CN112331352A - Intelligent information matching system based on dengue fever - Google Patents
Intelligent information matching system based on dengue fever Download PDFInfo
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Abstract
The invention provides an intelligent matching system for information based on dengue fever. The method comprises the following steps: the acquisition module is used for acquiring a dengue characteristic information text and establishing a first characteristic vector set according to the dengue characteristic information text; the acquisition module is used for acquiring a characteristic information text of the dengue to be matched, extracting characteristic information to be matched in the characteristic information text of the dengue to be matched through TF-IDF, and establishing a second characteristic vector set; the calculation module is used for setting an original meaning conversion rule and a symbol conversion rule, converting the first feature vector set and the second feature vector set according to the original meaning conversion rule and the symbol conversion rule and calculating the similarity between the two converted feature vector sets; and the weighting module is used for carrying out weighted summation according to the similarity, obtaining the result of the weighted summation and generating a corresponding matching report according to the result. The invention greatly improves the accuracy of intelligent information matching and simultaneously improves the speed of information matching through the TF-IDF algorithm and weighted summation.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to an intelligent matching system for information based on dengue fever.
Background
Dengue Fever (DF) is an acute infectious disease caused by dengue virus, primarily transmitted by aedes aegypti and aedes albopictus, with the major clinical manifestations of fever, rash, headache, myalgia, anorexia, gastrointestinal dysfunction and systemic failure. In addition, dengue virus can cause dengue hemorrhagic fever and dengue shock syndrome, and the latter two types of clinical symptoms are critical and have higher fatality rate.
Dengue fever is generally diagnosed through routine and biochemical examination, serological examination, virus isolation, reverse transcription polymerase chain reaction, and related symptoms and epidemic history, and the diagnosis depends on the judgment of a clinician, which not only has large workload, but also may cause certain errors, so that the improvement of the existing method is urgently needed.
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 an intelligent matching system for information based on dengue fever, and aims to solve the technical problem that the accuracy of intelligent matching of information cannot be improved by acquiring feature information for multiple times and weighting in the prior art.
The technical scheme of the invention is realized as follows:
in one aspect, the present invention provides an intelligent matching system for dengue fever-based information, comprising:
the acquisition module is used for acquiring a dengue characteristic information text and establishing a first characteristic vector set according to the dengue characteristic information text;
the acquisition module is used for acquiring a characteristic information text of the dengue to be matched, extracting characteristic information to be matched in the characteristic information text of the dengue to be matched through TF-IDF, and establishing a second characteristic vector set according to the characteristic information to be matched;
the calculation module is used for setting and defining an original conversion rule and a symbol conversion rule, converting the first feature vector set and the second feature vector set according to the original conversion rule and the symbol conversion rule, and calculating the similarity between the two converted feature vector sets;
and the weighting module is used for carrying out weighted summation according to the similarity, obtaining the result of the weighted summation and generating a corresponding matching report according to the result.
On the basis of the above technical solution, preferably, the obtaining module includes a first feature vector set establishing module, configured to obtain a dengue feature information text, where the dengue feature information text includes: the method comprises the steps of establishing a first feature vector set according to epidemiological history feature information, clinical sign feature information and etiological feature information, and storing the epidemiological history feature information and the etiological feature information into the first feature vector set.
On the basis of the above technical scheme, preferably, the acquisition module includes a second feature vector set establishment module, configured to establish a TF-IDF algorithm, set a word frequency range, acquire the feature information text of the dengue fever to be matched, calculate the word frequency of each word in the feature information text of the dengue fever to be matched through the TF-IDF algorithm, determine the feature information word of the feature information text of the dengue fever to be matched through the word frequency range, and establish a corresponding second feature vector set.
On the basis of the above technical solution, preferably, the calculation module includes a conversion module, configured to set an original conversion rule and a symbol conversion rule, and convert the first feature vector set and the second feature vector set according to the primitive conversion rule and the symbol conversion rule to obtain a first basic primitive description, other basic primitive descriptions, a relation primitive description, and a relation symbol description.
