Pharmaceutical economics multi-attribute decision evaluation method based on MAUT method

Abstract

The invention discloses a drug economics multi-attribute decision evaluation method based on an MAUT method, and relates to the technical field of drug decision and evaluation, and the method comprises the following steps: firstly, constructing a drug economics evaluation system based on the MAUT method, and then completing the collection of evaluation attributes, then the utility value difference degree, the optimal utility value difference degree and the utility value difference degree change value within the time threshold value are obtained, a fusion module completes data fusion, and then the future utility value difference degree, the optimal future utility value difference degree and the future utility value difference degree change value within the future time threshold value are obtained according to requirements; according to the method, the future time threshold value of the treatment cycle of the patient can be dealt with, predictive prediction and evaluation are carried out on each medicine combination scheme through the future time threshold value, the medicine combination most likely to generate a good effect is selected, blind attempts of different medicine combinations are avoided, the treatment accuracy is improved, and the treatment efficiency is improved. The illness state can be controlled more quickly.

Description

Technical Field

[0001] The present invention relates to the technical field of drug decision-making and evaluation, and specifically to a multi-attribute decision-making and evaluation method for pharmacoeconomics based on the MAUT method. Background Art

[0002] Drug decision-making refers to the process of selecting the most suitable drug treatment plan for patients after comprehensive consideration of various factors such as the specific condition, physical condition, age, gender, genetic factors of patients, as well as the characteristics, efficacy, and safety of drugs during medical treatment. Drug evaluation is a process of comprehensively evaluating and studying the safety, effectiveness, quality controllability, etc. of drugs to determine the value and risk of drugs in clinical applications.

[0003] In the prior art, the multi-attribute decision-making and evaluation method for pharmacoeconomics based on the MAUT method is a method that comprehensively considers multiple attributes to evaluate the pharmacoeconomic value. However, the multi-attribute decision-making and evaluation method for pharmacoeconomics based on the MAUT method in the prior art still has the following defects:

[0004] (1) In the traditional drug decision-making and evaluation method, during the drug decision-making and evaluation process, the difference degree cannot be accurately obtained. Since there are many types of drugs for treating a certain disease, but the effects of different types of drugs and the combined effects are different, there are differences between them. Therefore, the traditional evaluation and decision-making method is not comprehensive and the evaluation and decision-making are single.

[0005] (2) In the traditional drug decision-making and evaluation method, during the drug decision-making and evaluation process, fewer factors are considered, such as the effects generated by the drug combination plan within the future time threshold. Thus, when patients need to achieve a certain amount of rehabilitation effect in the short term, the required drug combination plan cannot be accurately obtained through evaluation and decision-making.

[0006] (3) In the traditional drug decision-making and evaluation method, during the drug decision-making and evaluation process, the volatility of clinical data trials and the patient's own condition, etc. are not considered. Therefore, there are error values in drug decision-making and evaluation.

[0007] After retrieval, as disclosed in a Chinese patent document, a pharmacoeconomic evaluation method based on the MAUT method (publication number: CN202110209908.3), the present invention discloses a pharmacoeconomic evaluation method based on the MAUT method, including step S1: constructing a MAUT scheme evaluation model, and collecting and sorting data for the MAUT scheme evaluation model to obtain the final effectiveness value, final safety value, and final economic value of different types of drugs; step S2: comprehensively evaluating the final effectiveness value, final safety value, and final economic value of the drug through the MAUT scheme evaluation model to obtain the total utility value of the drug use plan for different types of drugs. The pharmacoeconomic evaluation method based on the MAUT method disclosed by the present invention uses MAUT to construct an evaluation model, comprehensively evaluates and compares the advantages and disadvantages of multiple drug use plans for a certain type of disease from dimensions such as effectiveness, safety, and economy, calculates the total utility value of each plan through a quantitative method, displays the final results of each evaluation plan, and determines the best treatment plan, with obvious effects, but there are still the following defects:

[0008] Although the above-mentioned pharmacoeconomic evaluation method based on the MAUT method realizes using MAUT to construct an evaluation model, comprehensively evaluating and comparing the advantages and disadvantages of multiple drug use plans for a certain type of disease from dimensions such as effectiveness, safety, and economy, calculating the total utility value of each plan through a quantitative method, displaying the final results of each evaluation plan, and determining the best treatment plan, with obvious effects, it still has the problems that it fails to accurately obtain the difference degree, does not consider the volatility of clinical data trials, and the patient's own conditions, etc., so there is an error value in the drug decision-making evaluation. Summary of the Invention

[0009] The purpose of the present invention is to provide a multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method to solve the problems mentioned in the above background technology, that is, it fails to accurately obtain the difference degree, does not consider the volatility of clinical data trials, and the patient's own conditions, etc., so there is an error value in the drug decision-making evaluation.

