CN111489809A - Traditional Chinese medicine diagnosis and treatment system based on set pair analysis - Google Patents
Traditional Chinese medicine diagnosis and treatment system based on set pair analysis Download PDFInfo
- Publication number
- CN111489809A CN111489809A CN202010267344.4A CN202010267344A CN111489809A CN 111489809 A CN111489809 A CN 111489809A CN 202010267344 A CN202010267344 A CN 202010267344A CN 111489809 A CN111489809 A CN 111489809A
- Authority
- CN
- China
- Prior art keywords
- analysis
- chinese medicine
- traditional chinese
- coefficient
- curative effect
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000003814 drug Substances 0.000 title claims abstract description 75
- 238000004458 analytical method Methods 0.000 title claims abstract description 56
- 238000003745 diagnosis Methods 0.000 title claims abstract description 19
- 230000000694 effects Effects 0.000 claims abstract description 42
- 208000011580 syndromic disease Diseases 0.000 claims abstract description 39
- 230000004069 differentiation Effects 0.000 claims abstract description 37
- 238000004364 calculation method Methods 0.000 claims abstract description 23
- 208000024891 symptom Diseases 0.000 claims abstract description 22
- 238000011160 research Methods 0.000 claims abstract description 19
- 229940079593 drug Drugs 0.000 claims abstract description 17
- 238000011156 evaluation Methods 0.000 claims abstract description 16
- 230000000007 visual effect Effects 0.000 claims abstract description 4
- 238000000034 method Methods 0.000 claims description 10
- 230000001225 therapeutic effect Effects 0.000 claims description 5
- 238000013211 curve analysis Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 2
- 238000011161 development Methods 0.000 abstract description 4
- 230000007774 longterm Effects 0.000 abstract description 3
- 201000010099 disease Diseases 0.000 description 9
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 9
- 238000005516 engineering process Methods 0.000 description 4
- 238000007792 addition Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- OVBPIULPVIDEAO-LBPRGKRZSA-N folic acid Chemical compound C=1N=C2NC(N)=NC(=O)C2=NC=1CNC1=CC=C(C(=O)N[C@@H](CCC(O)=O)C(O)=O)C=C1 OVBPIULPVIDEAO-LBPRGKRZSA-N 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 201000004182 Congenital stromal corneal dystrophy Diseases 0.000 description 1
- 208000032023 Signs and Symptoms Diseases 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 229940126678 chinese medicines Drugs 0.000 description 1
- 238000004587 chromatography analysis Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 229940124301 concurrent medication Drugs 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 210000001503 joint Anatomy 0.000 description 1
- 238000003032 molecular docking Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000001717 pathogenic effect Effects 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 229940126680 traditional chinese medicines Drugs 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/90—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to alternative medicines, e.g. homeopathy or oriental medicines
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Biomedical Technology (AREA)
- Pharmacology & Pharmacy (AREA)
- Alternative & Traditional Medicine (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to a traditional Chinese medicine diagnosis and treatment system based on set pair analysis, which provides a visual operation interface with the following contents for traditional Chinese medicine and western medicine: symptom evaluation and prediction, a pair analysis and syndrome differentiation model, symptom medication evaluation and prediction, a curative effect curve and a curative effect triangle, traditional Chinese medicine syndromes and western medicine index models, and a user can perform calculation and online storage according to data requirements on line according to needs. The invention captures the inspiration of scientific research: clinical data are accumulated in a full amount through the platform, and the CRF generated by similar cases is intelligently screened through long-term observation and research, so that the development of scientific research value is facilitated; creating a case database: case data are collected through the platform, permanently stored and rapidly exported, and scientific research projects are conveniently managed; participating in a multi-center study: the multi-center research is initiated or participated in through the platform, the grouping efficiency of the patients is improved together, and the research progress is accelerated.
Description
Technical Field
The invention relates to the technical field of traditional Chinese medicines, in particular to a traditional Chinese medicine diagnosis and treatment system based on set-pair analysis.
