JSON-based medical record document information display method and system

Abstract

The application relates to a JSON-based medical record document information display method and system, which comprises the following steps: acquiring a first medical record document in JSON format, extracting a field path of the first medical record document using a JSON Path syntax and classifying to obtain a data type and clinical sensitivity of the field path; generating a privacy parameter table according to a preset privacy protection level and the clinical sensitivity; and after carrying out noise processing on the first medical record document according to the data type and the privacy parameter table, carrying out medical semantic correction processing to obtain a third medical record document and display the third medical record document. According to the application, the privacy parameter is dynamically adjusted according to different privacy requirements, so that a balance is achieved between data availability and privacy protection, and the data confidentiality in the information display process of the medical record document is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for displaying information in medical records based on JSON. Background Technology

[0002] JSON is a lightweight data-interchange format that organizes data in key-value pairs, clearly representing various information in medical records, such as basic patient information, medical history, examination results, and treatment plans. For example, basic information such as a patient's name, age, and gender can be stored as key-value pairs, making them easy to understand and manipulate. Medical records contain sensitive patient information, such as personal identification information, disease diagnoses, and treatment processes. When using JSON to store and transmit this information, strict security measures are required to protect the confidentiality, integrity, and availability of the data. For example, data needs to be encrypted during storage and transmission, access permissions restricted, and data leakage and unauthorized tampering prevented.

[0003] Existing methods for displaying medical records use a fixed privacy budget allocation to protect data, which cannot adapt to the varying sensitivity of different fields in medical records, resulting in technical issues with insufficient confidentiality of case data. Summary of the Invention

[0004] To improve the confidentiality of medical record data, this application provides a method and system for displaying information in medical records based on JSON.

[0005] To address the aforementioned technical problems, this invention provides a method for displaying information in medical records based on JSON, comprising the following steps:

[0006] Obtain the first medical record document; wherein, the first medical record document is in JSON format;

[0007] The field paths of the first medical record were extracted using JSON Path syntax and categorized to obtain the categorization results of the field paths. The categorization results include: data type and clinical sensitivity.

[0008] Generate a privacy parameter table based on the preset privacy protection level and clinical sensitivity;

[0009] Based on the data type and privacy parameter table, noise is added to the first medical record document to obtain the second medical record document.

[0010] The second medical record document undergoes medical semantic correction processing to obtain the third medical record document;

[0011] The third case document is presented.

[0012] As a preferred approach, the step of adding noise to the first medical record document based on the data type and privacy parameter table to obtain the second medical record document includes the following steps:

[0013] Data types include continuous fields and discrete fields;

[0014] Based on the privacy parameter table, the continuous fields of the first medical record are noise-added using the Laplace mechanism to obtain the first processing result;

[0015] Based on the privacy parameter table, the discrete fields of the first medical record are noise-added using an exponential mechanism to obtain the second processing result;

[0016] Based on the results of the first and second processing, the second medical record document is obtained.

[0017] As a preferred approach, the step of extracting and classifying the field paths of the first medical record document using JSON Path syntax to obtain the classification results includes the following steps:

[0018] Use JSON Path syntax to extract field paths from the first medical record document;

[0019] The field paths are classified in three dimensions based on the medical knowledge base to obtain the classification results;

[0020] The classification results include: data type, clinical sensitivity, and constraints.

[0021] As a preferred approach, the step of generating a privacy parameter table based on a preset privacy protection level and clinical sensitivity includes the following steps:

[0022] Set the preset values ​​for privacy parameters according to the preset privacy protection level;

[0023] Based on clinical sensitivity, the preset values ​​of privacy parameters are adjusted to obtain the adjusted values ​​of privacy parameters;

[0024] After anomaly handling of the privacy parameter adjustment values, the corrected privacy parameter values ​​are obtained;

[0025] A privacy parameter table is generated based on the privacy parameter correction values; the privacy parameter table is used to represent the mapping relationship between field paths and privacy parameter correction values.

