Information display method and system of medical record document based on JSON (JavaScript Object Notation)

Abstract

The invention relates to a JSON-based medical record document information display method and system, and the method comprises the steps: obtaining a first medical record document in a JSON format, extracting a field path of the first medical record document through the JSON Path grammar, and carrying out the classification, and obtaining the data type and clinical sensitivity of the field path; generating a privacy parameter table according to a preset privacy protection level and clinical sensitivity; and according to the data type and the privacy parameter table, carrying out noise addition processing on the first medical record document, then carrying out medical semantic correction processing, and obtaining and displaying a third medical record document. According to the method, the privacy parameters are dynamically adjusted according to different privacy requirements, so that balance between data availability and privacy protection is achieved, and the data confidentiality in the information display process of the medical record document is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for information display of medical record documents based on JSON. Background Art

[0002] JSON is a lightweight data interchange format that organizes data in key-value pairs and can clearly represent various information in medical record documents, such as patient basic information, medical history, examination results, treatment plans, etc. For example, basic information such as the patient's name, age, and gender can be stored as key-value pairs, which are easy to understand and operate. Medical record documents contain sensitive information of patients, such as personal identity information, disease diagnosis, treatment process, etc. When using JSON to store and transmit this information, strict security measures need to be taken to protect the confidentiality, integrity, and availability of the data. For example, it is necessary to encrypt the storage and transmission of data, restrict access rights to the data, and prevent data leakage and illegal tampering.

[0003] The existing information display method for medical record documents uses a fixed privacy budget allocation method to protect data, which cannot adapt to the sensitivity differences of multiple fields in medical records, and there is a technical problem of low confidentiality of case data. Summary of the Invention

[0004] In order to improve the confidentiality of case data, the present application provides a method and system for information display of medical record documents based on JSON.

[0005] To solve the above technical problems, an embodiment of the present invention provides a method for information display of medical record documents based on JSON, and the method includes the following steps:

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

[0007] Use JSON Path syntax to extract the field paths of the first medical record document and classify them to obtain a classification result of the field paths; wherein, the classification result includes: data type and clinical sensitivity;

[0008] Generate a privacy parameter table according to a preset privacy protection level and clinical sensitivity;

[0009] Perform noise addition processing on the first medical record document according to the data type and the privacy parameter table to obtain a second medical record document;

[0010] Perform medical semantic correction processing on the second medical record document to obtain a third medical record document;

[0011] Display the third medical record document.

[0012] As a preferred solution, in the step of performing noise addition processing on the first medical record document according to the data type and the privacy parameter table to obtain the second medical record document, the following steps are included:

[0013] The data type includes continuous fields and discrete fields;

[0014] According to the privacy parameter table, combined with the Laplace mechanism, perform noise addition processing on the continuous fields of the first medical record document to obtain the first processing result;

[0015] According to the privacy parameter table, combined with the exponential mechanism, perform noise addition processing on the discrete fields of the first medical record document to obtain the second processing result;

[0016] According to the first processing result and the second processing result, obtain the second medical record document.

[0017] As a preferred solution, in the step of using the JSON Path syntax to extract the field paths of the first medical record document and classify them to obtain the classification result of the field paths, the following steps are included:

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

[0019] According to the medical knowledge base, perform three-dimensional classification on the field paths to obtain the classification result;

[0020] Among them, the classification result includes: data type, clinical sensitivity, and constraint conditions.

[0021] As a preferred solution, in the step of generating the privacy parameter table according to the preset privacy protection level and clinical sensitivity, the following steps are included:

[0022] According to the preset privacy protection level, set the preset value of the privacy parameter;

[0023] According to the clinical sensitivity, adjust the preset value of the privacy parameter to obtain the adjusted value of the privacy parameter;

[0024] After performing anomaly processing on the adjusted value of the privacy parameter, obtain the corrected value of the privacy parameter;

[0025] Generate the privacy parameter table according to the corrected value of the privacy parameter; among them, the privacy parameter table is used to represent the mapping relationship between the field path and the corrected value of the privacy parameter.

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

[0027] According to the constraint conditions, perform single-field verification on the second medical record document to obtain the first verification result;

[0028] Based on the resampling algorithm based on Markov Chain Monte Carlo, perform multi-field logical verification on the first verification result to obtain the second verification result;

[0029] Perform term consistency verification on the second verification result to obtain the medical semantic correction processing result;

[0030] Obtain the third medical record document according to the medical semantic correction processing result.

