Traditional Chinese Medicine Information Granularity Management Method

Abstract

The present invention provides a method for granular management of traditional Chinese medicine information, which relates to the technical field of traditional Chinese medicine information data processing. The method includes collecting traditional Chinese medicine consultation information and establishing a traditional Chinese medicine consultation database; extracting object-based consultation data from the traditional Chinese medicine consultation database, and performing information granularity division to form traditional Chinese medicine information granularity division data; according to the traditional Chinese medicine information granularity division data, extracting disease-based diagnosis and treatment data, and performing trend analysis to form disease epidemic trend analysis result data; according to the traditional Chinese medicine information granularity division data, extracting object-based diagnosis and treatment data, and performing treatment effect analysis to form disease treatment effect analysis result data. This method realizes accurate disease prediction and treatment effect analysis using traditional Chinese medicine big data by reasonably granulating the collected traditional Chinese medicine big data, promotes clinical progress and also realizes reasonable monitoring of epidemics.

Description

Technical Field

[0001] The present invention relates to the technical field of traditional Chinese medicine information data processing, and more particularly, to a method for granular management of traditional Chinese medicine information. Background Art

[0002] For traditional Chinese medicine (TCM) medical treatment, empirical medical information is important reference information for diagnosis and treatment. Therefore, TCM medical big data is of great significance for promoting the progress of medicine and improving the effectiveness of clinical treatment. Currently, in order to improve the utilization rate of TCM medical data, a big data for TCM diagnosis and treatment has been gradually established to provide a big data basis for subsequent in-depth TCM medical research.

[0003] Information granulation can make data information present reasonable classification and processing under the guidance of information, enabling efficient organization and utilization of complex data information. TCM data is characterized by a large amount of information and complex data information. If reasonable information granulation is carried out on TCM information, it can fully realize the efficient organization and utilization of TCM information, and structured TCM data can fully become important basic data for clinical and medical research.

[0004] Therefore, designing a method for granular management of TCM information to achieve accurate disease prediction and treatment effect analysis using TCM big data through reasonable information granulation of the collected TCM big data, promoting clinical progress and realizing reasonable monitoring of epidemics, is an urgent problem to be solved currently. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for granular management of TCM information. By establishing a TCM medical database to collect TCM medical information, basic big data required for information granulation is formed. Then, the TCM medical information in the database is used for information granulation to form granular information that can provide data reference guidance for TCM diagnosis and treatment. At the same time, due to the information granulation of TCM data, information mining can be further carried out on the medical data to realize the analysis of the epidemic trend of diseases, fully realizing the early warning of disease epidemics, which has an important role in promoting social medical care. In addition, effective treatment plans for different diseases can be mined from the medical data, providing important data reference for improving the treatment effect of diseases.

[0006] In a first aspect, the present invention provides a method for granular management of traditional Chinese medicine information, including collecting traditional Chinese medicine medical treatment information and establishing a traditional Chinese medicine medical treatment database; extracting object-based medical treatment data from the traditional Chinese medicine medical treatment database, and performing information granularity division to form traditional Chinese medicine information granularity division data; according to the traditional Chinese medicine information granularity division data, extracting disease-based diagnosis and treatment data, and performing trend analysis to form disease epidemic trend analysis result data; according to the traditional Chinese medicine information granularity division data, extracting object-based diagnosis and treatment data, and performing treatment effect analysis to form disease treatment effect analysis result data.

[0007] In the present invention, this method collects traditional Chinese medicine medical treatment information by establishing a traditional Chinese medicine medical treatment database to form the basic big data required for information granularity. Then, the traditional Chinese medicine medical treatment information in the database is used for information granularity to form granular information that can provide data reference guidance for traditional Chinese medicine diagnosis and treatment. At the same time, due to the information granularity of traditional Chinese medicine data, it is possible to further mine the medical treatment data to realize the analysis of the epidemic trend of diseases, fully realize the early warning of the epidemic of diseases, and play an important role in promoting social medical care. In addition, it is also possible to mine effective treatment plans for different diseases from the medical treatment data, providing important data reference for improving the treatment effect of diseases.

[0008] As a possible implementation manner, extracting object-based medical treatment data from the traditional Chinese medicine medical treatment database, and performing information granularity division to form traditional Chinese medicine information granularity division data includes: extracting object-based medical treatment data from the traditional Chinese medicine medical treatment database, and performing information granularity division based on the diagnosis result to form diagnosis result information granularity division data; extracting object-based medical treatment data from the traditional Chinese medicine medical treatment database, and combining the diagnosis result information granularity division data to perform information granularity division for the treatment plan to form treatment method information granularity division data; combining the diagnosis result information granularity division data and the treatment method information granularity division data to form traditional Chinese medicine information granularity division data.

[0009] In the present invention, the granularity division of information is carried out with reference to the amount of information or information unit, that is, each division can realize the stripping of specific information to better locate accurate information. This application considers the granularity of traditional Chinese medicine medical treatment information mainly to provide data reference guidance for traditional Chinese medicine treatment. Therefore, the first information to be stripped during information granularity is the diagnosis information and treatment information. Furthermore, it is possible to further deeply informatize the diagnosis information and treatment information respectively, and finally, a surprising information granularity effect, that is, the correspondence between diseases and treatments, can be generated comprehensively.

[0010] As a possible implementation, object-based medical visit data in the traditional Chinese medicine (TCM) medical visit database is extracted, and information granularity division based on the diagnosis results is performed to form diagnosis result information granularity division data, including: extracting symptom information based on disease types from the medical visit data of different objects in the TCM medical visit database to form object disease type symptom information granularity data; performing symptom level clustering based on disease types on different object disease type symptom information granularity data to form disease type symptom level information granularity data; aggregating the disease type symptom level information granularity data of different disease types to form diagnosis result information granularity division data.

