Intelligent grading classification and privacy protection system for medical data

Abstract

The invention belongs to the technical field of data security. The invention provides an intelligent hierarchical classification and privacy protection system for medical data. The system comprises a data receiving unit used for receiving medical data needing privacy protection, and the medical data comprise corresponding disease type information and diagnosis and treatment means information; the data grading and classifying unit is used for evaluating according to the disease type information and the diagnosis and treatment means to obtain a potential application intensity grade of the corresponding medical data, and grading or classifying the medical data according to the potential application intensity grade; and the privacy processing unit is used for determining a privacy processing scheme for the medical data according to grading or classification, and performing privacy processing on the medical data according to the privacy processing scheme. According to the scheme, the potential applied strength of each piece of medical data can be predicted, and then different privacy processing schemes are adopted, so that relative balance between privacy protection and reasonable application of the medical data is realized.

Description

Technical Field

[0001] The present invention relates to the field of data security technology, and in particular to an intelligent grading and classification and privacy protection system for medical data. Background Art

[0002] In the digital age, the medical field is undergoing profound changes, and the value of medical data is becoming increasingly prominent.

[0003] On the one hand, medical data covers the patient's medical history, diagnosis results, treatment plans, genetic information, etc. These data have a huge driving force for disease research, clinical decision support, medical quality assessment, and drug development. For example, by analyzing the medical data of a large number of patients, researchers can discover the underlying laws of the disease and develop more effective treatments; medical institutions can also optimize medical service processes and improve diagnosis and treatment efficiency based on these data. On the other hand, the high sensitivity and privacy of medical data cannot be ignored. Once these data are leaked, they will have serious negative impacts on patients, such as personal privacy exposure, social discrimination, and economic losses.

[0004] Therefore, how to achieve a relative balance between the privacy protection and reasonable application of medical data is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In this regard, the present invention provides an intelligent grading and classification and privacy protection system for medical data, an electronic device, a computer storage medium and a computer program product to solve the above-mentioned technical problems.

[0006] The present invention provides an intelligent grading, classification and privacy protection system for medical data, the system comprising a data receiving unit, a data grading and classification unit, and a privacy processing unit; the data receiving unit is used to receive various medical data that need to be privacy protected, the medical data containing corresponding disease type information and diagnosis and treatment means information; the data grading and classification unit is used to evaluate the corresponding potential application intensity level of the medical data according to the disease type information and the diagnosis and treatment means information, and grade or classify the medical data according to the potential application intensity level; the privacy processing unit is used to determine a privacy processing scheme for the medical data according to the grading or the classification, and perform privacy processing on the medical data according to the privacy processing scheme.

[0007] Optionally, the receiving of each medical data requiring privacy protection includes: after the data receiving unit receives the medical data, the data receiving unit parses the medical data to determine whether the parsing result contains identity information; if the identity information is contained, the data is determined to be the medical data requiring privacy protection.

[0008] Optionally, the data grading and classification unit includes an associated information query subunit, a feature extraction subunit, and an application strength analysis subunit; the associated information query subunit searches several designated data sources according to the disease type information and the diagnosis and treatment means, and obtains a first search result corresponding to the disease type information and a second search result corresponding to the disease type information and the diagnosis and treatment means information, respectively; the feature extraction subunit extracts features from the first search result and the second search result to obtain disease features; the application strength analysis subunit calls a deep analysis model to process the disease features to obtain an evaluated potential application strength level of the medical data.

[0009] Optionally, the feature extraction of the first search result and the second search result to obtain disease characteristics includes: the feature extraction subunit uses a feature extraction model to extract the first result quantity and result time span corresponding to the disease type information from the first search result; uses a semantic analysis component to extract multiple pathogenesis principles corresponding to the disease type information from the first search result, counts the second result quantity of pathogenesis principles whose result number is higher than a preset value, and extracts a diagnosis and treatment means data set corresponding to the disease type information from the second search result, and obtains the diagnosis and treatment means evolution trend based on the analysis of the diagnosis and treatment means data set; vectorizes the first result quantity, the result time span, the second result quantity, and the diagnosis and treatment means evolution trend to obtain the disease characteristics.