[0010] To achieve the above purpose, the present invention provides the following technical solutions: A multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method, including the following steps:

[0011] S1: Construct a pharmacoeconomic evaluation system based on the MAUT method;

[0012] S2: Complete the collection of evaluation attributes through the collection module contained in the pharmacoeconomic evaluation system;

[0013] S3: Set a monitoring time threshold to obtain the difference degree of utility values, the difference degree of the best utility value, and the change value of the difference degree of utility values within the time threshold;

[0014] S4: Complete data fusion through the fusion module contained in the pharmacoeconomic evaluation system;

[0015] S5: Obtain the reliable utility value difference degree, the best reliable utility value difference degree, and the change value of the reliable utility value difference degree for each drug plan;

[0016] S6: Through the prediction module contained in the pharmacoeconomic evaluation system, based on the reliable utility value difference degree, the best reliable utility value difference degree, and the change value of the reliable utility value difference degree, obtain the future utility value difference degree, the best future utility value difference degree, and the change value of the future utility value difference degree within the future time threshold according to the requirements;

[0017] S7: Finally, through the decision-making evaluation module contained in the pharmacoeconomic evaluation system, generate a drug decision-making evaluation based on the future utility value difference degree, the best future utility value difference degree, and the change value of the future utility value difference degree within the future time threshold.

[0018] Preferably, in the above S1, constructing a pharmacoeconomic evaluation system based on the MAUT method specifically includes the following steps:

[0019] S1.1: Complete the collection of the drug basic parameter data constructed for the pharmacoeconomic evaluation system based on the MAUT method;

[0020] S1.2: Determine several groups of drug combination plans according to the collected drug basic data through rationality factors;

[0021] S1.3: Determine individual factors for several groups of drug combination plans;

[0022] S1.4: Determine the cost-benefit for several groups of drug combination plans.

[0023] Preferably, the function of the collection module is to complete the collection of the evaluation attribute data applied to the pharmacoeconomic evaluation system, and the evaluation attributes specifically include drug effectiveness, drug safety, drug economy, and drug quality of life.

[0024] Preferably, in the above S3, the specific steps for obtaining the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree within the time threshold are as follows:

[0025] S3.1: Determine at least two drug experimental plans and each time point within the time threshold;

[0026] S3.2: Collect the utility value data at the same time point through the determined drug test plan;

[0027] S3.3: Obtain the utility values of the drug test plan for every two time points;

[0028] S3.4: Conduct statistical analysis on all utility values within the time threshold;

[0029] S3.5: Obtain the utility value difference degree and the best utility value difference degree of the drug trial plan;

[0030] S3.6: Visualize the utility value data of each drug trial plan using a utility value curve;

[0031] S3.7: Obtain the change value and floating value of the utility value difference degree through the utility value curves of each drug trial plan.

[0032] Preferably, the function of the fusion module is to receive the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree, and collect the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree obtained from the same drug plan in the previous period, complete weighted fusion, and obtain the reliable utility value difference degree, the best reliable utility value difference degree, and the reliable change value of the utility value difference degree of each drug plan.

[0033] Preferably, data sorting is performed on all the data received by the fusion module. First, the data formats and units of the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree are the same as those of the previous period data, and the data integrity is checked. Then, data standardization is performed to convert data with different ranges and magnitudes into comparable standard data. The Z-score standardization method is used to complete the data standardization process. The specific expression is as follows:

[0034]

[0035] In the formula, x is the original data, μ represents the mean, and σ represents the standard deviation.