[0011] In the present invention, for the granularity of diagnostic information, considering that the significance of diagnosis lies in the accurate positioning and judgment of diseases, the granularity of diagnostic information mainly includes two aspects. On the one hand, it is the granularity of information based on disease types, and the information generated by the granularity can help provide guiding references for positioning and judging disease types. On the second hand, it is the degree of development of disease symptoms. Different degrees of disease symptoms correspond to different subsequent treatment plan selections and also help to accurately and reasonably position and judge the development of the disease condition.

[0012] As a possible implementation, according to the medical visit data of different objects in the TCM medical visit database, symptom information is extracted based on disease types to form object disease type symptom information granularity data, including: extracting all medical visit examination information from the medical visit data of different objects and parameterizing it according to the examination category to form object medical visit examination parameter data; extracting the medical visit examination items for each object to form object medical visit examination item information; aggregating the object medical visit examination parameter data and object medical visit examination item information corresponding to different objects to form object disease type symptom information granularity data of different objects.

[0013] In the present invention, for the granularity of information based on disease types, the information to be granularized is mainly the examination information used for positioning and judging disease types. Considering the personalized situations of different doctors and different medical visit objects, the examination information is not the same. This difference is not only manifested in the volatility of examination parameter values but also in the non-uniformity of examination items. Since these inconsistent examination information can be used to judge disease types, there must be golden indicators for positioning and judging disease types among them. By parameterizing the examination information and extracting the examination item information for granularity, it provides a data basis for subsequently stripping out important information that can position and judge disease types.

[0014] As a possible implementation method, for the granularity data of symptom information of different object disease types, perform symptom level clustering based on disease types to form disease type symptom level information granularity data, including: clustering the object visit examination parameter data of different objects, and performing symptom level division in the following manner according to the onset time: arranging the object visit examination parameter data of different objects in ascending order of onset time to form initial disease type symptom level data; for the initial disease type symptom level data, determine the negative parameter values of different objects , where: , n represents the sequential number of different objects in the initial disease type symptom level data, k represents the number of different visit examination items with negative trends in the object visit examination parameter data of the object numbered n, represents the equivalent difference of the visit examination item numbered k with negative trend in the object visit examination parameter data of the object numbered n, represents the actual parameter value of the visit examination item numbered k with negative trend in the object visit examination parameter data of the object numbered n, represents the standard parameter value of the visit examination item numbered k with negative trend in the object visit examination parameter data of the object numbered n; according to the negative parameter values of different objects , adjust the order of the initial disease type symptom level data in ascending order to form the final disease type symptom level data; set the necessary examination parameter limit, for the objects whose negative parameter values do not exceed the necessary examination parameter limit, determine the object visit examination item information with the fewest visit examination items and label it as the disease type visit necessary examination item information; aggregate the corresponding final disease type symptom level data and disease type visit necessary examination item information under different disease types to form the disease type symptom level information granularity data.

[0015] In the present invention, after the granulation process of inspection information is completed, the inspection characteristic information of different types of diseases has been initially grasped. In order to provide more in-depth and accurate disease type positioning and judgment references, it is necessary to further granulate the information regarding the degree of disease symptoms. It can be understood that the development of disease symptoms is basically conditional on time. Therefore, during the process of granulating information regarding the development of disease symptoms, arranging the inspection information of the same disease type in chronological order can generally grasp the entire development process of the disease. However, considering the differences in the onset characteristics of individuals, the non-continuous changes of the disease conditions in individuals, and the differences in individuals' perception of their own disease conditions, there are still some deviations in the chronological order arrangement. Therefore, reasonable adjustments can be made using the inspection information. It should be noted that for the inspection item information, normal inspection result information basically does not contain references for positioning and judging disease symptom characteristics, while abnormal and negative parameter information presented by the inspection item information can determine the manifestation of disease symptoms. Therefore, when making reasonable order adjustments for the disease symptom development processes presented to different objects, it is mainly based on the inspection items with negative information. Of course, different inspection items have different manifestations for reflecting the degree of symptom development. Therefore, comprehensive equivalent judgments are required to accurately grasp the trend of symptom development. In addition, considering the individual differences of different objects and the differences in the medical treatment situations, the categories of inspection items will be different for different individuals, and there are gold standard inspection items for judging disease types and disease symptom stages. By extracting the inspection item information of the objects before the reasonable sequential quantity from the granulated data with adjusted sorting, the necessary inspection items for the disease type can be formed.

[0016] As a possible implementation method, extract the medical treatment data based on the object from the traditional Chinese medicine medical treatment database, and combine the diagnosis result information granulation data to perform information granulation for the treatment plan, forming treatment method information granulation data, including: according to the medical treatment data of different objects, determine the treatment plans corresponding to the medical treatment inspection parameter data at different sequential levels in the final disease type symptom level data; for the treatment plans at different sequential levels, perform the following combination divisions of treatment means: if there is an intersection among all the treatment means combinations, then determine the treatment means items corresponding to the intersection as the necessary treatment means at the level, and determine the treatment means combinations other than the necessary treatment means at the level under different treatment plans as the optional treatment means groups at the level; if there is no intersection among all the treatment means combinations, then determine the treatment means combination corresponding to each treatment plan as the optional treatment means group at the level; aggregate the necessary treatment means at the level and the optional treatment means groups at the level corresponding to different sequential levels in the final disease type symptom level data under each disease type to form the treatment method information granulation data.

