CN112086200A - System, device and storage medium for predicting diseases based on bitter taste - Google Patents

System, device and storage medium for predicting diseases based on bitter taste Download PDF

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CN112086200A
CN112086200A CN202010981317.3A CN202010981317A CN112086200A CN 112086200 A CN112086200 A CN 112086200A CN 202010981317 A CN202010981317 A CN 202010981317A CN 112086200 A CN112086200 A CN 112086200A
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杜登斌
杜小军
杜乐
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Wuzheng Intelligent Technology Beijing Co ltd
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Abstract

The invention discloses a system, a device and a storage medium for predicting diseases based on bitter taste, wherein the system comprises: the symptom acquisition module is used for acquiring bitter taste accompanying symptoms on the premise of determining bitter taste as a clinical first chief complaint symptom; the disease library module is used for establishing a bitter taste disease library of the incidence relation between bitter taste accompanying symptoms and corresponding diseases and disease symptoms; the preprocessing module is used for vectorizing and expressing various disease symptom information in the disease library in a text processing mode; the disease clustering module is used for clustering diseases in the bitter disease library by adopting an optimized k-means clustering algorithm; a large-class division module: clustering feature vectors of bitter taste accompanying symptoms to be identified; and the disease prediction module is used for predicting diseases by calculating the semantic similarity in the class.

Description

System, device and storage medium for predicting diseases based on bitter taste
Technical Field
The invention relates to the field of disease auxiliary diagnosis equipment, in particular to a system, equipment and a storage medium for predicting diseases based on bitter taste.
Background
In common speaking, "smelling the nose and smelling the smell, tasting the tongue with five flavors". The sour, sweet, bitter, spicy and salty taste information is transmitted by the tiny papillae densely distributed on the tongue surface and taste cells called tongue buds, and then excited by the taste center of the cerebral cortex, and the analysis activity of the whole taste is completed by a feedback loop neurohumoral system. However, some people have a bad taste in their mouth when eating food, or feel a bad taste without eating the mouth. This often suggests that a disease may be acquired. Bitter taste in the mouth means bitter taste in the mouth. Bitter taste corresponds to heart in TCM invisibility, but is rarely seen in heart-related diseases. Acute inflammation is usually seen, mainly liver and gallbladder inflammation, which is usually related to bile metabolism. Bitter taste can also be seen in cancer. Devis doctor, an American medicier, also found that cancer patients lost the taste of sweet foods and had an increasing sensation of bitterness in foods, which was associated with impaired tongue blood circulation and altered salivary contents.
Disclosure of Invention
In view of the above, the present invention provides a system, an apparatus, and a storage medium for predicting a disease based on bitter taste, which are used to solve the problem that in the prior art, accurate auxiliary diagnosis of a disease cannot be performed under the symptoms of bitter taste.
In a first aspect of the present invention, a system for predicting a disease based on bitter taste is disclosed, the system comprising:
a symptom acquisition module: acquiring bitter taste accompanying symptoms on the premise of determining bitter taste as clinical first chief complaint symptoms;
disease library module: acquiring all disease and disease symptom information corresponding to bitter taste, and establishing a bitter taste disease library of the incidence relation between bitter taste accompanying symptoms and corresponding diseases and disease symptoms;
a preprocessing module: vectorizing and expressing the information of various disease symptoms in a disease library in a text processing mode to obtain characteristic vectors of various disease symptoms;
a disease clustering module: determining a clustering category according to the causes of the bitter taste, and clustering diseases in a bitter taste disease library by adopting a k-means clustering algorithm optimized by a wolf-longicorn algorithm;
a large-class division module: acquiring bitter taste accompanying symptoms to be identified, establishing bitter taste accompanying symptom feature vectors, and clustering the bitter taste accompanying symptom feature vectors to obtain a large-class division result;
a disease prediction module: and according to the large class division result, predicting the diseases by calculating the semantic similarity in the class.
Preferably, the clustering categories are divided into five categories of psychogenic bitter taste, oral inflammation bitter taste, stomach heat bitter taste, certain chronic diseases bitter taste, and liver and gall diseases bitter taste.