[0036] Preferably, the specific method for using the weighted fusion to complete the fusion of the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree and the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree obtained from the same drug plan in the previous period is as follows:

[0037] Step 1: First, determine the data quality, reliability, and importance of the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree obtained from the same drug plan in the previous period;

[0038] Step 2: Obtain the characteristics of the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree obtained from the same drug plan in the previous period;

[0039] Step 3: Determine the data points according to the obtained data characteristics;

[0040] Step 4: Assign weights according to the determined data points;

[0041] Step Five: Calculate the weighted average or weighted sum to achieve data fusion;

[0042] Step Six: Calculate the mean square error and the mean absolute error;

[0043] Step Seven: Compare the data obtained through fusion in Step Five with the actual observed values to evaluate the accuracy of the fusion result;

[0044] In the said Step Five, the specific method of calculating the weighted average or weighted sum to achieve data fusion is as follows:

[0045] Express the weight of past data as w1 and the weight of current data as w2, then w1 + w2 = 1, and the difference expression of the utility value after fusion is as follows:

[0046] D fu = w1D pr + w2D cu

[0047] In the formula, D pr represents the difference degree of the past utility value D cu represents the difference degree of the current utility value.

[0048] Preferably, in the said S6, obtaining the future utility value difference degree, the best future utility value difference degree, and the change value of the future utility value within the future time threshold is completed by using a linear regression model. The independent variables in the linear regression model are the reliable utility value difference degree, the best reliable utility value difference degree, and the change value of the reliable utility value difference degree, and the dependent variable in the linear regression model is the corresponding index value within the future time threshold.

[0049] Preferably, in the said S7, the specific steps of generating a drug decision evaluation according to the future utility value difference degree, the best future utility value difference degree, and the change value of the future utility value within the future time threshold are as follows:

[0050] S7.1: First, convert the future utility value difference degree, the best future utility value difference degree, and the change value of the future utility value within the future time threshold into dimensionless utility values;

[0051] S7.2: Construct an evaluation model according to the dimensionless utility values of each drug plan;

[0052] S7.3: Obtain the condition factors of the user;

[0053] S7.4: Analyze the dimensionless utility values of each drug plan and the condition factors;

[0054] S7.5: Obtain the drug decision evaluation.

[0055] Preferably, the pharmacoeconomic evaluation system includes a collection module, a fusion module, a prediction module, and a decision-making evaluation module. The collection module is electrically connected to the fusion module, the fusion module is electrically connected to the prediction module, and the prediction module is electrically connected to the decision-making evaluation module. The collection module, the fusion module, the prediction module, and the decision-making evaluation module are all interactively connected to the remote evaluation service terminal of pharmacoeconomics.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0057] 1. During the use of the present invention, by obtaining the basic evaluation attributes and obtaining the difference degree of utility values, the best difference degree of utility values, and the change value of the difference degree of utility values within the time threshold through the monitoring time threshold, and then through the fusion module, the difference degree of utility values, the best difference degree of utility values, and the change value of the difference degree of utility values obtained by the same drug regimen in the past are weighted and fused. In this way, a large number of multi-attribute data can be fused by using the MAUT method. The obtained parameter data are based on a large amount of clinical data and weighted fusion of a large amount of past data. The obtained data are reliable and accurate, thereby improving the accuracy in the decision-making evaluation process.

[0058] 2. During the use of the present invention, the difference degree of utility values, the best difference degree of utility values, and the change value of the difference degree of utility values between a large number of drug combination regimens can be obtained, so as to comprehensively complete the evaluation and decision-making. In solving a certain disease, the drug combination regimens applied to the disease can be evaluated and decided in a multi-dimensional manner, and the multi-effect attributes of the drugs can be analyzed for early warning of advantages and disadvantages, and the best difference degree of utility values of the drugs can be known in real time, so as to judge the effectiveness, safety, economy, and quality of life of the drugs most suitable for patients, thereby improving the comprehensiveness in the decision-making evaluation process.

[0059] 3. During the use of the present invention, it can cope with the future time threshold of the patient's treatment cycle, and then through the future time threshold, predictive prediction and evaluation are carried out on each drug combination regimen, so as to understand in advance the possible efficacy of a certain drug combination on a specific patient. Therefore, according to the specific situation of the patient, such as disease type, severity, individual physiological characteristics, etc., the drug combination most likely to produce good results is selected, avoiding blindly trying different drug combinations, improving the accuracy of treatment, controlling the condition faster, and not only considering the efficacy, but also comprehensively evaluating the risk of adverse reactions. The drug combination with good efficacy and relatively small adverse reactions is selected to reduce the pain and risk suffered by the patient due to drug adverse reactions, and at the same time, it also reduces the medical expenses increased due to the treatment of adverse reactions.

