CN111863240A - Disease cognitive system based on abnormal change of human body fluid - Google Patents
Disease cognitive system based on abnormal change of human body fluid Download PDFInfo
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Abstract
The invention provides a disease cognitive system based on abnormal changes of human body fluid. The method comprises the following steps: the data storage module is used for acquiring characteristic information of abnormal change of human body fluid and corresponding disease symptom information and performing classified storage; the database module is used for establishing an event tree according to the stored characteristic information of the abnormal change of the human body fluid and the corresponding disease symptom information; the matching module is used for establishing a complex correlation coefficient algorithm, acquiring the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed, and calculating the correlation probability between the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed and the event tree by utilizing the complex correlation coefficient algorithm; and the prediction module is used for diagnosing and predicting the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed. According to the method, the rapid diagnosis of the disease cognition of the abnormal change of the human body fluid can be realized through the complex correlation coefficient algorithm, and meanwhile, the method is combined with the event tree, so that the disease cognition of the abnormal change of the human body fluid can be predicted, and the user experience is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a disease cognitive system based on abnormal change of human body fluid.
Background
Generally, the human body contains a large amount of water, and this water and various substances dispersed in the water are collectively called body fluid, and account for about 60% of the body weight. Which kind of abnormal water regulation of the body is likely to indicate the condition of the relevant organs. That is, the abnormal changes of body fluids such as color, smell and character are closely related to diseases, and the observation of the abnormal changes of body fluids to judge the health condition and diseases of human body is one of the indispensable steps for medical institutions to diagnose diseases.
At present, the condition of the patient is preliminarily judged by inquiring of a doctor, and then a final inspection result is obtained through the inspection of an instrument, but the inspection process is complex, time is consumed, and user experience is not facilitated, so that a disease cognition system based on abnormal change of human body fluid is urgently needed, and the speed and the efficiency of disease cognition can be improved.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
In view of this, the invention provides a disease cognition system based on abnormal change of human body fluid, and aims to solve the technical problems that in the prior art, quick matching of abnormal body fluid and disease cognition can not be realized through complex correlation coefficients, and the disease cognition speed and efficiency are improved.
The technical scheme of the invention is realized as follows:
in one aspect, the present invention provides a disease recognition system based on abnormal changes of human body fluid, including:
the data storage module is used for acquiring characteristic information of abnormal change of human body fluid and corresponding disease symptom information and performing classified storage;
the database module is used for establishing an event tree according to the stored characteristic information of the abnormal change of the human body fluid and the corresponding disease symptom information;
the matching module is used for establishing a complex correlation coefficient algorithm, acquiring the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed, and calculating the correlation probability between the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed and the event tree by utilizing the complex correlation coefficient algorithm;
and the prediction module is used for diagnosing and predicting the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed according to the relevant probability and the event tree.
On the basis of the above technical solution, preferably, the data storage module includes a classification data storage module, which is used to obtain characteristic information of abnormal changes of human body fluid and corresponding disease symptom information, and the human body fluid includes: sweat, tears, sputum, gastric juice, saliva, semen, menses, and leucorrhea, the characteristic information including: color, smell, character, form and quantity change, and the disease symptom information comprises: the characteristic information of abnormal change of the body fluid of the human body and the corresponding disease symptom information are classified and stored according to the body fluid of the human body.
On the basis of the above technical scheme, preferably, the database module includes an event tree establishing module, which is used for establishing an event tree by using the classified and stored human body fluid as a root node, using the characteristic information of abnormal changes of the human body fluid as a child node, and using the corresponding disease symptom information as a leaf node.
On the basis of the above technical solution, preferably, the matching module includes a matching calculation module, configured to establish a complex correlation coefficient algorithm, acquire characteristic information of the abnormal change of the body fluid of the human body to be diagnosed, and calculate, by using the complex correlation coefficient algorithm, a correlation probability between the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed and each root node and corresponding child node of the event tree.