[0010] Optionally, the application intensity analysis subunit calls the deep analysis model to process the disease characteristics to obtain the evaluated potential application intensity level of the medical data, including: the application intensity analysis subunit calls the deep analysis model to process the disease characteristics to obtain the evaluated initial potential application intensity level of the medical data; the application intensity analysis subunit also obtains the medical data sharing history of the institution that owns the medical data, obtains the sharing scale of data that is the same or similar to the medical data based on the medical data sharing history, and determines an optimization coefficient based on the sharing scale; and uses the optimization coefficient to optimize the initial potential application intensity level to the potential application intensity level.

[0011] Optionally, grading or classifying the medical data according to the potential application strength level includes: obtaining a corresponding grading label / classification label according to the potential application strength level matching, and associating the grading label / classification label with the medical data.

[0012] Optionally, determining the privacy processing scheme for the medical data based on the grading or the classification includes: determining the privacy processing scheme for each medical data based on the potential application strength level corresponding to the grading label or the classification label, and different privacy processing schemes are achieved by erasing the identity information in the medical data to varying degrees and / or encrypting the medical data using encryption algorithms with different encryption levels.

[0013] The present invention also discloses an electronic device, applied to the system as described in any of the preceding items, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

[0014] The present invention further discloses a computer storage medium, which is applied to the system as described in any of the preceding items, and the computer-readable storage medium stores a computer program.

[0015] The present invention further discloses a computer program product, which is applied to the system as described in any of the preceding items, wherein the computer program includes a number of computer codes.

[0016] The above-mentioned scheme of the present invention can predict the potential application intensity of each piece of medical data, and then adopt different privacy processing schemes, so as to achieve a relative balance between the privacy protection and reasonable application of medical data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a structural diagram of an intelligent grading and classification and privacy protection system for medical data disclosed in an embodiment of the present invention.

[0019] Figure 2 It is a structural schematic diagram of a data grading and classification unit disclosed in an embodiment of the present invention.

[0020] Figure 3 It is a schematic diagram of the structure of the feature extraction subunit disclosed in an embodiment of the present invention.

[0021] Figure 4 It is a structural schematic diagram of the application intensity analysis subunit disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0023] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0024] like Figure 1 As shown, an embodiment of the present invention discloses an intelligent grading and classification and privacy protection system for medical data, which includes a data receiving unit, a data grading and classification unit, and a privacy processing unit.

[0025] The data receiving unit is used to receive various medical data that need to be privacy protected, and the medical data contains corresponding disease type information and diagnosis and treatment means information.

[0026] The data receiving unit is the key interface for the entire system to interact with external data. It is used to accurately connect to various medical data sources and then receive multiple medical data, such as patient diagnosis and treatment information from the hospital's electronic medical record system, test reports from medical testing institutions, and monitoring data from telemedicine devices, wearable health monitoring devices, etc. In addition to the patient's identity information (name, gender, age, contact information, place of origin, home address, medical history, etc.), these medical data also include disease type information and diagnosis and treatment information, such as pneumonia, thyroid cancer, influenza, etc., and diagnosis and treatment information includes taking certain drugs (capsules, tablets), surgery, liquid nitrogen freezing, laser, chemotherapy, targeted drugs, etc.

[0027] The data grading and classification unit is used to evaluate the potential application intensity level of the corresponding medical data based on the disease type information and the diagnosis and treatment means information, and grade or classify the medical data according to the potential application intensity level.

[0028] Since different medical data play different roles in disease research, clinical decision-making, medical quality assessment, etc., there are obvious differences in their potential application strength. Therefore, the data grading and classification unit of the present invention adopts a suitable evaluation method to evaluate the potential application strength level of medical data based on disease type information and diagnosis and treatment information. The potential application strength level in the present invention refers to the intensity of medical data being applied across systems and regions. For example, the probability that a certain medical data is applied across more systems (or institutions) and across more regions (countries, provinces, cities, etc.) is higher, and the corresponding potential application strength level is higher, and vice versa. The system here refers to different medical data management systems, such as between different medical institutions, official medical data management systems and third-party commercial medical data application systems, etc.