[0017] In the present invention, the purpose of information granulation of treatment plans is to form alternative treatment plans for different diseases, providing a reference guide for subsequent disease treatment. After the granulation of diagnostic information is completed, clustering of treatment plans can be first carried out based on the granulated clustering data, and then the plans can be further refined. It can be understood that generally, treatment plans in traditional Chinese medicine are used in combination, such as herbs, acupuncture, massage, etc. Of course, more specifically, it is the dispensing of herbs, the selection of acupuncture points, etc. For specific disease types, there may also be necessary traditional Chinese medicine treatment means. Therefore, during the process of information granulation, the necessary treatment means and alternative treatment means of the treatment plan can be further distinguished to provide a reasonable and accurate reference guide for subsequent treatment plan formulation.

[0018] As a possible implementation method, according to the data divided by the information granulation of traditional Chinese medicine, the diagnosis and treatment data based on diseases are extracted, and trend analysis is carried out to form the disease epidemic trend analysis result data, including: obtaining the historical disease epidemic trend data and establishing the average disease epidemic trend curve of the morbidity volume in the order of the time dimension. ; Establish the diagnosis and treatment trend curve for different disease types in the order of the time dimension according to the number of times and the citation time of the data divided by the information granulation of treatment methods under different disease types in the traditional Chinese medicine diagnosis database. , where m represents the number of different disease types; according to the average disease epidemic trend curve and the diagnosis and treatment trend curve of different disease types , carry out epidemic trend analysis to form the disease epidemic trend analysis result data of different disease types.

[0019] In the present invention, after the information granulation of both diagnosis and treatment is completed, unexpected effects can also be achieved, that is, for different disease types, granulated treatment data can be extracted to conduct epidemic trend analysis on the corresponding disease types, which can provide data reference for public medical safety. For diseases with epidemic trends, from a social attribute perspective, the development trends of epidemics are basically similar. Therefore, the average epidemic trend curve can be extracted using the big data of the epidemic trends of epidemics, and then the trend data formed by the treatment volume in the time dimension using the granulated treatment data can be compared to achieve the analysis and judgment of trends.

[0020] As a possible implementation method, according to the average disease epidemic trend curve and the diagnosis and treatment trend curve of different disease types , carry out epidemic trend analysis to form the disease epidemic trend analysis result data of different disease types, including: setting the trend judgment duration threshold T, and according to the average disease epidemic trend curve And the diagnostic and treatment trend curves of different disease types , conduct trend analysis and judgment in the following way: If within a duration not less than the trend judgment duration threshold T the following conditions are met: , and , then label the corresponding disease type as the currently prevalent disease; otherwise, do not label it. Among them, represents the disease prevalence cumulative quantity judgment threshold corresponding to the disease type numbered m, represents the disease prevalence rate judgment threshold corresponding to the disease type numbered m, represents the derivative in the time dimension, represents the derivative in the time dimension.

[0021] In the present invention, the judgment of trend analysis is mainly to quantitatively determine whether the prevalence trend curve of the disease type extracted from the granulated data is close to the average prevalence trend curve obtained from big data. The closeness is determined from two aspects. On the one hand, it is the magnitude of the morbidity. After all, an epidemic requires a certain basis of morbidity. On the second hand, it is the change trend of the incidence rate. The change trend of the incidence rate is an important indicator for judging whether a disease is prevalent. Of course, the thresholds determined for judgment can be determined according to the actual situation or based on big data analysis.

[0022] As a possible implementation method, divide the data according to the TCM information granularity, extract the diagnosis and treatment data based on the object, and conduct treatment effect analysis to form the disease treatment effect analysis result data, including: the number of times each sequential level corresponding to different levels of optional treatment means groups is cited under the final disease type symptom level data in the treatment method information granularity division data of different disease types in the TCM medical record database and the number of follow-up consultations after the corresponding level of optional treatment means groups are adopted , where r represents the number of different sequential levels in the final disease type symptom level data in the treatment method information granularity division data, and i represents the number of different optional treatment means under the sequential level numbered r; for the number of times each different level of optional treatment means groups corresponding to different sequential levels in the final disease type symptom level data are cited and the number of follow-up consultations , conduct treatment effect analysis to form the disease treatment effect analysis result data.

[0023] In the present invention, of course, after granulating the information, these data can also be used to evaluate the effectiveness of treatment plans, so as to provide a reasonable guidance for selecting treatment plans for subsequent treatment. When evaluating the effectiveness of different treatment plans, it is mainly carried out from two aspects. One is the quantity of the selected plan, and the other is the follow-up visit rate of the selected plan. Both aspects have a certain impact on the effectiveness evaluation.

[0024] As a possible implementation method, for the number of times cited corresponding to different groups of optional treatment means at different rank levels in the final disease type symptom level data and the number of follow-up visits , perform treatment effect analysis to form disease treatment effect analysis result data, including: for the number of times cited corresponding to different groups of optional treatment means at the same rank level in the final disease type symptom level data and the number of follow-up visits , perform effect analysis in the following way: set the citation number impact factor and the follow-up visit number impact factor , and determine the effect factor corresponding to different optional treatment means , where ; according to the effect factor , arrange different groups of optional treatment means at the same rank level from large to small to form disease treatment effect analysis result data at the same rank level.

[0025] In the present invention, for determining the effectiveness of treatment plans by using the selection quantity of the plan and the subsequent follow-up visit rate, it can be comprehensively analyzed and judged through impact factors. For the impact factors, they can be determined according to the actual situation and can also be determined based on big data analysis.