On the basis of the above technical solution, preferably, the matching module further includes a complex correlation coefficient algorithm unit, and the complex correlation coefficient algorithm is:
wherein R represents a complex correlation coefficient, y represents characteristic information of abnormal changes of the body fluid of the human body to be diagnosed,
x represents disease symptom information corresponding to characteristic information of abnormal change of human body fluid in the event tree,
represents the regression coefficient, and K represents the number of leaf nodes in the event tree.
On the basis of the above technical solution, preferably, the prediction module includes a diagnosis prediction module, which is configured to diagnose the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed according to the complex correlation coefficient, and predict the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed by combining with the event tree.
Still further preferably, the disease recognition device based on abnormal changes in body fluid of a human body comprises:
the data storage unit is used for acquiring characteristic information of abnormal change of human body fluid and corresponding disease symptom information and performing classified storage;
the database unit is used for establishing an event tree according to the stored characteristic information of the abnormal change of the human body fluid and the corresponding disease symptom information;
the matching unit is used for establishing a complex correlation coefficient algorithm, acquiring the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed, and calculating the correlation probability between the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed and the event tree by using the complex correlation coefficient algorithm;
and the prediction unit is used for predicting the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed according to the relevant probability and the event tree.
Compared with the prior art, the disease cognitive system based on the abnormal change of the body fluid of the human body has the following beneficial effects:
(1) through the complex correlation coefficient algorithm, the diagnosis speed and accuracy of the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed can be improved, and the user experience is improved;
(2) by establishing the event tree and utilizing the event tree and the related probability, the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed can be predicted, and meanwhile, the speed of the whole diagnosis process is improved by the event tree.
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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 block diagram of the structure of a first embodiment of the disease recognition system based on abnormal changes in human body fluid according to the present invention;
FIG. 2 is a structural diagram of a second embodiment of the disease recognition system based on abnormal changes of human body fluid according to the present invention;
FIG. 3 is a block diagram of a disease recognition system based on abnormal changes in body fluids according to a third embodiment of the present invention;
FIG. 4 is a block diagram of a disease recognition system based on abnormal changes in body fluids according to a fourth embodiment of the present invention;
FIG. 5 is a block diagram of a fifth embodiment of the disease recognition system based on abnormal changes in body fluids;
fig. 6 is a structural block diagram of a disease cognition device based on abnormal change of human body fluid.
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, fig. 1 is a block diagram illustrating a first embodiment of a disease recognition system based on abnormal changes in body fluids according to the present invention. Wherein the disease cognitive system based on abnormal changes of human body fluid comprises: a data acquisition module 10, a learning model construction module 20, a diagnostic model module 30, and a diagnostic prediction module 40.
The data storage module 10 is used for acquiring characteristic information of abnormal changes of human body fluid and corresponding disease symptom information, and performing classified storage;
the database module 20 is used for establishing an event tree according to the stored characteristic information of the abnormal change of the human body fluid and the corresponding disease symptom information;
the matching module 30 is configured to establish a complex correlation coefficient algorithm, obtain characteristic information of the abnormal change of the body fluid of the human body to be diagnosed, and calculate a correlation probability between the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed and the event tree by using the complex correlation coefficient algorithm;
and the prediction module 40 is used for diagnosing and predicting the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed according to the relevant probability and the event tree.
It should be understood that the present embodiment further includes: the system comprises a characteristic information base module for abnormal change of human body fluid, a disease database, a disease knowledge base, an input module, a matching retrieval module and an output module.
The characteristic information base module for the abnormal change of the human body fluid mainly collects the characteristic information of the abnormal change of the human body fluid (including sweat, tears, sputum, gastric juice, saliva, semen, menstruation, leucorrhea and the like) and stores the characteristic information in a classified manner.
The disease database collects symptom characteristic information of diseases caused by abnormal changes of various different types of body fluids, and generates historical cases for classified storage according to the symptom characteristic information of the diseases.
The disease knowledge base stores and records the name of the disease and the information of the expression symptoms of each stage of the disease, and establishes an event tree based on the disease symptoms for the diseases with the same initial symptoms respectively.
