CN115186112A - Medicine data retrieval method and device based on syndrome differentiation mapping rule - Google Patents
Medicine data retrieval method and device based on syndrome differentiation mapping rule Download PDFInfo
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Abstract
The invention discloses a medicine data retrieval method and device based on dialectical mapping rules, and relates to the technical field of medicine information. The method comprises the following steps: acquiring a text to be retrieved; inputting the text to be retrieved into a synonym standard database to obtain the attribute category of the keyword of the text to be retrieved; and inputting the attribute types into a mapping rule base to obtain a medicine data retrieval result of the text to be retrieved. The invention constructs a keyword query method, obtains the input text of the user, automatically divides words, and extracts keywords through a synonym standard database. And constructing a dialectical mapping reasoning method, after classifying the attributes of the keywords, performing professional expansion and mapping on each attribute of the keywords through a mapping word database, associating the information of the disease and syndrome method, forming the mapping relations from symptoms to syndrome, from syndrome to treatment, from symptoms to diseases, from diseases to symptoms, from treatment to prescription, from diseases to prescription and the like, and constructing a comprehensive and accurate dialectical mapping reasoning relation.
Description
Technical Field
The invention relates to the technical field of medical information, in particular to a medical data retrieval method and device based on syndrome differentiation mapping rules.
Background
Medical data retrieval is a process by which a user obtains desired relevant information from large-scale medical data. With the development of the digital era and the popularization of medical digital equipment, some traditional information retrieval methods, such as Boolean logic retrieval, word retrieval and the like, are generally applied to medical data retrieval, which meet the requirements of users on retrieval speed to a certain extent, but do not meet the requirements on accuracy of retrieved data, and cannot provide more information after reasoning and prediction for users through data in a database.
Because of its special characteristics, TCM uses syndrome differentiation as the basic principle of clinical diagnosis and treatment, and its symptoms are fuzzy, complex and diverse. Meanwhile, the traditional Chinese medicine recognizes the diseases not only by syndrome differentiation, but also by five different levels of symptoms, diseases, methods and prescriptions, and the basic means of diagnosis and treatment in the traditional Chinese medicine is provided by syndrome differentiation, disease differentiation and disease and syndrome combination, and method based on the symptoms and the methods. However, the existing search in the field of traditional Chinese medicine is not constructed completely based on the thinking of 'treatment by syndrome differentiation' of traditional Chinese medicine, so that the search cannot obtain higher accuracy.
The existing traditional Chinese medicine data retrieval mode is generally carried out by adopting a mode of matching search words, receiving words input by a user, comparing the received words with information in a database, and capturing information associated with the words. The traditional Chinese medicine retrieval method has certain defects, if the information in the database is huge, the quantity of the information retrieved and associated according to the vocabulary is large, and the user needs to search again in the large quantity of information, so that the retrieval efficiency of the user is reduced, and the information in the existing retrieval database cannot be inferred and predicted, so that the problems that the retrievers retrieve by using some existing retrieval methods, the retrieved contents are few, and the queried data is inaccurate occur.
Disclosure of Invention
The invention provides the method for searching the data of the electronic map by the aid of the data query, and aims to solve the problems that a searcher searches through some existing searching methods, the obtained searched content is few, and the searched data is inaccurate.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the present invention provides a medical data retrieval method based on dialectical mapping rules, where the method is implemented by an electronic device, and the method includes:
s1, obtaining a text to be retrieved.
And S2, inputting the text to be retrieved into a synonym standard database to obtain the keywords with the classified attributes.
And S3, inputting the keywords after attribute classification into a mapping rule base to obtain a medicine data retrieval result of the text to be retrieved.
Optionally, the text to be retrieved in S1 is a professional descriptive information text or a non-professional descriptive information text.
Optionally, the step S2 of inputting the text to be retrieved into the synonym criterion database, and obtaining the attribute category of the keyword of the text to be retrieved includes:
s21, inputting the text to be retrieved into the synonym standard database, and obtaining the keywords of the text to be retrieved according to the synonym standard table of the synonym standard database.
And S22, obtaining the attribute category of the key word of the text to be retrieved according to the key word and the multiple sub-databases of the synonym standard database.
