Medical data evaluation method and device based on data application requirements

Abstract

The invention discloses a medical data evaluation method and device based on a data application demand, and the method comprises the steps: collecting source data which is generated by each medical institution and consists of different types of data sets, and storing the source data in a preset data warehouse; when the source data in the data warehouse needs to be subjected to single data set evaluation, obtaining a calculated index weight vector under a data application demand, and performing data quality evaluation according to the index weight vector in combination with the fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector of a single data set; and when the source data in the data warehouse needs to be subjected to multi-data-set evaluation, acquiring a data set weight vector of each data set, which is set by a data application party according to own requirements, and determining a multi-data-set evaluation result according to the data set weight vector and the comprehensive evaluation vector of the single data set. The problem that in the prior art, when data quality evaluation is carried out, the requirement of specific data application for data quality emphasis degree differences of different data sets cannot be met is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a medical data evaluation method and device based on data application requirements. Background Art

[0002] At present, the evaluation scheme of regional medical data quality includes the following steps: first, the data is integrated into a data warehouse through ETL tools, then the integrity, consistency, validity and timeliness of all incrementally extracted data sets are calculated, and then the calculation results are statistically analyzed to form a report, and finally the data quality problems statistically analyzed in the report are used to guide the correction of data problems. After the quality problems are corrected, the data is pushed to the corresponding database according to the needs of different data users.

[0003] However, data quality assessment is only concentrated in the data warehouse, without considering that the data sets and data quality requirements required by various data application parties are different, and cannot meet the needs of specific data applications for different data sets with different emphasis on data quality. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a medical data evaluation method and device based on data application requirements, aiming to solve the problem that the prior art cannot meet the needs of specific data applications for different data sets when performing data quality evaluation.

[0005] The present invention is achieved in that: A medical data evaluation method based on data application requirements, the method comprising: Collect source data composed of different types of data sets generated by various medical institutions, and store the source data in a preset data warehouse; When the source data in the data warehouse needs to be evaluated for a single data set, the indicator weight vector calculated under the data application requirements is obtained, and the data quality is evaluated according to the indicator weight vector combined with the fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector of the single data set; When the source data in the data warehouse needs to be evaluated for multiple data sets, the data set weight vector of each data set set by the data application party according to its own needs is obtained, and the evaluation result of the multiple data sets is determined based on the data set weight vector and the comprehensive evaluation vector of the single data set.

[0006] Furthermore, in the above-mentioned medical data evaluation method based on data application requirements, the step of obtaining the indicator weight vector calculated under the data application requirements includes: Obtaining the standard deviation of each indicator under the data application requirements and the correlation coefficient between the indicators, and constructing a quantitative indicator representing the conflict between the indicators based on the correlation coefficient; Calculate the amount of information contained in each indicator based on the quantitative indicator and the standard deviation, and calculate the objective indicator based on the information amount; The indicator weight vector of each indicator is determined according to the amount of information contained in each indicator and the objective indicator.

[0007] Furthermore, in the above-mentioned medical data evaluation method based on data application requirements, the calculation formula of the standard deviation is: ; The calculation formula of the correlation coefficient is: ; in, represents the standard matrix, m represents the number of columns of the standard matrix, n represents the number of rows of the standard matrix, represents the mean value of the i-th indicator, represents the covariance of the i-th row and j-th column of the standard matrix.

[0008] Furthermore, in the above-mentioned medical data evaluation method based on data application requirements, the calculation formula of the information volume is: ; The calculation formula of the objective indicator is: ; in, A quantitative indicator representing the conflict between the i-th indicator and other indicators, Indicates the standard deviation of each indicator.

[0009] Furthermore, in the above-mentioned medical data evaluation method based on data application requirements, the step of evaluating data quality according to the indicator weight vector combined with the fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector of a single data set includes: Constructing a comment set according to the scoring criteria, and constructing a membership function of the indicator weight vector corresponding to the comment set, wherein the comment set is a collection of all evaluation results made by the evaluator on the evaluation object; The membership of the indicator weight vector to different levels in the comment set is calculated respectively to determine the evaluation matrix of the data set, and a comprehensive evaluation matrix representing the comprehensive evaluation vectors of all data sets is determined according to the evaluation matrix of the data set.