Background
The set-to-set analysis is a systematic mathematical theory proposed by the professor Zhao Ke Li of China in 1989, and has advantages in studying deterministic information, uncertain information and related microscopic effects (prediction functions), so that the set-to-set analysis is widely applied to the fields of astronomy, hydraulic resources and other multiple subjects, and more than 8000 CSCD papers and more than 200 SCI papers are published at present, while the application in the field of traditional Chinese medicine is just started. Syndrome differentiation is the basic thought and method of traditional Chinese medicine, syndrome is the summary of pathological characteristics of a certain stage of the disease according to constitution, age and previous disease of a patient, the comprehensive causes of disease, disease position, disease nature, the relationship between pathogenic factors and positive factors and the like on the basis of determining the disease, belongs to relatively uncertain factors, and a set pair is a mathematical concept consisting of two sets and is not in conflict with the yin-yang theory of the theory of syndrome differentiation and treatment of traditional Chinese medicine, so the thought of combining the theory of syndrome differentiation and treatment of traditional Chinese medicine and the set pair analysis theory has the wonderful effect of having different natures, the mathematical expectation based on the joint coefficient corresponds to the actual situation of the patient, the therapeutic rule of the treatment based on syndrome differentiation and the treatment and the corresponding adjustment of the used medicines are fed back, and the curative effect is gradually improved. The diagnosis and treatment of traditional Chinese medicine are combined by 'overall syndrome differentiation' and 'local syndrome differentiation', the 'disease differentiation' and 'syndrome differentiation' are combined, the 'disease differentiation' focuses on grasping the determined factors, and the grasping of uncertain factors (such as the constitution of a patient, the climate environment and the like) is the key of the diagnosis and treatment of traditional Chinese medicine. Aiming at the problem, a set pair analysis method is introduced firstly, and the thought characteristic is that objective recognition is given to various kinds of objectively existing uncertainties (differences of treatment methods, objects, environments, treatment results and the like), and the determinacy and the uncertainties are used as a definite and uncertain homological-heterological system to carry out syndrome differentiation analysis and mathematical treatment, so that the system is summarized into objective recognition, system description, quantitative depiction and specific analysis, various uncertain factors are found out, and addition and subtraction changes of medicines are carried out in a targeted manner, so that the treatment based on syndrome differentiation is more objective, and the curative effect level is improved.
The patient efficacy rating is a complex system with uncertain factors such as constitutional difference, environmental difference, treatment difference and the like. There are many methods for researching medical efficacy rating currently, such as chromatography and fuzzy comprehensive evaluation methods, which have respective advantages and disadvantages, and mainly have subjectivity in weight assignment, neglect of uncertainty in evaluation process, and the like. The technology optimizes the weight of different clinical symptom importance degrees after being combined with the cloud model, and the weight is upgraded into an instant input, storage, analysis and export integrated intelligent platform.
The main technical key of the existing team V1.0 is four traditional Chinese medicine retrospective analysis modules based on a set pair analysis algorithm (SPA): 1. differentiation factors (screening clinical differentiation factors to improve accuracy); 2. symptom assessment prediction (screening of refractory differentiation factors); 3. the assessment and prediction of symptomatic medication (assessing the microscopic trend of the patient's condition development after the symptomatic medication); 4. curative effect curve and curative effect triangle (Chinese medicine or compound prescription is used as research object to evaluate the curative effect of Chinese medicine). And V2.0 is the analysis of partial union coefficient by combining set pair, calculates the traditional Chinese medicine syndrome and Western medicine index, and evaluates the curative effect of the patient.
The V1.0 version and the V2.0 version are exe program software, are used by a single person and a single machine, and are not beneficial to the acquisition of big data and the establishment of a database. V3.0 is a webpage version further developed by Meddoad Cloud of Meddroad of Meyer medical services of union method, contains five module contents of V1.0 and V2.0, adopts a multilayer distributed architecture B/S technology, has an interface connected with an external system, allows partial application functions to be connected with Internet, and has special technical measures to ensure the safety of data. The system is highly portable and supports various mainstream relational database systems such as DB2, Oracle and the like. The project adopts J2EE architecture design, which is the mainstream technology at present, and the robustness, openness and expansibility of the system are fully ensured by adopting a mature multilayer enterprise-level architecture system of J2 EE. Can be selectively deployed in various system environments to meet the requirements of different types and different scales. The system meets the management system structure, the management mode and the operation program of the current clinical test of the medicine, and can meet the requirements of the current and future informatization long-term development of the clinical test base of the medicine.