On the basis of the above technical solution, preferably, the calculation module includes a similarity calculation module, configured to calculate a similarity between the first feature vector set and the second feature vector set according to the first basic semantic description, the other basic semantic descriptions, the relational semantic description, and the relational symbol description.
On the basis of the above technical solution, preferably, the weighting module includes a weighted average module for setting a similarity threshold, obtaining a final similarity by weighted averaging the similarity, comparing the final similarity with the similarity threshold, and generating a corresponding matching report according to a comparison result.
Still further preferably, the dengue fever based information intelligent matching device comprises:
the acquisition unit is used for acquiring a dengue feature information text and establishing a first feature vector set according to the dengue feature information text;
the acquisition unit is used for acquiring a characteristic information text of the dengue to be matched, extracting characteristic information to be matched in the characteristic information text of the dengue to be matched through TF-IDF, and establishing a second characteristic vector set according to the characteristic information to be matched;
the calculation unit is used for setting an original conversion rule and a symbol conversion rule, converting the first feature vector set and the second feature vector set according to the original conversion rule and the symbol conversion rule, and calculating the similarity between the two converted feature vector sets;
and the weighting unit is used for carrying out weighted summation according to the similarity, obtaining the result of the weighted summation and generating a corresponding matching report according to the result.
Compared with the prior art, the intelligent matching system for information based on dengue has the following beneficial effects:
(1) by accurately extracting the characteristic information text of the dengue fever by using the TF-IDF, the accuracy of a subsequent information matching process is improved, and the time of the information matching process is saved;
(2) by utilizing the original conversion rule and the symbol conversion rule of the definition, the comparison between characters is converted into the comparison between vectors, the text information can be accurately matched, and the matching speed of the system is improved.
Drawings
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 a first embodiment of the intelligent matching system for dengue-based information of the present invention;
FIG. 2 is a block diagram of a second embodiment of the intelligent matching system for dengue-based information according to the present invention;
FIG. 3 is a block diagram illustrating a third exemplary embodiment of the intelligent matching system for dengue-based information;
FIG. 4 is a block diagram illustrating a fourth embodiment of the intelligent matching system for dengue-based information according to the present invention;
FIG. 5 is a block diagram illustrating a fifth embodiment of the intelligent matching system for dengue-based information according to the present invention;
FIG. 6 is a block diagram of the dengue fever based information intelligent matching device of 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, fig. 1 is a block diagram illustrating a first embodiment of an intelligent matching system for dengue fever based information according to the present invention. Wherein the intelligent matching system for information based on dengue fever comprises: the device comprises an acquisition module 10, an acquisition module 20, a calculation module 30 and a weighting module 40.
The acquiring module 10 is configured to acquire a dengue feature information text, and establish a first feature vector set according to the dengue feature information text;
the acquisition module 20 is used for acquiring a feature information text of dengue to be matched, extracting feature information to be matched in the feature information text of dengue to be matched through TF-IDF, and establishing a second feature vector set according to the feature information to be matched;
the calculation module 30 is configured to set an original conversion rule and a symbol conversion rule, convert the first feature vector set and the second feature vector set according to the original conversion rule and the symbol conversion rule, and calculate a similarity between the two converted feature vector sets;
and the weighting module 40 is configured to perform weighted summation according to the similarity, obtain a result of the weighted summation, and generate a corresponding matching report according to the result.
Further, as shown in fig. 2, a structural block diagram of a second embodiment of the intelligent matching system for dengue fever based information according to the foregoing embodiments is provided, in this embodiment, the obtaining module 10 further includes:
a first feature vector set establishing module 101, configured to obtain a dengue feature information text, where the dengue feature information text includes: the method comprises the steps of establishing a first feature vector set according to epidemiological history feature information, clinical sign feature information and etiological feature information, and storing the epidemiological history feature information and the etiological feature information into the first feature vector set.