Optionally, the step S21 of inputting the text to be retrieved into the synonym standard database, and obtaining the keywords of the text to be retrieved according to the synonym standard table of the synonym standard database includes:
and S211, automatically segmenting the text to be retrieved.
S212, information comparison is carried out on the segmented text and a synonym standard table preset in a synonym standard database, and keywords of the text to be retrieved are obtained.
Optionally, the synonym criteria database in S21 includes a plurality of pieces of structured information.
Each piece of structural information in the plurality of pieces of structural information comprises a positive name of a keyword, a different name of the keyword and an attribute category of the keyword.
Wherein the attribute categories include disease category, symptom category, syndrome category, efficacy category and prescription category.
Optionally, the plurality of sub-databases of the synonym criteria database in S22 includes a disease database, a symptom database, a syndrome database, an efficacy database, and a prescription database.
Optionally, obtaining attribute categories of the keywords of the text to be retrieved according to the multiple sub-databases of the keyword and the synonym standard database in S22 includes:
and S221, classifying the keywords according to the attribute types.
S222, respectively inputting the classified keywords into a sub-database of the synonym standard database for retrieval, and obtaining the attribute categories of the keywords of the text to be retrieved.
Optionally, the building process of the mapping rule base in S3 includes:
s31, acquiring the number of the series of the linguistic variables and the number of the mapping rules, and establishing the form of the mapping rules according to the number of the series of the linguistic variables and the number of the mapping rules.
S32, acquiring the mapping relation among the disease category, the symptom category, the syndrome category, the treatment category and the prescription category.
And S33, constructing a mapping rule base according to the form of the mapping rule and the mapping relation.
Optionally, the step of inputting the attribute category into the mapping rule base in S3 to obtain a medical data search result of the text to be searched includes:
constructing an index based on a syndrome differentiation mapping reasoning rule according to the mapping relation of the mapping rule base and the parent-child hierarchical conceptual structure of the keyword classification;
and obtaining a medicine data retrieval result of the text to be retrieved according to the attribute category and the index based on the dialectical mapping reasoning rule.
The medical data retrieval result comprises prescription suggestions obtained based on disease categories, symptom categories, syndrome categories, treatment categories and prescription categories of texts to be retrieved.
In another aspect, the present invention provides a medical data retrieval device based on dialectical mapping rules, which is applied to implement a medical data retrieval method based on dialectical mapping rules, and the device includes:
and the acquisition module is used for acquiring the text to be retrieved.
And the database module is used for inputting the text to be retrieved into the synonym standard database to obtain the attribute category of the key word of the text to be retrieved.
And the rule base module is used for inputting the attribute types into the mapping rule base to obtain a medicine data retrieval result of the text to be retrieved.
Optionally, the text to be retrieved is a professional descriptive information text or a non-professional descriptive information text.
Optionally, the database module is further configured to:
s21, inputting the text to be retrieved into the synonym standard database, and obtaining the keywords of the text to be retrieved according to the synonym standard table of the synonym standard database.
And S22, obtaining the attribute category of the key word of the text to be retrieved according to the key word and the plurality of sub-databases of the synonym standard database.
Optionally, the database module is further configured to:
and S211, automatically segmenting the text to be retrieved.
S212, information comparison is carried out on the segmented text and a synonym standard table preset in a synonym standard database, and keywords of the text to be retrieved are obtained.
Optionally, the synonym criteria database includes a plurality of pieces of structured information.
Each piece of structural information in the plurality of pieces of structural information comprises a positive name of a keyword, a different name of the keyword and an attribute category of the keyword.
Wherein the attribute categories include disease category, symptom category, syndrome category, efficacy category and prescription category.
Optionally, the plurality of sub-databases of the synonym criteria database includes a disease database, a symptom database, a syndrome database, an efficacy database, and a prescription database.
Optionally, the database module is further configured to:
and S221, classifying the keywords according to the attribute types.
S222, respectively inputting the classified keywords into a sub-database of the synonym standard database for retrieval, and obtaining the attribute categories of the keywords of the text to be retrieved.