Disclosure of Invention
The first objective of the present invention is to provide a diagnosis and treatment system based on set-pair analysis for TCM, aiming at the deficiencies in the prior art.
In order to achieve the first purpose, the invention adopts the technical scheme that:
a traditional Chinese medicine diagnosis and treatment system based on set pair analysis provides a visual operation interface with the following contents for traditional Chinese medicine and western medicine: the system comprises symptom evaluation prediction, a pair analysis syndrome differentiation model, symptom medication evaluation prediction, a curative effect curve and a curative effect triangle, traditional Chinese medicine syndromes and a western medicine index model, wherein a user can calculate and store on line according to data requirements according to needs, and output factors of the symptom evaluation prediction are partial coefficient analysis, isobaric analysis and heterodromous analysis and symptom evaluation prediction; the output factors of the pair-collecting analysis syndrome differentiation model are the U value and the mu value of the syndrome differentiation factor and the pair-collecting analysis syndrome differentiation model; the output factors of the symptomatic medication evaluation prediction are partial coefficient analysis and isobaric analysis; the input factors of the curative effect curve and the curative effect triangle are curative effect curve analysis, Chinese medicine optimization, curative effect curve and curative effect triangle analysis; the calculation process in the traditional Chinese medicine syndrome and western medicine index model is as follows: u is total a number + total b number i + total c number j; said
The quadruple coefficient of the output curative effect is mu (gt) ═ at + bti + ctj + dtk (t ═ 1,2 …) a + b + c + d ═ 100%
The third-order partial positive coefficient, the third-order partial negative coefficient and the third-order full partial coefficient are ranked from large to small, and if the third-order partial positive coefficient, the third-order partial negative coefficient and the third-order full partial coefficient are 0, the mark is zero.
The output factors of the set pair analysis syndrome differentiation model are a syndrome differentiation factor U value and a mu value: u ═ c (a + b)/(a + b + c) -d; μ ═ U/(a + b + c + d).
In the assessment and prediction of symptomatic drug use:
the quaternary coefficient of therapeutic effect is mu (gt) ═ at + bti + ctj + dtk (t ═ 1,2 …) a + b + c + d ═ 100%
The curative effect curve and the curative effect triangle have a curative effect quadruple coefficient of
μ(gt)＝at+bti+ctj+dtk(t＝1,2…)a+b+c+d＝100％，
The difference between the sequence numbers of St and at is denoted as t, the sequence number of t is denoted as "same" (the sequence numbers of both sequences are the same), the sequence number of 1 ≦ t ≦ X1 is denoted as "same", the sequence number of X1 ≦ t ≦ X2 is denoted as "opposite", and the sequence number of X2< t is denoted as "opposite"
X1 is St rank maximum/4, X2 is St rank maximum/2.
The traditional Chinese medicine diagnosis and treatment system based on set pair analysis can obtain results after being input, interfaces are switched, the results are permanently stored and deleted after calculation, a doctor can export the stored results at any time according to needs, and the traditional Chinese medicine diagnosis and treatment system based on set pair analysis is provided with authority classification.
The permanently storing and deleting of the result after calculation means that after a doctor enters the patient data, the corresponding calculation result of the input data can be stored in the system by clicking for storage, so that the patient data can be checked and read at any time later, and the result which is wrong in calculation or does not need to be presented can be deleted at any time according to the stored result.
The authority classification means that the account number authority of an administrator is set to be that all calculation results can be calculated, stored, deleted and exported at any time according to the requirements at present; account authorities of other researchers only can see the content operated by the researchers according to different research centers, and the rest of the content is invisible.
The traditional Chinese medicine diagnosis and treatment system based on set pair analysis adopts a multilayer distributed architecture B/S technology, has an interface connected with an external system, and allows part of application functions to be connected with the Internet.
