TOPSIS-RSR-based antibacterial drug disease sensitivity evaluation method, system and equipment and storage medium

Abstract

The invention discloses an antibacterial drug disease sensitivity evaluation method, system and device based on TOPSIS-RSR and a storage medium, and relates to the technical field of antibacterial drug sensitivity evaluation.The method comprises the following steps that pathogenic bacteria culture and drug sensitivity data of infectious diseases in a specific time period are obtained, drug sensitivity data of common pathogenic bacteria are selected as analysis objects, and the analysis objects are analyzed; counting the sensitivity rate of the common antibacterial agent to be evaluated to common pathogenic bacteria; the composition ratio of each pathogenic bacterium is used as a weight, multi-strain sensitivity rate data of infectious diseases is comprehensively analyzed through a weighted TOPSIS method, and an antibacterial drug disease sensitivity index is constructed so as to quantify the comprehensive coverage capability of the antibacterial drug on infectious disease pathogenic bacterium populations; by applying an RSR grading method, according to the ADSI value of each antibacterial drug, clinical common antibacterial drugs are divided into a high-sensitivity grade, a medium-sensitivity grade and a low-sensitivity grade, and a decision basis with higher guiding value is provided for clinicians to reasonably select the antibacterial drugs.

Description

Technical Field

[0001] The present invention relates to the technical field of antimicrobial drug sensitivity evaluation, and in particular to an antimicrobial drug disease sensitivity evaluation method, system, device and storage medium based on TOPSIS-RSR. Background Art

[0002] The existing research method for antimicrobial sensitivity analysis is a linear evaluation model of "single bacteria-single drug" oriented towards a single pathogen, that is, the sensitivity characteristics of a specific antimicrobial drug are evaluated for a specific pathogen, and finally a discrete single bacteria-single drug sensitivity analysis result is formed. When the clinic needs to evaluate the antimicrobial sensitivity of a specific infection type (such as urinary tract infection), it is first necessary to obtain the distribution spectrum of urinary tract infection pathogens in the region during a certain period, and then conduct a one-to-one analysis of the drug sensitivity of each target pathogen to each antimicrobial drug, and finally form a discrete "single bacteria-single drug" sensitivity analysis result.

[0003] The research conclusions of the existing "single bacteria-single drug" linear evaluation model can only reflect the sensitivity of antibiotics to a specific pathogen. The sensitivity rate data output by this model is strictly bound to the specific pathogen-drug combination, and it is impossible to establish an overall sensitivity evaluation system for antibiotics to the potential pathogen spectrum of the disease, resulting in the inability to quantify the comprehensive coverage ability of drugs for pathogen groups. In the clinical application practice of antibiotics for infectious diseases, due to the time delay or the possibility of negative results in the return of pathogen culture results, clinicians often face the dilemma of unclear pathogens when selecting initial antibiotics, and often need to select drugs when the pathogens are unknown. The discrete single bacteria number output by the current technology cannot directly support the probability coverage analysis of multiple bacterial species, and has limited guiding value for the rational clinical selection of antibiotics. This has led to the frequent use of "broad-spectrum coverage" empirical medication strategies in clinical practice, significantly increasing the risk of abuse of broad-spectrum antibiotics. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a method, system, device and storage medium for evaluating disease sensitivity of antimicrobial drugs based on TOPSIS-RSR, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: the method for evaluating disease sensitivity of antimicrobial drugs comprises the following steps:

[0006] S1. Obtain pathogen culture and drug sensitivity data for infectious diseases during a specific period of time, and select drug sensitivity data of common pathogens as the analysis object;

[0007] S2. Calculate the sensitivity rates of clinically commonly used antimicrobial drugs to common pathogens to form a multi-index raw data matrix;

[0008] S3. Normalize the original data of the multi-index original data matrix, establish a standardized multi-objective decision matrix, eliminate the dimension differences of the indicators, and determine the optimal and worst values ​​of the indicators;

[0009] S4. Then, the distance between the index value of each evaluation unit and the optimal value and the worst value is calculated, and the relative closeness between the index value of each evaluation unit and the optimal value is calculated;

[0010] S5. Use relative proximity value instead of rank sum ratio, apply RSR classification method to classify the evaluation results of TOPSIS method, count their distribution and calculate Probit value;

[0011] S6. Based on the Probit value and regression equation, antibiotics are divided into three sensitivity levels: low, medium, and high. The rationality of the grading is then verified through the homogeneity of variance test and F test. In actual application, the medication strategy can be formulated based on the grading results.