[0031] As a preferred solution, it further includes:

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

[0033] Calculate the hash value according to the operation record;

[0034] Write the hash value into the medical alliance chain;

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

[0036] As a preferred solution, the medical alliance chain includes multiple metadata;

[0037] The metadata includes: data identifier, operation type, and privacy parameter;

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

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

[0040] The privacy parameter is used to represent the privacy parameter correction value and clinical sensitivity

[0041] Correspondingly, the present invention also provides an information display system for medical record documents based on JSON, including: 8. Acquisition module, classification module, generation module, noise addition module, correction module, and display module;

[0042] Among them, the acquisition module is used to acquire the first medical record document; wherein, the first medical record document is in JSON format;

[0043] The classification module is used to extract the field path of the first medical record document using JSON Path syntax and classify it to obtain the classification result of the field path; wherein, the classification result includes: data type and clinical sensitivity;

[0044] The generation module is used to generate a privacy parameter table according to the preset privacy protection level and clinical sensitivity;

[0045] The noise addition module is used to perform noise addition processing on the first medical record document according to the data type and the 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 display the third medical record document.

[0048] Correspondingly, 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, wherein the processor implements the steps of the information display method of a medical record document based on JSON as described in any one of the above when executing the computer program.

[0049] Correspondingly, the present invention also provides a storage medium storing a computer program, wherein the computer program implements the steps of the information display method of a medical record document based on JSON as described in any one of the above when executed by a processor.

[0050] The technical solution of the present invention obtains the first medical record document in JSON format, extracts the field paths of the first medical record document using JSON Path syntax and classifies them to obtain the data types and clinical sensitivities of the field paths; generates a privacy parameter table according to the preset privacy protection level and clinical sensitivity; performs noise addition processing on the first medical record document according to the data type and the privacy parameter table to obtain the second medical record document; performs medical semantic correction processing on the second medical record document to obtain the third medical record document; and displays the third medical record document. The information display method of the medical record document based on JSON provided by the present invention performs noise addition processing on the first medical record document according to different data types and privacy parameter tables, so that the statistical results of the data are still available while protecting privacy; at the same time, the privacy protection parameters are dynamically adjusted according to different privacy requirements, so as to achieve a balance between data availability and privacy protection, and solve the problem that the existing information display method of medical record documents uses fixed privacy budget allocation to protect data and cannot adapt to the sensitivity differences of multiple fields in medical records, thereby effectively improving the data confidentiality in the information display process of medical record documents. Further, the technical solution of the present invention performs medical semantic correction processing on the medical record document, which can avoid the generation of invalid data and effectively ensure the data accuracy in the information display process of the medical record document. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flowchart of the steps of an information display method of a medical record document based on JSON in an embodiment of the present application;

[0052] Figure 2It is a structural diagram of an information display system for medical record documents based on JSON in an embodiment of the present application;

[0053] Figure 3 It is a schematic hardware structure diagram of an electronic device in an embodiment of the present application.

[0054] Reference numerals in the accompanying drawings:

[0055] Obtaining module 201, classification module 202, generation module 203, noise addition module 204, correction module 205, and display module 206. Detailed implementation manners

[0056] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. When the following description involves the accompanying drawings, unless otherwise indicated, the same numerals in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the embodiments of the present application. They are only examples of devices and methods that are consistent with some aspects of the embodiments of the present application.

[0057] It can be understood that the terms "first", "second", etc. used in the present application can be used in this document to describe various concepts, but unless otherwise specified, 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 the present application, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information. Depending on the context, the words "if", "when" as used herein can be interpreted as "when...", "when...", or "in response to determining".

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

[0059] Unless otherwise defined, all technical and scientific terms used in this document have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used in this document are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0060] The existing information display method for medical record documents uses fixed privacy budget allocation to protect data, which cannot adapt to the sensitivity differences of multiple fields in medical records, and there is a technical problem of low confidentiality of case data.