The invention has the advantages that:
1. catching the inspiration of scientific research: clinical data are accumulated in a full amount through the platform, and the CRF generated by similar cases is intelligently screened through long-term observation and research, so that the development of scientific research value is facilitated; 2. creating a case database: case data are collected through the platform, permanently stored and rapidly exported, and scientific research projects are conveniently managed; 3. participating in a multi-center study: the multi-center research is initiated or participated in through the platform, the grouping efficiency of the patients is improved together, and the research progress is accelerated; 4. the academic position is promoted: a medical research network is built through a platform, a disease diagnosis and treatment standard and a patient diagnosis and treatment model are created, and the improvement of discipline construction and clinical decision making capacity is facilitated; 5. culture research-type team: good habits of doctors in departments actively recording data for observing clinical problems are cultured through a platform, scientific research innovation thinking is stimulated, and the scientific research level of a team is effectively improved; 6. optimizing doctor-patient management: patient follow-up visits are managed through the platform, doctor-patient communication is promoted, good doctor-patient relations are constructed, and the rate of re-diagnosis is improved; 7. is beneficial to market popularization.
Drawings
Figure 1 is a graph of efficacy based on a quaternary efficacy cascade.
FIG. 2 shows that the interfaces of the five calculations can be switched arbitrarily as required.
Fig. 3 shows that the stored result may be subjected to deletion processing for a result that is calculated erroneously or does not need to be presented at any time.
Fig. 4 is a diagram for analyzing the results after storage, and the results with calculation errors or without presentation can be deleted at any time.
FIG. 5 shows that the physician can export the saved results at any time according to the needs.
Detailed Description
The following examples are provided to illustrate specific embodiments of the present invention.
Example 1
Collection pair analysis and medical access cloud docking requirements
The set of analysis, statistics and analysis software and the medical cloud are in butt joint with 5 modules, all calculation formulas are realized through software, indexes are required to be input according to requirements, and finally a specified table is output.
The method comprises the following steps:
1. symptom assessment prediction
The input symptoms, a, b, c, d (number of people) include: a is cured; b represents the effect; c is improved; invalid d (where it is implemented by a form)
Examples are: see Table 1 symptom assessment prediction data
For computing
at＝a/(a+b+c+d)
bt＝b/(a+b+c+d)
ct＝c/(a+b+c+d)
dt＝d/(a+b+c+d)
Output of
The four-element coefficient of curative effect is mu (gt) ═ at+bti+ctj+dtk(t＝1,2…)a+b+c+d＝100％
The third order partial positive coefficient, the third order partial negative coefficient and the third order full partial coefficient are ranked from large to small, if 0, the mark is marked (none)
Output 1: analyzing a partial union coefficient:
see table 2 for data on partial coefficient analysis
And (3) calculating:
total valid/invalid ═ a + b + c)/d
Rank total valid/invalid from large to small
Ranking the full-biased connection coefficient from large to small based on the third order
The difference between the two serial numbers of the same or different inverse-total effective/ineffective and third-order full-bias-combined coefficient is recorded ast，t0 is called "same" (the two ranking numbers are the same), 1 ≦ 1tX1 is called "partial homology", X1<t ≦ X2 called "partial inversion", X2<tReferred to as "trans"
X1 is the maximum value/4 of the rank of the third-order full-biased cascade coefficient, and X2 is the maximum value/2 of the rank of the third-order full-biased cascade coefficient
And (3) outputting 2: the same or different reaction analysis is shown in Table 3
And (3) calculating:
and classifying according to the same order and the different orders, and sorting from small to large in each class according to the ranking of the three-order full-biased union coefficients. And (3) outputting: symptom assessment prediction see Table 4 symptom assessment prediction data
2. Collection-pair analysis dialectical model
The input of differentiation factors a, b, c and d (number of people) comprises: a is cured; b represents the effect; c is improved; invalid d (where it is implemented by a form)
Table 5 below shows the syndrome differentiation factor data (number, syndrome differentiation factor, recovery, effective and ineffective data as input contents)