It should be understood that, in this embodiment, the system first obtains a text of dengue fever feature information, where the text of dengue fever feature information includes: the method comprises the following steps of establishing a first feature vector set according to epidemiological history feature information, clinical sign feature information and etiological feature information, and storing the epidemiological history feature information and the etiological feature information into the first feature vector set, wherein the epidemiological history feature information, the clinical sign feature information and the etiological feature information comprise: epidemiological characteristic information: living in dengue fever epidemic areas or in areas of 15 days which are in the epidemic areas, and 5-9 days before the disease occurs, the people have a history of being bitten by mosquitoes; clinical performance characteristic information: the disease suddenly starts. Aversion to cold and fever (39-40 ℃ in 24-36 h. a few patients present with bimodal fever); fatigue, nausea, vomiting, etc.; with more severe headache, orbital pain, and muscular, joint, and skeletal pain; with flushing of the face, neck and chest, conjunctival congestion; superficial lymphadenectasis. In addition, rashes may occur: the disease course is 5-7 days, and the disease is manifested as various rashes (measles-like rashes, scarlet fever-like rashes), subcutaneous bleeding spots and the like. The rash is distributed on the limbs, trunk or head and face, and is mostly itchy without desquamation for 3-5 days; a small number of patients can present with encephalitis-like encephalopathy symptoms and signs; bleeding tendency (positive arm restraint test) is caused, and gingival bleeding, epistaxis, gastrointestinal bleeding, subcutaneous bleeding, hemoptysis, hematuria, vaginal bleeding or pleuroperitoneal bleeding generally occur within 5-8 d of the course of disease; multiple organ profuse hemorrhage; hepatomegaly; with shock, etc.; laboratory examination: peripheral blood examination: thrombocytopenia (100X 109/L of hypotony). The total number of white blood cells is reduced, and the differential count of lymphocytes and single child cells is relatively increased; the volume of the red blood cells is increased by more than 20 percent; a single serum specific IgG antibody positive; positive serum specific IgM antibody; the serum specific IgG antibody in the convalescent phase is increased by 4 times or more than that in the acute phase; DV is isolated from the serum, plasma, blood cell layer or autopsy organ of acute stage patient, or DV antigen is detected.
Further, as shown in fig. 3, a structural block diagram of a third embodiment of the intelligent matching system for dengue fever based information according to the foregoing embodiments is provided, in this embodiment, the acquiring module 20 further includes:
the second feature vector set establishing module 201 is configured to establish a TF-IDF algorithm, set a word frequency range, acquire a feature information text of the dengue fever to be matched, calculate a word frequency of each word in the feature information text of the dengue fever to be matched through the TF-IDF algorithm, determine a feature information word of the feature information text of the dengue fever to be matched through the word frequency range, and establish a corresponding second feature vector set.
It should be understood that the system will establish a TF-IDF algorithm, set a word frequency range, simultaneously acquire a feature information text of the dengue to be matched, calculate the word frequency of each word in the feature information text of the dengue to be matched through the TF-IDF algorithm, determine the feature information word of the feature information text of the dengue to be matched through the word frequency range, and establish a corresponding second feature vector set, and it should be noted that since most of epidemiological history, clinical signs and the like to be diagnosed by a patient are descriptive texts, feature information extraction, that is, feature participle extraction, needs to be performed on the text. The characteristic items are selected mainly by using TF-IDF, characteristic information or characteristic word segmentation contained in an epidemiological history and a clinical sign text to be identified is acquired, and a vector set of the epidemiological history and the clinical sign characteristic information of Dengue Fever (DF) is established respectively so as to facilitate subsequent calculation and cognition.
Further, as shown in fig. 4, a block diagram of a fourth embodiment of the intelligent matching system for dengue fever based information according to the foregoing embodiments is provided, in this embodiment, the calculating module 30 includes:
the conversion module 301 is configured to set an original conversion rule and a symbol conversion rule, and convert the first feature vector set and the second feature vector set according to the primitive conversion rule and the symbol conversion rule to obtain a first basic primitive description, other basic primitive descriptions, a relationship primitive description, and a relationship symbol description.
