A Medical Information Input Method and System Involving Minors' Infringement

Abstract

The present invention discloses a medical information entry method and system for minors-related infringements. The method includes: first, obtaining the medical information to be entered for minors-related infringements; then, performing classified label detection on the medical information to be entered according to the sensitive data search algorithm to determine the medical data to be declassified in the medical information to be entered; obtaining the mapping relationship between multiple preset types of target keywords of the medical data to be declassified and the substitution functions corresponding to the target keywords according to the medical data parsing model; and finally, based on the target keywords and the mapping relationship, generating a target entry result corresponding to the medical information to be entered in response to the entry request of the medical data to be declassified. It can improve the privacy protection level in the process of entering medical information for minors-related infringements.

Description

Technical Field

[0001] The present invention belongs to the technical field of information systems, and particularly relates to a medical information entry method and system for minors' infringement. Background Art

[0002] For the management of medical information related to minors' infringement, national judicial organs and medical institutions need to respect the personal dignity and privacy rights of minors and take special protection measures when handling cases involving minors to ensure their healthy growth. Since the crime problem related to minors' infringement is a complex social problem, the medical information related to minors' infringement involves the privacy and safety of minors and requires even stricter protection. However, the data security protection of many current medical information systems is not perfect, and there are risks of information leakage and being attacked, which has become an urgent problem to be solved. Summary of the Invention

[0003] The purpose of the present invention is to provide a medical information entry method and system for minors' infringement to solve the deficiencies in the prior art. It obtains multiple target keywords of preset types of medical data to be decrypted and the mapping relationship of substitution functions corresponding to the target keywords through a medical data analysis model, and generates a target entry result corresponding to the medical information to be entered, improving the privacy protection in the process of entering medical information related to minors' infringement.

[0004] An embodiment of the present application provides a medical information entry method for minors' infringement, and the method includes:

[0005] Obtain the medical information to be entered related to minors' infringement;

[0006] Perform classified label detection on the medical information to be entered according to the sensitive data search algorithm to determine the medical data to be decrypted in the medical information to be entered;

[0007] Obtain multiple target keywords of preset types of the medical data to be decrypted and the mapping relationship of substitution functions corresponding to the target keywords according to the medical data analysis model;

[0008] Based on the target keywords and the mapping relationship, in response to the entry request of the medical data to be decrypted, generate a target entry result corresponding to the medical information to be entered.

[0009] Optionally, before obtaining the medical information to be entered related to minors' infringement, the method further includes:

[0010] Obtain the medical data characteristics related to minors' infringement;

[0011] Determine the proportional value of the medical data feature based on the medical data feature and the historical medical privacy data related to the medical data feature;

[0012] Calculate the importance value of the medical data feature according to the proportional value and the preset coefficient related to the medical data feature; wherein, the importance value represents the degree of confidentiality of the medical data feature for the medical information to be entered related to the infringement of minors.

[0013] Optionally, the calculating the importance value of the medical data feature according to the proportional value and the preset coefficient related to the medical data feature includes:

[0014] Calculate the importance value of the medical data feature through the following formula:

[0015]

[0016] wherein, Z i represents the importance value of the i-th medical data feature, β represents the proportional value, N represents the number of medical data features, μ i represents the preset coefficient corresponding to the i-th medical data feature, and σ i represents the preset clustering degree of the i-th medical data feature.

[0017] Optionally, the obtaining the medical information to be entered related to the infringement of minors includes:

[0018] Obtain the conversation data between the medical staff and the minor during the consultation process;

[0019] Based on the conversation data, determine the injury type information related to the infringement of minors;

[0020] Based on the injury type information and the conversation data, generate a consultation feature vector;

[0021] Based on the consultation feature vector, generate the medical information to be entered during the consultation process.

[0022] Optionally, the performing the confidential label detection on the medical information to be entered according to the sensitive data search algorithm to determine the medical data to be declassified in the medical information to be entered includes:

[0023] According to the sensitive data search algorithm, add random segment signals to the sensitive attribute fields of the medical information to be entered;

[0024] Set confidential labels for the sensitive attribute fields according to the added random segment signals;

[0025] Perform the confidential label detection on the medical information to be entered to determine the medical data to be declassified in the medical information to be entered.