[0026] As a preferred approach, the step of performing medical semantic correction processing on the second medical record document to obtain the third medical record document includes the following steps:

[0027] Based on the constraints, a single-field validation is performed on the second medical record document to obtain the first validation result;

[0028] Based on the Markov chain Monte Carlo resampling algorithm, the first verification result is subjected to multi-field logical verification to obtain the second verification result;

[0029] The second verification result is subjected to terminology consistency verification to obtain the medical semantic correction processing result.

[0030] Based on the results of medical semantic correction, the third case document was obtained.

[0031] As a preferred option, it also includes:

[0032] Retrieve the operation record of the first medical record document; the operation record includes: field path, privacy parameter correction value, first processing result, second processing result, and operation timestamp;

[0033] Calculate the hash value based on the operation record;

[0034] Write the hash value into the medical consortium blockchain;

[0035] When the privacy parameter correction value is less than the privacy parameter preset value, a privacy operation audit chain is obtained.

[0036] As a preferred option, the medical consortium blockchain includes multiple metadata entries;

[0037] Metadata includes: data identifier, operation type, and privacy parameters;

[0038] Among them, the data identifier is used to represent the medical record ID;

[0039] The operation type is used to indicate the first processing result and the second processing result;

[0040] Privacy parameters are used to represent privacy parameter correction values ​​and clinical sensitivity.

[0041] Accordingly, the present invention also provides an information display system for medical records based on JSON, comprising: 8. an acquisition module, a classification module, a generation module, a noise-adding module, a correction module, and a display module;

[0042] The acquisition module is used to acquire the first medical record document; the first medical record document is in JSON format.

[0043] The classification module is used to extract field paths from the first medical record document using JSON Path syntax and classify them to obtain the classification results of the field paths; the classification results include: data type and clinical sensitivity;

[0044] The generation module is used to generate a privacy parameter table based on preset privacy protection levels and clinical sensitivity.

[0045] The noise-adding module is used to add noise to the first medical record document according to the data type and privacy parameter table to obtain the second medical record document;

[0046] The correction module is used to perform medical semantic correction processing on the second medical record document to obtain the third medical record document;

[0047] The display module is used to showcase the third case document.

[0048] Accordingly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of any of the above-mentioned methods for displaying information of JSON-based medical records.

[0049] Accordingly, the present invention also provides a storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of any of the above-mentioned methods for displaying information in JSON-based medical records.

[0050] The technical solution of this invention obtains a first medical record document in JSON format, extracts and categorizes its field paths using JSON Path syntax to obtain the data type and clinical sensitivity of the field paths, generates a privacy parameter table based on preset privacy protection levels and clinical sensitivity, adds noise to the first medical record document according to the data type and privacy parameter table to obtain a second medical record document, performs medical semantic correction on the second medical record document to obtain a third medical record document, and displays the third medical record document. This invention provides a JSON-based medical record document information display method that adds noise to the first medical record document according to different data types and privacy parameter tables, ensuring that the statistical results of the data are usable while protecting privacy. Simultaneously, it dynamically adjusts privacy protection parameters according to different privacy needs, thereby achieving a balance between data usability and privacy protection. This solves the problem that existing medical record document information display methods, which use fixed privacy budget allocation for data protection, cannot adapt to the differences in sensitivity of multiple fields in medical records, thus effectively improving data confidentiality during the information display process. Furthermore, the technical solution of this invention performs medical semantic correction on the medical record document, which can avoid the generation of invalid data and effectively ensure data accuracy during the information display process. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the steps of a JSON-based method for displaying medical records in an embodiment of this application.

[0052] Figure 2This is a structural diagram of a JSON-based information display system for medical records, as described in an embodiment of this application.

[0053] Figure 3 This is a schematic diagram of the hardware structure of the electronic device in the embodiments of this application.

[0054] Icon labels:

[0055] The module includes an acquisition module 201, a classification module 202, a generation module 203, a noise addition module 204, a correction module 205, and a display module 206. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application.

[0057] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0058] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0060] Existing methods for displaying information in medical records use a fixed privacy budget allocation to protect data, which cannot adapt to the differences in sensitivity of multiple fields in medical records, resulting in technical problems with low confidentiality of case data.

