CN112768055A - Intelligent triage method based on expert experience reasoning - Google Patents
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Abstract
The invention belongs to the technical field of artificial intelligence, and particularly relates to an intelligent triage method based on expert experience reasoning. The invention mainly comprises the following steps: obtaining a descriptive statement input by a user, wherein the descriptive statement at least comprises symptoms and the corresponding degree of the symptoms, and extracting the symptoms and the corresponding degree as input; obtaining triage information based on an expert experience reasoning method according to symptoms and corresponding degrees; the expert experience reasoning mainly depends on the experience of the expert, and has more accurate characteristic compared with the expert. The invention has the advantages that the diagnosis process is intelligentized, and the accuracy of intelligent diagnosis is improved.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to an intelligent triage method based on expert experience reasoning.
Background
With the continuous improvement of living conditions of people, the demand for health is more and more vigorous. In recent years, the number of outpatient emergency treatment in each large hospital has increased dramatically, especially in the hospitals that are the leading positions in the industry. And the corresponding problems still faced include: the lack of medical health knowledge in patients makes it unclear what department to visit, further exacerbating the pressure of medical triage. The workload of the doctor is high, the doctor does not have enough time to answer all the questions of the patient, the doctor-patient relationship is stressed to some extent, and meanwhile, the medical quality is difficult to guarantee. On the patient side, part of patients can see a doctor quickly, and a preferred emergency treatment mode is often adopted, so that the treatment efficiency of the hospital is further reduced.
In order to solve the problems, at present, a manual diagnosis and treatment platform and an application program for providing autonomous diagnosis and treatment service for a user mainly aim at pre-diagnosis consultation and guide diagnosis and treatment. The current triage program has high redundancy and low intelligent degree, so that the user experience is poor, and the purpose of intelligent triage cannot be effectively realized.
Disclosure of Invention
The invention aims to provide an intelligent triage method based on expert experience reasoning, which can provide triage service for a user only by acquiring daily description sentences of the user and performing a series of conversion processing in the background, and can effectively improve the accuracy of triage based on the expert experience reasoning.
The technical scheme of the invention is as follows: an intelligent triage method based on expert experience reasoning is characterized by comprising the following steps:
obtaining a descriptive statement input by a user, wherein the descriptive statement at least comprises symptoms and the corresponding degree of the symptoms, and extracting the symptoms and the corresponding degree as input;
and obtaining triage information based on an expert experience reasoning method according to the symptoms and the corresponding degree.
Furthermore, the degree of severity corresponding to the symptom is expressed by fuzzy quantifier, and the fuzzy quantifier at least comprises special, comparative and general.
The range of the fuzzy quantifier is wide, and according to general experience, a description mode of more grades such as particularly severe, very severe, relatively severe, general, somewhat, slight, none, and the like can be adopted, and correspondingly, in the processing process, for convenience, the fuzzy quantifier can be written as "particularly severe" 0, "very severe" 1, "severe" 2, "relatively severe" 3, "general" 4, "somewhat" 5, "slightly" 6, "none" 7, and the fuzzy grade can be adjusted.
Further, the specific method for obtaining triage information based on the expert experience reasoning method comprises the following steps:
1) constructing a database based on expert experience: the database is composed of rules, each rule is composed of a disease name, corresponding symptoms and corresponding symptom degrees, fuzzy quantifier for describing symptom degrees is numbered by Arabic numerals, different fuzzy quantifiers correspond to different numbers, and the number size is consistent with a set fuzzy grade rule;
2) carrying out fuzzy reasoning according to the input symptoms and the corresponding degrees to obtain the possible diseases of the user and the probability of the diseases, and putting the diseases and the probability into a temporary database, wherein the database is sorted according to the probability of the diseases from big to small;
3) when the number of the input symptoms reaches the upper limit, the system finishes reasoning, and M diseases with the probability greater than P are obtained according to the disease probability threshold value P;
4) and according to a preset triage rule, performing triage according to the rules corresponding to the M diseases, and outputting a triage result.
4. The intelligent triage method based on patent experience reasoning according to claim 3, further comprising a probability of each symptom when the database is constructed based on expert experience.