[0026] The beneficial effects of a traditional Chinese medicine information granulation management method provided by the present invention are as follows:

[0027] This method collects traditional Chinese medicine diagnosis and treatment information by establishing a traditional Chinese medicine diagnosis and treatment database to form the basic big data required for information granulation. Then, it uses the traditional Chinese medicine diagnosis and treatment information in the database for information granulation to form granulated information that can provide data reference guidance for traditional Chinese medicine diagnosis and treatment. At the same time, due to the information granulation of traditional Chinese medicine data, it can further mine the diagnosis and treatment data to realize the analysis of the epidemic trend of diseases, fully realize the early warning of disease epidemics, and has an important role in promoting social medical treatment. In addition, it can also mine effective treatment plans for different diseases from the diagnosis and treatment data, providing important data reference for improving the treatment effect of diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments of the present invention. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0029] Figure 1 It is a step diagram of the traditional Chinese medicine information granulation management method provided by the embodiments of the present invention. Specific implementation manners

[0030] The following will describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention.

[0031] For traditional Chinese medicine medical treatment, empirical medical information is important diagnostic reference information. Therefore, traditional Chinese medicine medical big data is of great significance for promoting the progress of medicine and improving the effect of clinical treatment. Currently, in order to improve the utilization rate of traditional Chinese medicine medical data, a diagnosis and treatment big data for traditional Chinese medicine has gradually been established to provide a big data basis for subsequent in-depth traditional Chinese medicine medical research.

[0032] Information granulation can enable data information to present reasonable classification and processing under the guidance of information, enabling efficient organization and utilization of complex data information. Traditional Chinese medicine data has the characteristics of large amount of information and complex data information. If reasonable information granulation of traditional Chinese medicine information can fully realize the efficient organization and utilization of traditional Chinese medicine information, structured traditional Chinese medicine data can fully become important basic data for clinical and medical research.

[0033] Reference Figure 1 , the embodiments of the present invention provide a traditional Chinese medicine information granulation management method. This method collects traditional Chinese medicine medical treatment information by establishing a traditional Chinese medicine medical treatment database to form the basic big data required for information granulation. Then, the traditional Chinese medicine medical treatment information in the database is used for information granulation to form granulated information that can provide data reference guidance for traditional Chinese medicine diagnosis and treatment. At the same time, due to the information granulation of traditional Chinese medicine data, it is possible to further mine the medical treatment data to realize the analysis of the epidemic trend of diseases, fully realize the early warning of disease epidemics, and play an important role in promoting social medical treatment. In addition, different treatment plans effective for treating diseases can also be mined from the medical treatment data, providing important data reference for improving the treatment effect of diseases.

[0034] The traditional Chinese medicine information granulation management method specifically includes the following steps:

[0035] S1: Collect traditional Chinese medicine medical treatment information and establish a traditional Chinese medicine medical treatment database.

[0036] Establishing a database for traditional Chinese medicine (TCM) medical treatment information can provide a big data foundation for subsequent granularity of TCM information.

[0037] S2: Extract object-based medical treatment data from the TCM medical treatment database, and perform information granularity division to form TCM information granularity division data.

[0038] Extract object-based medical treatment data from the TCM medical treatment database, and perform information granularity division to form TCM information granularity division data, including: extracting object-based medical treatment data from the TCM medical treatment database, and performing information granularity division based on the diagnosis result to form diagnosis result information granularity division data; extracting object-based medical treatment data from the TCM medical treatment database, and combining the diagnosis result information granularity division data to perform information granularity division for the treatment plan to form treatment method information granularity division data; aggregating the diagnosis result information granularity division data and the treatment method information granularity division data to form TCM information granularity division data.

[0039] The division of information granularity is carried out with reference to the amount of information or information units, that is, each division can achieve the stripping of specific information to better locate accurate information. This application considers the granularity of TCM medical treatment information mainly to provide a data reference guide for TCM treatment. Therefore, the first information to be stripped during information granularity is the diagnosis information and treatment information. Furthermore, further in-depth informatization can be carried out for the diagnosis information and treatment information respectively, and finally, an unexpected information granularity effect can be generated comprehensively, that is, the correspondence between diseases and treatments.

[0040] Extract object-based medical treatment data from the TCM medical treatment database, and perform information granularity division based on the diagnosis result to form diagnosis result information granularity division data, including: extracting symptom information based on the disease type according to the medical treatment data of different objects in the TCM medical treatment database to form object disease type symptom information granularity data; performing symptom level clustering based on the disease type for different object disease type symptom information granularity data to form disease type symptom level information granularity data; aggregating the disease type symptom level information granularity data of different disease types to form diagnosis result information granularity division data.

[0041] For the granularity of diagnosis information, considering that the significance of diagnosis lies in the accurate positioning and judgment of diseases, the granularity of diagnosis information mainly includes two aspects. On the one hand, it is the information granularity based on the disease type, and the information generated by the granularity can help provide a guiding reference for positioning and judging the disease type. On the second hand, it is the development degree of disease symptoms. Different degrees of disease symptoms correspond to different subsequent treatment plan selections, and also help to accurately and reasonably position and judge the development of the disease condition.

[0042] Based on the medical treatment data of different objects in the traditional Chinese medicine medical treatment database, extract symptom information based on disease types to form object disease type symptom information granularity data, including: for the medical treatment data of different objects, extract all medical examination information and parameterize it according to the examination category to form object medical examination parameter data; extract the medical examination items of each object to form object medical examination item information; aggregate the object medical examination parameter data and object medical examination item information corresponding to different objects to form object disease type symptom information granularity data for different objects.