[0061] In view of this, this application provides a method and system for displaying information from JSON-based medical records. This method obtains a first medical record document in JSON format, extracts and categorizes its field paths using JSON Path syntax to obtain the data type and clinical sensitivity of the field paths, generates a privacy parameter table based on preset privacy protection levels and clinical sensitivity, adds noise to the first medical record document according to the data type and privacy parameter table to obtain a second medical record document, performs medical semantic correction on the second medical record document to obtain a third medical record document, and then displays the third medical record document. The JSON-based method for displaying information from JSON provided by this invention adds noise to the first medical record document according to different data types and privacy parameter tables, ensuring that the statistical results of the data are usable while protecting privacy. Simultaneously, it dynamically adjusts privacy protection parameters according to different privacy needs, thereby achieving a balance between data usability and privacy protection. This solves the problem that existing methods for displaying medical records using fixed privacy budget allocation cannot adapt to the differences in sensitivity of multiple fields in medical records, thus effectively improving data confidentiality during the information display process. Furthermore, the technical solution of the present invention performs medical semantic correction processing on medical records, which can avoid the generation of invalid data and effectively ensure the data accuracy in the information display process of medical records.

[0062] The information display method and system for JSON-based medical records provided in this application relates to the field of data processing technology. The information display method and system for JSON-based medical records provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing a method for calculating the vehicle's center of gravity sideslip angle, but is not limited to the above forms.

[0063] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in a first context of computer-executable instructions, such as program modules, executed by a computer. Firstly, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0064] The present application will be further described in detail below with reference to the accompanying drawings.

[0065] In one embodiment, such as Figure 1 As shown, this application discloses a method for displaying information in medical records based on JSON, which specifically includes the following steps:

[0066] S101: Obtain the first medical record document.

[0067] The first medical record is in JSON format.

[0068] S102: Use JSON Path syntax to extract the field paths of the first medical record document and classify them to obtain the classification results of the field paths.

[0069] The classification results include: data type and clinical sensitivity.

[0070] In this embodiment, the step of extracting and classifying the field paths of the first medical record document using JSON Path syntax to obtain the classification results of the field paths includes the following steps:

[0071] Use JSON Path syntax to extract field paths from the first medical record document;

[0072] The field paths are classified in three dimensions based on the medical knowledge base to obtain the classification results;

[0073] The classification results include: data type, clinical sensitivity, and constraints.

[0074] S103: Generate a privacy parameter table based on the preset privacy protection level and clinical sensitivity.

[0075] S103 corresponds to the dynamic privacy budget allocation process. Clinical sensitivity can be set to three levels, corresponding to L1-L3. For example, HIV diagnosis is tagged as L3 (highest sensitivity), and blood pressure is tagged as L1 (lowest sensitivity). Data types include continuous fields and discrete fields; for example, continuous fields include blood glucose values, and discrete fields include diagnostic codes.

[0076] The constraints are specifically medical constraints, including both numerical ranges and terminology sets. For example, the numerical range includes a heart rate range of 30-200, and the terminology set includes ICD-11 codes.

[0077] In this embodiment, the step of generating a privacy parameter table based on a preset privacy protection level and clinical sensitivity includes the following steps:

[0078] Set the preset values ​​for privacy parameters according to the preset privacy protection level;

[0079] Based on clinical sensitivity, the preset values ​​of privacy parameters are adjusted to obtain the adjusted values ​​of privacy parameters;

[0080] After anomaly handling of the privacy parameter adjustment values, the corrected privacy parameter values ​​are obtained;

[0081] A privacy parameter table is generated based on the privacy parameter correction values; the privacy parameter table is used to represent the mapping relationship between field paths and privacy parameter correction values.

[0082] Specifically, based on the preset privacy protection level, preset values ​​for privacy parameters are set as follows:

[0083] The preset privacy protection levels include strict mode, normal mode, and easy mode.

[0084] For example, in strict mode, the privacy parameter preset value ε is set to 3 for the L1 field, 1 for the L2 field, and 0.5 for the L3 field. This step is used to determine the overall protection strength of the medical record data.