The input module is used for a user to input characteristic information including abnormal change of the body fluid.
The matching module matches the characteristic information of the abnormal change of the body fluid, which is recorded by the input module, with the content of the historical case stored in the disease database, and sends the matching result to the disease knowledge base and the output module respectively.
The output module generates conclusion information and sends the conclusion information to the user according to the matching result output by the matching module and the manifestation symptom information of each stage of the disease in the disease knowledge base; the output module also generates new cases according to the further matching results of the verified modules and sends the new cases to the disease database for storage.
Further, as shown in fig. 2, a structural block diagram of a second embodiment of the disease recognition system based on abnormal changes of body fluid of the human body according to the present invention is proposed based on the above embodiments, in this embodiment, the data storage module 10 further includes:
the classification data storage module 101 is configured to obtain feature information of abnormal changes of human body fluid and corresponding disease symptom information, where the human body fluid includes: sweat, tears, sputum, gastric juice, saliva, semen, menses, and leucorrhea, the characteristic information including: color, smell, character, form and quantity change, and the disease symptom information comprises: classifying and storing characteristic information of abnormal changes of human body fluid and corresponding disease symptom information according to the human body fluid;
it should be understood that the system may obtain the characteristic information of the abnormal change of the body fluid of the human body and the corresponding disease symptom information from other ways, which may be a network, an administrator input, and historical data in this embodiment.
It should be understood that the characteristic information of abnormal changes of body fluids mainly refers to the characteristic information of abnormal changes of color, smell, character, form and quantity of body fluids such as sweat, tears, sputum, gastric juice, saliva, semen, menstruation, leucorrhea and the like.
Further, as shown in fig. 3, a structural block diagram of a third embodiment of the disease recognition system based on abnormal changes in human body fluid is provided based on the above embodiments, in this embodiment, the database module 20 further includes:
the event tree establishing module 201 is configured to establish an event tree by using the classified and stored human body fluid as a root node, using the characteristic information of abnormal changes of the human body fluid as a child node, and using the corresponding disease symptom information as a leaf node.
It should be understood that the method for establishing the event tree is as follows: body fluid (such as sweat) abnormal change characteristic information and corresponding disease symptom and sign information with the same category are classified into one category by respectively establishing an event tree based on the symptom characteristic information; and draw the event tree with the abnormal change characteristic information in color, smell, property, shape and quantity in the category as the child node; the leaf nodes of the event tree correspond to disease symptom characteristic information (symptoms) until all end nodes of the event tree, namely the leaf nodes, correspond to diseases corresponding to symptoms finally. For example, the trunk of the event tree is a human body fluid; the branches and trunks include blood, gastric juice, tears, saliva, sputum, semen, sweat, nasal discharge, urine, menstruation, leucorrhea and other categories; branches are the changing features of each specific category (such as sweat), namely, the color, smell, character, shape, quantity, etc. of the categories of blood, gastric juice, tears, saliva, sputum, semen, sweat, nasal discharge, urine, menstruation, leucorrhea, etc. That is, each specific category (e.g., sweat) includes several changing features, i.e., color, odor, character, shape, quantity, etc.; the leaves (nodes) are the specific feature information in each specific variation feature. For example, the color of sweat is red sweat, white sweat, black sweat, etc. Here, each specific variation feature contains many feature information. For example, the sputum color changes to red, white, etc.; the sputum smell changes and has fishy smell, stink and the like; the sputum has the shape changes of block, sticky and the like; white sticky sputum and red massive sputum due to the change of the sputum properties; there are also a large amount of sputum and a small amount of sputum.
It should be understood that each specific characteristic information in each specific variation characteristic corresponds to one or more diseases. For measuring one variable y and a plurality of other variables x1,x2,...xKBy constructing a correlation coefficient with respect to x1,x2,...xKBy calculating a simple correlation coefficient between the linear combination and y as the variables y and x1,x2,...xKA complex correlation coefficient therebetween.