[0061] In view of this, an information display method and system for medical record documents based on JSON are provided in the embodiments of the present application. The solution obtains the first medical record document in JSON format, extracts the field paths of the first medical record document using JSON Path syntax and classifies them to obtain the data types and clinical sensitivities of the field paths; generates a privacy parameter table according to the preset privacy protection level and clinical sensitivity; performs noise addition processing on the first medical record document according to the data types and the privacy parameter table to obtain a second medical record document; performs medical semantic correction processing 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 medical record documents based on JSON provided by the present invention performs noise addition processing on the first medical record document according to different data types and privacy parameter tables, so that the statistical results of the data are still available while protecting privacy; at the same time, the privacy protection parameters are dynamically adjusted according to different privacy requirements, so as to achieve a balance between data availability and privacy protection, and solve the problem that the existing information display method for medical record documents cannot adapt to the sensitivity differences of multiple fields in medical records due to the use of fixed privacy budget allocation for data protection, thereby effectively improving the data confidentiality in the information display process of medical record documents. Further, the technical solution of the present invention performs medical semantic correction processing on medical record documents, which can avoid the generation of invalid data and effectively ensure the data accuracy in the information display process of medical record documents.

[0062] The information display method and system for medical record documents based on JSON provided in the embodiments of the present application relate to the technical field of data processing. The information display method and system for medical record documents based on JSON provided in the embodiments of the present application can be applied to a terminal, can also be applied to a server, or can be software running on a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, etc., but is not limited thereto; the server side can be configured as an independent physical server, can also be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, 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 for implementing the vehicle sideslip angle calculation method, etc., but is not limited to the above forms.

[0063] This application can be used in numerous general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in a first context of computer-executable instructions executed by a computer, such as program modules. First, 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 a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] The following further elaborates on this application in conjunction with the accompanying drawings.

[0065] In one embodiment, as Figure 1 shown, this application discloses a method for displaying information of medical record documents based on JSON, specifically including the following steps:

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

[0067] Among them, the first medical record document 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 result of the field paths.

[0069] Among them, the classification result includes: data type and clinical sensitivity.

[0070] In this embodiment, in the step of using JSON Path syntax to extract the field paths of the first medical record document and classify them to obtain the classification result of the field paths, the following steps are included:

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

[0072] Perform three-dimensional classification on the field paths according to the medical knowledge base to obtain the classification result;

[0073] Among them, the classification result includes: data type, clinical sensitivity, and constraint conditions.

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

[0075] S103 corresponds to the dynamic privacy budget allocation process. Among them, the clinical sensitivity can be set to three levels of sensitivity, corresponding to L1 - L3. For example, the HIV diagnosis marker is L3 (highest sensitivity), and the blood pressure marker is L1 (lowest sensitivity). The data types include continuous fields and discrete fields. Exemplarily, the continuous fields include blood glucose values, and the discrete fields include diagnosis codes.

[0076] The constraint conditions are specifically medical constraint conditions, including two aspects: numerical range and term set. Exemplarily, the numerical range includes a heart rate range of 30 - 200, and the term set includes ICD - 11 codes.

[0077] In this embodiment, in the step of generating a privacy parameter table according to the preset privacy protection level and clinical sensitivity, the steps are as follows:

[0078] Set the preset value of the privacy parameter according to the preset privacy protection level;

[0079] Adjust the preset value of the privacy parameter according to the clinical sensitivity to obtain an adjusted value of the privacy parameter;

[0080] After performing anomaly processing on the adjusted value of the privacy parameter, obtain a corrected value of the privacy parameter;

[0081] Generate a privacy parameter table according to the corrected value of the privacy parameter; among them, the privacy parameter table is used to represent the mapping relationship between the field path and the corrected value of the privacy parameter.

[0082] Among them, setting the preset value of the privacy parameter according to the preset privacy protection level is specifically:

[0083] The preset privacy protection levels include strict mode, general mode, and simple mode.

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

[0085] Furthermore, adjusting the preset value of the privacy parameter according to the clinical sensitivity to obtain an adjusted value of the privacy parameter is specifically:

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

[0087] Among them, ε_final is the adjusted value of the privacy parameter, ε_base is the preset value of the privacy parameter, and Sensitivity_Weight is the clinical sensitivity.

[0088] Specifically, perform exception handling on the privacy parameter adjustment value, including:

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

[0090] S104: Perform noise addition processing on the first medical record document according to the data type and the privacy parameter table to obtain the second medical record document.

[0091] In this embodiment, in the step of performing noise addition processing on the first medical record document according to the data type and the privacy parameter table to obtain the second medical record document, the steps include:

[0092] The data type includes continuous fields and discrete fields;

[0093] According to the privacy parameter table, perform noise addition processing on the continuous fields of the first medical record document in combination with the Laplace mechanism to obtain the first processing result;

[0094] According to the privacy parameter table, perform noise addition processing on the discrete fields of the first medical record document in combination with the exponential mechanism to obtain the second processing result;

[0095] According to the first processing result and the second processing result, obtain the second medical record document.