A similarity calculation module 302, configured to calculate a similarity between the first feature vector set and the second feature vector set according to the first basic semantic description, the other basic semantic descriptions, the relationship semantic descriptions, and the relationship symbol descriptions.
It should be understood that, based on the result of the above feature information extraction, similarity calculation is performed on any type of feature information vector set (including epidemiological history, clinical signs, and etiological data) in the first feature information vector set and the second feature information vector set, respectively. Regarding the calculation of the similarity, several existing basic methods are based on a Vector (Vector), that is, the distance between two vectors is calculated, and the closer the distance is, the greater the similarity is. The specific calculation model and calculation method are as follows:
because the above-mentioned characteristic information representation (word or phrase) is not organized in a tree-like hierarchy, but a kind of network structure; concepts may thus be described by means of semaphores and symbols. For two entries w1(dengue epidemiological history to be screened, clinical sign trait information vector set) and w2(vector set of dengue epidemiological history, clinical sign criteria signature information), w1There are n characteristic information semantic items (concepts, participles or phrases): s11,s12,...,s1n，w2There is an m-feature information semantic (concept, word or phrase): s11,s12,...,s1mThen, w1And w2The similarity of (2) is the maximum value of the similarity of each feature information semantic item (concept, word or phrase), that is:
it should be understood that, in the above calculation model, in order to calculate the semantic similarity between them more accurately, we can express their description as a feature structure containing the following four features:
the first basic semantic description: the value is a basic meaning, and the similarity of the parts of the two concepts is marked as Sim 1(s)1，s2)；
Other basic meanings describe: corresponding to all the basic semantic meaning description expressions except the first basic semantic meaning description expression in the semantic expression, the value of the basic semantic meaning description expression is a set of basic semantic meanings, and the similarity of the part of the two concepts is marked as Sim 2(s)1，s2)；
Description of relationship semantics: corresponding to all relation-meaning descriptors in the semantic expression, the value of the relation-meaning descriptor is a characteristic structure, and for each characteristic of the characteristic structure, the attribute of the relation-meaning descriptor is a relation-meaning, and the value of the relation-meaning descriptor is a basic meaning or a specific word. The similarity of this part of the two concepts is denoted as Sim 3(s)1，s2)；
The relationship notation describes: corresponding to all relational symbolic descriptors in the semantic expression, the value of which is also a feature structure, for each feature of which the attribute is a relational primitive, the attribute of which is a relational primitiveA value is a set, the elements of which are a basic semantic, or a specific word. The similarity of this part of the two concepts is denoted as Sim 4(s)1，s2)；
It can be seen that, because the hierarchies of the respective meanings are different, the influence degrees of the respective meanings on the word similarity are different, that is, the weight of the partial similarity in the overall similarity is different, and the weight (percentage) is represented by β, so the overall similarity of the concepts can be expressed as:
wherein β (1. ltoreq. i. ltoreq.4) is an adjustable parameter and has: beta is a1+β2+β3+β4＝1,β1≥β2≥β3≥β4. The latter reflects Sim1(s1，s2) To Sim4(s1，s2) The effect on the overall similarity decreases in turn. Since the first independent semantic expression reflects the most important feature of a concept, the weight should be defined to be relatively large, generally above 0.5, and in the above calculation, when finally calculating the weighted average, all parts take equal weight. Thus, the similarity problem between two words is reduced to the similarity problem between two concepts.
Further, as shown in fig. 5, a block diagram of a fifth embodiment of the intelligent matching system for dengue fever based information according to the foregoing embodiments is provided, in this embodiment, the weighting module 40 includes:
the weighted average module 401 is configured to set a similarity threshold, obtain a final similarity by performing weighted average on the similarity, compare the final similarity with the similarity threshold, and generate a corresponding matching report according to a comparison result.