Optionally, the rule base module is further configured to:
s31, acquiring the number of the series of the linguistic variables and the number of the mapping rules, and establishing the form of the mapping rules according to the number of the series of the linguistic variables and the number of the mapping rules.
S32, acquiring the mapping relation among the disease category, the symptom category, the syndrome category, the treatment category and the prescription category.
And S33, constructing a mapping rule base according to the form and the mapping relation of the mapping rule.
Optionally, the rule base module is further configured to:
constructing an index based on a syndrome differentiation mapping reasoning rule according to the mapping relation of the mapping rule base and the parent-child hierarchical concept structure of the keyword classification;
and obtaining a medicine data retrieval result of the text to be retrieved according to the attribute category and the index based on the dialectical mapping reasoning rule.
The medical data retrieval result comprises prescription suggestions obtained based on disease categories, symptom categories, syndrome categories, treatment categories and prescription categories of texts to be retrieved.
In one aspect, an electronic device is provided, and the electronic device includes a processor and a memory, where the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the medical data retrieval method based on the syndrome differentiation mapping rule.
In one aspect, a computer-readable storage medium is provided, where at least one instruction is stored, and the at least one instruction is loaded and executed by a processor to implement the medical data retrieval method based on dialectical mapping rules.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
in the scheme, the synonym standard database is constructed to realize the matching retrieval of the professional keywords, which is data retrieval based on basic knowledge of traditional Chinese medicine, so that the content of inspection is more accurate and professional. In addition, the synonym standard table database comprises obscure and unintelligible professional terms and popular and easily understood daily terms, so that the range of the standard words is as comprehensive as possible, multiple invalid searches of a user are avoided, and the search efficiency of the user is effectively improved.
By utilizing the rule base based on dialectical mapping and establishing indexes during retrieval according to the relations, a method of 'disease-symptom-syndrome-method' staggered association mapping is adopted, and mapping retrieval words of other categories are retrieved based on the dialectical mapping rule base according to the categories of retrieval keywords, so that an accurate and comprehensive dialectical retrieval model can be constructed, the ambiguity during dialectical treatment is reduced, the accuracy and the efficiency of dialectical retrieval are improved, and the retrieval recall ratio is ensured.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a medical data retrieval method based on dialectical mapping rules according to an embodiment of the present invention;
fig. 2 is a block diagram of a medical data retrieval device based on dialectical mapping rules according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, an embodiment of the present invention provides a medical data retrieval method based on a dialectical mapping rule, and the method may be implemented by an electronic device. As shown in fig. 1, a flow chart of a medical data retrieval method based on syndrome differentiation mapping rules may include the following steps:
s1, obtaining a text to be retrieved.
Optionally, the text to be retrieved in S1 is a professional descriptive information text or a non-professional descriptive information text.
And S2, inputting the text to be retrieved into the synonym standard database to obtain the attribute category of the keyword of the text to be retrieved.
Optionally, the step S2 of inputting the text to be retrieved into the synonym criterion database, and obtaining the attribute category of the keyword of the text to be retrieved includes:
s21, inputting the text to be retrieved into the synonym standard database, and obtaining the keywords of the text to be retrieved according to the synonym standard table of the synonym standard database.
Optionally, the step S21 of inputting the text to be retrieved into the synonym standard database, and obtaining the keywords of the text to be retrieved according to the synonym standard table of the synonym standard database includes:
and S211, automatically segmenting the text to be retrieved.
S212, information comparison is carried out on the segmented text and a synonym standard table preset in a synonym standard database, and keywords of the text to be retrieved are obtained.
Optionally, the synonym criteria database in S21 includes a plurality of pieces of structured information.
Each piece of structural information in the plurality of pieces of structural information comprises a positive name of a keyword, a different name of the keyword and an attribute category of the keyword.
Wherein the attribute categories include disease category, symptom category, syndrome category, efficacy category and prescription category.