[0043] Based on disease types for information granularity, the information to be granularized mainly is the examination information used to locate and judge disease types. Considering the individual situations of different doctors and different medical treatment objects, the examination information is not the same. This difference is not only manifested in the volatility of examination parameter values but also in the non-uniformity of examination items. And these inconsistent examination information can achieve the judgment of disease types. Then there must be a gold standard for locating and judging disease types among them. By parameterizing the examination information and extracting the examination item information for granularization, it provides a data basis for subsequently stripping out the important information that can locate and judge disease types.

[0044] For the object disease type symptom information granularity data of different objects, perform symptom level clustering based on disease types to form disease type symptom level information granularity data, including: cluster the object medical examination parameter data of different objects and perform symptom level division in the following way according to the onset time: arrange the object medical examination parameter data of different objects in ascending order according to the onset time to form initial disease type symptom level data; for the initial disease type symptom level data, determine the negative parameter values of different objects , where: , , n represents the sequential number of different objects in the initial disease type symptom level data, k represents the number of different medical examination items with negative directions in the object medical examination parameter data of the object numbered n, represents the equivalent difference of the medical examination item numbered k with negative direction in the object medical examination parameter data of the object numbered n, represents the actual parameter value of the medical examination item numbered k with negative direction in the object medical examination parameter data of the object numbered n, represents the standard parameter value of the medical examination item numbered k with negative direction in the object medical examination parameter data of the object numbered n; according to the negative parameter values of different objects , sort the initial disease type symptom level data in ascending order to form the final disease type symptom level data; set the necessary examination parameter limits, and for the objects whose negative parameter values do not exceed the necessary examination parameter limits, determine the information of the examination items with the fewest visits for the object with the fewest visits, and label it as the necessary examination item information for disease type visits; collect the corresponding final disease type symptom level data and disease type visit necessary examination item information under different disease types to form the disease type symptom level information granularity data.

[0045] After completing the granulation process of the examination information, the examination characteristic information of different types of diseases has been initially grasped. In order to provide more in-depth and accurate disease type positioning and judgment references, it is necessary to further granulate the information of the disease symptom degree. It can be understood that the development of disease symptoms is basically conditional on time. Therefore, in the process of granulating the information of the development of disease symptoms, arranging the examination information of the same disease type in chronological order can generally grasp the entire development process of the disease. However, considering the differences in the onset characteristics of individuals, the discontinuous changes of the disease symptoms in individuals, and the differences in the individual's perception of the disease symptoms, there are still some deviations in the chronological order arrangement. Therefore, reasonable adjustments can be made using the examination information. It should be noted that for the examination item information, the normal examination result information basically does not contain references for the positioning and judgment of disease symptom characteristics, while the abnormal and negative parameter information presented in the examination item information can determine the manifestation of the disease symptoms. Therefore, when making reasonable order adjustments for the development process of disease symptoms presented to different objects, it is mainly based on the examination items with negative information. Of course, different examination items have different manifestations of the symptom development degree, so comprehensive equivalent judgment is required to accurately grasp the trend of symptom development. In addition, considering the individual differences of different objects and the differences in the visit situations, the categories of examination items will be different for different individuals, and there are gold standard examination items for judging the disease type and the disease symptom stage. By extracting the examination item information of the objects before the reasonable order quantity of the sorted granulation data, the necessary examination items for the disease type can be formed.

[0046] Extract the object-based medical treatment data from the traditional Chinese medicine medical treatment database, and combine the data divided by the information granularity of the diagnosis result information to perform information granularity division for the treatment plan, forming the information granularity division data of the treatment method, including: determining the treatment plan corresponding to the medical examination parameter data at different order levels in the final disease type symptom level data according to the medical treatment data of different objects; performing the following treatment means combination division on the treatment plans at different order levels: if there is an intersection among all the treatment means combinations, determine the treatment means items corresponding to the intersection as the necessary treatment means at the level, and determine the treatment means combinations except the necessary treatment means at the level under different treatment plans as the optional treatment means group at the level; if there is no intersection among all the treatment means combinations, determine the treatment means combination corresponding to each treatment plan as the optional treatment means group at the level; collect the necessary treatment means at the level and the optional treatment means group corresponding to different order levels in the final disease type symptom level data under each disease type to form the information granularity division data of the treatment method.

[0047] The purpose of information granularity of the treatment plan is to form optional treatment plans for different diseases, providing a reference guide for subsequent disease treatment. Here, after completing the granularity of the diagnosis information, the clustering of the treatment plan can be first carried out based on the clustered data after granularity, and then the plan can be further refined. It can be understood that generally, treatment plans in traditional Chinese medicine are used in combination, such as herbs, acupuncture, massage, etc. Of course, more specifically, it is the dispensing of herbs, the selection of acupuncture points, etc. For specific disease types, there may also be necessary traditional Chinese medicine treatment means. Therefore, in the process of information granularity, the necessary treatment means and optional treatment means of the treatment plan can be further distinguished to provide a reasonable and accurate reference guide for subsequent treatment plans.

[0048] S3: Extract the diagnosis and treatment data based on diseases according to the traditional Chinese medicine information granularity division data, and perform trend analysis to form the disease epidemic trend analysis result data.