Output 1: syndrome differentiation factor U value and mu value
U＝(a+b)+c*(a+b)/(a+b+c)-d
μ＝U/(a+b+c+d)
Table 6 below shows the values of the differentiation factors U and μ (the numerical values of the differentiation factors U and μ are the output contents)
And (3) outputting 2: collection-pair analysis dialectical model
U＝(a+b)+c*(a+b)/(a+b+c)-d
μ＝U/(a+b+c+d)
Sorting the mu values from large to small and outputting the attribute of the syndrome differentiation factor
Mu is more than or equal to 0.75 to output main syndrome differentiation factors
0.75 mu is more than or equal to 0.5, and a secondary dialectical factor is output
0.5 & gtmu output secondary syndrome differentiation factor
Table 7 below sets forth the analytical syndrome differentiation model data (serial number, syndrome differentiation factor and μ value as output contents)
3. Symptomatic drug assessment prediction
Inputting: the input symptoms, a, b, c and d (number of people) and the concomitant medication comprise: a is cured; b represents the effect; c is improved; invalid is d
Such as: TABLE 8 assessment of symptomatic drug use and prediction input data
And (3) calculating:
at＝a/(a+b+c+d)
bt＝b/(a+b+c+d)
ct＝c/(a+b+c+d)
dt＝d/(a+b+c+d)
output of
The four-element coefficient of curative effect is mu (gt) ═ at+bti+ctj+dtk(t＝1,2…)a+b+c+d＝100％
Total valid/invalid ═ a + b + c)/d
Rank total valid/invalid from large to small
Ranking the three-order partial positive coefficient, the three-order partial negative coefficient and the three-order full partial coefficient from large to small, and marking (without) if the three-order partial positive coefficient, the three-order partial negative coefficient and the three-order full partial coefficient are 0
The difference between the two ranking numbers of the same or different, total effective/ineffective and third-order full-bias-combined coefficient is recorded ast，t0 is called "same" (the two ranking numbers are the same), 1 ≦ 1tX1 is called "partial homology", X1<t ≦ X2 called "partial inversion", X2<tReferred to as "trans"
X1 is the maximum value/4 of the rank number of the third-order full-deviation relation number, and X2 is the maximum value/2 of the rank number of the third-order full-deviation relation number
Third order partial negative coefficient of coupling
Output 1: analyzing a partial union coefficient; see table 9 for data analysis of partial coefficients
And (3) outputting 2: iso-trans analysis
See Table 10 analysis of identity and difference
And (3) calculating:
sorting according to the same or different categories, and sorting from small to large in each category according to the ranking of three-order full-biased cascade coefficients
And (3) outputting: symptomatic drug assessment prediction
See Table 11 for the assessment of the prediction data of symptomatic drug use
4. Curative effect curve and curative effect triangle
Inputting: inputting Chinese medicines, a, b, c and d (number of people) comprises: a is cured; b represents the effect; c is improved; invalid is d
Such as: TABLE 12 curative effect curves and triangular data
And (3) calculating:
at＝a/(a+b+c+d)
bt＝b/(a+b+c+d)
ct＝c/(a+b+c+d)
dt＝d/(a+b+c+d)
the quaternary coefficient of output curative effect is mu (gt) ═ at+bti+ctj+dtk (t is 1,2 …) a + b + c + d is 100%, and the difference between the two row numbers of St and at is expressed ast，t0 is called "same" (the two ranking numbers are the same), 1 ≦ 1tX1 is called "partial homology", X1<t ≦ X2 called "partial inversion", X2<tReferred to as "trans"
X1-St maximum rank/4, X2-St maximum rank/2
Output 1: efficacy curve analysis
See table 13 efficacy Curve analysis data
And (3) calculating:
classifying according to the same type and different types, and outputting 2 in various types according to the order of st from small to large: chinese medicine optimal selection
See Table 14 for preferred Chinese medicine data
And (3) calculating: drawing a curative effect curve according to the values of a, b, c and d of the medicines
And (3) outputting: the therapeutic effect is shown in FIG. 1
And (3) calculating: whether a triangle can be formed or not is determined to be a + b-c, a + c-b, b + c-a >0
Area of triangle
The triangle area is ranked from large to small, if the triangle cannot be formed, the triangle area and the area are output in a ranking way "
And (4) outputting: trigonometric analysis of therapeutic effect
See Table 14 for efficacy triangulation data
5. Model for treating symptoms and signs of traditional Chinese medicine and western medicine
Inputting: the Chinese medicine and western medicine indexes of a, b and c (the Chinese medicine syndrome and the number of the western medicine indexes) are input separately, for example: selecting a, wherein the weight is 0.6, then the number of a is calculated to be 0.6a, and the rest is analogized;
see Table 15 for Chinese medicine syndrome and Western medicine model input data
And (3) calculating:
