 1 in that, first, the disease probability calculation system based on fuzzy decision in the present embodiment is applied to a personal mobile terminal or a tablet computer, so that a user can use the system at home instead of going to a hospital, so that the user can make a probability estimation of a disease before going to the hospital.
Secondly, the embodiment further comprises a camera on the mobile terminal or the tablet computer, which is used for shooting case files and prescription files after each visit of the user, and then extracting case texts and prescription texts through a text extraction module, wherein the texts comprise diagnosis results, disease descriptions, doctors and time on the cases, prescriptions of the prescriptions such as time, dosage and medicine-dispensing doctors and all information displayed by characters on the cases, so that an individual existing case library of the user is formed; in addition, the embodiment also comprises an expert prescription library which stores common medicines and dosage information corresponding to various diseases and diseases with different severity degrees; the medicine matching module receives the reasoning result from the reasoning module, inquires the common medicines and the dosage according to the disease name and the severity in the result, and displays the common medicines and the dosage through a display screen of the mobile terminal or the tablet computer; meanwhile, whether the same medicine and the amount of the medicine on the prescription are contained in the prescription within a certain time period or not is inquired from the existing library and displayed on a display screen; the form is that "you may have bought XX g of XX medicine in XX month and please see if there is any residue", so that the user can clearly know that there is some medicine in home, and after verification, the doctor is informed of the medicine in home, and if the doctor needs to take the same medicine or similar medicine, the medicine consumption can be reduced, and the medicine is prevented from being repeatedly taken out and wasted.
In addition, the embodiment also comprises a medicine knowledge graph database of medicines which can not be used simultaneously and a medicine reminding module, wherein the module obtains medicines of a recent prescription by inquiring the prescription in the recent time at the time, inquires medicines which can not be used together with the medicines from the medicine knowledge graph database and displays the medicines on a screen, and the medicines are in the form that' you can take XX medicines in the recent time and pay attention to that the medicines can not be used together with XX medicines and pay attention to that the doctor can remind the doctor to take the medicines according to the information or pay attention to whether the medicines prescribed by the doctor can not be taken together with the medicines after the user checks. The function avoids the harm to the body caused by newly opening the medicine because a user forgets the medicine which is taken recently.
In addition, the system also comprises a new case and prescription comparison module which is used for inquiring the past case in a past case base according to the disease and the severity in the case when the new case and the prescription are received, comparing the prescription information corresponding to the same disease and severity, reminding a user if the two cases have a larger difference, inquiring whether a doctor has other reasons to cause a great difference of the prescription on the spot, wherein the difference of the prescription can be calculated by using a key characteristic set to represent the prescription and the corresponding disease and using an equivalent dimension calculation method.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (4)
1. The expert experience reasoning system based on fuzzy reasoning is characterized in that: the system comprises a symptom obtaining module, a symptom obtaining module and a symptom display module, wherein the symptom obtaining module is used for obtaining symptom information of a user, and the symptom information comprises names of symptoms and severity information of the symptoms;
the expert experience rule base is used for storing a plurality of expert rules, wherein the rules are a certain disease, all symptoms of the disease and the severity of the symptoms;
the fuzzification module is used for fuzzifying the severity information through a membership function; the membership function is a Gaussian membership function;
a fuzzy inference module: the system is used for calculating diseases corresponding to the symptom information of the user as inference results by fuzzy inference according to the fuzzified symptom information and rules in the expert experience rule base;
the symptom acquisition module, the expert experience rule base, the fuzzification module and the fuzzy reasoning module are all integrated on the mobile terminal; the mobile terminal comprises a camera and is used for shooting case files and prescription files after the user visits a doctor;
the mobile terminal also comprises a character extraction module, an expert prescription library, a prior library, a medicine matching module, a medicine knowledge map database and a medicine reminding module;
the text extraction module is used for extracting a case and a text on the prescription, wherein the text comprises a diagnosis result, a disease description, a doctor and time on the case, the time, the dosage and the doctor for prescription on the case, and an existing case library of a user is formed;
the expert prescription library stores common medicines and dosage information corresponding to various diseases and diseases with different severity degrees;
the past library is used for storing the prescriptions according to a time sequence;
the medicine matching module is used for receiving the reasoning result of the reasoning module, inquiring the common medicines and the dosage according to the disease names and the severity in the result, displaying the common medicines and the dosage, and inquiring whether the prescription contains the same medicines and the dosage of the medicines on the prescription in a preset time period from a previous library and displaying the same medicines and the dosage;
the medicine knowledge map database stores medicines which cannot be used simultaneously;
the medicine reminding module is used for obtaining medicines on a recent prescription by inquiring the prescription in preset time in a past library, inquiring medicines which cannot be used together with the medicines from the medicine knowledge map database and displaying the medicines through the mobile terminal;
and the comparison module is used for inquiring the previous case in the previous case library according to the disease and the severity in the case when a new case and a new prescription are received, comparing prescription information corresponding to the same disease and severity, and reminding a user if the two are in a larger difference.
2. The expert empirical reasoning system based on fuzzy inference of claim 1, characterized in that: and the fuzzy inference module is used for calculating the closeness of the blurred symptom information and each rule in the expert experience rule base and the big data case rule base as the probability of the illness of the user, and selecting the disease name and the probability corresponding to one or more rules with the highest closeness as the inference result.
3. The expert empirical reasoning system based on fuzzy inference according to claim 2, characterized in that: the fuzzy inference module is further used for selecting the rule with the closeness degree larger than the set threshold value, selecting the disease information corresponding to the rule with the highest closeness degree as the inference result, and if no rule with the closeness degree larger than the set threshold value exists, not outputting any inference result.
4. The expert empirical reasoning system based on fuzzy inference according to claim 2 or 3, characterized in that: and the correction step is also included, the closeness of the rule with the maximum closeness is corrected by using a correction function, and the probability of the disease information corresponding to the rule represented by the final correction result is obtained.
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