[0096] In a specific embodiment, the process of calculating the 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 the Laplace noise.

[0098] If the value after noise addition exceeds the medical constraint conditions, truncate it to the nearest valid value and record the audit event.

[0099] In a specific embodiment, the process of performing noise addition processing on the discrete fields is as follows:

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

[0101] Output the replacement value according to the probability P(x)exp(ε·u(x)), where the utility function u(x) reflects the semantic distance from the original value;

[0102] Among them, u(x) is the candidate value.

[0103] S104 corresponds to the process of hierarchical differential privacy execution. Differential privacy technology can not only protect privacy, but also support complex statistical analysis and machine learning tasks. For example, in medical image data, by combining wavelet transform and differential privacy technology, the visual utility and classification utility of image data can be improved while protecting privacy. This technology can be widely applied in fields such as disease prediction and medical image processing. In the process of medical data sharing, differential privacy technology can effectively prevent privacy leakage, enabling medical institutions to safely share and analyze data while protecting the privacy of patients. 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 the model accuracy can be reduced while protecting privacy.

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

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

[0106] According to the constraint conditions, perform single-field verification on the second medical record document to obtain the first verification result;

[0107] According to the resampling algorithm based on Markov chain Monte Carlo, perform multi-field logical verification on the first verification result to obtain the second verification result;

[0108] Perform term consistency verification on the second verification result to obtain the medical semantic correction processing result;

[0109] According to the medical semantic correction processing result, obtain the third medical record document.

[0110] Among them, single-field verification is used to check whether the single field in the second medical record document meets the medical constraint conditions. Consistency verification is used to ensure that discrete values are within the original term set (such as diagnosis codes do not appear invalid values).

[0111] S106: Display the third medical record document.

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

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

[0114] Calculate the hash value according to the operation record;

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

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

[0117] Among them, the medical alliance chain includes multiple metadata;

[0118] The metadata includes: a data identifier, an operation type, and a privacy parameter;

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

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

[0121] The privacy parameter is used to represent the privacy parameter correction value and the clinical sensitivity.

[0122] Among them, the privacy operation audit chain provides verifiability guarantee for all operations and meets the requirements of regulations such as GDPR. A privacy protection proof is generated through zk-SNARK for verification by the regulatory party.

[0123] In a specific embodiment, taking the example of encrypting the blood glucose data of diabetic patients:

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

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

[0126] Calibration: If the noise causes the value to be -0.2, it is corrected to 3.9 (normal lower limit);

[0127] Auditing: 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 (meeting differential privacy with ε = 3), the data can still be used for clinical diagnosis (meeting the medical range), and all operations are traceable (blockchain evidence storage).

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

[0130]

[0131] Table 1: Technical comparison table

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

[0133]

[0134] Table 2: Comparative Table of Actual Performance Measurements

[0135] Among them, the lower the privacy protection uniformity, the more precise the protection difference of different sensitivity fields.

[0136] The fixed privacy budget allocation method encrypts the whole of {"vitals":{"bp":120},"diagnosis":"HIV"}, resulting in the blood pressure data being overly noisy due to the high sensitivity of HIV (ε = 0.1). In the present invention, ε = 5 is allocated to $.vitals.bp and ε = 0.1 is allocated to $.diagnosis, reducing the error of blood pressure measurement values from ±15 mmHg to ±2 mmHg.

[0137] Furthermore, when doctors or researchers need to access and use this medical record information, they first need to log in to the system through the system's identity authentication mechanism. For example, after a doctor logs in to the system using their work account and password, the system will allocate corresponding operation permissions according to their role and permissions. If the doctor is authorized to conduct medical research, then he can query and use the medical record information in the system within the authorized scope; if it is a person in the nurse position, they may only be able to view part of the information related to nursing work. When an authorized user initiates a data query request, the system will perform a decryption operation on the encrypted data according to the user's permission verification result. For example, when a researcher is conducting an epidemiological study on pneumonia, after authorization, they can query the medical record information of relevant patients. 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, the researcher can, on the premise of complying with relevant regulations and ethical guidelines, conduct data analysis and research work, such as counting the incidence and symptom manifestations of patients in different age groups, providing valuable reference for medical research.