[0010] Furthermore, in the above-mentioned medical data evaluation method based on data application requirements, the step of determining the evaluation results of multiple data sets according to the data set weight vector and the comprehensive evaluation vector of the single data set includes: The data set weight vector is multiplied by the comprehensive evaluation vector of the single data set to obtain the multi-data set evaluation result.

[0011] Another object of the present invention is to provide a medical data evaluation device based on data application requirements, the device comprising: A source data collection module is used to collect source data composed of different types of data sets generated by various medical institutions, and store the source data in a preset data warehouse; A single data evaluation module is used to obtain the indicator weight vector calculated under the data application requirements when the source data in the data warehouse needs to be evaluated for a single data set, and to evaluate the data quality according to the indicator weight vector combined with the fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector for the single data set; The multi-data evaluation module is used to obtain the data set weight vector of each data set set by the data application party according to its own needs when the source data in the data warehouse needs to be evaluated on multiple data sets, and determine the multi-data set evaluation result based on the data set weight vector and the comprehensive evaluation vector of the single data set.

[0012] Another object of the present invention is to provide a readable storage medium having a computer program stored thereon, wherein the program implements the steps of the above method when executed by a processor.

[0013] Another object of the present invention is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above method when executing the program.

[0014] The present invention collects source data composed of different types of data sets generated by various medical institutions, and stores the source data in a preset data warehouse; when the source data in the data warehouse needs to be evaluated for a single data set, the indicator weight vector calculated under the data application requirements is obtained, and the data quality is evaluated according to the indicator weight vector combined with a fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector of the single data set; when the source data in the data warehouse needs to be evaluated for multiple data sets, the data set weight vector of each data set set by the data application party according to its own requirements is obtained, and the evaluation results of the multiple data sets are determined according to the data set weight vector and the comprehensive evaluation vector of the single data set. The data application party can set the indicator weight vector and the data set weight vector according to its own application requirements, taking into account the relative importance factors of different data sets, and solving the problem in the prior art that when performing data quality assessment, the requirements of specific data applications for different data set quality emphasis are not met. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a medical data evaluation method based on data application requirements in a first embodiment of the present invention; Figure 2 A schematic diagram of a scoring standard in a medical data evaluation method based on data application requirements in one embodiment of the present invention; Figure 3 Schematic diagram of a data quality evaluation index system in a medical data evaluation method based on data application requirements in one embodiment of the present invention; Figure 4 This is a schematic diagram of the result of calculating the indicator weight coefficient using the CRITIC method in the medical data evaluation method based on data application requirements in one embodiment of the present invention; Figure 5 It is a structural block diagram of a medical data evaluation device based on data application requirements in the third embodiment of the present invention.

[0016] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0017] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0018] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the relevant listed types.

[0020] The following will describe in detail how to adapt to the different data quality preferences of different data sets when performing data quality assessment in conjunction with specific embodiments and drawings.

[0021] Embodiment 1 See also Figure 1 , shown is a medical data evaluation method based on data application requirements in the first embodiment of the present invention, and the method includes steps S10 to S12.

[0022] Step S10, collecting source data composed of different types of data sets generated by various medical institutions, and storing the source data in a preset data warehouse.

[0023] Among them, the source system data of each medical institution is collected, including but not limited to the inpatient medical record homepage, admission record, admission medical record, examination and inspection record, hospitalization doctor's order, surgical treatment record, medical record, cost information, etc., and stored in the data warehouse. In the specific implementation, in order to ensure the accuracy of the data, the data can also be cleaned before data storage.

[0024] Step S11, when the source data in the data warehouse needs to be evaluated for a single data set, obtain the indicator weight vector calculated under the data application requirements, and perform data quality evaluation based on the indicator weight vector combined with the fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector of the single data set.