[0085] Furthermore, based on clinical sensitivity, the preset values ​​of the privacy parameters are adjusted to obtain the adjusted privacy parameter values, specifically:

[0086] ε_final=ε_base×(1+0.1*Sensitivity_Weight)

[0087] Where ε_final is the privacy parameter adjustment value, ε_base is the privacy parameter preset value, and Sensitivity_Weight is the clinical sensitivity.

[0088] Specifically, anomaly handling is performed on privacy parameter adjustment values, including:

[0089] Perform ε-value co-calibration on interdependent fields (such as systolic / diastolic blood pressure) to avoid logical contradictions and obtain privacy parameter correction values.

[0090] S104: Based on the data type and privacy parameter table, add noise to the first medical record document to obtain the second medical record document.

[0091] In this embodiment, the step of adding noise to the first medical record document according to the data type and privacy parameter table to obtain the second medical record document includes the following steps:

[0092] Data types include continuous fields and discrete fields;

[0093] Based on the privacy parameter table, the continuous fields of the first medical record are noise-added using the Laplace mechanism to obtain the first processing result;

[0094] Based on the privacy parameter table, the discrete fields of the first medical record are noise-added using an exponential mechanism to obtain the second processing result;

[0095] Based on the results of the first and second processing, the second medical record document is obtained.

[0096] In one specific embodiment, the process of calculating Laplace noise is as follows:

[0097] noise = Laplace(0, Sensitivity_Weigh / ε), where Sensitivity_Weigh is the clinical sensitivity, ε is the privacy parameter correction value in the privacy parameter table, and noise is Laplace noise.

[0098] If the value after adding noise exceeds the medical constraints, it is truncated to the most recent valid value and an audit event is recorded.

[0099] In one specific embodiment, the process of adding noise to discrete fields is as follows:

[0100] Select 10 candidate values ​​with similar semantics from the discrete field;

[0101] The replacement value is output with probability P(x)exp(ε·u(x)), where the utility function u(x) reflects the semantic distance from the original value;

[0102] Where u(x) is a candidate value.

[0103] S104 corresponds to the hierarchical differential privacy execution process. Differential privacy technology not only protects privacy but also supports complex statistical analysis and machine learning tasks. For example, in medical image data, combining wavelet transform and differential privacy technology can improve the visual and classification utility of image data while protecting privacy. This technology can be widely applied in disease prediction, medical image processing, and other fields. In the process of medical data sharing, differential privacy technology can effectively prevent privacy leaks, enabling medical institutions to securely share and analyze data while protecting patient privacy. This is of great significance for improving the quality of medical services and promoting medical research. Optimization algorithms combined with differential privacy (such as DPDLDA) can improve the efficiency and accuracy of data analysis. For example, by injecting appropriate noise into the gradient and optimizing the gradient clipping method, the impact of noise on model accuracy can be reduced while protecting privacy.

[0104] S105: Perform medical semantic correction on the second medical record document to obtain the third medical record document.

[0105] In this embodiment, the step of performing medical semantic correction processing on the second medical record document to obtain the third medical record document includes the following steps:

[0106] Based on the constraints, a single-field validation is performed on the second medical record document to obtain the first validation result;

[0107] Based on the Markov chain Monte Carlo resampling algorithm, the first verification result is subjected to multi-field logical verification to obtain the second verification result;

[0108] The second verification result is subjected to terminology consistency verification to obtain the medical semantic correction processing result.

[0109] Based on the results of medical semantic correction, the third case document was obtained.

[0110] The single-field validation is used to check whether a single field in the second medical record meets the medical constraints. The consistency validation is used to ensure that discrete values ​​are within the original terminology set (e.g., no invalid values ​​appear in the diagnostic code).

[0111] S106: Present the third case document.

[0112] In one specific embodiment, it further includes:

[0113] Retrieve the operation record of the first medical record document; the operation record includes: field path, privacy parameter correction value, first processing result, second processing result, and operation timestamp;

[0114] Calculate the hash value based on the operation record;

[0115] Write the hash value into the medical consortium blockchain;

[0116] When the privacy parameter correction value is less than the privacy parameter preset value, a privacy operation audit chain is obtained.