Preferably, the disease clustering module specifically comprises:
an initialization unit: the method is used for initializing the gray wolf algorithm, setting the group scale, solving the space dimension and initializing the wolf group;
wolf of head screening unit: respectively calculating the fitness of each gray wolf, and screening three gray wolfs with the minimum fitness as head wolfs;
a location update unit: respectively calculating the distances from other wolfs to the wolf, and updating the positions of the wolfs in combination with competition consciousness in a bird swarm algorithm:
Figure BDA0002687616140000021
Figure BDA0002687616140000022
Figure BDA0002687616140000023
wherein X (t +1) is the gray wolf position at t +1 iterations, X1、X2、X3For updating the intermediate variables according to the estimated grey wolf positions of the head wolf positions and the distances, b1 and b2 are belonged to [0,2 ]]K is [1, N ]]And k ≠ i, fi、fkThe fitness values of the ith and the k wolfs are respectively; sumf is the sum of fitness of the whole population, and is a constant close to 0, meanjIs the average fitness value of the population;
returning the updating result of the location of the wolf individualThe fitness calculating unit carries out iterative operation until reaching the maximum iterative times and outputs
Figure BDA0002687616140000024
As the optimized cluster centroid.
Preferably, in the fitness evaluating unit, the fitness function may be the sum of the intra-class distances,
Figure BDA0002687616140000031
where K is the number of cluster categories, d (X)i,Cj) For each gray wolf object X in the jth cluster categoryiTo the cluster center point CjThe distance of (c).
Preferably, in the large-class classification module, the Euclidean distance between the feature vector of bitter accompanying symptoms and each cluster centroid is calculated, and the cluster class with the smallest distance is selected as the large-class classification result.
Preferably, in the disease prediction module, the similarity between the feature vector of bitter taste accompanying symptoms to be classified and the feature vector of each disease symptom in the large classification result is calculated, and the N kinds of diseases with the highest similarity are taken as the preliminary disease prediction result; and for various diseases in the preliminary disease prediction result, if the same disease corresponds to a plurality of accompanying symptoms, respectively calculating the similarity between the feature vector of the bitter taste accompanying symptoms to be classified and the plurality of accompanying symptoms, carrying out comprehensive sequencing on the similarity, and taking N diseases with the highest similarity as the final disease prediction result.
In a second aspect of the present invention, an electronic device is disclosed, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, which are invoked by the processor to implement the system of the first aspect of the invention.
In a third aspect of the invention, a computer-readable storage medium is disclosed, which stores computer instructions for causing a computer to implement the system of the first aspect of the invention.
Compared with the prior art, the invention has the following beneficial effects:
1) the invention establishes a bitter taste disease library, clusters the disease categories by adopting a clustering algorithm optimized based on a longicorn beard-wolf algorithm, divides the diseases with similar disease symptoms into a large category, determines the large category of the cases to be classified, and then calculates the similarity in the category without comparing and judging with all the diseases, thereby greatly reducing the calculation amount, improving the speed and the accuracy of disease diagnosis, being capable of accurately deducing and predicting possible diseases and health problems, and being a simple and practical auxiliary diagnosis system.
2) The invention combines the gray wolf algorithm and the bird swarm algorithm for clustering optimization, fully exerts the advantages of cooperative trapping and mutual competition among the populations, on one hand, the wolf with better fitness can be close to the current optimal position at the fastest speed, on the other hand, other wolfs with poorer fitness can search the potential optimal position near the current optimal position, and the global optimal solution can be searched at the fastest speed.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the system for bitter taste-based disease prediction according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, the present invention provides a system for predicting diseases based on bitter taste, which comprises a symptom obtaining module 100, a disease library module 200, a preprocessing module 300, a disease clustering module 400, a large class partitioning module 500, and a disease prediction module 600;
a symptom obtaining module 100 for obtaining bitter taste accompanying symptoms on the premise of determining bitter taste as a clinical first chief complaint symptom; for example, the traditional Chinese medicine considers that people with bitter taste often have symptoms of headache, dizziness, red face and eyes, acute and irritability, dry stool, reddish tongue, thin and yellow fur, wiry and rapid pulse and the like, and are mostly caused by heat of liver and gallbladder; bitter taste is often accompanied by alternating chills and fever, restlessness and vomiting, bitter and fullness in chest and hypochondrium, silence without appetite, yellow urine, etc., which are mostly caused by gallbladder heat steaming upwards;
the disease library module 200 is used for acquiring all diseases and disease symptom information corresponding to the bitter taste and establishing a bitter taste disease library of the incidence relation between the bitter taste accompanying symptoms and the corresponding diseases and disease symptoms; for example, the main symptoms of gallbladder heat syndrome are: fullness in the chest and hypochondrium, alternating chills and fever, bitter taste in the mouth, dry throat, which may or may not be vomited; or hypochondriac distending pain, nausea, vomiting, bile discharge, thin and yellow tongue coating, and wiry and thready pulse; the main symptoms of liver depression transforming into heat are: bitter taste and dry throat, vexation and irritability, dizziness and headache, conjunctival congestion and hypochondriac pain, yellow and short urine, dry stool, red tongue edge and tip, thin and yellow fur, and wiry and rapid pulse;
because each disease corresponds to a plurality of disease symptoms, and the diagnosis can be confirmed without satisfying all the symptoms, the invention establishes the corresponding relation between the disease and the disease symptoms according to the accompanying symptoms of the mouth salt and the mouth salt, and different accompanying symptoms of the mouth salt can correspond to the same disease.