[0025] Specifically, the comprehensive evaluation of a single data set is to use the indicator weights calculated under specific data application requirements combined with the fuzzy comprehensive evaluation model to conduct a comprehensive evaluation of data quality. The final evaluation result is a comprehensive evaluation vector of the single data set for the comment set. If the largest element in the evaluation vector is less than the requirement, technical processing is required to eliminate low-quality data until the requirement is met.

[0026] Among them, in some optional embodiments of the present invention, the step of performing data quality evaluation according to the indicator weight vector combined with the fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector of a single data set includes: Constructing a comment set according to the scoring criteria, and constructing a membership function of the indicator weight vector corresponding to the comment set, wherein the comment set is a collection of all evaluation results made by the evaluator on the evaluation object; The membership of the indicator weight vector to different levels in the comment set is calculated respectively to determine the evaluation matrix of the data set, and a comprehensive evaluation matrix representing the comprehensive evaluation vectors of all data sets is determined according to the evaluation matrix of the data set.

[0027] Among them, the fuzzy comprehensive evaluation method is used to evaluate the quality of regional medical data under specific data application requirements. The first step is to construct a comment set and a membership function. The comment set refers to the set of all evaluation results made by the evaluator on the evaluation object, represented by V. For example, three evaluation levels are selected to establish a comment set, that is, V = {normal, attention, abnormal}. The scoring criteria are as follows Figure 2 shown.

[0028] In specific implementation, the value of the indicator vector is automatically calculated by the system according to the quality verification rules. The embodiment of the present invention adopts a trapezoidal distribution function to establish an indicator membership function. The closer the value is to 1, the higher the quality satisfaction. The membership function of the indicator corresponding to the three states of the comment set is as follows: ; in, x As the score value, a fuzzy comprehensive evaluation is performed on a single data set, and their evaluation on the comment set is calculated respectively. Different levels of membership , from this we can conclude that Evaluation matrix of the dataset , we can get the comprehensive evaluation matrix of all data sets under specific data application requirements: .

[0029] Step S12, when the source data in the data warehouse needs to be evaluated for multiple data sets, obtain the data set weight vector of each data set set by the data application party according to its own needs, and determine the multiple data set evaluation result based on the data set weight vector and the comprehensive evaluation vector of the single data set.

[0030] For data sets involved in specific data application requirements, data application parties can set the weight of each data set according to their own requirements, thereby forming a personalized data set requirement weight vector. Then, matrix operations are performed with the evaluation matrix formed by the comprehensive evaluation of a single data set to obtain the final evaluation result.

[0031] Specifically, based on the comprehensive evaluation of a single dataset, the weight vector of the dataset is combined to obtain the comprehensive evaluation result of multiple datasets, that is, the membership vector of all datasets in the current scene to the comment set. , the calculation formula is as follows: ; Among them, A is the weight vector of the data set, and R is the comprehensive evaluation matrix of all data sets.

[0032] In summary, the medical data evaluation based on data application requirements in the above-mentioned embodiments of the present invention is a medical data evaluation method based on data application requirements, by collecting source data composed of different types of data sets generated by various medical institutions, and storing the source data in a preset data warehouse; when the source data in the data warehouse needs to be evaluated on a single data set, the indicator weight vector calculated under the data application requirements is obtained, and the data quality is evaluated according to the indicator weight vector combined with the fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector of the single data set; when the source data in the data warehouse needs to be evaluated on multiple data sets, the data set weight vector of each data set set by the data application party according to its own needs is obtained, and the evaluation results of the multiple data sets are determined according to the data set weight vector and the comprehensive evaluation vector of the single data set. The data application party can set the indicator weight vector and the data set weight vector according to its own application needs, taking into account the relative importance factors of different data sets, and solving the problem that the prior art cannot meet the needs of specific data applications for different degrees of emphasis on data quality of different data sets when performing data quality evaluation.