[0029] For example, for research data on rare diseases such as ALS (amyotrophic lateral sclerosis), because it is of vital importance in revealing the pathogenesis of the disease and exploring effective treatment methods, the potential application intensity level is high, that is, the greater the value of the medical data, the greater the possibility of being used in multiple ways by more institutions. For the common cold, the treatment method is already very standardized and complete, so generally speaking, the potential application intensity level of this type of medical data (such as symptom records, simple blood test results, etc.) is relatively low, that is, the smaller the value of the medical data, the smaller the possibility of being used in multiple ways by more institutions.

[0030] According to the potential application intensity level obtained through evaluation, medical data can be reasonably graded or classified. For example, data with high application intensity can be classified into level 1, which is used for in-depth scientific research and accurate diagnosis and treatment of difficult diseases; data with low application intensity can be classified into level 3, which is mainly used for daily medical statistical analysis.

[0031] The present invention abandons the traditional extensive data processing mode, so that data of different values ​​and sensitivities can be sorted out in an orderly manner, which can significantly improve the pertinence and efficiency of subsequent data processing and use.

[0032] The privacy processing unit is used to determine a privacy processing scheme for the medical data according to the classification or the classification, and perform privacy processing on the medical data according to the privacy processing scheme.

[0033] Different classifications / grades correspond to different potential application strength levels and correspondingly adopt different privacy processing solutions. The privacy processing solutions include but are not limited to different degrees of erasure of identity information, encryption methods of medical data (different encryption methods correspond to different encryption strengths), etc.

[0034] The above-mentioned scheme of the present invention can predict the potential application intensity of each piece of medical data, and then adopt different privacy processing schemes, so as to achieve a relative balance between the privacy protection and reasonable application of medical data.

[0035] Optionally, the receiving of each medical data requiring privacy protection includes: after the data receiving unit receives the medical data, the data receiving unit parses the medical data to determine whether the parsing result contains identity information; if the identity information is contained, the data is determined to be the medical data requiring privacy protection.

[0036] In this embodiment, the medical data received by the data receiving unit comes from a wide range of sources, including hospital information systems, medical testing agency reports, remote medical equipment, and wearable health monitoring equipment. Some medical data may not contain the patient's identity information, so there is no need for privacy processing. In this regard, the present invention is configured that when the data receiving unit receives the medical data, it will immediately parse it (including operations such as identifying the data format and extracting the data content), and determine whether it contains identity information. If it contains identity information, the medical data is determined to be data that needs to be protected by privacy, and it will be used subsequently after privacy processing.

[0037] Through the above method, the amount of medical data that needs privacy protection can be effectively reduced, thereby reducing the data processing load.

[0038] Alternatively, if Figure 2 As shown, the data grading and classification unit includes an associated information query subunit, a feature extraction subunit, and an application strength analysis subunit; the associated information query subunit searches in several designated data sources according to the disease type information and the diagnosis and treatment means, and obtains a first search result corresponding to the disease type information and a second search result corresponding to the disease type information and the diagnosis and treatment means information, respectively; the feature extraction subunit extracts features from the first search result and the second search result to obtain disease features; the application strength analysis subunit calls a deep analysis model to process the disease features to obtain an evaluated potential application strength level of the medical data.

[0039] In this embodiment, refer to Figure 2 As shown, the data classification unit further includes the above three subunits. Among them: the associated information query subunit searches in several designated data sources such as authoritative medical databases, professional medical research literature libraries, and case databases of large medical institutions based on the received disease type information and diagnosis and treatment information. For example, when the disease type information is "leukemia" and the diagnosis and treatment method is "bone marrow transplantation", the associated information query subunit searches in the above data sources with "leukemia", "leukemia" + "bone marrow transplantation" respectively.