[0012] Preferably, in S2, the optimal vector Z of the indicator is determined according to the decision matrix + and the worst vector Z - , Z + is the optimal vector, which is the maximum value of each indicator, Z - is the worst vector, which is the minimum value of each indicator;

[0013] The optimal Euclidean distance and the worst Euclidean distance The calculation formula is as follows:

[0014]

[0015] It can be seen from this that the relative proximity C i The value is calculated as:

[0016]

[0017] Where i=1,2,……,n.

[0018] Preferably, the calculation according to the Probit value and the regression equation in S6 is specifically as follows:

[0019] With ADSI as the dependent variable and Probit as the independent variable, correlation and regression analysis show that ADSI and Probit have a linear correlation. The specific formula is as follows:

[0020] ADSI=a+bProbit

[0021] Among them, a is the intercept value of the regression equation, b is the slope of the regression equation, and ADSI is the relative closeness C of the TOPSIS method. iValue, Probit is the distribution probability of RSR binning method.

[0022] Preferably, the normalization process of S3 establishes a normalized multi-objective decision matrix, and the specific formula is as follows:

[0023]

[0024] Among them, i=1, 2, ..., n, j=1, 2, ..., n, X is the value of each indicator, i is the number of rows of the indicator, and j is the number of columns of the indicator.

[0025] Preferably, the medication strategy is formulated based on the grading results in S6, specifically as follows:

[0026] For patients with mild infections, moderately sensitive antimicrobial drugs are selected as the initial antibiotics based on the specific circumstances of the infection;

[0027] For critically ill patients, highly sensitive antimicrobial drugs are selected as the initial antibiotics based on the specific circumstances of the patient's infection.

[0028] Preferably, in the antibiotic de-escalation treatment strategy, if the culture result indicates negative, antibiotics with higher ADSI values ​​in the moderately sensitive range can be preferentially used for empirical de-escalation treatment based on the patient's specific situation;

[0029] For patients with infection, avoid using low-susceptibility antimicrobials as initial antibiotics.

[0030] A TOPSIS-RSR-based antimicrobial drug disease sensitivity evaluation system, comprising:

[0031] The data input module is used to receive pathogen culture and drug sensitivity data input by the user, and supports table import or manual entry;

[0032] Data processing module, used to clean data and calculate the composition ratio of pathogens, and screen the main pathogens;

[0033] TOPSIS model calculation module, which calculates the ADSI value of antibiotics based on the weighted TOPSIS method;

[0034] RSR classification module, which classifies drug sensitivity based on ADSI values ​​and generates antimicrobial disease sensitivity grade results;

[0035] The result output module is used to output the classification results in a visual form, and the output content includes but is not limited to: classification tables and trend charts.

[0036] Preferably, the input content of the data input module includes: pathogen name, strain number and antibiotic to be evaluated; the data processing module calculates the pathogen composition ratio as follows:

[0037]

[0038] Among them, B is the composition ratio, X is the number of strains, and Z is the total number of strains. The pathogens are arranged in descending order by composition ratio, and the pathogens with a cumulative composition ratio ≥ 95% are retained.

[0039] An electronic device comprises: a processor, a memory and a communication bus, wherein the processor and the memory communicate with each other via the communication bus;

[0040] The memory is used to store computer programs;

[0041] The processor is used to execute the program stored in the memory to implement the antimicrobial drug disease sensitivity evaluation method.

[0042] A computer-readable storage medium stores a computer program, which implements the method for evaluating disease sensitivity of antimicrobial drugs when executed by a processor.

[0043] The present invention provides a method, system, device, and storage medium for evaluating disease susceptibility of antimicrobial drugs based on TOPSIS-RSR. The method has the following beneficial effects:

[0044] (1) The composition ratio of each pathogen was used as a weight, and the multi-species sensitivity rate data of infectious diseases were comprehensively analyzed by the weighted TOPSIS method. The antimicrobial disease sensitivity index was constructed to quantify the comprehensive coverage of antimicrobial drugs against the pathogenic bacteria of infectious diseases. The RSR classification method was used to classify the commonly used clinical antimicrobial drugs into highly sensitive, moderately sensitive and low sensitive grades according to the ADSI value of each antimicrobial drug, providing more guiding decision-making basis for clinical physicians to rationally select antimicrobial drugs.