[0033] Embodiment 2 The embodiment of the present invention also proposes a medical data evaluation method based on data application requirements. The medical data evaluation method based on data application requirements proposed in the embodiment of the present invention is different from the medical data evaluation method based on data application requirements proposed in the first embodiment of the present invention in that: The step of obtaining the calculated indicator weight vector under the data application requirement includes: Obtaining the standard deviation of each indicator under the data application requirements and the correlation coefficient between the indicators, and constructing a quantitative indicator representing the conflict between the indicators based on the correlation coefficient; Calculate the amount of information contained in each indicator based on the quantitative indicator and the standard deviation, and calculate the objective indicator based on the information amount; The indicator weight vector of each indicator is determined according to the amount of information contained in each indicator and the objective indicator.

[0034] Among them, firstly, a relevant indicator system for regional medical data quality evaluation can be constructed according to actual needs. For example, in the embodiment of the present invention, the relevant indicator system for regional medical data quality evaluation includes 4 indicator dimensions and 10 specific indicators of the 4 indicator dimensions. Among them, the data quality evaluation indicator system is as follows: Figure 3 shown.

[0035] Specifically, the determination part of the indicator weight vector adopts the CRITIC analysis method, which comprehensively determines the objective weight of the indicator through the size of the change within the indicator and the conflict between the indicators. The 10 secondary indicators selected in the embodiment of the present invention can all collect objective indicator values, and there is a certain volatility and correlation between the indicators. The indicator weight in a specific scenario is calculated through the following steps.

[0036] Based on the correlation coefficients between the indicators, a quantitative expression is constructed to represent the conflict to express the conflict between the indicators. The standard deviation of each indicator can be obtained The correlation coefficient between the Where represents the mean value of the i-th indicator, Represents the standard matrix Covariance of row i and column j: ; ; Calculate the amount of information contained in each indicator according to the CRITIC method Where A quantitative indicator that represents the conflict between the i-th indicator and other indicators. The larger it is, the more information is contained in the i-th indicator, and the greater the weight of the indicator is: The calculation formula of information volume is: ; The calculation formula of objective indicators is: ; The indicator weight vector of each indicator is determined according to the amount of information contained in each indicator and the objective indicator.

[0037] In order to further describe the implementation process of the present invention in detail, illustratively, a regional medical research scenario is selected for example analysis. First, the data quality verification index calculation result data of 6 data sets involved in the 60-day research scenario (hospitalization medical record homepage, admission record, admission medical record, examination and testing record, hospitalization doctor's order, surgical treatment record) are selected to perform index weight calculation. Secondly, the data to be evaluated on a certain day is selected and a single data set evaluation is performed on these 6 data sets. Then, a comprehensive evaluation is performed on multiple data sets to obtain the membership matrix of all data sets to the comment set in the scientific research scenario. Finally, the data quality evaluation result of this day is determined according to the maximum membership principle.

[0038] Due to the different dimensions of indicators, the indicators are first standardized, and then the indicator weight coefficients are calculated using the CRITIC method. Figure 4 shown.

[0039] The fuzzy comprehensive evaluation of the six data sets was performed by combining the collected daily index calculation results with the membership function, and the membership matrix of the single data set to the comment set was obtained. as follows: ; Based on the comprehensive evaluation of a single dataset, the weight vector of the dataset is combined Calculate the membership vector of all data sets in the scientific research scenario to the comment set In this example, the dataset weight vector is specified by the user as , the process of calculating the membership matrix of all data sets to the comment set in the scenario of scientific research data application requirements is as follows: ; After calculation, we get ,According to the membership range of the above evaluation levels, the ,data quality evaluation results in the scientific research scenario of this example are at the ,“abnormal” level.