[0040] The feature extraction subunit extracts key features, namely the above-mentioned disease features, from the above-mentioned search results. The application strength analysis subunit then calls the deep analysis model to process the extracted disease features, thereby obtaining the potential application strength level of the medical data. The deep analysis model can be a model built based on a machine learning algorithm, such as a neural network model, a general-purpose large model (DeepSeek, GPT, BERT, etc.).

[0041] Optionally, the feature extraction of the first search result and the second search result to obtain disease characteristics includes: the feature extraction subunit uses a feature extraction model to extract the first result quantity and result time span corresponding to the disease type information from the first search result; uses a semantic analysis component to extract multiple pathogenesis principles corresponding to the disease type information from the first search result, counts the second result quantity of pathogenesis principles whose result number is higher than a preset value, and extracts a diagnosis and treatment means data set corresponding to the disease type information from the second search result, and obtains the diagnosis and treatment means evolution trend based on the analysis of the diagnosis and treatment means data set; vectorizes the first result quantity, the result time span, the second result quantity, and the diagnosis and treatment means evolution trend to obtain the disease characteristics.

[0042] In this embodiment, if Figure 3 As shown, the feature extraction subunit uses a feature extraction model (such as a convolutional network) to statistically obtain the number of relevant information from the first search results corresponding to the disease type information, namely, the number of first results and the time span of the results. The two are used to characterize the degree to which the disease has been studied and discussed. Generally speaking, the more the number of first results and the longer the time span of the results, the higher the degree to which the disease has been studied and discussed. Correspondingly, it also indicates that the disease is a common disease and the probability of mature treatment methods is higher; otherwise, the lower.

[0043] The feature extraction subunit also embeds a semantic analysis component, which can perform in-depth semantic analysis on the first search result, thereby extracting multiple pathogenesis corresponding to the disease type information. For example, for diabetes, the pathogenesis may include insulin resistance, pancreatic beta cell dysfunction, etc. The pathogenesis with a number of results higher than a preset value (e.g., 500) is determined as a pathogenesis with a higher confidence, and the number of second results of all the pathogenesis with a higher confidence is counted.

[0044] The semantic analysis component also extracts the diagnosis and treatment means data set corresponding to the disease type information from the second search result, and obtains the evolution trend of the diagnosis and treatment means based on the analysis of the diagnosis and treatment means data set. For example, when the evolution trend of the diagnosis and treatment means shows that the quantity curves of various diagnosis and treatment means tend to be stable (for example, the quantity curves are generally horizontal), it indicates that the treatment means of the disease are already very mature. When it is shown that the quantity curves of various diagnosis and treatment means have not tended to be stable or are still in a messy state of being intertwined with each other, it indicates that the treatment means of the disease are still in the research stage (for example, the disease is a new disease, or a difficult and complicated disease that has not been conquered).

[0045] The number of first results, the time span of the results, the number of second results, and the evolution trend of diagnostic and treatment methods extracted and analyzed above are vectorized to obtain disease characteristics in the form of numerical vectors.

[0046] Alternatively, if Figure 4 As shown, the application strength analysis subunit calls the deep analysis model to process the disease characteristics to obtain the evaluated potential application strength level of the medical data, including: the application strength analysis subunit calls the deep analysis model to process the disease characteristics to obtain the evaluated initial potential application strength level of the medical data; the application strength analysis subunit also obtains the medical data sharing history of the institution that owns the medical data, obtains the sharing scale of data that is the same or similar to the medical data based on the medical data sharing history, and determines the optimization coefficient based on the sharing scale; and uses the optimization coefficient to optimize the initial potential application strength level to the potential application strength level.