In a possible implementation, according to the input text searched by the user, the text may be professional or non-professional description information, the system automatically performs word segmentation on the input information, and performs information comparison with a synonym standard table preset in a synonym standard database, so as to extract matched keywords, and each keyword has a corresponding attribute category (including disease, symptom, syndrome, efficacy and prescription). Each piece of structure standardization information stored in the synonym standard database is a professional keyword in the traditional Chinese medicine field, and comprises a positive name, a synonym and corresponding attribute categories of the keyword. They are extracted from a large number of authoritative medical books and existing traditional Chinese medicine databases.
Further, when structuring the criteria table in the synonym criteria database, it is not limited to the arrangement of written words, but is expanded to spoken language descriptions. In order to make syndrome differentiation, diagnosis and treatment more accurate, the standard table also creates a multi-level father-son concept during sorting, so that the scope of standard words is enlarged, and the proper names and different names can be more accurately corresponding.
And S22, obtaining the attribute category of the key word of the text to be retrieved according to the key word and the plurality of sub-databases of the synonym standard database.
Optionally, the plurality of sub-databases of the synonym criteria database in S22 includes a disease database, a symptom database, a syndrome database, an efficacy database, and a prescription database.
Optionally, obtaining the attribute category of the keyword of the text to be retrieved according to the keyword and the multiple sub-databases of the synonym standard database in S22 includes:
and S221, classifying the keywords according to the attribute types.
S222, respectively inputting the classified keywords into a sub-database of the synonym standard database for retrieval, and obtaining the attribute categories of the keywords of the text to be retrieved.
In one possible embodiment, the matched keywords are searched in the sub-databases (disease, symptom, syndrome, efficacy, and prescription databases) of the respective synonym criteria database according to the corresponding attribute categories.
Further, when the keywords are searched in the synonym standard database, the corresponding relation between the professional written vocabulary and the spoken vocabulary is established, for example, when the user searches for ' I don't want to eat ' or ' poor appetite ', the system can automatically match the keywords with corresponding attributes- ' poor appetite ' after word segmentation.
In addition, a multi-level father-son concept is created, wherein the father-son concept can also be called a top-level concept, a bottom-level concept, a ' 1, 2, 3 ', 8230a ' level concept and other level concepts, and the main function of the hierarchical concept is to subdivide the matched keywords to the maximum extent and enable the synonyms and the standard words to correspond more accurately.
For example, when the input information submitted by the user is a father concept, namely "sweating", the search is performed at all levels, and clinically, the description of various levels such as "sweating, head sweating, hand sweating, virtual sweating" of sweating conditions needs to be performed at all levels as sub-concepts; <xnotran> — "" , " , " "" , , "" , , "" ", , , , , , , , , , , , " , . </xnotran>
And S3, inputting the attribute types into a mapping rule base to obtain a medicine data retrieval result of the text to be retrieved.
Optionally, the building process of the mapping rule base in S3 includes:
s31, acquiring the number of the series of the linguistic variables and the number of the mapping rules, and establishing the form of the mapping rules according to the number of the series of the linguistic variables and the number of the mapping rules.
S32, acquiring the mapping relation among the disease category, the symptom category, the syndrome category, the treatment category and the prescription category.
And S33, constructing a mapping rule base according to the form and the mapping relation of the mapping rule.
Optionally, the step of inputting the attribute category into the mapping rule base in S3 to obtain a medical data search result of the text to be searched includes:
constructing an index based on a syndrome differentiation mapping reasoning rule according to the mapping relation of the mapping rule base and the parent-child hierarchical conceptual structure of the keyword classification;
and obtaining a medicine data retrieval result of the text to be retrieved according to the attribute category and the index based on the dialectical mapping reasoning rule.
The medicine data retrieval result comprises prescription suggestions obtained based on the disease category, symptom category, syndrome category, treatment category and prescription category of the text to be retrieved.
In a feasible implementation mode, the mapping relationship among disease categories, symptom categories, syndrome categories, therapeutic categories and prescription categories is determined according to the basic principle of treatment based on syndrome differentiation of traditional Chinese medicine and by combining with the standard established by professional authoritative books and industries. The disease name terms belong to the disease category, the symptom name terms belong to the symptom category, the syndrome name terms belong to the syndrome category, the therapeutic rules, the therapeutic methods and the treatment name terms belong to the therapeutic methods category, the prescription name terms belong to the prescription category, a professional mapping rule base is formed, and indexes during retrieval are established according to the relations.