It should be understood that, finally, the system sets a similarity threshold, obtains a final similarity by performing weighted average on the similarities, compares the final similarity with the similarity threshold, and generates a corresponding matching report according to a comparison result, for example: 1. epidemiological data: living in dengue fever epidemic areas or in areas of 15 days which are in the epidemic areas, and 5-9 days before the disease occurs, the people have a history of being bitten by mosquitoes; 2. the clinical manifestations are as follows: 2.1 sudden onset of disease. Aversion to cold and fever (39-40 ℃ in 24-36 h. few patients present with bimodal fever). Fatigue, nausea, vomiting, etc.; 2.2 with more severe headache, orbital pain, and muscular, joint, and skeletal pain; 2.3 with flush of face, neck and chest, conjunctival congestion; 2.4 superficial lymphadenectasis; 2.5 rash: the disease course is 5-7 days, and the disease is manifested as various rashes (measles-like rashes, scarlet fever-like rashes), subcutaneous bleeding spots and the like. The rash is distributed on the limbs, trunk or head and face, and is mostly itchy without desquamation for 3-5 days; 2.6 a minority of patients can present with encephalitis-like encephalopathy symptoms and signs; 2.7 has bleeding tendency (positive arm restraint test), and generally gum bleeding, epistaxis, alimentary tract bleeding, subcutaneous bleeding, hemoptysis, hematuria, vaginal bleeding or pleuroperitoneal bleeding are performed in the course of 5-8 d; 2.8 multiple organ profuse hemorrhage; 2.9 hepatomegaly; 2.10 with shock; 3. laboratory examination: 3.1 peripheral blood test: thrombocytopenia (100X 109/L of hypotony). The total number of white blood cells is reduced, and the differential count of lymphocytes and single child cells is relatively increased; 3.2 the volume of the red blood cells is increased by more than 20 percent; 3.3 a single serum specific IgG antibody positive; 3.4 positive serum specific IgM antibody; 3.5 serum specific IgG antibody in convalescent phase is increased by 4 times or more than that in acute phase; 3.6 isolating DV from the serum, plasma, blood cell layer or autopsy organ of acute stage patient or detecting DV antigen; 4. case classification: 4.1 suspected cases: has at least one of 3.1, 3.2.1, 3.2.2, and 3.2.3 to 3.2.7; 4.2 clinical diagnosis cases: suspected cases plus 3.3.1 (dengue epidemics have been established) or plus 3.3.3 (sporadic cases or epidemics have not been established); 4.3 confirmed cases: dengue fever: any one of 3.3.4, 3.3.5 and 3.3.6 is added in clinical diagnosis cases.
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 provides an intelligent matching system for dengue-based information, which includes: the acquisition module is used for acquiring a dengue characteristic information text and establishing a first characteristic vector set according to the dengue characteristic information text; the acquisition module is used for acquiring a characteristic information text of the dengue to be matched, extracting characteristic information to be matched in the characteristic information text of the dengue to be matched through TF-IDF, and establishing a second characteristic vector set according to the characteristic information to be matched; the calculation module is used for setting and defining an original conversion rule and a symbol conversion rule, converting the first feature vector set and the second feature vector set according to the original conversion rule and the symbol conversion rule, and calculating the similarity between the two converted feature vector sets; and the weighting module is used for carrying out weighted summation according to the similarity, obtaining the result of the weighted summation and generating a corresponding matching report according to the result. According to the embodiment, the accuracy of intelligent information matching is greatly improved through the TF-IDF algorithm and the weighted summation, and meanwhile, the speed of information matching is improved.
In addition, the embodiment of the invention also provides information intelligent matching equipment based on dengue fever. As shown in fig. 6, the dengue-based information intelligent matching device includes: an acquisition unit 10, an acquisition unit 20, a calculation unit 30 and a weighting unit 40.