[0026] Optionally, the substitution function includes:

[0027] The substitution function is constructed by the following formula:

[0028]

[0029] where L T represents a substitute word for the target keyword, L represents the target keyword, α represents a preset coefficient of the medical data parsing model, represents the correspondence degree of the mapping relationship, and s represents the number of neurons in the medical data parsing model.

[0030] Optionally, the method further includes:

[0031] The cloud server receives the target input result corresponding to the medical information to be input, and during the process of receiving the target input result corresponding to the medical information to be input, performs network environment security verification on the receiving-end server; wherein, the cloud server is generated based on the consortium chain technology.

[0032] Another embodiment of the present application provides a medical information input system for minors-related infringements, and the system includes:

[0033] A first acquisition module, configured to acquire medical information to be input for minors-related infringements;

[0034] A detection module, configured to perform classified label detection on the medical information to be input according to the sensitive data search algorithm, and determine the medical data to be declassified in the medical information to be input;

[0035] A second acquisition module, configured to acquire multiple preset types of target keywords of the medical data to be declassified and the mapping relationship of the substitution function corresponding to the target keywords according to the medical data parsing model;

[0036] A generation module, configured to generate a target input result corresponding to the medical information to be input in response to an input request for the medical data to be declassified based on the target keywords and the mapping relationship.

[0037] Another embodiment of the present application provides a storage medium, in which a computer program is stored, and wherein the computer program is configured to implement the above-mentioned method when running.

[0038] Another embodiment of the present application provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to implement the above-mentioned method.

[0039] Compared with the prior art, the present invention first obtains the medical information to be entered related to the infringement of minors, then performs classified label detection on the medical information to be entered according to the sensitive data search algorithm to determine the medical data to be decrypted in the medical information to be entered, obtains the target keywords of multiple preset types of the medical data to be decrypted and the mapping relationship of the substitution function corresponding to the target keywords according to the medical data parsing model, and finally generates the target entry result corresponding to the medical information to be entered based on the target keywords and the mapping relationship in response to the entry request of the medical data to be decrypted. It can improve the privacy protection level in the process of entering medical information related to the infringement of minors. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 FIG. is a block diagram of the hardware structure of a computer terminal for a method of entering medical information related to the infringement of minors provided by an embodiment of the present invention;

[0041] Figure 2 FIG. is a schematic flowchart of a method of entering medical information related to the infringement of minors provided by an embodiment of the present invention;

[0042] Figure 3 FIG. is a schematic diagram of the modules of a system for entering medical information related to the infringement of minors provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present invention and should not be construed as limiting the present invention.

[0044] An embodiment of the present invention first provides a method of entering medical information related to the infringement of minors. This method can be applied to electronic devices, such as computer terminals, specifically, such as ordinary computers, quantum computers, etc.

[0045] The following takes running on a computer terminal as an example to explain it in detail. Figure 1 FIG. is a block diagram of the hardware structure of a computer terminal for a method of entering medical information related to the infringement of minors provided by an embodiment of the present invention. As Figure 1 shown, the computer terminal may include one or more ( Figure 1 only one is shown in the figure) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the above computer terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown in the figure is only schematic and does not limit the structure of the above computer terminal. For example, the computer terminal may further include more or fewer components than Figure 1 shown in the figure, or have the same asFigure 1 The different configurations shown.

[0046] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / modules corresponding to the medical information entry method for minors' infringement in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the computer terminal through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0047] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by a communication provider of the computer terminal. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0048] See Figure 2 , Figure 2 is a schematic flowchart of a method for entering medical information for minors' infringement provided by an embodiment of the present invention, and may include the following steps:

[0049] S201: Obtain the medical information to be entered for minors' infringement.

[0050] The medical information to be entered refers to the patient information that has not been entered into the medical system or database, including information such as the patient's basic information, condition, diagnosis result, treatment plan, etc. This information is crucial for doctors to make correct diagnoses and treatment plans, so it needs to be entered into the medical system or database in a timely and accurate manner.