The preprocessing module 300 is used for vectorizing and expressing various disease symptoms in the disease library in a text processing mode to obtain characteristic vectors of various disease symptoms;
specifically, word segmentation and word removal processing are carried out on disease symptom information corresponding to various diseases; performing mathematical modeling by adopting a vector space model, performing word segmentation weight calculation on the text data after word segmentation by adopting TF-IDF, and extracting key words in the text data; and (3) carrying out Word vectorization by using a Word2Vec model, using the text data vector after vectorization as the characteristic vector of each disease symptom, and inputting the data of characteristic vector clustering of each disease symptom.
The disease clustering module 400 determines the clustering category according to the cause of the bitter taste, and clusters the diseases in the bitter taste disease library by adopting a k-means clustering algorithm optimized by a wolf-longicorn algorithm; the disease clustering module specifically comprises:
a category determination unit: according to the causes of the bitter taste, determining clustering categories, wherein the clustering categories are divided into five categories of psychogenic bitter taste, oral inflammation bitter taste, stomach heat bitter taste, certain chronic diseases bitter taste and liver and gall diseases bitter taste.
1) The modern work and life pressure causes a large number of people to have large work mental pressure, irregular life, insufficient sleep and rest, lack of movement and the like for a long time, thereby causing bitter taste; excessive smoking, excessive drinking, snore, sleep with mouth open and the like are also easy to cause dry mouth and bitter mouth; 2) oral inflammation, if gingivitis, gingival bleeding and other oral diseases are common causes of bitter taste; 3) if the diet is improper, the gastrointestinal function is dull, the retention time of the eaten food in the gastrointestinal tract is too long, the eating food is very easy to generate damp heat, and the bitter taste can be caused; 4) some chronic diseases and diabetes mellitus are often accompanied by bitter taste; in addition, cancer patients feel bitter in the oral cavity due to the fact that the sweet taste valve is increased and the bitter taste valve is reduced, and the vigilance should be improved; 5) the bitter taste in the morning is caused by damp-heat, and the bitter taste in the mouth is caused by inflammation in the liver and gallbladder. For example, when the liver or gallbladder is inflamed, abnormal bile excretion results in bitter taste in the mouth.
An initialization unit: for the gray Wolf algorithm (GWO, Grey Wolf Optimi)zer), setting the group size N, the solution space dimension N and the wolf group initialization Xi＝(x1,x2,......,xn) Where i ∈ [ N ]](ii) a Selecting a case as an initial clustering center for psychogenic bitter taste, oral inflammation bitter taste, stomach heat bitter taste, some chronic diseases bitter taste and liver and gall disease bitter taste respectively;
fitness evaluation unit: respectively calculating the fitness of each gray wolf, and screening out three gray wolf alpha, beta and beta with the minimum fitness, wherein the corresponding positions are X respectivelyα、Xβ、XIs mixing Xα、Xβ、XAn input position updating unit; the fitness function may be the sum of the intra-class distances,
Figure BDA0002687616140000061
where K is the number of cluster categories, d (X)i,Cj) For each gray wolf object X in the jth cluster categoryiTo its clustering center point CjThe distance of (c).