The index based on dialectical mapping inference rule is established on the basis of mapping rule library, and its mapping rule form is "IF \8230; THEN \8230;" 8230; "statement" in computer program language. The mapping rules are determined by the number of input and output physical quantities and the required accuracy. In designing the mapping rule base, it is necessary to determine the appropriate number of linguistic variable series and mapping rules, and to establish the correct rule form. The mapping rule base is established according to the mutual mapping relation among related disease names, symptoms, syndromes, treatment methods and prescriptions, is used for searching disease names through the symptom names, searching syndrome names through the symptom names/disease names, searching treatment method names through the syndrome names, searching prescription names through the disease names, and establishing the index based on the unified objective syndrome-differentiation mapping reasoning rule on the basis of the index and the father-son hierarchical concept structure classified according to the keywords and the mapping relation among the disease names, the symptom names, the syndrome names, the treatment method names and the prescription names, so that the disease-symptom-syndrome-method-prescription can be effectively combined, and the disease names, the syndromes and the treatment methods are synthesized based on the symptoms to give suggestions to the prescription, thereby improving the retrieval accuracy and efficiency. If the symptom of nasal obstruction is searched, the disease name of cold is found, the treatment method of dispelling cold and dredging orifices is found, the syndrome of wind-cold is found, and the prescription of magnolia flower powder is found.
The invention adopts a method for extracting effective information from a simple to a complex multilayer to preprocess indexes of syndrome, symptom, disease name, efficacy and prescription to form a related synonym standard table and a mapping word table, and professionally expands and maps related information of syndrome, disease and the like to associate the information of a disease and symptom prescription method, thereby forming a search method based on a syndrome differentiation mapping rule aiming at traditional Chinese medicine and pharmacy information. The technical problems solved by the invention comprise the following points:
(1) The invention discloses a keyword query method, which is used for acquiring an input text of a user, automatically segmenting words and extracting keywords through a synonym standard database. Each piece of structured information in the synonym standard database comprises the proper name and the different name of the keyword and the corresponding attribute category, and the attribute category comprises diseases, symptoms, syndromes, efficacies and prescriptions, so that the efficiency of traditional Chinese medicine retrieval can be greatly improved.
(2) After the keywords are subjected to attribute classification, professional expansion and mapping are performed on each attribute of the keywords through a mapping word database, information of a disease syndrome method is associated, mapping relations from symptoms to syndromes, syndromes to treatment methods, symptoms to diseases, diseases to symptoms, treatment methods to prescriptions, diseases to prescriptions and the like are formed, and comprehensive and accurate syndrome differentiation mapping reasoning relations are constructed.
In the embodiment of the invention, the synonym standard database is constructed to realize the matching retrieval of the professional keywords, which is a data retrieval based on the basic knowledge of the traditional Chinese medicine, so that the inspected content is more accurate and professional. In addition, the synonym standard table database comprises obscure and unintelligible professional terms and popular and easily understood daily terms, so that the range of the standard words is as comprehensive as possible, multiple invalid searches of a user are avoided, and the search efficiency of the user is effectively improved.
By utilizing the rule base based on dialectical mapping and establishing indexes during retrieval according to the relations, a method of 'disease-symptom-syndrome-method' staggered association mapping is adopted, and mapping retrieval words of other categories are retrieved based on the dialectical mapping rule base according to the categories of retrieval keywords, so that an accurate and comprehensive dialectical retrieval model can be constructed, the ambiguity during dialectical treatment is reduced, the accuracy and the efficiency of dialectical retrieval are improved, and the retrieval recall ratio is ensured.
As shown in fig. 2, an embodiment of the present invention provides a medical data retrieving apparatus 200 based on dialectical mapping rules, where the apparatus 200 is applied to implement a medical data retrieving method based on dialectical mapping rules, and the apparatus 200 includes:
and the acquisition module is used for acquiring the text to be retrieved.
And the database module is used for inputting the text to be retrieved into the synonym standard database to obtain the attribute category of the key word of the text to be retrieved.