Further, as shown in fig. 4, a block diagram of a fourth embodiment of the disease recognition system based on abnormal changes of human body fluid according to the present invention is proposed based on the above embodiments, and in this embodiment, the matching module 30 includes:
the matching calculation module 301 is configured to establish a complex correlation coefficient algorithm, obtain characteristic information of abnormal changes of the body fluid of the human body to be diagnosed, and calculate, by using the complex correlation coefficient algorithm, a correlation probability between the characteristic information of the abnormal changes of the body fluid of the human body to be diagnosed and each root node and corresponding child node of the event tree.
A complex correlation coefficient algorithm unit 302, where the complex correlation coefficient algorithm is:
wherein R represents a complex correlation coefficient, y represents characteristic information of abnormal changes of the body fluid of the human body to be diagnosed,
x represents disease symptom information corresponding to characteristic information of abnormal change of human body fluid in the event tree, 
Representing the regression coefficients, K representing the number of leaf nodes in the event tree,
represents the average value.
It should be understood that the matching calculation module compares the current abnormal change feature information of the body fluid with each item of feature information of diseases in the event tree item by item to calculate the related probability and coefficient. In general, abnormal changes in the color, smell, character, form, quantity, etc. of body fluids indicate a problem with the health of a human or what disease may be suffering from it, or a precursor to it. For example, sputum, normal people should have no sputum or small amount of white or colorless secretion. If the sputum is red or brownish red, blood may be mixed with the sputum, which is commonly seen in pulmonary tuberculosis, bronchiectasis, acute pulmonary edema, lung cancer, etc.; if it is yellow or yellowish green phlegm, suppurative inflammation of respiratory tract such as chronic bronchitis, pulmonary tuberculosis, etc.; green excessive phlegm is pseudomonas aeruginosa infection; rust-colored lobar pneumonia, pulmonary infarction, etc.; mucus-like phlegm is commonly seen in bronchitis, bronchial asthma, early pneumonia, etc.; serous phlegm is mostly seen in pulmonary edema; purulent sputum, purulent infection of respiratory tract, bronchiectasis, lung abscess, etc.; the black phlegm is caused by long-term smoking or inhaling air and dust in a serious area polluted by the atmosphere.
It should be understood that the value of the complex correlation coefficient is [0,1], and there are several cases:
1. when the correlation coefficient is 0, the x and y variables have no relation; 2. when the value of x is increased (decreased), the value of y is increased (decreased), the two variables are positively correlated, and the correlation coefficient is between 0.00 and 1.00; 3. when the value of x increases (decreases) and the value of y decreases (increases), the two variables are negatively correlated, with a correlation coefficient between-1.00 and 0.00. That is, the larger the absolute value of the correlation coefficient is, the stronger the correlation is, the closer the correlation coefficient is to 1 or-1, and the stronger the correlation is, the closer the correlation coefficient is to 0, the weaker the correlation is.
The specific calculation process is as follows:
first, using y to x1,x2,...xKMaking regression to obtain:
then calculate y and
the simple correlation coefficients are y and x1,x2,...xKThe complex correlation coefficient between the two is calculated by the formula:
the complex correlation coefficient is represented by R because the square of R is just the coefficient of determination of the linear regression equation, i.e. R is a complex correlation coefficient
Thereby it can be deduced，
It should be understood that the complex correlation coefficient differs from the simple correlation coefficient in that the simple correlation coefficient has a value range of [ -1,1], and the complex correlation coefficient has a value range of [0,1 ]. This is because, in the case of two variables, the regression coefficient has a positive or negative score, so that there are positive and negative scores when the correlation is studied; however, when there are a plurality of variables, the partial regression coefficients have two or more values, and the signs of the partial regression coefficients have positive or negative values, and cannot be distinguished according to the positive or negative values, so that the complex correlation coefficient only takes a positive value.
Further, as shown in fig. 5, a block diagram of a fifth embodiment of the disease recognition system based on abnormal changes in body fluid of the human body according to the present invention is proposed based on the above embodiments, and in this embodiment, the prediction module 40 includes:
the diagnosis and prediction module 401 is configured to diagnose the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed according to the complex correlation coefficient, and predict the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed by combining the event tree.