[0049] Extract the diagnosis and treatment data based on diseases according to the traditional Chinese medicine information granularity division data, and perform trend analysis to form the disease epidemic trend analysis result data, including: obtaining the historical disease epidemic trend data, and establishing the average disease epidemic trend curve of the incidence volume in the time dimension order ; establishing the diagnosis and treatment trend curve for different disease types in the time dimension order according to the number of times and the reference time of the information granularity division data of the treatment method under different disease types in the traditional Chinese medicine medical treatment database , where m represents the number of different disease types; according to the average disease epidemic trend curve and the diagnosis and treatment trend curve of different disease types , conduct trend analysis to form disease epidemic trend analysis result data for different disease types.

[0050] After completing the information granularity in both diagnosis and treatment, unexpected effects can also be achieved. That is, for different disease types, granularized treatment data can be extracted to conduct epidemic trend analysis for the corresponding disease types, which can provide data reference for public medical security. For diseases with epidemic trends, from a social attribute perspective, the development trends of epidemics are basically similar. Therefore, the average epidemic trend curve can be extracted from the big data of epidemic trends, and then the trend data formed by the treatment volume in the time dimension using the granularized treatment data can be compared to achieve the analysis and judgment of the trend.

[0051] According to the average disease epidemic trend curve and the diagnosis and treatment trend curves of different disease types , conduct trend analysis to form disease epidemic trend analysis result data for different disease types, including: setting a trend judgment duration threshold T, and according to the average disease epidemic trend curve and the diagnosis and treatment trend curves of different disease types , conduct trend analysis and judgment in the following way: If it satisfies within a duration not less than the trend judgment duration threshold T : and , then calibrate the corresponding disease type as the currently prevalent disease, otherwise do not calibrate. Among them, represents the disease epidemic cumulative amount judgment threshold corresponding to the disease type numbered m, represents the disease epidemic rate judgment threshold corresponding to the disease type numbered m, represents the derivative of in the time dimension, represents the derivative of

[0052] The judgment of trend analysis is mainly to quantitatively determine whether the epidemic trend curve of the disease type extracted from the granularized data is close to the average epidemic trend curve obtained from big data. The closeness is determined through two aspects. On the one hand is the size of the incidence, after all, an epidemic requires a certain incidence basis. The second aspect is the changing trend of the incidence rate, and the changing trend of the incidence rate is an important indicator for judging whether a disease is prevalent. Of course, for the determined thresholds of judgment, they can be determined according to the actual situation or based on big data analysis.

[0053] S4: Divide the data according to the granularity of traditional Chinese medicine information, extract the diagnosis and treatment data based on the object, and conduct treatment effect analysis to form disease treatment effect analysis result data.

[0054] Divide the data according to the information granularity of traditional Chinese medicine, extract the diagnosis and treatment data based on the object, and conduct an analysis of the treatment effect to form the result data of the disease treatment effect analysis, including: the number of times each sequential level corresponding to different levels of optional treatment means groups is cited under the final disease type symptom level data in the treatment method information granularity division data of different disease types in the traditional Chinese medicine diagnosis and treatment database and the number of follow-up visits after the corresponding level of optional treatment means group is adopted , where r represents the number of different sequential levels under the final disease type symptom level data in the treatment method information granularity division data, and i represents the number of different optional treatment means under the sequential level numbered r; the number of times cited corresponding to different levels of optional treatment means groups under different sequential levels in the final disease type symptom level data and the number of follow-up visits , conduct an analysis of the treatment effect to form the result data of the disease treatment effect analysis

[0055] Of course, after granulating the information, these data can also be used to evaluate the effect of the treatment plan to provide a reasonable guidance for the selection of the treatment plan for the follow-up treatment. When evaluating the effects of different treatment plans, it is mainly carried out through two aspects. One is the quantity of the plan being selected, and the other is the follow-up visit rate of the selected plan. Both aspects have a certain impact on the effect evaluation

[0056] The number of times cited corresponding to different levels of optional treatment means groups under different sequential levels in the final disease type symptom level data and the number of follow-up visits , conduct an analysis of the treatment effect to form the result data of the disease treatment effect analysis, including: the number of times cited of different levels of optional treatment means groups under the same sequential level in the final disease type symptom level data and the number of follow-up visits , conduct the effect analysis in the following way: set the citation number impact factor and the follow-up visit number impact factor , and determine the effect factor corresponding to different optional treatment means , where ; according to the effect factor , arrange the different levels of optional treatment means groups under the same sequential level from large to small to form the result data of the disease treatment effect analysis under the same sequential level

[0057] For determining the effect of the treatment plan by using the selection quantity of the plan and the subsequent follow-up visit rate, it can be comprehensively analyzed and judged through the impact factor. For the impact factor, it can be determined according to the actual situation and can also be determined based on the analysis of big data

[0058] In summary, the beneficial effects of the traditional Chinese medicine information granulation management method provided by the embodiments of the present invention are as follows:

[0059] This method collects traditional Chinese medicine diagnosis information by establishing a traditional Chinese medicine diagnosis database to form the basic big data required for information granulation. Then, the traditional Chinese medicine diagnosis information in the database is used for information granulation to form granulated information that can provide data reference guidance for traditional Chinese medicine diagnosis and treatment. At the same time, due to the information granulation of traditional Chinese medicine data, it is possible to further mine the diagnosis data to realize the analysis of the epidemic trend of diseases, fully realize the early warning of the disease epidemic, and play an important role in promoting social medical care. In addition, it is also possible to mine effective treatment plans for different diseases from the diagnosis data, providing important data reference for improving the treatment effect of diseases.