[0117] The medical consortium blockchain includes multiple metadata entries;

[0118] Metadata includes: data identifier, operation type, and privacy parameters;

[0119] Among them, the data identifier is used to represent the medical record ID;

[0120] The operation type is used to indicate the first processing result and the second processing result;

[0121] Privacy parameters are used to represent privacy parameter correction values ​​and clinical sensitivity.

[0122] The privacy operation audit chain provides verifiability assurance for all operations, meeting regulatory requirements such as GDPR. Privacy protection certificates are generated via zk-SNARK for verification by regulators.

[0123] In one specific embodiment, let's take the encryption of blood glucose data from a diabetic patient as an example:

[0124] Original value: {"glucose":8.7} (Preset privacy protection level is strict mode, clinical sensitivity is L1, and the preset privacy parameter value ε = 3).

[0125] Add noise: Add Laplace noise → 8.7 ± 0.3 (95% confidence interval);

[0126] Correction: If noise causes a value of -0.2, then correct it to 3.9 (lower limit of normal).

[0127] Audit: Record the hash of {"field":"glucose","ε":3,"noise":-0.5,"final":8.2} to the blockchain.

[0128] This process ensures that: attackers cannot infer the true value (satisfying differential privacy with ε=3), the data can still be used for clinical diagnosis (within the scope of medicine), and all operations are traceable (blockchain evidence).

[0129] Compared with the fixed privacy budget allocation method, the technical differences of the JSON medical record structure encryption process based on dynamic hierarchical differential privacy provided in this application are shown in Table 1:

[0130]

[0131] Table 1: Technical Comparison Table

[0132] The performance comparison is shown in Table 2:

[0133]

[0134] Table 2: Performance Measurement Comparison Table

[0135] Among them, the lower the uniformity of privacy protection, the more accurate the difference in protection between different sensitive fields.

[0136] The fixed privacy budget allocation method encrypts the entire {"vitals":{"bp":120},"diagnosis":"HIV"}, causing blood pressure data to be excessively interfered with by noise due to the high sensitivity of HIV (ε=0.1). This invention, however, allocates ε=5 to $.vitals.bp and ε=0.1 to $.diagnosis, reducing the error in blood pressure measurement from ±15mmHg to ±2mmHg.

[0137] Furthermore, when doctors or researchers need to access and use these medical records, they first need to log in to the system through its authentication mechanism. For example, after a doctor logs in using their work account and password, the system assigns corresponding access permissions based on their role and privileges. If the doctor is authorized to conduct medical research, they can query and use medical records within the authorized scope; a nurse may only be able to view some information related to nursing work. When an authorized user initiates a data query request, the system decrypts the encrypted data based on the user's permission verification results. For example, when researchers are conducting an epidemiological study on pneumonia, they can access the medical records of relevant patients after authorization. The system uses the AES decryption algorithm and the correct key to decrypt the ciphertext, restoring the data after differential privacy processing. After obtaining this data, researchers can conduct data analysis and research work, such as statistically analyzing the incidence and symptoms of patients in different age groups, providing valuable reference data for medical research, provided that relevant regulations and ethical guidelines are followed.

[0138] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0139] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented in this device embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. Please refer to... Figure 2 , Figure 2This paper illustrates an information display system for medical records based on JSON, characterized in that the system includes: an acquisition module 201, a classification module 202, a generation module 203, a noise addition module 204, a correction module 205, and a display module 206;

[0140] The acquisition module 201 is used to acquire the first medical record document; the first medical record document is in JSON format.

[0141] The classification module 202 is used to extract the field paths of the first medical record document using JSON Path syntax and classify them to obtain the classification results of the field paths; among which, the classification results include: data type and clinical sensitivity;

[0142] The generation module 203 is used to generate a privacy parameter table based on the preset privacy protection level and clinical sensitivity.

[0143] The noise-adding module 204 is used to add noise to the first medical record document according to the data type and privacy parameter table to obtain the second medical record document;

[0144] The correction module 205 is used to perform medical semantic correction processing on the second medical record document to obtain the third medical record document;

[0145] The display module 203 is used to display the third case document.

[0146] This system connects to the hospital's information system via an interface to automatically collect primary medical records in JSON format, including basic patient information (name, age, gender, etc.), symptom descriptions, examination and test results (blood routine, imaging examinations, etc.), and diagnostic results. This data is then transmitted in JSON format to the JSON-based medical record information display system of this invention.