[0051] It should be noted that to obtain the medical information to be entered for minors' infringement, it is first necessary to clarify the scope and source of the medical information to be entered for minors' infringement, such as medical records, diagnostic reports, inspection reports, etc. involving minors.

[0052] Before obtaining the medical information to be entered for minors' infringement, the method may further include:

[0053] 1. Obtain the medical data characteristics related to underage abuse.

[0054] Specifically, obtaining the medical data characteristics related to underage abuse is an important basis for relevant research and preventing underage people from being abused. Among them, the medical data characteristics can at least include:

[0055] Injury type and location: In the medical data related to underage abuse, common injury types include fractures, contusions, lacerations, etc., and the injury locations may involve the head, torso, limbs, etc. These characteristics can reflect the severity and nature of the abuse behavior.

[0056] Scars and marks: In the medical data related to underage abuse, there may be some special scars and marks, such as knife wounds, gunshot wounds, beating injuries, etc. These characteristics can provide information about the means and tools of abuse, which is of great significance for identifying the suspects and the nature of the case.

[0057] Injuries to body tissues and organs: In the medical data related to underage abuse, there may be injuries to body tissues and organs, such as internal organ injuries, sexual organ injuries, etc. These characteristics can reflect the violence and cruelty of the abuse behavior, which is of great significance for evaluating the degree of injury and prognosis of the victim.

[0058] Mental state and psychological condition: In the medical data related to underage abuse, the mental state and psychological condition of the victim may be involved, such as emotional instability, anxiety, depression, etc. These characteristics can reflect the impact of the abuse behavior on the physical and mental health of the victim, which is of great significance for evaluating the psychological support and intervention needed by the victim.

[0059] Disease course and treatment plan: In the medical data related to underage abuse, the disease course and treatment plan are also one of the important characteristics. For example, the need for surgery, special care or long-term rehabilitation may indicate a more serious degree of injury to the victim. These characteristics can reflect the evaluation of the victim's condition and treatment plan by medical professionals, which is of great significance for formulating corresponding treatment plans and prognostic measures.

[0060] 2. Determine the proportional value of the medical data characteristics based on the medical data characteristics and the historical medical privacy data related to the medical data characteristics.

[0061] Specifically, the proportional value of the medical data characteristics can be determined by statistically analyzing the historical medical privacy data.

[0062] For example, first clean and preprocess the historical medical privacy data to remove irrelevant information and noisy data, ensuring the accuracy and reliability of the data. Extract relevant features from the medical data according to the problems to be solved. These features can include, but are not limited to, the basic information of the patient, the condition, the diagnosis result, the treatment plan, etc. Select features related to the medical data features and exclude irrelevant features to reduce the computational complexity and improve the accuracy of the model. Conduct statistical analysis on the selected features and calculate the proportional value of each feature. For example, this can be achieved by using statistical software or statistical analysis libraries in programming languages. Finally, interpret and evaluate the medical data features based on the calculated feature proportional values. For example, the occurrence frequency and distribution of a certain feature in the historical medical privacy data can be analyzed to understand the importance and impact degree of this feature in the medical data.

[0063] 3. Calculate the importance value of the medical data feature according to the proportional value and a preset coefficient related to the medical data feature; wherein, the importance value characterizes the degree of confidentiality of the medical data feature for the medical information to be entered related to the infringement of minors.

[0064] Specifically, calculating the importance value of the medical data feature according to the proportional value and the preset coefficient can further evaluate the degree of confidentiality of the medical data feature for the medical information to be entered related to the infringement of minors.

[0065] First, the preset coefficient is a parameter used to adjust the feature proportional value and can be set according to the actual situation. The value range of the preset coefficient can be adjusted according to actual needs and is usually between 0 and 1. According to the feature proportional value and the preset coefficient, the importance value of each medical data feature can be calculated. The calculation formula of the importance value can be designed according to actual needs, and generally, the weighted average or weighted summation method is adopted. The importance value can be used to evaluate the degree of confidentiality of the medical data feature for the medical information to be entered related to the infringement of minors. The higher the importance value, the greater the contribution of this feature to the degree of confidentiality, and more attention needs to be paid to the confidentiality measures for this feature. Finally, corresponding confidentiality strategies can be formulated according to the evaluation results of the importance value. For example, for features with higher importance values, more strict encryption and access control measures can be adopted to ensure the security and confidentiality of the data.