And the rule base module is used for inputting the attribute types into the mapping rule base to obtain the medicine data retrieval result of the text to be retrieved.
Optionally, the text to be retrieved is a professional descriptive information text or a non-professional descriptive information text.
Optionally, the database module is further configured to:
s21, inputting the text to be retrieved into the synonym standard database, and obtaining the keywords of the text to be retrieved according to the synonym standard table of the synonym standard database.
And S22, obtaining the attribute category of the key word of the text to be retrieved according to the key word and the plurality of sub-databases of the synonym standard database.
Optionally, the database module is further configured to:
and S211, automatically segmenting the text to be retrieved.
S212, information comparison is carried out on the segmented text and a synonym standard table preset in a synonym standard database, and keywords of the text to be retrieved are obtained.
Optionally, the synonym criteria database includes a plurality of pieces of structured information.
Each piece of structural information in the plurality of pieces of structural information comprises a positive name of a keyword, a different name of the keyword and an attribute category of the keyword.
Wherein the attribute categories include disease category, symptom category, syndrome category, efficacy category and prescription category.
Optionally, the plurality of sub-databases of the synonym criteria database includes a disease database, a symptom database, a syndrome database, an efficacy database, and a prescription database.
Optionally, the database module is further configured to:
and S221, classifying the keywords according to the attribute types.
S222, respectively inputting the classified keywords into a sub-database of the synonym standard database for retrieval, and obtaining the attribute categories of the keywords of the text to be retrieved.
Optionally, the rule base module is further configured to:
s31, acquiring the number of the series of the linguistic variables and the number of the mapping rules, and establishing the form of the mapping rules according to the number of the series of the linguistic variables and the number of the mapping rules.
S32, acquiring the mapping relation among the disease category, the symptom category, the syndrome category, the treatment category and the prescription category.
And S33, constructing a mapping rule base according to the form of the mapping rule and the mapping relation.
Optionally, the rule base module is further configured to:
constructing an index based on a syndrome differentiation mapping reasoning rule according to the mapping relation of the mapping rule base and the parent-child hierarchical concept structure of the keyword classification;
and obtaining a medicine data retrieval result of the text to be retrieved according to the attribute category and the index based on the dialectical mapping reasoning rule.
The medical data retrieval result comprises prescription suggestions obtained based on disease categories, symptom categories, syndrome categories, treatment categories and prescription categories of texts to be retrieved.
In the embodiment of the invention, the synonym standard database is constructed to realize the matching retrieval of the professional keywords, which is a data retrieval based on the basic knowledge of the traditional Chinese medicine, so that the inspected content is more accurate and professional. In addition, the synonym standard table database comprises obscure and unintelligible professional terms and popular and easily understood daily terms, so that the range of the standard words is as comprehensive as possible, multiple invalid searches of a user are avoided, and the search efficiency of the user is effectively improved.
By utilizing the rule base based on dialectical mapping and establishing indexes during retrieval according to the relations, a method of 'disease-symptom-syndrome-method' staggered association mapping is adopted, and mapping retrieval words of other categories are retrieved based on the dialectical mapping rule base according to the categories of retrieval keywords, so that an accurate and comprehensive dialectical retrieval model can be constructed, the ambiguity during dialectical treatment is reduced, the accuracy and the efficiency of dialectical retrieval are improved, and the retrieval recall ratio is ensured.
Fig. 3 is a schematic structural diagram of an electronic device 300 according to an embodiment of the present invention, where the electronic device 300 may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 301 and one or more memories 302, where the memory 302 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 301 to implement the following medical data retrieval method based on the syndrome mapping rule:
s1, obtaining a text to be retrieved.
And S2, inputting the text to be retrieved into the synonym standard database to obtain the attribute category of the keyword of the text to be retrieved.
And S3, inputting the attribute types into a mapping rule base to obtain a medicine data retrieval result of the text to be retrieved.