[0138] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or posterior, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0139] It can be understood that the content in the above method embodiments is applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments. Please refer to Figure 2 , Figure 2Schematically shows an information display system for medical record documents 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] Among them, the acquisition module 201 is used to acquire the first medical record document; among them, 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 result of the field paths; among them, the classification result includes: data type and clinical sensitivity;

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

[0143] The noise addition module 204 is used to perform noise addition processing on the first medical record document according to the data type and the 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 medical record document.

[0146] This system is connected through an interface with the hospital information system to automatically collect the first medical record document in JSON format, such as the patient's basic information (name, age, gender, etc.), symptom description, examination and test results (blood routine, imaging examination, etc.), diagnosis results, etc. These data are transmitted in JSON format to the information display system for medical record documents of JSON of the present invention.

[0147] In a specific embodiment, in the step of performing noise addition processing on the first medical record document according to the data type and the privacy parameter table to obtain the second medical record document, the following steps are included:

[0148] The data type includes continuous fields and discrete fields;

[0149] According to the privacy parameter table, the continuous fields of the first medical record document are processed by adding noise in combination with the Laplace mechanism to obtain the first processing result;

[0150] According to the privacy parameter table, the discrete fields of the first medical record document are processed by adding noise in combination with the exponential mechanism to obtain the second processing result;

[0151] According to the first processing result and the second processing result, the second medical record document is obtained.

[0152] In a specific embodiment, in the step of extracting the field path of the first medical record document using JSON Path syntax and classifying it to obtain the classification result of the field path, the following steps are included:

[0153] Extract the field path of the first medical record document using JSON Path syntax;

[0154] Perform three-dimensional classification on the field path according to the medical knowledge base to obtain the classification result;

[0155] Among them, the classification result includes: data type, clinical sensitivity, and constraint conditions.

[0156] In a specific embodiment, in the step of generating a privacy parameter table according to the preset privacy protection level and clinical sensitivity, the following steps are included:

[0157] Set the preset value of the privacy parameter according to the preset privacy protection level;

[0158] Adjust the preset value of the privacy parameter according to the clinical sensitivity to obtain the adjusted value of the privacy parameter;

[0159] After performing anomaly processing on the adjusted value of the privacy parameter, obtain the corrected value of the privacy parameter;

[0160] Generate a privacy parameter table according to the corrected value of the privacy parameter; among them, the privacy parameter table is used to represent the mapping relationship between the field path and the corrected value of the privacy parameter.

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

[0162] Perform single-field verification on the second medical record document according to the constraint conditions to obtain the first verification result;

[0163] Perform multi-field logical verification on the first verification result according to the resampling algorithm based on Markov chain Monte Carlo to obtain the second verification result;

[0164] Perform term consistency verification on the second verification result to obtain the result of medical semantic correction processing;

[0165] Obtain the third medical record document according to the result of medical semantic correction processing.

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

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

[0168] Calculate the hash value according to the operation record;

[0169] Write the hash value into the medical alliance chain;

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

[0171] In a specific embodiment, the medical alliance chain includes multiple metadata;

[0172] The metadata includes: a data identifier, an operation type, and a privacy parameter;

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

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

[0175] The privacy parameter is used to represent the privacy parameter correction value and the clinical sensitivity.

[0176] Furthermore, when doctors or researchers need to access and use this medical record document information, they first need to log in to the system through the system's identity authentication mechanism. For example, after a doctor logs in to the system using their work account and password, the system will assign corresponding operation permissions according to their role and permissions. If the doctor is authorized to conduct medical research, then he can query and use the medical record document information in the system within the authorized scope; if it is a nurse, they may only be able to view some information related to nursing work. When an authorized user initiates a data query request, the system will decrypt the encrypted data according to the user's permission verification result. For example, when a researcher is conducting an epidemiological study on pneumonia, after authorization, they can query the medical record document information of relevant patients. The system uses the AES decryption algorithm and the correct key to decrypt the ciphertext and restore the data after differential privacy processing. After obtaining this data, the researcher can, on the premise of complying with relevant regulations and ethical guidelines, conduct data analysis and research work, such as counting the incidence and symptom manifestations of patients in different age groups, providing valuable reference basis for medical research.