[0060] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. If the information indicated by a certain piece of information is called the information to be indicated, then in the specific implementation process, there are many ways to indicate the information to be indicated. For example, but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated, etc. It is also possible to indirectly indicate the information to be indicated by indicating other information, where there is an association relationship between the other information and the information to be indicated. It is also possible to only indicate a part of the information to be indicated, while the other parts of the information to be indicated are known or pre-agreed. For example, it is also possible to realize the indication of specific information by relying on the arrangement order of each piece of information pre-agreed (such as protocol regulations), thereby reducing the indication overhead to a certain extent. At the same time, it is also possible to identify the common parts of each piece of information and uniformly indicate them to reduce the indication overhead caused by separately indicating the same information.

[0061] In addition, the specific indication method can also be various existing indication methods, such as but not limited to, the above indication methods and their various combinations, etc. The specific details of various indication methods can refer to the prior art and will not be elaborated herein. As described above, for example, when it is necessary to indicate multiple pieces of information of the same type, there may be a situation where the indication methods of different pieces of information are different. In the specific implementation process, the required indication method can be selected according to specific needs. The embodiments of the present application do not limit the selected indication method. In this way, the indication methods involved in the embodiments of the present application should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0062] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub - information and sent separately. Moreover, the sending periods and / or sending timings of these sub - information can be the same or different. The specific sending method is not limited in the embodiments of the present application. Among them, the sending periods and / or sending timings of these sub - information can be predefined, for example, predefined according to a protocol, or can be configured by the sending - end device by sending configuration information to the receiving - end device.

[0063] "Pre - defined" or "pre - configured" can be achieved by pre - saving corresponding codes, tables or other means that can be used to indicate relevant information in the device. The embodiments of the present application do not limit its specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be separately set, or can be integrated in an encoder, a decoder, a processor, or a communication device. The one or more memories can also be partly separately set and partly integrated in a decoder, a processor, or a communication device. The type of the memory can be any form of storage medium, which is not limited in the embodiments of the present application.

[0064] The "protocol" involved in the embodiments of the present application can refer to a protocol family in the communication field, a standard protocol with a frame structure similar to that of a protocol family, or a relevant protocol applied to a future communication system. The embodiments of the present application do not make specific limitations on this.

[0065] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if", and "if" all mean that the device will perform corresponding processing under a certain objective situation, which does not limit time, and does not require the device to have a judgment action when implemented, nor does it mean the existence of other limitations.

[0066] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B may represent A or B. The "and / or" in the embodiments of the present application is merely a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Also, in the description of the embodiments of the present application, unless otherwise specified, "a plurality of" means two or more than two. "At least one (item)" or its similar expression refers to any combination of these items, including any combination of a single item or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple. Additionally, for the convenience of clearly describing the technical solutions of the embodiments of the present application, in the embodiments of the present application, terms such as "first" and "second" are used to distinguish between identical or similar items with basically the same functions and roles. Those skilled in the art can understand that terms such as "first" and "second" do not limit the quantity and execution order, and "first", "second", etc. do not necessarily mean different. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, using words such as "exemplary" or "for example" aims to present relevant concepts in a specific manner for easy understanding.

[0067] It should be understood that the processor in the embodiments of the present application can be a central processing unit (CPU), and this processor can also be other general - purpose processors, digital signal processors (DSPs), application - specific integrated circuits (ASICs), field - programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general - purpose processor can be a microprocessor or this processor can also be any conventional processor, etc.

[0068] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0069] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0070] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be understood specifically by referring to the context before and after.

[0071] In the present application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0072] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not imply the order of execution. The order of execution of each process should be determined based on its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0073] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0074] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be electrical, mechanical, or other forms.

[0076] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0078] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0079] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for granular management of traditional Chinese medicine information, characterized in that: include: Collect TCM consultation information and establish a TCM consultation database; Extracting object-based medical consultation data from the TCM consultation database, and dividing the information into granularity to form TCM information granularity division data; According to the data of TCM information granularity division, the diagnosis and treatment data based on the disease are extracted, and trend analysis is performed to form disease epidemic trend analysis result data; According to the data divided by the TCM information granularity, the diagnosis and treatment data based on the object are extracted, and the treatment effect analysis is performed to form the disease treatment effect analysis result data; The object-based medical treatment data is extracted from the TCM medical treatment database, and the information is divided into granularity to form TCM information granularity division data, including: Extracting the medical consultation data based on the object in the TCM medical consultation database, and performing information granularity division based on the diagnosis result to form diagnosis result information granularity division data; Extracting the patient-based medical treatment data in the TCM medical treatment database, and performing information granularity division for treatment plans in combination with the diagnosis result information granularity division data to form treatment method information granularity division data; The diagnosis result information granularity division data and the treatment method information granularity division data are collected to form the traditional Chinese medicine information granularity division data; Extracting the medical consultation data based on the object in the TCM medical consultation database, and performing information granularity division based on the diagnosis result to form diagnosis result information granularity division data, including: Extracting symptom information based on disease type according to the medical data of different subjects in the TCM medical database to form granular data of symptom information of disease type of the subject; For different granular data of symptom information of the object disease type, clustering the symptom level based on the disease type to form granular data of symptom level information of the disease type; Gather the disease type symptom level information granularity data of different disease types to form the diagnosis result information granularity division data; According to the medical treatment data of different subjects in the TCM medical treatment database, symptom information is extracted based on the disease type to form granular data of symptom information of the disease type of the subject, including: For the medical data of different subjects, all medical examination information is extracted and parameterized according to the examination category to form the subject medical examination parameter data; Extracting the medical examination items of each subject to form the medical examination item information of the subject; Gather the medical examination parameter data and medical examination item information of different subjects to form granular data of disease type and symptom information of different subjects; For different granular data of the symptom information of the object disease type, clustering of the symptom level based on the disease type is performed to form granular data of the symptom level of the disease type, including: The patient examination parameter data of different subjects are clustered, and the symptom levels are divided according to the onset time in the following manner: Arrange the examination parameter data of different subjects in ascending order according to the onset time to form initial disease type symptom level data; For the initial disease type symptom level data, determine the negative parameter values ​​of different objects ,in: , n represents the sequential numbers of different subjects in the initial disease type symptom level data, k represents the numbers of different negative examination items in the subject examination parameter data of the subject numbered n, represents the equivalent difference of the negative examination item numbered k in the examination parameter data of the subject numbered n, represents the actual parameter value of the negative-going medical examination item numbered k in the medical examination parameter data of the subject numbered n, represents the standard parameter value of the negative-going medical examination item numbered k in the medical examination parameter data of the subject numbered n; According to the negative parameter values ​​of different objects , sequentially adjusting the initial disease type symptom level data in order from small to large to form final disease type symptom level data; Setting necessary examination parameter limits, for subjects whose negative parameter values ​​do not exceed the necessary examination parameter limits, determining the subject's examination item information with the least examination items, and marking it as necessary examination item information for disease type visits; The final disease type symptom level data corresponding to different disease types and the necessary examination item information for the disease type are collected to form the disease type symptom level information granularity data.