u is total a number + total b number i + total c number j
Here, the values of the contact components i and j are set
And (3) outputting: first order fully biased coupled coefficients of ternary coupled coefficients: the login interface and the analysis interface of a/(a + b) + b/(b + c) i + b/(a + b) i + c/(b + c) j are separated from each other, wherein the input interface of a doctor (including the name of the patient and the doctor) and the input interface of the following symptoms (the grading condition of each symptom of the patient) are separated from each other. The calculation process needs to be corrected, and the letter code is updated. The previous output, mu, 7+4i +6j, now requires a normalization process,
μ＝7/(7+4+6)+4/(7+4+6)i+6/(7+4+6)j
output μ 7/17+4/17i +6/17j
Calculating first order full bias coefficient
Where a is 7/17b 4/17c is 6/17
Output of
And define i j to obtain
The specific numerical value of (1).
The system provides a visual operation interface with the following contents for traditional Chinese medicine and western medicine: symptom evaluation and prediction, a pair analysis and syndrome differentiation model, symptom medication evaluation and prediction, a curative effect curve and a curative effect triangle, traditional Chinese medicine syndromes and a western medicine index model. The user can calculate and store on line according to the data requirement on line according to the requirement.
1 the result is obtained after the input
After the input is finished, the analysis result can be displayed below the interface by clicking the calculation.
2 switching between interfaces
The interfaces of the five kinds of calculation can be switched randomly according to the requirements. See FIG. 2
3 permanently saving and deleting results after calculation
After the doctor enters the patient data, the doctor clicks to save, and then the corresponding calculation result of the input data can be saved in the system, so that the doctor can look up and retrieve the data at any time in the later period. For the stored result, the result which is wrong in calculation or does not need to be presented can be deleted at any time. See fig. 3, fig. 4.
4 derivation of the results
The doctor can export the stored results at any time according to the needs. See fig. 5.
5. Authority classification
At present, according to requirements, the account number authority of an administrator is set to be that all calculation results can be calculated, stored, deleted and exported at any time; account authorities of other researchers only can see the content operated by the researchers according to different research centers, and the rest of the content is invisible.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and additions can be made without departing from the method of the present invention, and these modifications and additions should also be regarded as the protection scope of the present invention.
Claims (8)
1. A traditional Chinese medicine diagnosis and treatment system based on set pair analysis provides a visual operation interface with the following contents for traditional Chinese medicine and western medicine: the system comprises a symptom evaluation prediction module, a pair analysis syndrome differentiation module, a symptom medication evaluation prediction module, a curative effect curve, a curative effect triangle, a traditional Chinese medicine syndrome and a western medicine index module, wherein a user can calculate and store on line according to data requirements according to needs, and output factors of the symptom evaluation prediction module are partial union coefficient analysis, homological-heterological analysis and symptom evaluation prediction; the output factors of the pair-collecting analysis syndrome differentiation model are the U value and the mu value of the syndrome differentiation factor and the pair-collecting analysis syndrome differentiation model; the output factors of the symptomatic medication evaluation prediction are partial coefficient analysis and isobaric analysis; the input factors of the curative effect curve and the curative effect triangle are curative effect curve analysis, Chinese medicine optimization, curative effect curve and curative effect triangle analysis; the calculation process in the traditional Chinese medicine syndrome and western medicine index model is as follows: u is total a number + total b number i + total c number j; the quaternary coefficient of the output curative effect is mu (gt) ═ at + bti + ctj + dtk (t ═ 1,2 …) a + b + c + d ═ 100%
The third-order partial positive coefficient, the third-order partial negative coefficient and the third-order full partial coefficient are ranked from large to small, and if the third-order partial positive coefficient, the third-order partial negative coefficient and the third-order full partial coefficient are 0, the mark is zero.