[0040] The medical data evaluation based on data application requirements in the above-mentioned embodiment of the present invention is a medical data evaluation method based on data application requirements, by collecting source data composed of different types of data sets generated by various medical institutions, and storing the source data in a preset data warehouse; when the source data in the data warehouse needs to be evaluated for a single data set, the indicator weight vector calculated under the data application requirements is obtained, and the data quality is evaluated according to the indicator weight vector combined with the fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector of the single data set; when the source data in the data warehouse needs to be evaluated for multiple data sets, the data set weight vector of each data set set by the data application party according to its own needs is obtained, and the evaluation results of the multiple data sets are determined according to the data set weight vector and the comprehensive evaluation vector of the single data set. The data application party can set the indicator weight vector and the data set weight vector according to its own application needs, taking into account the relative importance factors of different data sets, and solving the problem that the prior art cannot meet the needs of specific data applications for different degrees of emphasis on data quality of different data sets when performing data quality evaluation.

[0041] In addition, data users can set the weights of data quality evaluation indicators according to their own application needs, which enhances the flexibility of the evaluation method and the accuracy of the evaluation results; A comprehensive evaluation of multiple data sets is conducted based on the data sets involved by the data users, taking into account the relative importance factors of different data sets, so that the evaluation results are more in line with actual needs.

[0042] Embodiment 3 See also Figure 5 , shown is a medical data evaluation device based on data application requirements proposed in the third embodiment of the present invention, the device comprising: The source data collection module 100 is used to collect source data composed of different types of data sets generated by various medical institutions, and store the source data in a preset data warehouse; The single data evaluation module 200 is used to obtain the index weight vector calculated under the data application requirements when the source data in the data warehouse needs to be evaluated for a single data set, and to perform data quality evaluation based on the index weight vector combined with the fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector for the single data set; The multi-data evaluation module 300 is used to obtain the data set weight vector of each data set set by the data application party according to its own needs when the source data in the data warehouse needs to be evaluated on multiple data sets, and determine the multi-data set evaluation result based on the data set weight vector and the comprehensive evaluation vector of the single data set.

[0043] Furthermore, in some optional embodiments of the present invention, the single data evaluation module includes: An acquisition unit, used to acquire the standard deviation of each indicator under the data application requirement and the correlation coefficient between the indicators, and construct a quantitative indicator representing the conflict between the indicators according to the correlation coefficient; A calculation unit, used to calculate the amount of information contained in each indicator according to the quantitative indicator and the standard deviation, and calculate the objective indicator according to the information amount; A determination unit is used to determine the indicator weight vector of each indicator according to the amount of information contained in each indicator and the objective indicator.

[0044] Furthermore, in some optional embodiments of the present invention, the calculation formula of the standard deviation is: ; The calculation formula of the correlation coefficient is: ; in, represents the standard matrix, m represents the number of columns of the standard matrix, n represents the number of rows of the standard matrix, represents the mean value of the i-th indicator, represents the covariance of the i-th row and j-th column of the standard matrix.

[0045] Furthermore, in some optional embodiments of the present invention, the calculation formula of the information amount is: ; The calculation formula of the objective indicator is: ; in, A quantitative indicator representing the conflict between the i-th indicator and other indicators, Indicates the standard deviation of each indicator.

[0046] Furthermore, in some optional embodiments of the present invention, the single data evaluation module further includes: A construction unit, used to construct a comment set according to the scoring standard, and to construct a membership function of the indicator weight vector corresponding to the comment set, wherein the comment set is a collection of all evaluation results made by the evaluator on the evaluation object; The membership of the indicator weight vector to different levels in the comment set is calculated respectively to determine the evaluation matrix of the data set, and a comprehensive evaluation matrix representing the comprehensive evaluation vectors of all data sets is determined according to the evaluation matrix of the data set.

[0047] Furthermore, in some optional embodiments of the present invention, the multi-data evaluation module is specifically used for: The data set weight vector is multiplied by the comprehensive evaluation vector of the single data set to obtain the multi-data set evaluation result.

[0048] The functions or operation steps implemented when the above modules are executed are substantially the same as those in the above method embodiments, and will not be repeated here.