[0060] 4. In the process of making decisions and evaluations, the present invention optimizes the data, simplifies the data into a single index, and converts all variables into dimensionless values, facilitating medical staff to more intuitively make judgments and autonomously optimize the plan during the decision-making and evaluation verification processes. At the same time, the efficiency of decision-making and evaluation in the present invention is optimized. By reducing the skewness of the data, the model can better learn the features and laws in the data, improve the adaptability and stability of the model to different data sets, and reduce the risk of model overfitting. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Schematic diagram of the multi-attribute decision-making evaluation method of pharmacoeconomics based on the MAUT method of the present invention;

[0062] Figure 2 Schematic diagram of the specific steps for constructing a pharmacoeconomic evaluation system based on the MAUT method in the present invention;

[0063] Figure 3 Schematic diagram of the specific steps for obtaining the difference degree of utility values, the best difference degree of utility values, and the change value of the difference degree of utility values within the time threshold in the present invention;

[0064] Figure 4 Schematic diagram of the specific steps for completing the fusion by using the weighted fusion in the present invention;

[0065] Figure 5 Schematic diagram of the specific steps for generating a drug decision-making evaluation in the present invention;

[0066] Figure 6 Schematic diagram of the module structure of the bioeconomic evaluation system in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0068] Embodiment 1

[0069] Please refer to Figure 1-6 , the present invention provides a multi-attribute decision-making evaluation method of pharmacoeconomics based on the MAUT method, including the following steps:

[0070] S1: Construct a pharmacoeconomic evaluation system based on the MAUT method;

[0071] S2: Complete the acquisition of evaluation attributes through the acquisition module contained in the pharmacoeconomic evaluation system;

[0072] S3: Set a monitoring time threshold to obtain the difference degree of utility values, the best difference degree of utility values, and the change value of the difference degree of utility values within the time threshold;

[0073] S4: Complete data fusion through the fusion module included in the pharmacoeconomic evaluation system;

[0074] S5: Obtain the reliable utility value differences, the best reliable utility value differences, and the changes in reliable utility value differences for each drug plan;

[0075] S6: Through the prediction module included in the pharmacoeconomic evaluation system, based on the reliable utility value differences, the best reliable utility value differences, and the changes in reliable utility value differences, obtain the future utility value differences, the best future utility value differences, and the changes in future utility value differences within the future time threshold according to requirements;

[0076] S7: Finally, through the decision-making evaluation module included in the pharmacoeconomic evaluation system, generate a drug decision evaluation based on the future utility value differences, the best future utility value differences, and the changes in future utility value differences within the future time threshold.

[0077] The role of the acquisition module is to complete the acquisition of evaluation attribute data applied to the pharmacoeconomic evaluation system, and the evaluation attributes specifically include drug effectiveness, drug safety, drug economy, and drug quality of life. Drug effectiveness is measured by indicators such as cure rate, symptom remission rate, and disease progression delay time. Drug safety is reflected by the incidence of adverse reactions and the incidence of serious adverse reactions. Drug economy includes the cost of drug procurement, the monitoring cost during treatment, and the additional treatment cost caused by adverse reactions. The drug quality of life assesses the improvement of patients in terms of physical, psychological, and social functions.

[0078] The role of the fusion module is to receive the utility value differences, the best utility value differences, and the changes in utility value differences, and collect the utility value differences, the best utility value differences, and the changes in utility value differences obtained from the same drug plans in the past period, complete weighted fusion, and obtain the reliable utility value differences, the best reliable utility value differences, and the changes in reliable utility value differences for each drug plan.

[0079] Data sorting is performed on all the data received by the fusion module. First, the data formats and units of the utility value differences, the best utility value differences, and the changes in utility value differences are the same as those of the past data, and the data integrity is checked. The records with missing values are deleted, or the data integrity is processed by using the mean filling method. Then, data standardization is performed to convert data with different ranges and magnitudes into comparable standard data. The Z-score standardization method is used to complete the data standardization process. The specific expression is as follows:

[0080]

[0081] In the formula, x represents the original data, μ represents the mean, and σ represents the standard deviation.