It should be understood that, in the embodiment, the event tree based on the abnormal change characteristic information of the body fluid of the human body is combined with the abnormal change characteristic information of the body fluid of the human body to be diagnosed, so as to predict the possible development trend of the abnormal change characteristic information of the body fluid of the human body to be diagnosed, and the occurrence probability of the event of the category (such as sweat, tear, and the like) is respectively multiplied by the occurrence probability of each branch of the node of the category characteristic (such as color, smell, and the like) so as to obtain the occurrence probability of each terminal node (such as sweat of the body fluid; color of the sweat; color of red; and sending the disease prediction result corresponding to each end node with the end node occurrence probability larger than 0.3 (hypothesis) to an output module. Thus, we can get that, when >0.90, the case representing the event tree is most similar to the current case, i.e. completely matched, and the matching result is sent to the output module and the disease knowledge base, respectively; and when the matching result is less than or equal to 0.90, the case representing the event tree is completely different from the current case, and the matching result is respectively sent to the rewriting module and the disease knowledge base. The conclusion information comprises the matching degree of the current case and the case of the event tree and the possible development trend of the current case. For example, urine is slightly pale and yellowish in color under normal drinking and eating conditions, that is, the normal color of urine is clear and yellowish. But recently, users find that urine is sometimes too thick, too dark, soy sauce, and even hematuria. Then, it is obvious that if the color is too dark, whether metabolic problems or diseases exist is considered, and the patient needs to be treated in time because the color changes abnormally.
It should be understood that, in this embodiment, the body fluid abnormal change feature information, the rules and models of the body fluid corresponding to and associated with the disease feature information are trained, so as to implement the adjustment of the applicability of the rules, then a new event tree case is generated by the case adjustment module according to the result of the verification, the matching retrieval module adds the abnormal change feature information of the new event tree case and the corresponding disease symptom feature information label in the corresponding body fluid abnormal change feature information space, thereby implementing autonomous learning and case supplement, improving the reliability and accuracy of the system, and finally, the continuous deep learning optimization of the generated intelligent rules is performed according to the actual application scenarios of the generated body fluid auxiliary diagnosis and inquiry rules in the system, in combination with limited manual intervention, and according to the continuously imported training data.
The above description is only for illustrative purposes and does not limit the technical solutions of the present application in any way.
As can be easily found from the above description, the present embodiment proposes a disease recognition system based on abnormal changes in body fluid of a human body, comprising: the data storage module is used for acquiring characteristic information of abnormal change of human body fluid and corresponding disease symptom information and performing classified storage; the database module is used for establishing an event tree according to the stored characteristic information of the abnormal change of the human body fluid and the corresponding disease symptom information; the matching module is used for establishing a complex correlation coefficient algorithm, acquiring the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed, and calculating the correlation probability between the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed and the event tree by utilizing the complex correlation coefficient algorithm; and the prediction module is used for diagnosing and predicting the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed according to the relevant probability and the event tree. The embodiment can realize the rapid diagnosis of the disease cognition of the abnormal change of the human body fluid through the complex correlation coefficient algorithm, and meanwhile, the method is combined with the event tree, so that the disease cognition of the abnormal change of the human body fluid can be predicted, and the user experience is improved.
In addition, the embodiment of the invention also provides disease cognitive equipment based on the abnormal change of the body fluid of the human body. As shown in fig. 6, the disease recognition apparatus based on abnormal changes in body fluid of a human body includes: a data storage unit 10, a database unit 20, a matching unit 30, and a prediction unit 40.