[0066] Exemplarily, the calculating the importance value of the medical data feature according to the proportional value and a preset coefficient related to the medical data feature may include:

[0067] Calculate the importance value of the medical data feature through the following formula:

[0068]

[0069] wherein, Z irepresents the importance value of the i-th medical data feature, β represents the proportional value, N represents the number of medical data features, μ i represents the preset coefficient corresponding to the i-th medical data feature, σ i represents the preset clustering degree of the i-th medical data feature.

[0070] Specifically, the obtaining of the medical information to be entered related to the infringement on minors may include:

[0071] Step 1: Obtain the conversation data between the medical staff and the minor during the consultation process.

[0072] Step 2: Based on the conversation data, determine the information on the type of injury related to the infringement on minors.

[0073] Exemplarily, preprocess the conversation data, including operations such as denoising, word segmentation, and part-of-speech tagging, and then extract the features related to the type of injury from the conversation data. These features may include keywords, sentiment tendencies, topics, etc. For example, keywords related to physical injury, psychological injury, sexual assault, etc. can be extracted, or the emotions and topic content expressed in the conversation can be analyzed. The extracted features and the labeled information on the type of injury can be used to train a classification model. By selecting appropriate classification algorithms, such as Naive Bayes, Support Vector Machine, Neural Network, etc., the model can be selected and adjusted according to actual needs. Finally, use the trained classification model to classify and predict new conversation data to determine the type of injury involved in the conversation. According to the classification results, the conversation data can be further processed and analyzed to deeply understand the situation of the infringement on minors.

[0074] Step 3: Based on the information on the type of injury and the conversation data, generate a consultation feature vector.

[0075] First, preprocess the conversation data, including operations such as denoising, standardization, and word segmentation, to improve the accuracy and readability of the data. At the same time, classify and label the information on the type of injury for subsequent feature extraction and model training. Extract the features related to the type of injury from the conversation data. These features may include voice features, text features, semantic features, etc. For example, parameters such as pitch, intensity, and duration in the voice can be extracted, or features such as keywords and sentiment tendencies can be extracted from the text. Select the features related to the type of injury and exclude the irrelevant features. Feature selection algorithms, such as statistics-based methods and model-based methods, can be used to select the most important features. Integrate the selected features into a feature vector. The length of the feature vector depends on the number and type of the selected features. Evaluate the quality and effectiveness of the generated feature vector. Some evaluation metrics, such as accuracy and recall, can be used to measure the performance of the feature vector in classifying or identifying the type of injury. According to the evaluation results, the feature vector can be adjusted and optimized.

[0076] Through the above steps, a feature vector can be generated based on the injury type information and session data. This feature vector can be used for further analysis and processing, such as classification, clustering, association rule mining, etc., to deeply understand the injuries suffered by minors and provide more accurate and comprehensive diagnosis and treatment plans.

[0077] Step 4: Generate medical information to be entered during the consultation based on the consultation feature vector.

[0078] To integrate the consultation feature vector with the existing medical information to form a complete medical information to be entered, the following steps are required:

[0079] First, extract key information from the consultation feature vector, such as injury type, symptoms, medical history, etc., and organize it into a structured data format. Then, organize the extracted information into a report, which can include detailed information such as the medical history, symptoms, injury type, treatment plan, etc. of the minor. Finally, enter the generated report into the medical information system or electronic medical record for doctors to carry out subsequent diagnosis and treatment work.

[0080] S202: According to the sensitive data search algorithm, perform classified label detection on the medical information to be entered, and determine the classified medical data to be declassified in the medical information to be entered.

[0081] Specifically, the sensitive data search algorithm is a search algorithm that can perform automated classified label detection on medical information according to preset rules and conditions.