[0082] In S6, to obtain the future utility value difference, the best future utility value difference, and the change value of the future utility value within the future time threshold, a linear regression model is used to complete the prediction. The independent variables in the linear regression model are the reliable utility value difference, the best reliable utility value difference, and the change value of the reliable utility value difference. The dependent variable in the linear regression model is the corresponding index value within the future time threshold. The linear regression model estimates the model parameters by methods such as the least squares method to obtain the linear relationship between the independent variable and the dependent variable, thereby making predictions. And error analysis and evaluation are performed on the predicted values, and the analysis and evaluation of errors are completed by calculating indicators such as the mean square error, the mean absolute error, and the mean absolute percentage error.

[0083] Example 2

[0084] Please refer to Figure 2 , the present invention provides a multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method. Constructing a pharmacoeconomic evaluation system based on the MAUT method specifically includes the following steps:

[0085] S1.1: Collect the drug basic parameter data for constructing the pharmacoeconomic evaluation system based on the MAUT method;

[0086] S1.2: Determine several groups of drug combination plans according to the collected drug basic data through rationality factors;

[0087] S1.3: Determine individual factors for several groups of drug combination plans;

[0088] S1.4: Determine the cost-benefit for several groups of drug combination plans.

[0089] In S1.2, the rationality factors include the treatment purpose and drug compatibility. The treatment purpose is to clarify the treatment goal according to different diseases or symptoms when determining the drug combination plan. Drug compatibility is to consider the interaction between drugs when determining the drug combination plan to avoid selecting drug combinations with adverse interactions.

[0090] In S1.3, determine individual factors for several groups of drug combination plans. The individual factors include at least individual characteristics such as the patient's age, gender, weight, liver and kidney function, and allergy history.

[0091] In S1.4, determine the cost-benefit for several groups of drug combination plans. The cost-benefit is to consider the cost of the drug on the premise of meeting the treatment effect and select a drug combination with high cost performance.

[0092] Example 3

[0093] Please refer toFigure 3 , the present invention provides a multi - attribute decision - making evaluation method for pharmacoeconomics based on the MAUT method. The specific steps for obtaining the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree within the time threshold are as follows:

[0094] S3.1: Determine at least two drug experimental schemes and each time point within the time threshold;

[0095] S3.2: Collect the utility value data of the determined drug test schemes at the same time point;

[0096] S3.3: Obtain the utility values of the drug test schemes for every two time points;

[0097] S3.4: Conduct statistical analysis on all the utility values within the time threshold;

[0098] S3.5: Obtain the utility value difference degree and the best utility value difference degree of the drug test schemes;

[0099] S3.6: Visualize the utility value data of each drug test scheme by using a utility value curve;

[0100] S3.7: Obtain the change value and the floating value of the utility value difference degree through the utility value curves of each drug test scheme.

[0101] In S3.2, the utility value data is obtained through clinical trials and observational research methods.

[0102] Example 4

[0103] Please refer to Figure 4 , the present invention provides a multi - attribute decision - making evaluation method for pharmacoeconomics based on the MAUT method. The specific way to complete the fusion of the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree with the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree obtained from the same drug scheme in the previous period is as follows:

[0104] Step 1: First, determine the data quality, reliability, and importance of the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree obtained from the same drug scheme in the previous period;

[0105] Step 2: Obtain the characteristics of the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree obtained from the same drug scheme in the previous period;

[0106] Step 3: Determine the data points according to the obtained data characteristics;

[0107] Step 4: Assign weights according to the determined data points;

[0108] Step Five: Calculate the weighted average or weighted sum to achieve data fusion;

[0109] Step Six: Calculate the mean square error and mean absolute error;

[0110] Step Seven: Compare the data obtained through fusion in Step Five with the actual observed values to evaluate the accuracy of the fusion result;

[0111] In Step Five, the specific method of calculating the weighted average or weighted sum to achieve data fusion is as follows:

[0112] Express the weight of past data as w1 and the weight of current data as w2, then w1 + w2 = 1, and the difference expression of the utility value after fusion is as follows:

[0113] D fu = w1D pr + w2D cu

[0114] In the formula, D pr represents the difference degree of the past utility value, and D cu represents the difference degree of the current utility value.