The acquiring unit 10 is configured to acquire a dengue feature information text, and establish a first feature vector set according to the dengue feature information text;
the acquisition unit 20 is used for acquiring a feature information text of dengue to be matched, extracting feature information to be matched in the feature information text of dengue to be matched through TF-IDF, and establishing a second feature vector set according to the feature information to be matched;
a calculating unit 30, configured to set an original conversion rule and a symbol conversion rule, convert the first feature vector set and the second feature vector set according to the original conversion rule and the symbol conversion rule, and calculate a similarity between the two converted feature vector sets;
and the weighting unit 40 is configured to perform weighted summation according to the similarity, obtain a result of the weighted summation, and generate a corresponding matching report according to the result.
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 elaborated in this embodiment may be referred to the intelligent matching system for dengue fever based information 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. An intelligent matching system for dengue fever-based information, which is characterized by comprising:
the acquisition module is used for acquiring a dengue characteristic information text and establishing a first characteristic vector set according to the dengue characteristic information text;
the acquisition module is used for acquiring a characteristic information text of the dengue to be matched, extracting characteristic information to be matched in the characteristic information text of the dengue to be matched through TF-IDF, and establishing a second characteristic vector set according to the characteristic information to be matched;
the calculation module is used for setting and defining an original conversion rule and a symbol conversion rule, converting the first feature vector set and the second feature vector set according to the original conversion rule and the symbol conversion rule, and calculating the similarity between the two converted feature vector sets;
and the weighting module is used for carrying out weighted summation according to the similarity, obtaining the result of the weighted summation and generating a corresponding matching report according to the result.
2. The dengue-based information intelligent matching system of claim 1, wherein: the obtaining module comprises a first feature vector set establishing module and is used for obtaining a dengue feature information text, wherein the dengue feature information text comprises: the method comprises the steps of establishing a first feature vector set according to epidemiological history feature information, clinical sign feature information and etiological feature information, and storing the epidemiological history feature information and the etiological feature information into the first feature vector set.
3. The dengue-based information intelligent matching system of claim 2, wherein: the acquisition module comprises a second feature vector set establishment module for establishing a TF-IDF algorithm, setting a word frequency range, acquiring a characteristic information text of the dengue to be matched, calculating the word frequency of each word in the characteristic information text of the dengue to be matched through the TF-IDF algorithm, determining the characteristic information word of the characteristic information text of the dengue to be matched through the word frequency range, and establishing a corresponding second feature vector set.
4. The dengue-based information intelligent matching system of claim 3, wherein: the calculation module comprises a conversion module which is used for setting a definition original conversion rule and a symbol conversion rule, and converting the first characteristic vector set and the second characteristic vector set according to the semantic original conversion rule and the symbol conversion rule to obtain a first basic semantic description, other basic semantic descriptions, a relation semantic description and a relation symbol description.
5. The dengue-based information intelligent matching system of claim 4, wherein: the calculation module comprises a similarity calculation module used for calculating the similarity between the first feature vector set and the second feature vector set according to the first basic semantic description, the other basic semantic descriptions, the relation semantic descriptions and the relation symbol descriptions.
6. The dengue-based information intelligent matching system of claim 5, wherein: the weighting module comprises a weighted average module which is used for setting a similarity threshold, obtaining the final similarity by carrying out weighted average on the similarity, comparing the final similarity with the similarity threshold and generating a corresponding matching report according to the comparison result.
7. An intelligent matching device for information based on dengue fever, characterized in that the intelligent matching device for information based on dengue fever comprises:
the acquisition unit is used for acquiring a dengue feature information text and establishing a first feature vector set according to the dengue feature information text;
the acquisition unit is used for acquiring a characteristic information text of the dengue to be matched, extracting characteristic information to be matched in the characteristic information text of the dengue to be matched through TF-IDF, and establishing a second characteristic vector set according to the characteristic information to be matched;
the calculation unit is used for setting an original conversion rule and a symbol conversion rule, converting the first feature vector set and the second feature vector set according to the original conversion rule and the symbol conversion rule, and calculating the similarity between the two converted feature vector sets;
and the weighting unit is used for carrying out weighted summation according to the similarity, obtaining the result of the weighted summation and generating a corresponding matching report according to the result.
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