[0147] In one specific embodiment, the step of adding noise to the first medical record document according to the data type and privacy parameter table to obtain the second medical record document includes the following steps:

[0148] Data types include continuous fields and discrete fields;

[0149] Based on the privacy parameter table, the continuous fields of the first medical record are noise-added using the Laplace mechanism to obtain the first processing result;

[0150] Based on the privacy parameter table, the discrete fields of the first medical record are noise-added using an exponential mechanism to obtain the second processing result;

[0151] Based on the results of the first and second processing, the second medical record document is obtained.

[0152] In one specific embodiment, the step of extracting and classifying the field paths of the first medical record document using JSON Path syntax to obtain the classification results of the field paths includes the following steps:

[0153] Use JSON Path syntax to extract field paths from the first medical record document;

[0154] The field paths are classified in three dimensions based on the medical knowledge base to obtain the classification results;

[0155] The classification results include: data type, clinical sensitivity, and constraints.

[0156] In one specific embodiment, the step of generating a privacy parameter table based on a preset privacy protection level and clinical sensitivity includes the following steps:

[0157] Set the preset values ​​for privacy parameters according to the preset privacy protection level;

[0158] Based on clinical sensitivity, the preset values ​​of privacy parameters are adjusted to obtain the adjusted values ​​of privacy parameters;

[0159] After anomaly handling of the privacy parameter adjustment values, the corrected privacy parameter values ​​are obtained;

[0160] A privacy parameter table is generated based on the privacy parameter correction values; the privacy parameter table is used to represent the mapping relationship between field paths and privacy parameter correction values.

[0161] In one specific embodiment, the step of performing medical semantic correction processing on the second medical record document to obtain the third medical record document includes the following steps:

[0162] Based on the constraints, a single-field validation is performed on the second medical record document to obtain the first validation result;

[0163] Based on the Markov chain Monte Carlo resampling algorithm, the first verification result is subjected to multi-field logical verification to obtain the second verification result;

[0164] The second verification result is subjected to terminology consistency verification to obtain the medical semantic correction processing result.

[0165] Based on the results of medical semantic correction processing, the third case document was obtained.

[0166] In one specific embodiment, it further includes:

[0167] Retrieve the operation record of the first medical record document; the operation record includes: field path, privacy parameter correction value, first processing result, second processing result, and operation timestamp;

[0168] Calculate the hash value based on the operation record;

[0169] Write the hash value into the medical consortium blockchain;

[0170] When the privacy parameter correction value is less than the privacy parameter preset value, a privacy operation audit chain is obtained.

[0171] In one specific embodiment, the medical consortium blockchain includes multiple metadata;

[0172] Metadata includes: data identifier, operation type, and privacy parameters;

[0173] Among them, the data identifier is used to represent the medical record ID;

[0174] The operation type is used to indicate the first processing result and the second processing result;

[0175] Privacy parameters are used to represent privacy parameter correction values ​​and clinical sensitivity.

[0176] Furthermore, when doctors or researchers need to access and use these medical records, they first need to log in to the system through its authentication mechanism. For example, after a doctor logs in using their work account and password, the system assigns corresponding access permissions based on their role and privileges. If the doctor is authorized to conduct medical research, they can query and use medical records within the authorized scope; a nurse may only be able to view some information related to nursing work. When an authorized user initiates a data query request, the system decrypts the encrypted data based on the user's permission verification results. For example, when researchers are conducting an epidemiological study on pneumonia, they can access the medical records of relevant patients after authorization. The system uses the AES decryption algorithm and the correct key to decrypt the ciphertext, restoring the data after differential privacy processing. After obtaining this data, researchers can conduct data analysis and research work, such as statistically analyzing the incidence and symptoms of patients in different age groups, providing valuable reference data for medical research, provided that relevant regulations and ethical guidelines are followed.