In an exemplary embodiment, a computer-readable storage medium, such as a memory, including instructions executable by a processor in a terminal to perform the medical data retrieval method based on the dialectical mapping rule is also provided. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
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 (10)
1. A medicine data retrieval method based on dialectical mapping rules is characterized by comprising the following steps:
s1, obtaining a text to be retrieved;
s2, inputting the text to be retrieved into a synonym standard database to obtain the attribute category of the keyword of the text to be retrieved;
and S3, inputting the attribute types into a mapping rule base to obtain a medicine data retrieval result of the text to be retrieved.
2. The method according to claim 1, wherein the text to be retrieved in S1 is a professional or non-professional description information text.
3. The method according to claim 1, wherein the step S2 of inputting the text to be retrieved into the synonym criterion database to obtain the attribute category of the keyword of the text to be retrieved includes:
s21, inputting the text to be retrieved into a synonym standard database, and obtaining keywords of the text to be retrieved according to a synonym standard table of the synonym standard database;
and S22, obtaining the attribute category of the key word of the text to be retrieved according to the key word and the plurality of sub-databases of the synonym standard database.
4. The method according to claim 3, wherein the step S21 of inputting the text to be retrieved into the synonym criterion database, and obtaining the keywords of the text to be retrieved according to the synonym criterion table of the synonym criterion database includes:
s211, automatically segmenting the text to be retrieved;
s212, information comparison is carried out on the segmented text and a synonym standard table preset in a synonym standard database, and keywords of the text to be retrieved are obtained.
5. The method according to claim 3, wherein the synonym criterion database in S21 comprises a plurality of pieces of structured information;
each piece of structural information in the plurality of pieces of structural information comprises a positive name of a keyword, a synonym of the keyword and an attribute category of the keyword;
wherein the attribute categories comprise disease categories, symptom categories, syndrome categories, efficacy categories and prescription categories.
6. The method of claim 3, wherein the plurality of sub-databases of the synonym criteria database in S22 comprises a disease database, a symptom database, a syndrome database, an efficacy database, and a prescription database.
7. The method according to claim 6, wherein the obtaining, in the S22, the attribute category of the keyword of the text to be retrieved according to the keyword and the multiple sub-databases of the synonym criterion database includes:
s221, classifying the keywords according to attribute categories;
s222, respectively inputting the classified keywords into a sub-database of the synonym standard database for retrieval, and obtaining the attribute categories of the keywords of the text to be retrieved.
8. The method according to claim 1, wherein the construction process of the mapping rule base in S3 comprises:
s31, acquiring the level number of the linguistic variable and the number of the mapping rules, and establishing the form of the mapping rules according to the level number of the linguistic variable and the number of the mapping rules;
s32, obtaining a mapping relation among a disease category, a symptom category, a syndrome category, a treatment category and a prescription category;
and S33, constructing a mapping rule base according to the form of the mapping rule and the mapping relation.
9. The method according to claim 8, wherein the step of inputting the attribute category into a mapping rule base in the step S3 to obtain the medicine data retrieval result of the text to be retrieved comprises:
constructing an index based on a syndrome differentiation mapping reasoning rule according to the mapping relation of the mapping rule base and the parent-child hierarchical concept structure of the keyword classification;
obtaining a medicine data retrieval result of the text to be retrieved according to the attribute category and the index based on the dialectical mapping reasoning rule;
the medicine data retrieval result comprises prescription suggestions obtained based on disease categories, symptom categories, syndrome categories, treatment categories and prescription categories of texts to be retrieved.