[0049] Embodiment 4 Another aspect of the present invention further provides a readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the steps of the method described in any one of the above-mentioned embodiments 1 to 2 are implemented.

[0050] Embodiment 5 Another aspect of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the program, the steps of the method described in any one of the above-mentioned embodiments 1 to 2 are implemented.

[0051] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0052] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0053] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0054] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0055] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0056] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A medical data evaluation method based on data application requirements, characterized in that: The method comprises: Collect source data composed of different types of data sets generated by various medical institutions, and store the source data in a preset data warehouse; When the source data in the data warehouse needs to be evaluated for a single data set, the indicator weight vector calculated under the data application requirements is obtained, and the data quality is evaluated according to the indicator weight vector combined with the fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector of the single data set; When the source data in the data warehouse needs to be evaluated for multiple data sets, a data set weight vector of each data set set by the data application party according to its own needs is obtained, and a multiple data set evaluation result is determined according to the data set weight vector and the comprehensive evaluation vector of the single data set; The step of evaluating data quality according to the indicator weight vector in combination with the fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector of a single data set includes: Constructing a comment set according to the scoring criteria, and constructing a membership function of the indicator weight vector corresponding to the comment set, wherein the comment set is a collection of all evaluation results made by the evaluator on the evaluation object; The membership of the indicator weight vector to different levels in the comment set is calculated respectively to determine the evaluation matrix of the data set, and a comprehensive evaluation matrix representing the comprehensive evaluation vectors of all data sets is determined according to the evaluation matrix of the data set.

2. The medical data evaluation method based on data application requirements according to claim 1, characterized in that: The step of obtaining the calculated indicator weight vector under the data application requirement includes: Obtaining the standard deviation of each indicator under the data application requirements and the correlation coefficient between the indicators, and constructing a quantitative indicator representing the conflict between the indicators according to the correlation coefficient; Calculate the amount of information contained in each indicator based on the quantitative indicator and the standard deviation, and calculate the objective indicator based on the information amount; The indicator weight vector of each indicator is determined according to the amount of information contained in each indicator and the objective indicator.

3. The medical data evaluation method based on data application requirements according to claim 2, characterized in that: The calculation formula of the standard deviation is: ； The calculation formula of the correlation coefficient is: ； in, represents the standard matrix, m represents the number of columns of the standard matrix, n represents the number of rows of the standard matrix, represents the mean value of the i-th indicator, represents the covariance of the i-th row and j-th column of the standard matrix.

4. The medical data evaluation method based on data application requirements according to claim 3 is characterized in that: The calculation formula of the information volume is: ； The calculation formula of the objective indicator is: ； in, A quantitative indicator representing the conflict between the i-th indicator and other indicators, Indicates the standard deviation of each indicator.

5. The medical data evaluation method based on data application requirements according to claim 1, characterized in that: The step of determining the evaluation results of multiple data sets according to the data set weight vector and the comprehensive evaluation vector of the single data set comprises: The data set weight vector is multiplied by the comprehensive evaluation vector of the single data set to obtain the multi-data set evaluation result.

6. A medical data evaluation device based on data application requirements, characterized in that: The device is used to implement the medical data evaluation method based on data application requirements as described in any one of claims 1 to 5, comprising: A source data collection module is used to collect source data composed of different types of data sets generated by various medical institutions, and store the source data in a preset data warehouse; A single data evaluation module is used to obtain the indicator weight vector calculated under the data application requirements when the source data in the data warehouse needs to be evaluated for a single data set, and to perform data quality evaluation based on the indicator weight vector combined with a fuzzy comprehensive evaluation model to obtain a comprehensive evaluation vector for the single data set; The multi-data evaluation module is used to obtain the data set weight vector of each data set set by the data application party according to its own needs when the source data in the data warehouse needs to be evaluated on multiple data sets, and determine the multi-data set evaluation result based on the data set weight vector and the comprehensive evaluation vector of the single data set.

7. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 5 when executing the program.

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