A location update unit: calculating the distance D from other gray wolfs omega to gray wolfs alpha and betaα、Dβ、D：
Dα＝|C1*Xα(t)-X|,Dβ＝|C2*Xβ(t)-X|,D＝|C3*X(t)-X|
Updating the intermediate variable X according to the gray wolf position estimated by the head wolf position and the distance1、X2、X3：
X1＝Xα(t)-A1*Dα,X2＝Xβ(t)-A2*Dβ,X3＝X(t)-A3*D
Grey wolf individual location updates are performed in conjunction with competition awareness in Bird Swarm Algorithm (BSA):
Figure BDA0002687616140000062
Figure BDA0002687616140000063
Figure BDA0002687616140000064
wherein, X (t +1) is the grey wolf position at t +1 times of iteration, A and C are two system vectors, A is 2a r1-a,C＝2a*r2,a＝a1(1-t/tmax)，r1、r2Is [0, 1 ]]Random vectors uniformly distributed over, a1Is a constant value of tmaxIs the maximum iteration number; b1, b 2E [0,2 ]]K is [1, N ]]And k ≠ i, fi、fkThe fitness values of the ith and the k wolfs are respectively; sumf is the sum of fitness of the whole population, and is a constant close to 0, meanjIs the average fitness value of the population.
Returning the updating result of the individual position of the wolf of wolf to the fitness calculation unit for iterative operation until the maximum iteration number is reached, and outputting XαAs the optimized cluster centroid.
The invention combines the gray wolf algorithm with the bird swarm algorithm, when the position is updated, the gray wolf algorithm is not directly moved to the next target position determined by the head wolf, but keeps alert, each head wolf tries to move to the center of the seed group, the behavior is influenced by competition among the species groups, the wolf with better adaptability has higher probability to fly to the center, and the optimal position can be found at the fastest speed, so the convergence speed is improved; and other wolves can search near the target position, so that the local search range is expanded, and the trapping of local optimum is avoided. Therefore, the wolf algorithm and the bird swarm algorithm are combined, so that the wolf with better fitness approaches to the current optimal position at the fastest speed on one hand, and other wolfs with poorer fitness search potential optimal positions near the current optimal position on the other hand, the advantages of cooperative capture and mutual competition among the populations are fully exerted on the two aspects, and the global optimal solution is searched at the fastest speed.
The major classification module 500 is used for acquiring bitter accompanying symptoms to be identified, establishing bitter accompanying symptom feature vectors through the preprocessing module 300, and clustering the bitter accompanying symptom feature vectors through the disease clustering module 400 to obtain major classification results;
specifically, the Euclidean distance between the characteristic vector of bitter accompanying symptoms and each clustering centroid is calculated, and the clustering class with the minimum distance is selected as a large-class division result.
And the disease prediction module 600 performs disease prediction by calculating semantic similarity in the classes according to the large class division result.
In the disease prediction module, calculating the similarity between the characteristic vectors of bitter accompanying symptoms to be classified and the characteristic vectors of various disease symptoms in the large classification result, and taking N kinds of diseases with the highest similarity as a preliminary disease prediction result; for each disease in the preliminary disease prediction result, if the same disease corresponds to a plurality of accompanying symptoms, since the accompanying symptoms are the most easily obtained symptoms clinically, the disease symptoms of each disease are possibly more, and the situation that the degree of distinction between the bitter accompanying symptoms to be classified and the disease symptoms is not large is simply compared, at this time, the similarity between the bitter accompanying symptom feature vector to be classified and the plurality of accompanying symptoms can be respectively calculated, the similarity is comprehensively reordered, and N diseases with the highest similarity are taken as the final disease prediction result. Specifically, a cosine similarity calculation method can be adopted to arrange cosine similarities in a descending order in the class, and the first N most disease prediction results with the highest similarity are recommended to medical care personnel.
The present invention also discloses an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions which can be executed by the processor, and the processor calls the program instructions to realize the system for intelligently identifying the cardiovascular system infection cases based on the current cardiovascular system infection diagnosis standard, which comprises a symptom acquisition module, a disease library module, a preprocessing module, a disease clustering module, a large-class dividing module and a disease prediction module.
The invention also discloses a computer readable storage medium, which stores computer instructions, and the computer instructions enable the computer to realize all the system or part of the system according to the embodiment of the invention. For example, the disease diagnosis and treatment system comprises a symptom acquisition module, a disease base module, a pretreatment module, a disease clustering module, a large-class division module and a disease prediction module. The storage medium includes: u disk, removable hard disk, ROM, RAM, magnetic disk or optical disk, etc.