[0045] (2) Antimicrobial drug sensitivity is classified by ADSI value to clarify the comprehensive sensitivity level of different drugs to specific diseases. Severe patients can directly choose high-sensitivity drugs in the classification, and mild patients give priority to drugs with higher ADSI in the moderately sensitive class and avoid using low-sensitivity drugs. This classification model provides a scientific basis for initial medication when the pathogen is not clear, reduces the necessity of blindly using broad-spectrum drugs, thereby reducing the risk of drug resistance, optimizing clinical drug use strategies, and alleviating the contradiction between dependence on broad-spectrum antimicrobial drugs and drug resistance prevention and control.

[0046] (3) Through the RSR classification method, a regression equation of ADSI value and Probit is constructed to achieve a rational classification of antimicrobial drug sensitivity. In step-down treatment, if the culture result is negative, drugs with higher scores in the moderately sensitive grade can be recommended according to the ADSI value for empirical step-down treatment to avoid excessive use of broad-spectrum antibiotics. In addition, individualized medication plans and regional drug resistance prevention and control recommendations can be automatically generated to help doctors accurately select drugs at each stage of treatment, shorten the time of empirical treatment when the pathogen is not clear, and improve the efficiency and accuracy of clinical decision-making, which has significant clinical translation value. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of an antimicrobial drug sensitivity analysis method of an antimicrobial drug disease sensitivity evaluation method based on TOPSIS-RSR of the present invention;

[0048] Figure 2 This is a flow chart of the clinical application of an antimicrobial drug sensitivity grading evaluation model of an antimicrobial drug disease sensitivity evaluation method based on TOPSIS-RSR of the present invention;

[0049] Figure 3 It is a fitted line graph of ADSI and Probit univariate linear regression of a method for evaluating disease sensitivity of antimicrobial drugs based on TOPSIS-RSR of the present invention;

[0050] Figure 4 A method step diagram of a TOPSIS-RSR-based antimicrobial drug disease sensitivity evaluation method of the present invention;

[0051] Figure 5 This is a system block diagram of an antimicrobial drug disease sensitivity evaluation system based on TOPSIS-RSR of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] Example 1

[0054] See also Figure 1-4 The present invention provides a method for evaluating the disease sensitivity of antimicrobial drugs based on TOPSIS-RSR. To achieve the above object, the present invention is implemented by the following technical scheme: the method for evaluating the disease sensitivity of antimicrobial drugs comprises the following steps:

[0055] S1. Obtain pathogen culture and drug sensitivity data for infectious diseases during a specific period, calculate the composition ratio of each pathogen, and select the drug sensitivity data of the top 95% of pathogens as the analysis object;

[0056] S2. Calculate the sensitivity of the clinically commonly used antimicrobial drugs to the top 95% of pathogens to form a multi-index raw data matrix

[0057] S3. Normalize the original data, establish a standardized multi-objective decision matrix, eliminate the dimension difference of the indicators, determine the optimal and worst values ​​of the indicators, and determine the optimal vector Z of the indicators based on the decision matrix. + and the worst vector Z - , Z + is the optimal vector, which is the maximum value of each indicator, Z - is the worst vector, which is the minimum value of each indicator;

[0058] S4, then calculate the distance between the index value of each evaluation unit and the optimal value and the worst value, that is, the optimal Euclidean distance and the worst Euclidean distance Calculate the relative closeness C between the index value of each evaluation unit and the optimal value i (i.e. ADSI value);

[0059] S5, with relative proximity C i The rank sum ratio is replaced by the value, and the evaluation results of the TOPSIS method are classified by the RSR classification method, and their distribution is counted and the probability, namely the Probit value, is calculated;

[0060] S6. Based on the Probit value and regression equation, antibiotics are divided into three sensitivity levels: low, medium, and high. The rationality of the grading is then verified through the homogeneity of variance test and F test to ensure that the differences between the grades are statistically significant. In actual application, the medication strategy can be formulated based on the grading results.