[0082] For example, according to the privacy protection regulations and laws and regulations in the medical industry, define the labels of classified medical data, such as sensitive information such as the name, ID number, contact phone number, address, etc. of minors. Use the sensitive data search algorithm to scan the medical information to be entered and match it with the preset classified labels. If the information to be entered contains data that matches the classified labels, mark it as classified medical data to be declassified. Through automated tools or programs, perform real-time or batch detection on medical information to identify and mark classified medical data to be declassified. For the results of automatic detection, manual review can also be carried out to ensure the accuracy and reliability of the classified labels. For the classified medical data marked to be declassified, take appropriate declassification measures, such as replacement, deletion, or encryption, etc., to protect the privacy and safety of minors.

[0083] In an alternative embodiment, the performing classified label detection on the medical information to be entered according to the sensitive data search algorithm and determining the classified medical data to be declassified in the medical information to be entered may include:

[0084] a. Add random fragment signals to the sensitive attribute fields of the medical information to be entered according to the sensitive data search algorithm.

[0085] Adding random fragment signals to the sensitive attribute fields of the medical information to be entered according to the sensitive data search algorithm can improve the security and confidentiality of the data during data storage, transmission, and processing.

[0086] First, determine the fields containing sensitive information in the medical information to be entered, such as the names of minors, ID numbers, contact phone numbers, etc. Generate random fragment signals of the corresponding length according to the length and format of the sensitive attribute fields. These signals can be random numbers, letters, or characters to ensure that they are not associated with the original data. Merge or superimpose the generated random fragment signals with the sensitive attribute fields to generate data with random fragment signals. This process can be achieved through programming or automated tools to ensure the accuracy and consistency of the random fragment signals. Store or transmit the data with random fragment signals. Due to the existence of the random fragment signals, even if the data is intercepted or stolen, it is not easy to restore the original sensitive information, thus improving the security and confidentiality of the data. When the data needs to be used, remove the random fragment signals in the sensitive attribute fields through appropriate algorithms or programs to restore the original data.

[0087] By adding random fragment signals, the security and confidentiality of the data can be improved during data storage, transmission, and processing. At the same time, this method can also be used to protect the privacy and security of minors and prevent sensitive information from being improperly disclosed or misused.

[0088] b. Set confidential labels for the sensitive attribute fields according to the added random fragment signals.

[0089] Exemplarily, setting confidential labels for the sensitive attribute fields can be achieved through the following steps:

[0090] First, it is necessary to determine which sensitive attribute fields need to be marked as confidential. These fields usually contain important or private information, such as ID numbers, contact numbers, addresses, etc. Generate a random label for each confidential attribute field. These labels can be a combination of numbers, letters, or characters to ensure that they are not associated with the original data. Add the generated random fragment signals (i.e., labels) to the corresponding sensitive attribute fields. This process can be achieved through programming or automated tools to ensure the accuracy and consistency of the labels. Store or transmit the data with labels. Due to the existence of labels, even if the data is intercepted or stolen, it is not easy to restore the original sensitive information, thus improving the security and confidentiality of the data. Ensure that each confidential attribute field has a unique label. Methods such as unique identifiers or hash functions can be used to ensure the uniqueness of the labels. For the already set confidential labels, a management system can be set up to track and update these labels. When some fields no longer need to be marked as confidential, the corresponding labels can be revoked.

[0091] c. Perform the detection of the confidential label for the to-be-entered medical information to determine the to-be-declassified medical data in the to-be-entered medical information.

[0092] Specifically, by performing the detection of the confidential label, the to-be-declassified medical data can be effectively identified and marked, ensuring the legal, safe, and effective utilization of the data.

[0093] S203: According to the medical data parsing model, obtain multiple target keywords of preset types for the to-be-declassified medical data and the mapping relationship of the substitution functions corresponding to the target keywords.

[0094] First, according to the characteristics of the medical data and the privacy protection requirements, define multiple target keywords of preset types. These keywords are usually sensitive or privacy-related information, such as the names of minors, ID numbers, contact numbers, etc. Use a large amount of labeled medical data to train a medical data parsing model, which can identify and extract the target keywords in the to-be-declassified medical data. Use the trained medical data parsing model to parse the to-be-declassified medical data and extract the target keywords therein. For each target keyword, define one or more substitution functions. The role of the substitution function is to replace the target keyword with other meaningless characters, strings, or random values.