[0047] In this embodiment, the fully trained deep analysis model predicts and processes the previously extracted and quantified disease features, and preliminarily evaluates the initial potential application intensity level of the disease medical data. For example, for a rare disease, if the number of relevant research papers is small (the number of first results is small) but the time span is long (indicating that it has been concerned for a long time but progressed slowly), the pathogenesis is complex (the number of second results is large), and the diagnosis and treatment methods are not yet stable (the evolution trend of diagnosis and treatment methods shows that the number of various diagnosis and treatment methods has not yet stabilized), then the initial potential application intensity level of the disease medical data is preliminarily evaluated to be high, that is, the relevant institutions have a large application demand for the medical data, because the pathogenesis and diagnosis and treatment methods of the disease corresponding to the medical data still have a large research space, and the research value and potential influence of the medical data are relatively large.

[0048] The above-mentioned initial potential application intensity level is analyzed without considering the willingness of the medical data ownership institution to share medical data. In fact, the willingness of the medical data ownership institution to share medical data also has a direct and significant impact on the potential application intensity level of medical data. In this regard, the present invention is set to also obtain the medical data sharing history of the medical data ownership institution, and obtain the sharing scale of the same or similar data as the medical data according to the medical data sharing history. Among them, the same or similar data refers to the data belonging to the above-mentioned initial potential application intensity level, that is, to analyze how many medical data of this type of initial potential application intensity level the ownership institution has shared; the sharing scale is mainly determined by factors such as the total amount of data shared, the number of shared batches, and the number of shared objects. Then, the optimization coefficient is determined based on the sharing scale (for example, by multiplication), and the optimization coefficient is actually used to reflect the degree to which the ownership institution hinders the sharing of medical data. Obviously, the larger the sharing scale, the lower the above-mentioned degree of obstruction, and the corresponding optimization coefficient is closer to 1, that is, the lower the initial potential application intensity level is reduced; and the smaller the sharing scale, the higher the above-mentioned degree of obstruction, and the corresponding optimization coefficient is less than 1, that is, the lower the initial potential application intensity level is increased.

[0049] Therefore, the initial potential application intensity level obtained above can be adjusted to a level closer to the actual situation by optimizing the coefficient, which is conducive to more appropriate grading or classification of medical data in the future.

[0050] Optionally, grading or classifying the medical data according to the potential application strength level includes: obtaining a corresponding grading label / classification label according to the potential application strength level matching, and associating the grading label / classification label with the medical data.

[0051] In this embodiment, after the corresponding potential application strength level is determined, the corresponding graded label or classification label can be matched according to the preset corresponding relationship and associated with the medical data. When used later, different privacy treatment solutions can be determined based on the label.

[0052] Optionally, determining the privacy processing scheme for the medical data based on the grading or the classification includes: determining the privacy processing scheme for each medical data based on the potential application strength level corresponding to the grading label or the classification label, and different privacy processing schemes are achieved by erasing the identity information in the medical data to varying degrees and / or encrypting the medical data using encryption algorithms with different encryption levels.

[0053] In this embodiment, the personalized privacy treatment scheme of the medical data can be determined according to the potential application strength level. For example, when the potential application strength level is high, it is more likely to be used by more institutions and across more regions. At this time, more content in its identity information can be erased, such as name, gender, age, contact information, place of origin, home address, past medical history, etc., while when the potential application strength level is low, only name, contact information, place of origin, home address, past medical history, etc. are erased.

[0054] The privacy processing of medical data can also be reflected through different encryption algorithms, which can reduce the probability of medical data being leaked during transmission and use. For example, when the potential application strength level is high, encryption algorithms such as AES and RSA with better encryption performance are used, while when the potential application strength level is low, encryption algorithms such as EDS and 3DES with slightly worse encryption performance are used.

[0055] The above is only for illustration and is not intended to limit the scope of protection of the present invention.

[0056] An embodiment of the present invention further discloses an electronic device, which is applied to the system as described in any of the preceding items, and includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

[0057] An embodiment of the present invention further discloses a computer storage medium, which is applied to the system as described in any of the preceding items, and the computer-readable storage medium stores a computer program.

[0058] An embodiment of the present invention further discloses a computer program product, which is applied to the system as described in any of the preceding items, wherein the computer program includes a number of computer codes.

[0059] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0060] The technical features of the above embodiments may 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.