The invention has the advantages that the diagnosis process is intelligentized, and the accuracy of intelligent diagnosis is improved.
Drawings
FIG. 1 is a schematic diagram of a fuzzy inference process;
FIG. 2 is an exemplary diagram of a blur distribution;
FIG. 3 is a flow chart of expert system reasoning.
Detailed Description
The technical scheme of the invention is described in detail in the following with reference to the attached drawings:
the scheme of the invention is based on expert experience reasoning, and the knowledge of an expert system is provided by experts and doctors and is used for simulating expert diagnosis to provide more accurate preliminary diagnosis results for users. The expert system knowledge mainly comprises diseases, symptoms and occurrence probability corresponding to each symptom of the diseases. The database of the expert system is mainly a rule base of the expert system and is used for storing various diseases and all corresponding symptoms and probabilities of the diseases, and each disease and all symptoms of the disease form a rule of the expert system. The expert system also comprises a temporary database for temporarily storing the fuzzy reasoning result each time.
The invention adopts fuzzy reasoning, and the principle of the fuzzy reasoning is as follows:
fuzzification, wherein the fuzzification process mainly converts a definition value into a membership function of a fuzzy set, and a fuzzy distribution diagram of a fuzzy quantity is designed as follows:
(1) fuzzy quantity/fuzzy distribution fuzzy distributions of discourse domains for a linguistic variable are often empirically given (lower, normal, higher), and figure 2 is an example of a fuzzy distribution.
(2) In practical application, fuzzy membership function design adopts several membership function curves such as single-point, triangular, trapezoidal and normal distribution functions.
(3) After determining the fuzzy distribution form and solving the membership functions of each fuzzy set, the input transformation needs to perform fuzzy transformation on the input quantity.
The medical evaluation criteria of each symptom are different, and in order to enable the system to better diagnose diseases, the membership functions of the evaluation criteria of each disease medical diagnosis are normally distributed, see table 1, wherein several fuzzy quantifiers and corresponding membership functions are given. For the convenience of computer processing, the interval is divided into 6 discrete points with equal intervals, and the result of discretization of the membership function of the fuzzy quantifier in the table 1 is shown in the table 2.
TABLE 1 membership function for disease severity
TABLE 5.2 discretization results
Knowledge base, fuzzy inference based on rule inference. It is the core of fuzzy reasoning. The formulation of the rules directly determines the accuracy of the overall system. The completeness and the reality of the inference rule are the final targets of forming the fuzzy inference rule base. The knowledge base of fuzzy inference is mainly composed of inference rules, and a perfect rule base is to be formed. The rule base is extracted from the decision of experts, the knowledge and the experience of engineers.
The first task established by the fuzzy expert system is to collect the fact, i.e. the information about the disease category, symptoms, etc., and the symptoms collected from the experts and books are called input, and the set of all symptoms is called input domain, which is in the form of:
X＝{x1，x2，…，x23}
wherein xiRepresentative of a disease condition is, for example: heat, cough, expectoration, nausea, vomiting, dizziness, etc.
The set of disease categories is called the output domain, denoted as Y:
Y＝{y1，y2，…，y26}
wherein y isiRepresentative of a disease is a disease such as: pulmonary tuberculosis, acute cholangitis, acute pancreatitis, cerebral hemorrhage, cerebral embolism, etc.
The fact base and the rule base of the fuzzy expert system we will build are given below.
TABLE 3 fact library
Table 3 is a fact library, and since there are multiple rules in the rule library, only a part of the rule library is listed as table 4 by way of example, for convenience, the fuzzy quantifier "especially severe" is 0, "very severe" is 1, "severe" is 2, "more severe" is 3, "general" is 4, "somewhat" is 5, "slightly" is 6, "none" is 7 (the fuzzy grade can be adjusted by an expert), and in table 4, rows represent rules and columns represent antecedents of each rule.
TABLE 4 partial rule base
Fuzzy reasoning: input variables are added to a set, using the control rule of "if-then". The results are combined together by inference of various control rules, producing a set of "fuzzy inference outputs". The invention adopts a closeness inference algorithm.