[0095] For example, the substitution function can include: constructing the substitution function through the following arithmetic expression:

[0096]

[0097] where L T represents the substitute word of the target keyword, L represents the target keyword, and α represents the preset coefficient of the medical data parsing model. It represents the correspondence degree of the mapping relationship, and s represents the number of neurons in the medical data parsing model.

[0098] Establish a mapping relationship table to associate each target keyword with its corresponding substitution function. This mapping relationship table can be in the form of key-value pairs, where the key is the target keyword and the value is the corresponding substitution function. When processing the medical data to be decrypted, replace the target keyword with its corresponding substitution function according to the mapping relationship table to achieve the decryption process of the data.

[0099] S204: Based on the target keyword and the mapping relationship, in response to the input request of the medical data to be decrypted, generate the target input result corresponding to the medical information to be input.

[0100] Specifically, based on the target keyword and the mapping relationship, in response to the input request of the medical data to be decrypted, generating the target input result corresponding to the medical information to be input can be achieved through the following steps:

[0101] Receive the input request of the medical data to be decrypted. For example, when there is medical data to be input, the system receives this request and obtains the medical data to be decrypted. Use the medical data parsing model to parse the medical data to be decrypted and identify the target keyword therein. According to the established mapping relationship table, replace the identified target keyword with its corresponding substitution function. Generate the target input result in accordance with the format or standard specified by the system for the processed medical data to be decrypted.

[0102] Through the above steps, based on the target keyword and the mapping relationship, the target input result corresponding to the medical information to be input can be generated. This method can protect the privacy and security of minors, and at the same time meet the privacy protection regulations and laws and regulations in the medical industry.

[0103] In an optional implementation manner, the method may further include:

[0104] The cloud server receives the target input result corresponding to the medical information to be input and performs network environment security verification on the receiving-end server during the process of receiving the target input result corresponding to the medical information to be input; wherein, the cloud server is generated based on the consortium blockchain technology.

[0105] Specifically, the cloud server receives the target input result corresponding to the medical information to be input. When the receiving server sends the target input result, the cloud server receives it. During the receiving process, the cloud server performs security verification on the network environment of the receiving server, which includes verifying the identity of the server, checking whether the network communication is encrypted, and preventing malicious attacks. The cloud server generates relevant data or information based on the consortium blockchain technology. Among them, the consortium blockchain technology can ensure the traceability, authenticity, and immutability of data, thereby ensuring the credibility and security of medical information.

[0106] Through the above steps, the cloud server can securely receive the target input result corresponding to the medical information to be input and generate relevant data or information based on the consortium blockchain technology. This verification can ensure the security of the network environment, prevent data from being tampered with or stolen, and protect the privacy and security of minors.

[0107] It can be seen that the present invention first obtains the medical information to be input related to the infringement of minors, then performs classified label detection on the medical information to be input according to the sensitive data search algorithm to determine the medical data to be decrypted in the medical information to be input, obtains multiple preset types of target keywords of the medical data to be decrypted and the mapping relationship of the substitution function corresponding to the target keywords according to the medical data analysis model, and finally generates the target input result corresponding to the medical information to be input based on the target keywords and the mapping relationship in response to the input request of the medical data to be decrypted. It can improve the degree of privacy protection in the process of inputting medical information related to the infringement of minors.

[0108] See Figure 3 , Figure 3 which is a schematic diagram of the modules of a medical information input system related to the infringement of minors provided by an embodiment of the present invention, and may include the following modules:

[0109] The first acquisition module 301 is used to acquire the medical information to be input related to the infringement of minors;

[0110] The detection module 302 is used to perform classified label detection on the medical information to be input according to the sensitive data search algorithm to determine the medical data to be decrypted in the medical information to be input;

[0111] The second acquisition module 303 is used to acquire multiple preset types of target keywords of the medical data to be decrypted and the mapping relationship of the substitution function corresponding to the target keywords according to the medical data analysis model;

[0112] The generation module 304 is used to generate the target input result corresponding to the medical information to be input based on the target keywords and the mapping relationship in response to the input request of the medical data to be decrypted.