[0177] Differential privacy technology not only protects privacy but also supports complex statistical analysis and machine learning tasks. For example, in medical image data, combining wavelet transform and differential privacy techniques can improve the visual and classification utility of image data while protecting privacy. This technology can be widely applied in disease prediction, medical image processing, and other fields. In the process of sharing medical data, differential privacy technology can effectively prevent privacy leaks, enabling medical institutions to securely share and analyze data while protecting patient privacy. This is of great significance for improving the quality of medical services and promoting medical research. Optimization algorithms combined with differential privacy (such as DPDLDA) can improve the efficiency and accuracy of data analysis. For example, by injecting appropriate noise into the gradient and optimizing the gradient clipping method, the impact of noise on model accuracy can be reduced while protecting privacy.

[0178] In summary, the method of encrypting JSON medical record information based on differential privacy technology can protect patient privacy while supporting efficient and accurate medical data analysis, and has significant application advantages.

[0179] For specific limitations regarding a JSON-based medical record information display system, please refer to the limitations of a JSON-based medical record information display method described above, which will not be repeated here. The various modules in the aforementioned JSON-based medical record information display system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in an electronic device, or stored in the memory of an electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0180] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0181] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the electronic device includes:

[0182] The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0183] The memory 802 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and called by the processor 801 to execute the vehicle center of gravity sideslip angle calculation method of the embodiments of this application.

[0184] The 803 input / output interface is used to implement information input and output.

[0185] The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0186] Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804);

[0187] The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0188] The electronic device's processor provides computing and control capabilities. Its memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage medium. The electronic device's database stores the database. Its network interface is used to communicate with external terminals via a network. When executed by the processor, the computer program implements a JSON-based method for displaying medical records.

[0189] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0190] Obtain the first medical record document; wherein, the first medical record document is in JSON format;

[0191] The field paths of the first medical record were extracted using JSON Path syntax and categorized to obtain the categorization results of the field paths. The categorization results include: data type and clinical sensitivity.

[0192] Generate a privacy parameter table based on the preset privacy protection level and clinical sensitivity;

[0193] Based on the data type and privacy parameter table, noise is added to the first medical record document to obtain the second medical record document.

[0194] The second medical record document undergoes medical semantic correction processing to obtain the third medical record document;

[0195] The third case document is presented.

[0196] In one embodiment, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0197] Obtain the first medical record document; wherein, the first medical record document is in JSON format;

[0198] The field paths of the first medical record were extracted using JSON Path syntax and categorized to obtain the categorization results of the field paths. The categorization results include: data type and clinical sensitivity.

[0199] Generate a privacy parameter table based on the preset privacy protection level and clinical sensitivity;

[0200] Based on the data type and privacy parameter table, noise is added to the first medical record document to obtain the second medical record document.

[0201] The second medical record document undergoes medical semantic correction processing to obtain the third medical record document;

[0202] The third case document is presented.

[0203] The information display method and system for JSON-based medical records provided in this application embodiment obtains a first medical record document in JSON format, extracts and categorizes the field paths of the first medical record document using JSON Path syntax, and obtains the data type and clinical sensitivity of the field paths; generates a privacy parameter table based on preset privacy protection levels and clinical sensitivity; adds noise to the first medical record document according to the data type and privacy parameter table to obtain a second medical record document; performs medical semantic correction on the second medical record document to obtain a third medical record document; and displays the third medical record document. The information display method for JSON-based medical records provided by this invention adds noise to the first medical record document according to different data types and privacy parameter tables, ensuring that the statistical results of the data are usable while protecting privacy; at the same time, it dynamically adjusts the privacy protection parameters according to different privacy needs, thereby achieving a balance between data usability and privacy protection. This solves the problem that existing information display methods for medical records, which use fixed privacy budget allocation for data protection, cannot adapt to the differences in sensitivity of multiple fields in medical records, thus effectively improving the data confidentiality during the information display process of medical records. Furthermore, the technical solution of the present invention performs medical semantic correction processing on medical records, which can avoid the generation of invalid data and effectively ensure the data accuracy in the information display process of medical records.