[0061] In this example, the antimicrobial susceptibility data of patients with urinary tract infections in the urology department of a hospital over the past year were analyzed;

[0062] The composition ratio of urinary tract infection pathogens in the urology department of the hospital in the past year was statistically analyzed, and the drug sensitivity data of the top 95% of all urinary tract infection pathogens were selected for the next step of analysis;

[0063] The antibiotics used were ampicillin, ampicillin-sulbactam, piperacillin-tazobactam, cefazolin, cefuroxime, cefoxitin, ceftriaxone, ceftazidime, ceftazidime-clavulanic acid, cefotaxime, cefoperazone-sulbactam, cefepime, gentamicin, amikacin, levofloxacin, ciprofloxacin, imipenem, meropenem, nitrofurantoin, and co-trimoxazole.

[0064] Table 1 shows the composition ratio of urinary tract infection pathogens in the urology department of a certain hospital in the past year:

[0065]

[0066]

[0067] The sensitivity rates of the above 20 antibiotics to Escherichia coli, Klebsiella pneumoniae, Enterococcus, Enterobacter, and Staphylococcus were calculated to establish five indicators (X1-X5). All of the above indicators are high-quality indicators.

[0068] Table 2 shows the sensitivity rates of 20 antibiotics to the top five pathogens of urinary tract infection in urology:

[0069]

[0070]

[0071] The research method of antimicrobial drug sensitivity evaluation has been upgraded from the traditional pathogen-oriented linear evaluation model to a disease-oriented comprehensive evaluation model. An innovative antimicrobial drug sensitivity grading evaluation model based on the weighted TOPSIS-RSR method has been constructed. The antibiotic sensitivity grading system designed based on the above antimicrobial drug sensitivity grading evaluation model can quickly obtain the antibiotic grading results of infectious diseases and the ADSI values ​​of each antibiotic in a set time period in a specific area. Based on this, and combined with specific circumstances, personalized antibiotic selection and antibiotic resistance prevention and control recommendations can be provided.

[0072] Example 2

[0073] Specifically: Normalization of S3 is performed to establish a standardized multi-objective decision matrix. The specific formula is as follows:

[0074]

[0075] Among them, i=1, 2, ..., n, j=1, 2, ..., n, X is the value of each indicator, i is the number of rows of the indicator, and j is the number of columns of the indicator.

[0076] Table 3 shows the normalized matrix values ​​of the sensitivity indexes of 20 antibiotics to 5 pathogens of urinary tract infection in urology department:

[0077]

[0078]

[0079] Optimal Euclidean distance and the worst Euclidean distance The calculation formula is as follows:

[0080]

[0081] From this we can know that the relative closeness C i The value is calculated as:

[0082]

[0083] Where i=1,2,……,n.

[0084] Table 4 shows the TOPSIS comprehensive evaluation data of the sensitivity of 20 antibiotics to urinary tract infections in urology:

[0085]

[0086]

[0087] The medication strategy is formulated based on the tiered results in S6, as follows:

[0088] For patients with mild infections, moderately sensitive antimicrobial drugs are selected as the initial antibiotics based on the patient's specific condition;

[0089] For critically ill patients, highly sensitive antimicrobial drugs are selected as the initial antibiotics based on the patient's specific condition;

[0090] In the antibiotic de-escalation therapy strategy, if the culture results are negative, antibiotics with higher ADSI values ​​in the moderately sensitive range can be used for empirical de-escalation therapy based on the patient's specific situation, and the use of low-sensitivity antibiotics as initial antibiotics can be avoided.

[0091] Table 5 shows the ADSI value distribution of commonly used antimicrobial drugs and the corresponding probability unit values:

[0092]

[0093]

[0094] Note: In the table #Probit is the standard normal deviation corresponding to p% plus 5;

[0095] With ADSI as the dependent variable and Probit as the independent variable, correlation and regression analysis showed that ADSI and Probit had a linear correlation (refer to Figure 3 ), the results of variance analysis: F = 50.121, P = 0.000, indicating that there is a linear relationship between ADSI and Probit, and the regression equation is as follows:

[0096] ADSI=-0.996+0.308Probit

[0097] Antibiotic RSR evaluation was used to rank drugs. Based on the calculated Probit value and regression equation, the above 20 antibiotics could be divided into three grades according to their comprehensive sensitivity to the top five pathogens of urinary tract infection in urology department.