[0177] Differential privacy technology can not only protect privacy, but also support complex statistical analysis and machine learning tasks. For example, in medical image data, by combining wavelet transform and differential privacy technology, the visual utility and classification utility of image data can be improved while protecting privacy. This technology can be widely applied in fields such as disease prediction and medical image processing. During the process of medical data sharing, differential privacy technology can effectively prevent privacy leakage, enabling medical institutions to safely share and analyze data while protecting the privacy of patients. 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 the model accuracy can be reduced while protecting privacy.

[0178] In summary, the method for encrypting JSON medical record document information based on differential privacy technology can support efficient and accurate medical data analysis while protecting the privacy of patients, and has significant application advantages.

[0179] The specific limitations of an information display system for JSON-based medical record documents can refer to the limitations of the information display method for JSON-based medical record documents in the above text, which will not be elaborated here. Each module in the above information display system for JSON-based medical record documents can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the electronic device in hardware form or be independent of it, or be stored in the memory of the electronic device in software form to facilitate the processor to call and execute the operations corresponding to the above modules.

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

[0181] In one embodiment, an electronic device is provided. The electronic device can be a server, and its internal structure diagram can be as Figure 3 shown. The electronic device includes:

[0182] A processor 801, which can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present application;

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

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

[0185] The communication interface 804 is used to implement communication interaction between this device and other devices. It can implement communication through a wired method (such as USB, network cable, etc.), or can also implement communication through a wireless method (such as a mobile network, WIFI, Bluetooth, etc.);

[0186] The bus 805 transmits information between various components of the device (such as the processor 801, the memory 802, the input / output interface 803, and the communication interface 804);

[0187] Among them, the processor 801, the memory 802, the input / output interface 803, and the communication interface 804 are communicatively connected to each other inside the device through the bus 805.

[0188] Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store the database. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for displaying information of a medical record document based on JSON.

[0189] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

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

[0191] Use JSON Path syntax to extract the field paths of the first medical record document and classify them to obtain a classification result of the field paths; wherein, the classification result includes: data type and clinical sensitivity;

[0192] Generate a privacy parameter table according to a preset privacy protection level and clinical sensitivity;

[0193] Perform noise addition processing on the first medical record document according to the data type and the privacy parameter table to obtain a second medical record document;

[0194] Perform medical semantic correction processing on the second medical record document to obtain a third medical record document;

[0195] Display the third medical record document.

[0196] In one embodiment, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

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

[0198] Use JSON Path syntax to extract the field paths of the first medical record document and classify them to obtain a classification result of the field paths; wherein, the classification result includes: data type and clinical sensitivity;

[0199] Generate a privacy parameter table according to a preset privacy protection level and clinical sensitivity;

[0200] Perform noise addition processing on the first medical record document according to the data type and the privacy parameter table to obtain a second medical record document;

[0201] Perform medical semantic correction processing on the second medical record document to obtain a third medical record document;

[0202] Display the third medical record document.

[0203] In the embodiments of the present application, an information display method and system for medical record documents based on JSON are provided. By obtaining a first medical record document in JSON format, using JSON Path syntax to extract the field paths of the first medical record document and classify them, the data types and clinical sensitivities of the field paths are obtained; a privacy parameter table is generated according to a preset privacy protection level and clinical sensitivity; according to the data types and the privacy parameter table, noise addition processing is performed on the first medical record document to obtain a second medical record document; medical semantic correction processing is performed on the second medical record document to obtain a third medical record document; and the third medical record document is displayed. The information display method for medical record documents based on JSON provided by the present invention performs noise addition processing on the first medical record document according to different data types and privacy parameter tables, so that the statistical results of the data are still available while protecting privacy; at the same time, the privacy protection parameters are dynamically adjusted according to different privacy requirements, so as to achieve a balance between data availability and privacy protection, and solve the problem that the existing information display method for medical record documents uses a fixed privacy budget allocation to protect data and cannot adapt to the sensitivity differences of multiple fields in medical records, thereby effectively improving the data confidentiality in the information display process of medical record documents. Further, the technical solution of the present invention performs medical semantic correction processing on medical record documents, which can avoid the generation of invalid data and effectively ensure the data accuracy in the information display process of medical record documents.