For example: as can be seen from Table 2:
severe a (x) ═ 0,0,0.04,0.136,0.99,0.98]
Slight b (x) ([ 1,0.53,0.08,0, 0]
The severity and mild closeness were calculated as:
SM(A,B)＝1-{[(0-1)2+(0-0.53)2+(0.04-0.08)2+(0.136-0)2+(0.99-0)2+(0.98-0)2]/6}1/2＝0.26
each disease corresponds to a set of symptoms, and two fuzzy sets K ═ K1，k2,…,kmL ═ L1,l2,…,lmThe closeness of is defined as:
the inference algorithm based on closeness of equation 2 is: and recording the observed set as K and the known set as L, firstly, calculating the closeness of the first element in the K and the L, and calculating the closeness of all the observed sets and the known set by analogy, and then, carrying out averaging operation.
By means of a closeness inference algorithm, the larger the obtained SM value is, the closer the input observation set and the rule are; the smaller the value, the more dissimilar. Based on the rule, one-time screening can be carried out, the threshold value of rule excitation is set to be sigma, the rule is excited only when the proximity value of the premise of the rule and the input premise is larger than sigma, and otherwise, the rule is not excited.
Defuzzification: the result obtained by the fuzzy reasoning process is only one fuzzy membership function or fuzzy subset. It is necessary to find a most representative fuzzy level from the fuzzy output result set and the exact quantity belonging to the fuzzy output result set, which is the defuzzification. The fuzzy inference output is defuzzified, i.e. an exact output value is found that is considered most representative within an output range. This is defuzzification.
The results of the rule screening process are:
1) a closeness threshold sigma is set, and a rule in the patient's symptoms and knowledge base is fired when the closeness of the rule is greater than or equal to sigma.
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, doctor consult is advised'.
3) When multiple rules are activated, the system automatically compares the values of the satisfied results, selects the rule with the largest proximity value because a larger proximity value indicates a closer rule, and outputs the conclusion part of the rule. 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.
For the results obtained based on closeness, a correction function is usually used to improve, so that the results obtained by the system are more in line with the actual situation. The text selects an attenuation type correction function and selects the fuzzy number multiplication operation of Zadeh.
Attenuation ofType correction function B*`＝B*×SM (3)
The method based on the proximity inference algorithm comprises the following steps:
the first step is as follows: initialization
1) And determining a similarity calculation formula by using the formula 1 and the formula 2.
2) The size of the threshold sigma is determined.
3) The correction function corresponding to the closeness is determined, using equation 3.
4) Let i equal 1 and j equal 1. Where i is 1, 2, … n, j is 1, 2, …, m, n is a regular number, and m is a number of known preconditions in the condition.
The second step is that: pattern matching
A in the conditioni *A in the antecedents and rulesiAnd carrying out pattern matching on the premise, and calculating the closeness. That is, the degree of closeness calculation is performed between the symptoms appearing in the patient and the symptoms corresponding to the diseases in the rule base.
rij＝SMi(Aj,A* j),SMi＝avgSMi(Aj,A* j) (5-4)
The third step: selection of rules
The fourth step: comparison operation of inference results
Comparing all the excited results, wherein the comparison operation is that the satisfied result is compared to be the maximum
(1) When SMi(Aj,A* j) When the value is larger than or equal to sigma, the rule is activated, and if i is equal to n and j is equal to m, the fourth step is carried out, otherwise, the second step is carried out;
(2) if SMi(Aj,A* j)<When the value is sigma, the rule is not excited, and if i is equal to n and j is equal to m, the fourth step is carried out, otherwise, the second step is carried out.
The fifth step: calculating inference results
And correspondingly correcting the closeness of the obtained maximum rule by using the selected correction function to obtain a final correction result.