[0061] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. An intelligent classification and privacy protection system for medical data, characterized by: The system includes a data receiving unit, a data grading and classification unit, and a privacy processing unit; the data receiving unit is used to receive various medical data that need to be privacy protected, and the medical data contains corresponding disease type information and diagnosis and treatment means information; the data grading and classification unit is used to evaluate the corresponding potential application intensity level of the medical data based on the disease type information and the diagnosis and treatment means information, and grade or classify the medical data according to the potential application intensity level; the privacy processing unit is used to determine the privacy processing plan for the medical data according to the grading or classification, and perform privacy processing on the medical data according to the privacy processing plan.

2. The intelligent classification and privacy protection system for medical data according to claim 1, characterized in that: The receiving of each medical data requiring privacy protection includes: after the data receiving unit receives the medical data, the data receiving unit analyzes the medical data to determine whether the analysis result contains identity information; if the identity information is contained, the data is determined to be the medical data requiring privacy protection.

3. The intelligent classification and privacy protection system for medical data according to claim 2, characterized in that: The data classification unit includes a correlation information query subunit, a feature extraction subunit, and an application intensity analysis subunit; The associated information query subunit searches in several designated data sources according to the disease type information and the diagnosis and treatment means, and obtains a first search result corresponding to the disease type information and a second search result corresponding to the disease type information and the diagnosis and treatment means information respectively; the feature extraction subunit extracts features from the first search result and the second search result to obtain disease features; The application strength analysis subunit calls the deep analysis model to process the disease characteristics to obtain an evaluated potential application strength level of the medical data.

4. The intelligent classification and privacy protection system for medical data according to claim 3, characterized in that: The feature extraction of the first search result and the second search result to obtain disease characteristics includes: the feature extraction subunit uses a feature extraction model to extract the first result quantity and result time span corresponding to the disease type information from the first search result; uses a semantic analysis component to extract multiple pathogenesis principles corresponding to the disease type information from the first search result, counts the second result quantity of pathogenesis principles whose result number is higher than a preset value, and extracts a diagnosis and treatment means data set corresponding to the disease type information from the second search result, and obtains the diagnosis and treatment means evolution trend based on the analysis of the diagnosis and treatment means data set; vectorizes the first result quantity, the result time span, the second result quantity, and the diagnosis and treatment means evolution trend to obtain the disease characteristics.

5. The intelligent classification and privacy protection system for medical data according to claim 4, characterized in that: The application strength analysis subunit calls the deep analysis model to process the disease characteristics to obtain the evaluated potential application strength level of the medical data, including: the application strength analysis subunit calls the deep analysis model to process the disease characteristics to obtain the evaluated initial potential application strength level of the medical data; the application strength analysis subunit also obtains the medical data sharing history of the institution that owns the medical data, obtains the sharing scale of data that is the same or similar to the medical data based on the medical data sharing history, and determines an optimization coefficient based on the sharing scale; and uses the optimization coefficient to optimize the initial potential application strength level to the potential application strength level.

6. The intelligent classification and privacy protection system for medical data according to claim 1, characterized in that: The grading or classifying the medical data according to the potential application strength level includes: obtaining a corresponding grading label / classification label according to the potential application strength level matching, and associating the grading label / classification label with the medical data.

7. The intelligent classification and privacy protection system for medical data according to claim 1, characterized in that: The determining of the privacy processing scheme for the medical data according to the grading or the classification includes: determining the privacy processing scheme for each medical data according to the potential application strength level corresponding to the grading label or the classification label, and different privacy processing schemes are achieved by erasing the identity information in the medical data to different degrees and / or encrypting the medical data using encryption algorithms with different encryption levels.

8. An electronic device, characterized in that: The system applied to any one of claims 1 to 7 comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

9. A computer storage medium, characterized in that: Applied to the system according to any one of claims 1 to 7, the computer storage medium stores a computer program.

10. A computer program product, applied to the system according to any one of claims 1 to 7, characterized in that: The computer program includes several computer codes.

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