The data storage unit 10 is used for acquiring characteristic information of abnormal changes of human body fluid and corresponding disease symptom information, and performing classified storage;
the database unit 20 is used for establishing an event tree according to the stored characteristic information of the abnormal change of the human body fluid and the corresponding disease symptom information;
the matching unit 30 is configured to establish a complex correlation coefficient algorithm, acquire feature information of the abnormal change of the body fluid of the human body to be diagnosed, and calculate a correlation probability between the feature information of the abnormal change of the body fluid of the human body to be diagnosed and the event tree by using the complex correlation coefficient algorithm;
and the prediction unit 40 is used for predicting the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed according to the relevant probability and the event tree.
In addition, it should be noted that the above-described embodiments of the apparatus are merely illustrative, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of the modules to implement the purpose of the embodiments according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment can be referred to the disease cognitive system based on the abnormal change of the body fluid of the human body provided in any embodiment of the present invention, and are not described herein again.
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 disease recognition system based on abnormal changes of human body fluid, which is characterized by comprising:
the data storage module is used for acquiring characteristic information of abnormal change of human body fluid and corresponding disease symptom information and performing classified storage;
the database module is used for establishing an event tree according to the stored characteristic information of the abnormal change of the human body fluid and the corresponding disease symptom information;
the matching module is used for establishing a complex correlation coefficient algorithm, acquiring the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed, and calculating the correlation probability between the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed and the event tree by utilizing the complex correlation coefficient algorithm;
And the prediction module is used for diagnosing and predicting the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed according to the relevant probability and the event tree.
2. A disease recognition system based on abnormal changes in body fluids of a human being as claimed in claim 1 wherein: the data storage module comprises a classification data storage module and is used for acquiring characteristic information of abnormal change of human body fluid and corresponding disease symptom information, and the human body fluid comprises: sweat, tears, sputum, gastric juice, saliva, semen, menses, and leucorrhea, the characteristic information including: color, smell, character, form and quantity change, and the disease symptom information comprises: the characteristic information of abnormal change of the body fluid of the human body and the corresponding disease symptom information are classified and stored according to the body fluid of the human body.
3. A disease recognition system based on abnormal changes in body fluids of a human being as claimed in claim 2 wherein: the database module comprises an event tree establishing module which is used for establishing an event tree by taking human body fluid which is stored in a classified mode as a root node, taking characteristic information of abnormal change of the human body fluid as a child node and taking corresponding disease symptom information as a leaf node.
4. A disease recognition system based on abnormal changes in body fluids of a human being as claimed in claim 3 wherein: the matching module comprises a matching calculation module which is used for establishing a complex correlation coefficient algorithm, acquiring the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed, and calculating the correlation probability between the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed and each root node and corresponding child nodes of the event tree by using the complex correlation coefficient algorithm.
5. A disease recognition system based on abnormal changes in body fluids of a human being as claimed in claim 4 wherein: the matching module also comprises a complex correlation coefficient algorithm unit, and the complex correlation coefficient algorithm is as follows:
wherein R represents a complex correlation coefficient, y represents characteristic information of abnormal changes of the body fluid of the human body to be diagnosed,
x represents disease symptom information corresponding to characteristic information of abnormal change of human body fluid in the event tree,
represents the regression coefficient, and K represents the number of leaf nodes in the event tree.
6. A disease recognition system based on abnormal changes in body fluids of a human being as claimed in claim 5 wherein: the prediction module comprises a diagnosis prediction module which is used for diagnosing the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed according to the complex correlation coefficient and predicting the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed by combining the event tree.
7. A disease recognition device based on abnormal changes of human body fluid, the disease recognition device based on abnormal changes of human body fluid is characterized by comprising:
the data storage unit is used for acquiring characteristic information of abnormal change of human body fluid and corresponding disease symptom information and performing classified storage;
the database unit is used for establishing an event tree according to the stored characteristic information of the abnormal change of the human body fluid and the corresponding disease symptom information;
the matching unit is used for establishing a complex correlation coefficient algorithm, acquiring the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed, and calculating the correlation probability between the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed and the event tree by using the complex correlation coefficient algorithm;
and the prediction unit is used for predicting the characteristic information of the abnormal change of the body fluid of the human body to be diagnosed according to the relevant probability and the event tree.
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