[0113] Compared with the prior art, the present invention first obtains the medical information to be entered related to the infringement of minors, then performs classified label detection on the medical information to be entered according to the sensitive data search algorithm to determine the medical data to be declassified in the medical information to be entered, obtains the target keywords of multiple preset types of the medical data to be declassified and the mapping relationship of the substitution function corresponding to the target keywords according to the medical data parsing model, and finally generates the target entry result corresponding to the medical information to be entered based on the target keywords and the mapping relationship in response to the entry request of the medical data to be declassified. It can improve the privacy protection level in the process of entering medical information related to the infringement of minors.

[0114] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, and the computer program is configured to implement the steps in the above method embodiment when running.

[0115] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps:

[0116] S201: Obtain the medical information to be entered related to the infringement of minors;

[0117] S202: Perform classified label detection on the medical information to be entered according to the sensitive data search algorithm to determine the medical data to be declassified in the medical information to be entered;

[0118] S203: Obtain the target keywords of multiple preset types of the medical data to be declassified and the mapping relationship of the substitution function corresponding to the target keywords according to the medical data parsing model;

[0119] S204: Generate the target entry result corresponding to the medical information to be entered based on the target keywords and the mapping relationship in response to the entry request of the medical data to be declassified.

[0120] Specifically, in this embodiment, the above storage medium may include but is not limited to: various media that can store computer programs such as USB flash drives, read-only memories (ROM for short), random access memories (RAM for short), mobile hard disks, magnetic disks or optical discs.

[0121] Compared with the prior art, the present invention first obtains the medical information to be entered related to the infringement of minors, then performs classified label detection on the medical information to be entered according to the sensitive data search algorithm to determine the medical data to be declassified in the medical information to be entered, obtains multiple preset types of target keywords of the medical data to be declassified and the mapping relationship of the substitution function corresponding to the target keywords according to the medical data parsing model, and finally generates the target entry result corresponding to the medical information to be entered based on the target keywords and the mapping relationship in response to the entry request of the medical data to be declassified. It can improve the privacy protection level in the process of entering medical information related to the infringement of minors.

[0122] An embodiment of the present invention further provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in the above method embodiment.

[0123] Specifically, the above electronic device may further include a transmission device and an input / output device. Among them, the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0124] Specifically, in this embodiment, the above processor may be configured to execute the following steps through a computer program:

[0125] S201: Obtain the medical information to be entered related to the infringement of minors;

[0126] S202: Perform classified label detection on the medical information to be entered according to the sensitive data search algorithm to determine the medical data to be declassified in the medical information to be entered;

[0127] S203: Obtain multiple preset types of target keywords of the medical data to be declassified and the mapping relationship of the substitution function corresponding to the target keywords according to the medical data parsing model;

[0128] S204: Generate the target entry result corresponding to the medical information to be entered based on the target keywords and the mapping relationship in response to the entry request of the medical data to be declassified.

[0129] Compared with the prior art, the present invention first obtains the medical information to be entered related to the infringement of minors, then performs classified label detection on the medical information to be entered according to the sensitive data search algorithm, determines the medical data to be declassified in the medical information to be entered, obtains the target keywords of multiple preset types of the medical data to be declassified and the mapping relationship of the substitution function corresponding to the target keywords according to the medical data parsing model, and finally generates the target entry result corresponding to the medical information to be entered based on the target keywords and the mapping relationship in response to the entry request of the medical data to be declassified. It can improve the privacy protection level in the process of entering medical information related to the infringement of minors.