[0115] Example 5

[0116] Please refer to Figure 5 , the present invention provides a multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method. The specific steps for generating a drug decision evaluation according to the future utility value difference degree, the best future utility value difference degree, and the change value of the future utility value within the future time threshold are as follows:

[0117] S7.1: First, convert the future utility value difference degree, the best future utility value difference degree, and the change value of the future utility value within the future time threshold into dimensionless utility values;

[0118] S7.2: Construct an evaluation model according to the dimensionless utility values of each drug plan;

[0119] S7.3: Obtain the condition factors of the user;

[0120] S7.4: Analyze the dimensionless utility values of each drug plan and the condition factors;

[0121] S7.5: Obtain the drug decision evaluation.

[0122] In S7.1, the future utility value difference degree, the best future utility value difference degree, and the change value of the future utility value within the future time threshold are all quantified by mathematical transformation.

[0123] In S7.3, the user's condition factors specifically include the user's age factor, gender factor, weight factor, pathological factor, and living condition factor. The user's pathological factor includes at least liver and kidney function factors and underlying disease factors, and the user's living condition factor includes the user's living and eating habit factor and daily habit factor.

[0124] In S7.4, the dimensionless utility values of each drug regimen are analyzed with the condition factors. Specifically, by comparing the user's condition factors with the recent condition factors, the difference value therebetween is obtained. Through the analysis of the difference value and the dimensionless utility values of each drug regimen, the impact that can be brought by the drug regimen that fits the user's own condition is obtained.

[0125] Embodiment 5

[0126] Please refer to Figure 6 , the present invention provides a multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method, including a pharmacoeconomics evaluation system. The pharmacoeconomics evaluation system includes a collection module, a fusion module, a prediction module, and a decision-making evaluation module. The collection module is electrically connected to the fusion module, the fusion module is electrically connected to the prediction module, and the prediction module is electrically connected to the decision-making evaluation module. The collection module, the fusion module, the prediction module, and the decision-making evaluation module are all interactively connected to the remote pharmacoeconomics evaluation service terminal;

[0127] The function of the remote pharmacoeconomics evaluation service terminal is to be able to integrate scattered data and transmit data in real time, and use the collected data. Then, with the support of a large amount of data, the accuracy of the subsequent drug evaluation and decision-making can be completed in real time, and at the same time, the data query for supporting the decision-making evaluation scheme can be completed more intuitively.

[0128] Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method, characterized in that: It includes the following steps: S1: Construct a pharmacoeconomic evaluation system based on the MAUT method; S2: Complete the collection of evaluation attributes through the collection module contained in the pharmacoeconomic evaluation system; S3: Set a monitoring time threshold, and obtain the difference degree of utility values, the difference degree of the best utility values, and the change value of the difference degree of utility values within the time threshold; S4: Complete data fusion through the fusion module contained in the pharmacoeconomic evaluation system; S5: Obtain the reliable difference degree of utility values, the difference degree of the best reliable utility values, and the change value of the difference degree of reliable utility values for each drug plan; S6: Through the prediction module contained in the pharmacoeconomic evaluation system, based on the reliable difference degree of utility values, the difference degree of the best reliable utility values, and the change value of the difference degree of reliable utility values, obtain the future difference degree of utility values, the difference degree of the best future utility values, and the change value of the difference degree of future utility values within the future time threshold according to the requirements; S7: Finally, through the decision-making evaluation module contained in the pharmacoeconomic evaluation system, generate a drug decision-making evaluation based on the future difference degree of utility values, the difference degree of the best future utility values, and the change value of the difference degree of future utility values within the future time threshold.

2. The multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method according to claim 1, characterized in that: In S1, constructing a pharmacoeconomic evaluation system based on the MAUT method specifically includes the following steps: S1.1: Complete the collection of drug basic parameter data for constructing a pharmacoeconomic evaluation system based on the MAUT method; S1.2: Determine several groups of drug combination plans according to the collected drug basic data through rationality factors; S1.3: Determine individual factors for several groups of drug combination plans; S1.4: Determine the cost-benefit for several groups of drug combination plans.

3. The multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method according to claim 1, wherein: The role of the collection module is to complete the collection of evaluation attribute data applied to the pharmacoeconomic evaluation system, and the evaluation attributes specifically include drug effectiveness, drug safety, drug economy, and drug quality of life.