2. The system according to claim 1, wherein the output factors of the set pair analysis dialectical model are the U value and the μ value of the dialectical factor: u ═ c (a + b)/(a + b + c) -d; μ ═ U/(a + b + c + d).
3. The system according to claim 1, wherein the evaluation and prediction of symptomatic medication comprises:
the quaternary coefficient of therapeutic effect is mu (gt) ═ at + bti + ctj + dtk (t ═ 1,2 …) a + b + c + d ═ 100%
4. The system of claim 1, wherein the four-element coefficient of efficacy in the efficacy curve and the efficacy triangle is
μ(gt)＝at+bti+ctj+dtk(t＝1,2…)a+b+c+d＝100％
The difference between the same or different order numbers St and at is recorded ast，t0 is called "same" (the two ranking numbers are the same), 1 ≦ 1tX1 is called "partial homology", X1<t ≦ X2 called "partial inversion", X2<tThe term "inverse" X1 is St rank maximum/4 and X2 is St rank maximum/2.
5. The system of claim 1, wherein the system of diagnosis and treatment of traditional Chinese medicine based on set-to-set analysis is capable of obtaining results after entering, switching between interfaces, permanently storing and deleting results after calculation, and the doctor can export the stored results at any time as required, and the system of diagnosis and treatment of traditional Chinese medicine based on set-to-set analysis is provided with permission classification.
6. The traditional Chinese medicine diagnosis and treatment system based on set pair analysis as claimed in claim 5, wherein the permanently storing and deleting after calculation means that after the doctor enters the patient data, the corresponding calculation result of the input data can be stored in the system by clicking for storage, so that the user can look up and look over at any time later, and the stored result can be deleted at any time when the result is wrong in calculation or the result is not required to be presented.
7. The traditional Chinese medicine diagnosis and treatment system based on set pair analysis as claimed in claim 5, wherein the permission classification means that the account permission of the administrator is set to be that all calculation results can be calculated, stored, deleted and exported at any time according to the requirement; account authorities of other researchers only can see the content operated by the researchers according to different research centers, and the rest of the content is invisible.
8. The system of claim 1, wherein the system is a multi-layer distributed B/S system, and has an interface to an external system, allowing part of the application functions to be connected to the Internet.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010267344.4A CN111489809A (en) | 2020-04-07 | 2020-04-07 | Traditional Chinese medicine diagnosis and treatment system based on set pair analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010267344.4A CN111489809A (en) | 2020-04-07 | 2020-04-07 | Traditional Chinese medicine diagnosis and treatment system based on set pair analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111489809A true CN111489809A (en) | 2020-08-04 |
Family
ID=71798297
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010267344.4A Pending CN111489809A (en) | 2020-04-07 | 2020-04-07 | Traditional Chinese medicine diagnosis and treatment system based on set pair analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111489809A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102034031A (en) * | 2011-01-07 | 2011-04-27 | 成都中医药大学 | Syndrome biological information data analyzing system |
| CN107491650A (en) * | 2017-08-25 | 2017-12-19 | 上海中医药大学附属岳阳中西医结合医院 | Chinese medicine screening method and system based on partial connection number method |
| CN107516013A (en) * | 2017-08-25 | 2017-12-26 | 上海中医药大学附属岳阳中西医结合医院 | It is a kind of that the preferable method and system of medicine are carried out by curative effect triangle |
| CN107564589A (en) * | 2017-08-25 | 2018-01-09 | 上海中医药大学附属岳阳中西医结合医院 | One kind is based on four-element connection number Symptomatic medicine assessment prediction method and system |
| CN109192320A (en) * | 2018-09-17 | 2019-01-11 | 上海中医药大学附属岳阳中西医结合医院 | Application of the Set Pair Analysis in the representative prescription medicine law research of disease |
| CN109326360A (en) * | 2018-09-28 | 2019-02-12 | 大连亿辉科技有限公司 | Traditional Chinese medicine data analysis management system |