2. The method for granular management of traditional Chinese medicine information according to claim 1, characterized in that: The extracting of the object-based medical treatment data from the TCM medical treatment database and combining the diagnosis result information granularity division data to perform information granularity division for treatment plans to form treatment method information granularity division data include: Determine, according to the medical consultation data of different subjects, treatment plans corresponding to the medical consultation examination parameter data at different order levels in the final disease type symptom level data; For treatment plans at different order levels, the following treatment method combinations are divided: If all treatment combinations have an intersection, the corresponding treatment items under the intersection are determined as necessary treatments, and the treatment combinations except the necessary treatments under different treatment plans are determined as optional treatment groups; If all treatment combinations do not have an intersection, the treatment combination corresponding to each treatment plan is determined as a hierarchical optional treatment group; The necessary treatment means and optional treatment means groups corresponding to different order levels in the final disease type symptom level data under each disease type are collected to form the treatment method information granularity division data.

3. The method for granular management of traditional Chinese medicine information according to claim 2, characterized in that: The data is divided according to the granularity of the traditional Chinese medicine information, the diagnosis and treatment data based on the disease is extracted, and trend analysis is performed to form disease epidemic trend analysis result data, including: Obtain historical disease epidemic trend data and establish an average disease epidemic trend curve of the incidence in the time dimension sequence ; According to the granularity of the treatment information under different disease types in the TCM consultation database, the number of citations and the citation time of the data are divided to establish the diagnosis and treatment trend curve for different disease types in the time dimension sequence. , where m represents the number of different disease types; According to the average disease prevalence trend curve and the diagnostic and treatment trend curves for different disease types , conduct epidemic trend analysis to form the disease epidemic trend analysis result data of different disease types.

4. The method for granular management of traditional Chinese medicine information according to claim 3, characterized in that: According to the average disease prevalence trend curve and the diagnostic and treatment trend curves for different disease types , conduct epidemic trend analysis, and form the epidemic trend analysis result data of different disease types, including: Set the trend judgment time threshold T, and according to the average disease epidemic trend curve and the diagnostic and treatment trend curves for different disease types , and conduct trend analysis and judgment in the following ways: If the duration is not less than the trend judgment duration threshold T The following meets: ,and , then the corresponding disease type is calibrated as the current epidemic disease, otherwise no calibration is performed, where, It represents the judgment threshold of the cumulative amount of disease epidemic corresponding to the disease type numbered m. represents the disease prevalence judgment threshold corresponding to the disease type numbered m, express The derivative in the time dimension is express Derivative in the time dimension.

5. The method for granular management of traditional Chinese medicine information according to claim 4, characterized in that: The data is divided according to the granularity of the traditional Chinese medicine information, the diagnosis and treatment data based on the object is extracted, and the treatment effect analysis is performed to form the disease treatment effect analysis result data, including: The number of times each order level corresponds to the number of times the optional treatment means groups of different levels are cited according to the final disease type symptom level data in the granularity classification data of the treatment methods under different disease types in the TCM consultation database and the number of follow-up visits after the corresponding level of optional treatment group is adopted , wherein r represents the number of different order levels under the final disease type symptom level data in the treatment method information granularity division data, and i represents the number of different optional treatment methods under the order level numbered r; The number of times the optional treatment means groups of different levels at different order levels in the final disease type symptom level data are cited and the number of follow-up visits , conduct treatment effect analysis and form the disease treatment effect analysis result data.

6. The method for granular management of traditional Chinese medicine information according to claim 5, characterized in that: The number of times the optional treatment means groups of different levels at different order levels in the final disease type symptom level data are cited and the number of follow-up visits , conduct treatment effect analysis to form the disease treatment effect analysis result data, including: The number of times the optional treatment groups of different levels at the same ordinal level under the final disease type symptom level data are cited and the number of follow-up visits , and conduct the following effect analysis: Set the citation impact factor Factors affecting the number of follow-up visits , and determine the effect factors corresponding to the different optional treatment methods ,in, ; According to the effect factor , arrange the optional treatment means groups of different levels at the same sequence level from large to small, and form the disease treatment effect analysis result data at the same sequence level.

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