4. The multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method according to claim 1, wherein: In S3, the specific steps for obtaining the difference degree of utility values, the difference degree of the best utility values, and the change value of the difference degree of utility values within the time threshold are as follows: S3.1: Determine at least two drug experimental plans and each time point within the time threshold; S3.2: Collect utility value data of the determined drug test plans at the same time point; S3.3: Obtain the utility values of the drug test plans for every two time points; S3.4: Conduct statistical analysis on all utility values within the time threshold; S3.5: Obtain the difference degree of utility values and the difference degree of the best utility values of the drug test plans; S3.6: Visualize the utility value data of each drug test plan by using a utility value curve; S3.7: Obtain the change value and the floating value of the difference degree of utility values through the utility value curves of each drug test plan.

5. The multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method according to claim 1, wherein: The role of the fusion module is to receive the difference degree of utility values, the difference degree of the best utility values, and the change value of the difference degree of utility values, and collect the difference degree of utility values, the difference degree of the best utility values, and the change value of the difference degree of utility values obtained from the same drug plans in the previous period, complete weighted fusion, and obtain the reliable difference degree of utility values, the difference degree of the best reliable utility values, and the change value of the difference degree of reliable utility values for each drug plan.

6. The multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method according to claim 5, characterized in that: For all the data received by the fusion module, data collation is adopted. First, the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree have the same data format and unit as the previous period data, and the data integrity is checked. Then, data standardization is carried out to convert data with different ranges and magnitudes into comparable standard units. The Z-score standardization method is used to complete the data standardization process, and the specific expression is as follows: In the formula, x is the original data, μ represents the mean value, and σ represents the standard deviation.

7. The multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method according to claim 5, characterized in that: The specific way to complete the fusion of the utility value difference degree, the best utility value difference degree, the change value of the utility value difference degree, and the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree obtained from the same drug regimen in the previous period by using the weighted fusion is as follows: Step 1: First, determine the data quality, reliability, and importance of the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree obtained from the same drug regimen in the previous period; Step 2: Obtain the characteristics of the utility value difference degree, the best utility value difference degree, and the change value of the utility value difference degree obtained from the same drug regimen in the previous period; Step 3: Determine the data points according to the obtained data characteristics; Step 4: Assign weights according to the determined data points; Step 5: Calculate the weighted average or weighted sum to achieve data fusion; Step 6: Calculate the mean square error and the mean absolute error; Step 7: Compare the data obtained by fusion in Step 5 with the actual observed values to evaluate the accuracy of the fusion result; In Step 5, the specific way to calculate the weighted average or weighted sum to achieve data fusion is as follows: Express the weight of the previous period data as w1 and the weight of the current data as w2, then w1 + w2 = 1, and the expression of the utility difference value after fusion is as follows: D fu = w1D pr + w2D cu In the formula, D pr represents the forward utility value difference degree and D cu represents the current utility value difference degree.

8. The multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method according to claim 1, wherein: In S6, to obtain the future utility value difference degree, the best future utility value difference degree, and the change value of the future utility value difference degree within the future time threshold, a linear regression model is used to complete the prediction. The independent variables in the linear regression model are the reliable utility value difference degree, the best reliable utility value difference degree, and the change value of the reliable utility value difference degree, and the dependent variable in the linear regression model is the corresponding index value within the future time threshold.

9. The multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method according to claim 1, characterized in that: In S7, the specific steps to generate a drug decision evaluation based on the future utility value difference degree, the best future utility value difference degree, and the change value of the future utility value difference degree within the future time threshold are as follows: S7.1: First, convert the future utility value difference degree, the best future utility value difference degree, and the change value of the future utility value difference degree within the future time threshold into dimensionless utility values; S7.2: Construct an evaluation model according to the dimensionless utility values of each drug regimen; S7.3: Obtain the condition factors of the user; S7.4: Analyze the dimensionless utility values of each drug regimen and the condition factors; S7.5: Obtain the drug decision evaluation.

10. The multi-attribute decision-making evaluation method for pharmacoeconomics based on the MAUT method according to claim 1, characterized in that: The drug economics evaluation system includes a collection module, a fusion module, a prediction module and a decision evaluation module. The collection module is electrically connected to the fusion module, the fusion module is electrically connected to the prediction module, the prediction module is electrically connected to the decision evaluation module, and the collection module, the fusion module, the prediction module and the decision evaluation module are all interactively connected to the drug economics remote evaluation service terminal.

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