| CN110634566A (en) * | 2019-09-24 | 2019-12-31 | 成都成信高科信息技术有限公司 | Traditional Chinese medicine clinical diagnosis data processing system and method and information data processing terminal |
-
2020
- 2020-04-07 CN CN202010267344.4A patent/CN111489809A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102034031A (en) * | 2011-01-07 | 2011-04-27 | 成都中医药大学 | Syndrome biological information data analyzing system |
| CN107491650A (en) * | 2017-08-25 | 2017-12-19 | 上海中医药大学附属岳阳中西医结合医院 | Chinese medicine screening method and system based on partial connection number method |
| CN107516013A (en) * | 2017-08-25 | 2017-12-26 | 上海中医药大学附属岳阳中西医结合医院 | It is a kind of that the preferable method and system of medicine are carried out by curative effect triangle |
| CN107564589A (en) * | 2017-08-25 | 2018-01-09 | 上海中医药大学附属岳阳中西医结合医院 | One kind is based on four-element connection number Symptomatic medicine assessment prediction method and system |
| CN109192320A (en) * | 2018-09-17 | 2019-01-11 | 上海中医药大学附属岳阳中西医结合医院 | Application of the Set Pair Analysis in the representative prescription medicine law research of disease |
| CN109326360A (en) * | 2018-09-28 | 2019-02-12 | 大连亿辉科技有限公司 | Traditional Chinese medicine data analysis management system |
| CN110634566A (en) * | 2019-09-24 | 2019-12-31 | 成都成信高科信息技术有限公司 | Traditional Chinese medicine clinical diagnosis data processing system and method and information data processing terminal |
Non-Patent Citations (1)
| Title |
|---|
| 李峰 等: ""应用集对分析法构建寻常型银屑病血热证辨证模型"" * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110415831B (en) | Medical big data cloud service analysis platform | |
| CN108492887B (en) | Medical knowledge map construction method and device | |
| CN112687397B (en) | Rare disease knowledge base processing method and device and readable storage medium | |
| US20090287503A1 (en) | Analysis of individual and group healthcare data in order to provide real time healthcare recommendations | |
| US11640853B2 (en) | Personal health record system with process decision support function | |
| CN109785927A (en) | Clinical document structuring processing method based on internet integration medical platform | |
| CN105940427A (en) | Identification of candidates for clinical trials | |
| CN109920535A (en) | A kind of mobile Chinese medical discrimination analysis method and device | |
| CN115497631A (en) | Clinical scientific research big data analysis system | |
| CN107767960A (en) | Data processing method, device and the electronic equipment of clinical detection project | |
| CN103384886A (en) | Learning and optimizing care protocols. | |
| CN107169259A (en) | Personalized medicine based on collaborative filtering and suggestion determines support system | |
| CN117612705A (en) | Clinical diagnosis and treatment decision support system based on personalized medical health knowledge graph | |
| CN109192312B (en) | Intelligent management system and method for adverse events of heart failure patients | |
| Jeyafzam et al. | Improvement of grey wolf optimizer with adaptive middle filter to adjust support vector machine parameters to predict diabetes complications | |
| Ren et al. | Hybrid collaborative filtering methods for recommending search terms to clinicians | |
| CN113903423A (en) | Medication scheme recommendation method, device, equipment and medium | |
| Li et al. | CCAE: cross-field categorical attributes embedding for cancer clinical endpoint prediction | |
| Jin et al. | Research on the construction and application of breast cancer-specific database system based on full data lifecycle | |
| Sun et al. | A framework for systematic assessment of clinical trial population representativeness using electronic health records data | |
| CN111489809A (en) | Traditional Chinese medicine diagnosis and treatment system based on set pair analysis | |
| CN115719640A (en) | System, device, electronic equipment and storage medium for recognizing primary and secondary symptoms of traditional Chinese medicine | |
| Chondrogiannis et al. | A novel approach for clinical data harmonization | |
| Mann et al. | Open drug knowledge graph | |
| Li et al. | ARB: Knowledge Discovery and Disease Diagnosis on Thyroid Disease Diagnosis integrating Association Rule with Bagging Algorithm. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200804 |
|
| RJ01 | Rejection of invention patent application after publication |