[0098] Table 6 shows the ranking results of the RSR evaluation of 20 antibiotics:

[0099]

[0100]

[0101] The results in Table 6 were tested for homogeneity of variance, and the results showed that the variances of the various grades were homogeneous (F=1.008, P>0.05); the differences between the various grades were statistically significant (F=29.557, P<0.05);

[0102] Therefore, the classification results in Table 6 are the best classifications, and the differences between each group are statistically significant (P < 0.05) after LSD test;

[0103] Through TOPSIS, the sensitivity rates of multiple bacterial species in infectious diseases are integrated to quantify the comprehensive coverage of antibiotics on the entire pathogen spectrum of the disease (ADSI value). The RSR grading method is used to construct a regression equation of ADSI value and Probit. The ADSI estimation critical value for the optimal grading is calculated using the regression equation. According to the ADSI critical value, the antibiotics to be evaluated are divided into three sensitivity levels: low, medium, and high. This overcomes the limitation of the traditional "single bacteria-single drug" analysis model that lacks comprehensive evaluation and ranking of multiple bacterial species of antimicrobial drugs, provides a scientific grading basis for initial medication when the pathogen is not clear, reduces the abuse of broad-spectrum antibiotics, and reduces the risk of drug resistance.

[0104] Example 3

[0105] Reference Figure 5 A TOPSIS-RSR-based antimicrobial drug disease sensitivity evaluation system is provided, which applies the TOPSIS-RSR-based antimicrobial drug disease sensitivity evaluation method, and is characterized by:

[0106] The antimicrobial disease susceptibility evaluation system includes:

[0107] The data input module is used to receive pathogen culture and drug sensitivity data input by the user, and supports table import or manual entry;

[0108] Data processing module, used to clean data and calculate the composition ratio of pathogens, and screen the main pathogens;

[0109] TOPSIS model calculation module, which calculates the ADSI value of antibiotics based on the weighted TOPSIS method;

[0110] RSR classification module, which classifies drug sensitivity based on ADSI values ​​and generates antimicrobial disease sensitivity grade results;

[0111] The result output module is used to output the classification results in a visual form. The output content includes but is not limited to: classification tables and trend charts. Based on the results of the result output module, clinical recommendations can be provided to doctors:

[0112] For severe patients, high-sensitivity drugs (such as amikacin and meropenem) should be given priority. For mild patients, drugs with higher ADSI in the intermediate-sensitivity range (such as cefoxitin and ampicillin-sulbactam) should be selected, and low-sensitivity drugs (such as ampicillin and co-trimoxazole) should be avoided as initial treatment.

[0113] The input content of the data input module includes: pathogen name, strain number and antibiotic to be evaluated;

[0114] The data processing module calculates the pathogen composition ratio as follows:

[0115]

[0116] Among them, B is the composition ratio, X is the number of strains, and Z is the total number of strains. The pathogens are arranged in descending order by composition ratio, and the pathogens with a cumulative composition ratio ≥ 95% are retained.

[0117] An electronic device comprises: a processor, a memory and a communication bus, wherein the processor and the memory communicate with each other via the communication bus;

[0118] memory for storing computer programs;

[0119] The processor is used to execute the program stored in the memory to implement the antimicrobial drug disease sensitivity evaluation method.

[0120] A computer-readable storage medium stores a computer program, which implements a method for evaluating disease sensitivity of antimicrobial drugs when executed by a processor.

[0121] In this embodiment, the program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0122] A computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable storage medium may also be any readable medium other than a computer-readable storage medium, which may transmit, propagate, or transfer programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0123] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0124] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A TOPSIS-RSR-based method for evaluating disease susceptibility to antimicrobial drugs, characterized by: The method for evaluating disease susceptibility to antimicrobial drugs comprises the following steps: S1. Obtain pathogen culture and drug sensitivity data for infectious diseases during a specific period of time, and select drug sensitivity data of common pathogens as the analysis object; S2. Calculate the sensitivity rates of clinically commonly used antimicrobial drugs to common pathogens to form a multi-index raw data matrix; S3. Normalize the original data of the multi-index original data matrix, establish a standardized multi-objective decision matrix, eliminate the dimension differences of the indicators, and determine the optimal and worst values ​​of the indicators; S4. Then, the distance between the index value of each evaluation unit and the optimal value and the worst value is calculated, and the relative closeness between the index value of each evaluation unit and the optimal value is calculated; S5. Use relative proximity value instead of rank sum ratio, apply RSR classification method to classify the evaluation results of TOPSIS method, count their distribution and calculate Probit value; S6. Based on the Probit value and regression equation, antibiotics are divided into three sensitivity levels: low, medium, and high. The rationality of the grading is then verified through the homogeneity of variance test and F test. In actual application, the medication strategy can be formulated based on the grading results.