The above-described system embodiments are merely illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts shown as units may or may not be physical units, i.e. may be distributed over a plurality of network units. Some or all of the modules may be selected according to the actual Xian to achieve the purpose of the solution of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A system for predicting a disease based on bitter taste, the system comprising:
a symptom acquisition module: acquiring bitter taste accompanying symptom information on the premise of determining bitter taste as a clinical first chief complaint symptom;
disease library module: acquiring all disease and disease symptom information corresponding to bitter taste, and establishing a bitter taste disease library of the incidence relation between bitter taste accompanying symptoms and corresponding diseases and disease symptoms;
a preprocessing module: vectorizing and expressing various disease symptom information and corresponding bitter accompanying symptom information in a disease library in a text processing mode to obtain characteristic vectors of various disease symptoms;
a disease clustering module: determining a clustering category according to the causes of the bitter taste, and clustering diseases in a bitter taste disease library by adopting a k-means clustering algorithm optimized by a wolf-bird swarm algorithm;
a large-class division module: acquiring bitter accompanying symptoms to be identified, establishing characteristic vectors of the bitter accompanying symptoms to be identified, and clustering the characteristic vectors of the bitter accompanying symptoms to be identified to obtain a large-class division result;
a disease prediction module: and according to the large class division result, predicting the diseases by calculating the semantic similarity in the class.
2. The system for predicting disease based on bitter taste as claimed in claim 1, wherein the cluster categories are divided into five categories of psychogenic bitter taste, oral inflammation bitter taste, stomach heat bitter taste, certain chronic bitter taste, liver and gall disease bitter taste.
3. The bitter taste-based disease prediction system of claim 2, wherein the disease clustering module specifically clusters diseases in the bitter taste disease library by using a k-means clustering algorithm optimized by a wolf-taurus algorithm, and comprises:
an initialization unit: the method is used for initializing the gray wolf algorithm, setting the group scale, solving the space dimension and initializing the wolf group; selecting a case as an initial clustering center for psychogenic bitter taste, oral inflammation bitter taste, stomach heat bitter taste, some chronic diseases bitter taste and liver and gall disease bitter taste respectively;
wolf of head screening unit: respectively calculating the fitness of each gray wolf, and screening three gray wolfs with the minimum fitness as head wolfs;
a location update unit: respectively calculating the distances from other wolfs to the wolf, and updating the positions of the wolfs in combination with competition consciousness in a bird swarm algorithm:
Figure FDA0002687616130000021
Figure FDA0002687616130000022
Figure FDA0002687616130000023
wherein X (t +1) is the gray wolf position at t +1 iterations, X1、X2、X3For updating the intermediate variables according to the estimated grey wolf positions of the head wolf positions and the distances, b1 and b2 are belonged to [0,2 ]]K is [1, N ]]And k ≠ i, fi、fkThe fitness values of the ith and the k wolfs are respectively; sumf is the sum of fitness of the whole population, and is a constant close to 0, meanjIs the average fitness value of the population;
returning the updating result of the individual position of the wolf of wolf to the fitness calculation unit for iterative operation until the maximum iteration number is reached, and outputting XαAs the optimized cluster centroid.
4. The bitter taste-based disease prediction system of claim 3, wherein the fitness evaluation unit is configured to determine the fitness function as the sum of the intra-class distances,
Figure FDA0002687616130000024
where K is the number of cluster categories, d (X)i,Cj) For each gray wolf object X in the jth cluster categoryiTo the cluster center point CjThe distance of (c).
5. The system for predicting diseases based on bitter taste as claimed in claim 1, wherein in the disease prediction module, the similarity between the feature vector of bitter taste accompanying symptoms to be classified and the feature vector of each disease symptom in the large classification result is calculated, and the N diseases with the highest similarity are taken as the preliminary disease prediction result; and for various diseases in the preliminary disease prediction result, if the same disease corresponds to a plurality of accompanying symptoms, respectively calculating the similarity between the feature vector of the bitter taste accompanying symptoms to be classified and the plurality of accompanying symptoms, carrying out comprehensive sequencing on the similarity, and taking N diseases with the highest similarity as the final disease prediction result.
6. An electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to implement the system of any one of claims 1-5.
7. A computer readable storage medium storing computer instructions which cause a computer to implement the system of any one of claims 1 to 5.
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