10. A medical data retrieval device based on dialectical mapping rules, the device comprising:
the acquisition module is used for acquiring a text to be retrieved;
the database module is used for inputting the text to be retrieved into the synonym standard database to obtain the attribute category of the key word of the text to be retrieved;
and the rule base module is used for inputting the attribute categories into a mapping rule base to obtain a medicine data retrieval result of the text to be retrieved.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117349452A (en) * | 2023-12-04 | 2024-01-05 | 长春中医药大学 | Information service system for traditional Chinese medicine retrieval |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5956711A (en) * | 1997-01-16 | 1999-09-21 | Walter J. Sullivan, III | Database system with restricted keyword list and bi-directional keyword translation |
| US6021404A (en) * | 1997-08-18 | 2000-02-01 | Moukheibir; Nabil W. | Universal computer assisted diagnosis |
| CN102043812A (en) * | 2009-10-13 | 2011-05-04 | 北京大学 | Method and system for retrieving medical information |
| US20110208776A1 (en) * | 2008-11-14 | 2011-08-25 | Min Ho Lee | Method and apparatus of semantic technological approach based on semantic relation in context and storage media having program source thereof |
| JP2013109597A (en) * | 2011-11-21 | 2013-06-06 | Panasonic Corp | Medical synonym dictionary creating device and medical synonym dictionary creating method |
| CN110413734A (en) * | 2019-07-25 | 2019-11-05 | 万达信息股份有限公司 | A kind of intelligent searching system and method for medical services |
| CN110688493A (en) * | 2019-09-26 | 2020-01-14 | 京东方科技集团股份有限公司 | Incidence relation construction method and device and electronic equipment |
| CN111028951A (en) * | 2019-11-13 | 2020-04-17 | 上海中医药大学 | Method and equipment for Chinese medicine diagnosis retrieval display and evaluation |
| CN112735597A (en) * | 2020-12-31 | 2021-04-30 | 荆门汇易佳信息科技有限公司 | Medical text disorder identification method driven by semi-supervised self-learning |
| US20210303630A1 (en) * | 2020-03-31 | 2021-09-30 | Roche Molecular Systems, Inc. | Text entry assistance and conversion to structured medical data |
-
2022
- 2022-06-20 CN CN202210697340.9A patent/CN115186112B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5956711A (en) * | 1997-01-16 | 1999-09-21 | Walter J. Sullivan, III | Database system with restricted keyword list and bi-directional keyword translation |
| US6021404A (en) * | 1997-08-18 | 2000-02-01 | Moukheibir; Nabil W. | Universal computer assisted diagnosis |
| US20110208776A1 (en) * | 2008-11-14 | 2011-08-25 | Min Ho Lee | Method and apparatus of semantic technological approach based on semantic relation in context and storage media having program source thereof |
| CN102043812A (en) * | 2009-10-13 | 2011-05-04 | 北京大学 | Method and system for retrieving medical information |
| JP2013109597A (en) * | 2011-11-21 | 2013-06-06 | Panasonic Corp | Medical synonym dictionary creating device and medical synonym dictionary creating method |
| CN110413734A (en) * | 2019-07-25 | 2019-11-05 | 万达信息股份有限公司 | A kind of intelligent searching system and method for medical services |
| CN110688493A (en) * | 2019-09-26 | 2020-01-14 | 京东方科技集团股份有限公司 | Incidence relation construction method and device and electronic equipment |
| CN111028951A (en) * | 2019-11-13 | 2020-04-17 | 上海中医药大学 | Method and equipment for Chinese medicine diagnosis retrieval display and evaluation |
| US20210303630A1 (en) * | 2020-03-31 | 2021-09-30 | Roche Molecular Systems, Inc. | Text entry assistance and conversion to structured medical data |
| WO2021202696A1 (en) * | 2020-03-31 | 2021-10-07 | F. Hoffmann-La Roche Ag | Text entry assistance and conversion to structured medical data |
| CN112735597A (en) * | 2020-12-31 | 2021-04-30 | 荆门汇易佳信息科技有限公司 | Medical text disorder identification method driven by semi-supervised self-learning |
Non-Patent Citations (3)
| Title |
|---|
| YIFAN WANG ET AL.: "Development of Test Engine for Chinese Medicine Scale", 《PROCEEDINGS OF THE 2ND INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE FOR MEDICINE SCIENCES》, pages 443 - 448 * |
| 刘东波;黄惠勇;: "基于中医药领域本体的信息检索模型研究", 湖南中医药大学学报, no. 02, pages 220 - 224 * |
| 李园白等: "关于中医医案的综合性分析研究近况", 《中国中医药信息杂志》, pages 91 - 93 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117349452A (en) * | 2023-12-04 | 2024-01-05 | 长春中医药大学 | Information service system for traditional Chinese medicine retrieval |
| CN117349452B (en) * | 2023-12-04 | 2024-02-09 | 长春中医药大学 | Information service system for traditional Chinese medicine retrieval |
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