2. The method for evaluating disease susceptibility of antimicrobial agents based on TOPSIS-RSR according to claim 1, wherein: In S2, the optimal vector Z of the indicator is determined according to the decision matrix + and the worst vector Z - , Z + is the optimal vector, which is the maximum value of each indicator, Z - is the worst vector, which is the minimum value of each indicator; The optimal Euclidean distance and the worst Euclidean distance The calculation formula is as follows: It can be seen from this that the relative proximity C i The value is calculated as: Where i=1,2,……,n.

3. The method for evaluating disease susceptibility of antimicrobial drugs based on TOPSIS-RSR according to claim 2, characterized in that: The calculation according to the Probit value and regression equation in S6 is specifically as follows: With ADSI as the dependent variable and Probit as the independent variable, correlation and regression analysis show that ADSI and Probit have a linear correlation. The specific formula is as follows: ADSI=a+bProbit Among them, a is the intercept value of the regression equation, b is the slope of the regression equation, and ADSI is the relative closeness C of the TOPSIS method. i Value, Probit is the distribution probability of RSR binning method.

4. The method for evaluating disease susceptibility of antimicrobial drugs based on TOPSIS-RSR according to claim 1, characterized in that: The normalization process of S3 establishes a normalized multi-objective decision matrix, and the specific formula is as follows: Among them, i=1, 2, ..., n, j=1, 2, ..., n, X is the value of each indicator, i is the number of rows of the indicator, and j is the number of columns of the indicator.

5. The method for evaluating disease susceptibility of antimicrobial drugs based on TOPSIS-RSR according to claim 1, characterized in that: The medication strategy is formulated based on the grading results in S6, as follows: For patients with mild infections, moderately sensitive antimicrobial drugs are selected as the initial antibiotics based on the specific circumstances of the infection; For critically ill patients, highly sensitive antimicrobial drugs are selected as the initial antibiotics based on the specific circumstances of the patient's infection.

6. The method for evaluating disease susceptibility of antimicrobial drugs based on TOPSIS-RSR according to claim 5, characterized in that: In the antibiotic de-escalation treatment strategy, if the culture results are negative, antibiotics with higher ADSI values ​​in the moderately sensitive range can be used for empirical de-escalation treatment based on the patient's specific situation. For patients with infection, avoid using low-susceptibility antimicrobials as initial antibiotics.

7. A TOPSIS-RSR-based antimicrobial drug disease sensitivity evaluation system, applied to the TOPSIS-RSR-based antimicrobial drug disease sensitivity evaluation method according to any one of claims 1 to 6, characterized in that: The antimicrobial drug disease sensitivity evaluation system comprises: The data input module is used to receive pathogen culture and drug sensitivity data input by the user, and supports table import or manual entry; Data processing module, used to clean data and calculate the composition ratio of pathogens, and screen the main pathogens; TOPSIS model calculation module, which calculates the ADSI value of antibiotics based on the weighted TOPSIS method; RSR classification module, which classifies drug sensitivity based on ADSI values ​​and generates antimicrobial disease sensitivity grade results; The result output module is used to output the classification results in a visual form, and the output content includes but is not limited to: classification tables and trend charts.

8. The antimicrobial drug disease sensitivity evaluation system based on TOPSIS-RSR according to claim 7, characterized in that: The input content of the data input module includes: pathogen name, strain number and antibiotic to be evaluated; The data processing module calculates the pathogen composition ratio as follows: Among them, B is the composition ratio, X is the number of strains, and Z is the total number of strains. The pathogens are arranged in descending order by composition ratio, and the pathogens with a cumulative composition ratio ≥ 95% are retained.

9. An electronic device, characterized in that: include: A processor, a memory, and a communication bus, wherein the processor and the memory communicate with each other via the communication bus; The memory is used to store computer programs; The processor is configured to execute the program stored in the memory to implement the method for evaluating disease sensitivity of antimicrobial drugs according to any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for evaluating disease sensitivity of antimicrobial drugs according to any one of claims 1 to 6 is implemented.

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