[0130] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0131] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] In several embodiments provided by the present invention, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the above division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0133] The units described as separate components above may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, the functional units in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0135] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods in various embodiments of the present invention. The aforementioned memory includes various media that can store program codes, such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0136] The above has introduced the embodiments of the present invention in detail. Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A medical information entry method involving minors' infringement, characterized in that, The method includes: Obtaining the important value of the medical data feature and the medical information to be entered related to the infringement of minors; among them, obtaining the important value of the medical data feature includes: Obtaining the medical data feature related to the infringement of minors; based on the medical data feature and the historical medical privacy data related to the medical data feature, determining the proportional value of the medical data feature; according to the proportional value and the preset coefficient related to the medical data feature, calculating the important value of the medical data feature; wherein, the important value represents the degree of confidentiality of the medical data feature for the medical information to be entered related to the infringement of minors. Performing a confidential label detection on the medical information to be entered according to the sensitive data search algorithm to determine the medical data to be decrypted in the medical information to be entered. Obtaining the target keywords of multiple preset types of the medical data to be decrypted and the mapping relationship of the substitution function corresponding to the target keywords according to the medical data parsing model; wherein, the substitution function is constructed by the following formula: Among them, represents a substitute word for the target keyword, represents the target keyword, represents a preset coefficient of the medical data analysis model, represents the correspondence degree of the mapping relationship, represents the number of neurons in the medical data analysis model; Based on the target keywords and the mapping relationship, in response to the input request of the medical data to be decrypted, generating the target input result corresponding to the medical information to be entered.

2. The method according to claim 1, wherein The calculating the important value of the medical data feature according to the proportional value and the preset coefficient related to the medical data feature includes: Calculating the important value of the medical data feature through the following formula: Among them, represents the importance value of the th medical data feature, represents the proportional value, represents the number of medical data features, represents the th preset coefficient corresponding to the medical data feature, represents the th preset clustering degree of the medical data feature.

3. The method according to claim 2, wherein The obtaining the medical information to be entered related to the infringement of minors includes: Obtaining the conversation data between the medical staff and the minor during the consultation process. Based on the conversation data, determining the injury type information related to the infringement of minors. Based on the injury type information and the conversation data, generating a consultation feature vector. Based on the consultation feature vector, generating the medical information to be entered for representing the consultation process.

4. The method according to claim 1 or 3, characterized in that, The performing a confidential label detection on the medical information to be entered according to the sensitive data search algorithm to determine the medical data to be decrypted in the medical information to be entered includes: Adding a random fragment signal to the sensitive attribute field of the medical information to be entered according to the sensitive data search algorithm. Setting a confidential label for the sensitive attribute field according to the added random fragment signal. Performing a confidential label detection on the medical information to be entered to determine the medical data to be decrypted in the medical information to be entered.

5. The method according to any one of claims 1 to 4, characterized in that The method further includes: The cloud server receives the target input result corresponding to the medical information to be entered and performs a network environment security verification on the receiving-end server during the receiving process of the target input result corresponding to the medical information to be entered; wherein, the cloud server is generated based on the consortium chain technology.

6. A medical information entry system involving minors' infringement, characterized in that, The system includes: A first obtaining module, configured to obtain the important value of the medical data feature and the medical information to be entered related to the infringement of minors; among them, obtaining the important value of the medical data feature includes: Obtain the medical data characteristics of minors-related infringements; determine the proportional value of the medical data characteristics based on the medical data characteristics and the historical medical privacy data related to the medical data characteristics; calculate the importance value of the medical data characteristics according to the proportional value and the preset coefficient related to the medical data characteristics; wherein, the importance value represents the degree of confidentiality of the medical data characteristics for the medical information to be entered regarding minors-related infringements; A detection module, configured to perform a confidentiality label detection on the medical information to be entered according to a sensitive data search algorithm, and determine the medical data to be decrypted in the medical information to be entered; A second acquisition module, configured to obtain multiple preset types of target keywords of the medical data to be decrypted and the mapping relationship of the substitution function corresponding to the target keywords according to a medical data parsing model; wherein, the substitution function is constructed by the following formula: Among them, represents a substitute word for the target keyword, represents the target keyword, represents a preset coefficient of the medical data analysis model, represents the correspondence degree of the mapping relationship, represents the number of neurons in the medical data analysis model; A generation module, configured to generate a target input result corresponding to the medical information to be entered based on the target keywords and the mapping relationship in response to an input request for the medical data to be decrypted; 7. A storage medium, characterized in that, A computer program is stored in the storage medium, wherein the computer program is configured to implement the method according to any one of claims 1-5 when running.

8. An electronic device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is configured to run the computer program to implement the method according to any one of claims 1-5.

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