[0204] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0205] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0206] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for displaying information in medical records based on JSON, characterized in that the method... include: Obtain the first medical record document; wherein, the first medical record document is in JSON format; Use JSON Path syntax to extract field paths from the first medical record document; The field paths are categorized based on the medical knowledge base to obtain the categorization results; The classification results include: data type, clinical sensitivity, and constraints, specifically medical constraints. A privacy parameter table is generated based on the preset privacy protection level and clinical sensitivity. The preset privacy protection levels include strict mode, normal mode and simple mode. The step of generating a privacy parameter table based on a preset privacy protection level and clinical sensitivity includes: setting preset values ​​for privacy parameters based on the preset privacy protection level; Based on clinical sensitivity, the preset values ​​of privacy parameters are adjusted to obtain the adjusted values ​​of privacy parameters; After anomaly handling of the privacy parameter adjustment values, the corrected privacy parameter values ​​are obtained; A privacy parameter table is generated based on the privacy parameter correction values; the privacy parameter table is used to represent the mapping relationship between field paths and privacy parameter correction values; Based on the data type and privacy parameter table, noise is added to the first medical record document to obtain the second medical record document. If the value after adding noise exceeds the medical constraints, it will be truncated to the nearest valid value and the audit event will be recorded. The second medical record document is subjected to medical semantic correction processing to obtain the third medical record document; Display the third medical record document; The method further includes the step of: obtaining the operation record of the first medical record document; Calculate the hash value based on the operation record; Write the hash value into the medical consortium blockchain; When the privacy parameter correction value is less than the privacy parameter preset value, a privacy operation audit chain is obtained.

2. The method for displaying information in medical records based on JSON according to claim 1, characterized in that, The step of adding noise to the first medical record document based on the data type and privacy parameter table to obtain the second medical record document includes the following steps: Data types include continuous fields and discrete fields; Based on the privacy parameter table, the continuous fields of the first medical record are noise-added using the Laplace mechanism to obtain the first processing result; Based on the privacy parameter table, the discrete fields of the first medical record are noise-added using an exponential mechanism to obtain the second processing result; Based on the results of the first and second processing, the second medical record document is obtained.

3. A method for displaying information in medical records based on JSON according to claim 2, characterized in that, The steps involved in performing medical semantic correction on the second medical record to obtain the third medical record include the following: Based on the constraints, a single-field validation is performed on the second medical record document to obtain the first validation result; Based on the Markov chain Monte Carlo resampling algorithm, the first verification result is subjected to multi-field logical verification to obtain the second verification result; The second verification result is subjected to terminology consistency verification to obtain the medical semantic correction processing result. Based on the results of medical semantic correction processing, the third medical record document is obtained.

4. A method for displaying information in medical records based on JSON according to claim 3, characterized in that, The medical consortium blockchain includes multiple metadata entries; Metadata includes: data identifier, operation type, and privacy parameters; Among them, the data identifier is used to represent the medical record ID; The operation type is used to indicate the first processing result and the second processing result; Privacy parameters are used to represent privacy parameter correction values ​​and clinical sensitivity.

5. A method for displaying information in medical records based on JSON according to claim 4, characterized in that, The operation record includes: field path, privacy parameter correction value, first processing result, second processing result, and operation timestamp.

6. A JSON-based medical record document information display system, applied to the JSON-based medical record document information display method described in any one of claims 1 to 5, characterized in that, The system includes: an acquisition module, a classification module, a generation module, a noise addition module, a correction module, and a display module; The acquisition module is used to acquire the first medical record document; the first medical record document is in JSON format. The classification module is used to extract field paths from the first medical record document using JSON Path syntax and classify them to obtain the classification results of the field paths; the classification results include: data type and clinical sensitivity; The generation module is used to generate a privacy parameter table based on preset privacy protection levels and clinical sensitivity. The noise-adding module is used to add noise to the first medical record document according to the data type and privacy parameter table to obtain the second medical record document; The correction module is used to perform medical semantic correction processing on the second medical record document to obtain the third medical record document; The display module is used to showcase third-party medical records.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes a computer program to implement the steps of a JSON-based method for displaying medical records as described in any one of claims 1 to 5.

8. A storage medium storing a computer program, characterized in that, When a computer program is executed by a processor, it implements the steps of a JSON-based method for displaying medical records as described in any one of claims 1 to 5.

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