[0204] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, storage, database or other medium used in the embodiments provided by the present 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), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0205] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above-mentioned functions can be allocated 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 the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An information display method for medical record documents based on JSON, characterized in that the method Including: Obtain the first medical record document; wherein, the first medical record document is in JSON format; Use JSON Path syntax to extract the field paths of the first medical record document and classify them to obtain the classification result of the field paths; wherein, the classification result includes: data type and clinical sensitivity; Generate a privacy parameter table according to the preset privacy protection level and clinical sensitivity; Perform noise addition processing on the first medical record document according to the data type and the privacy parameter table to obtain the second medical record document; Perform medical semantic correction processing on the second medical record document to obtain the third medical record document; Display the third medical record document.

2. The information display method of a medical record document based on JSON according to claim 1, characterized in that In the step of performing noise addition processing on the first medical record document according to the data type and the privacy parameter table to obtain the second medical record document, the following steps are included: The data type includes continuous fields and discrete fields; Perform noise addition processing on the continuous fields of the first medical record document according to the privacy parameter table in combination with the Laplace mechanism to obtain the first processing result; Perform noise addition processing on the discrete fields of the first medical record document according to the privacy parameter table in combination with the exponential mechanism to obtain the second processing result; Obtain the second medical record document according to the first processing result and the second processing result.

3. A method for displaying information of medical record documents based on JSON according to claim 2, characterized in that, In the step of using JSON Path syntax to extract the field paths of the first medical record document and classify them to obtain the classification result of the field paths, the following steps are included: Use JSON Path syntax to extract the field paths of the first medical record document; Perform three-dimensional classification on the field paths according to the medical knowledge base to obtain the classification result; Wherein, the classification result includes: data type, clinical sensitivity and constraint conditions.

4. A method for displaying information of a medical record document based on JSON according to claim 3, characterized in that, In the step of generating a privacy parameter table according to the preset privacy protection level and clinical sensitivity, the following steps are included: Set the preset value of the privacy parameter according to the preset privacy protection level; Adjust the preset value of the privacy parameter according to the clinical sensitivity to obtain the adjusted value of the privacy parameter; Perform anomaly processing on the adjusted value of the privacy parameter to obtain the corrected value of the privacy parameter; Generate a privacy parameter table according to the corrected value of the privacy parameter; wherein, the privacy parameter table is used to represent the mapping relationship between the field path and the corrected value of the privacy parameter.

5. A method for displaying information of a medical record document based on JSON according to claim 4, characterized in that, In the step of performing medical semantic correction processing on the second medical record document to obtain the third medical record document, the following steps are included: Perform single-field verification on the second medical record document according to the constraint conditions to obtain the first verification result; Perform multi-field logical verification on the first verification result according to the resampling algorithm based on Markov chain Monte Carlo to obtain the second verification result; Perform term consistency verification on the second verification result to obtain the result of medical semantic correction processing; Obtain the third medical record document according to the result of medical semantic correction processing.

6. A method for displaying information of a medical record document based on JSON according to claim 5, characterized in that, It also includes: Obtain the operation record of the first medical record document; wherein, the operation record includes: field path, corrected value of the privacy parameter, first processing result, second processing result and operation timestamp; Calculate the hash value according to the operation record; Write the hash value into the medical consortium chain; When the corrected value of the privacy parameter is less than the preset value of the privacy parameter, obtain the privacy operation audit chain.

7. A method for displaying information of a medical record document based on JSON according to claim 6, characterized in that, The medical consortium chain includes multiple metadata; The metadata includes: data identifier, operation type and privacy parameter; Among them, the data identifier is used to represent the medical record ID; The operation type is used to represent the first processing result and the second processing result; The privacy parameter is used to represent the privacy parameter correction value and the clinical sensitivity.

8. An information display system for medical record documents based on JSON, 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; Among them, the acquisition module is used to acquire the first medical record document; among them, the first medical record document is in JSON format; The classification module is used to extract the field path of the first medical record document using JSON Path syntax and classify it to obtain the classification result of the field path; among them, the classification result includes: data type and clinical sensitivity; The generation module is used to generate a privacy parameter table according to the preset privacy protection level and clinical sensitivity; The noise addition module is used to perform noise addition processing on the first medical record document according to the data type and the privacy parameter table to obtain a second medical record document; The correction module is used to perform medical semantic correction processing on the second medical record document to obtain a third medical record document; The display module is used to display the third medical record document.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of an information display method for a medical record document based on JSON according to any one of claims 1 to 7.

10. A storage medium stores a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of an information display method for a medical record document based on JSON according to any one of claims 1 to 7.

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