The specific process of expert reasoning is as follows: as shown in fig. 3, after the speech information of the user is analyzed semantically, the user's symptoms and the degree of the symptoms are provided to the expert system as the input of the expert system, for example, if the user inputs a very headache, the input of the expert is the symptoms-headache and degree-very. The expert system carries out fuzzy reasoning according to the input symptoms and symptom degrees to obtain diseases possibly suffered by the user and the probability of suffering from the diseases, and the diseases are put into a temporary database, and the database is sorted according to the probability of the diseases from large to small. When the input symptom number reaches the upper limit (the symptom number threshold N is temporarily set to 5 here), the system ends the inference, and outputs M diseases with the maximum probability greater than P according to the disease probability threshold P (the disease output number M is set to 3 here, for example, after fuzzy inference, 4 diseases with the maximum probability greater than P, and 3 diseases with the maximum output probability).
Establishing a database: the expert system knowledge is stored and expressed by a database, and the main content of the expert system knowledge is a rule base constructed according to disease knowledge mastered by experts and doctors. The contents of the rule base and the templates are shown in table 5.
TABLE 5 rule base content template
The degree of symptoms and the probability of symptoms are both specified based on expert experience.
The symptom degree indicates how much the symptom is likely to be a disease, and the symptom can be classified into 3 grades (slight, general and severe) (specifically, how to classify the symptom and the grade number are specified by experts), wherein the larger the grade is, the more serious the symptom is. Is a key index for fuzzy reasoning.
The probability of a symptom indicates the probability of the corresponding disease producing the symptom, and is mainly used for returning the symptom to be continuously asked to the question-answering system. Since each disease usually involves key symptoms, common symptoms and symptoms with a small occurrence probability, in order to reduce the difficulty of making the symptom probability, the symptom probability is divided into 3 ranges, namely key symptoms- (90-100%), common symptoms- (70-80%), and small probability symptoms- (40-50%) (which can be modified according to specific situations).
TABLE 6 symptom probability
| Key symptoms | Common symptoms | Minor probability of symptoms |
| 90％-100％ | 70％-80％ | 40％-50％ |
For example, the colds in table 5 indicate that a cold may be experienced when there is a severe headache, a light runny nose. If a cold is encountered, the probability of a headache is 80% and the probability of a runny nose is 70%.
Claims (4)
1. An intelligent triage method based on expert experience reasoning is characterized by comprising the following steps:
obtaining a descriptive statement input by a user, wherein the descriptive statement at least comprises symptoms and the corresponding degree of the symptoms, and extracting the symptoms and the corresponding degree as input;
and obtaining triage information based on an expert experience reasoning method according to the symptoms and the corresponding degree.
2. The intelligent triage method based on patent experience reasoning according to claim 1, wherein the degree of severity corresponding to the symptom is expressed by fuzzy quantifier, and the fuzzy quantifier at least comprises special, comparative and general.
3. The intelligent triage method based on patent empirical reasoning according to claim 2, wherein the specific method for obtaining triage information based on expert empirical reasoning comprises:
1) constructing a database based on expert experience: the database is composed of rules, each rule is composed of a disease name, corresponding symptoms and corresponding symptom degrees, fuzzy quantifier for describing symptom degrees is numbered by Arabic numerals, different fuzzy quantifiers correspond to different numbers, and the number size is consistent with a set fuzzy grade rule;
2) carrying out fuzzy reasoning according to the input symptoms and the corresponding degrees to obtain the possible diseases of the user and the probability of the diseases, and putting the diseases and the probability into a temporary database, wherein the database is sorted according to the probability of the diseases from big to small;
3) when the number of the input symptoms reaches the upper limit, the system finishes reasoning, and M diseases with the probability greater than P are obtained according to the disease probability threshold value P;
4) and according to a preset triage rule, performing triage according to the rules corresponding to the M diseases, and outputting a triage result.
4. The intelligent triage method based on patent experience reasoning according to claim 3, further comprising a probability of each symptom when the database is constructed based on expert experience.
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| CN112768052A (en) * | 2021-01-07 | 2021-05-07 | 重庆中肾网络科技有限公司 | Intelligent triage method based on knowledge graph reasoning |
| CN112786183A (en) * | 2021-01-07 | 2021-05-11 | 重庆中肾网络科技有限公司 | Intelligent triage system |
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Application publication date: 20210507 |