WO2021151326A1 - Procédé et appareil de criblage de dossiers médicaux électroniques sur la base d'un réseau adverse, dispositif et support - Google Patents

Procédé et appareil de criblage de dossiers médicaux électroniques sur la base d'un réseau adverse, dispositif et support Download PDF

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WO2021151326A1
WO2021151326A1 PCT/CN2020/124219 CN2020124219W WO2021151326A1 WO 2021151326 A1 WO2021151326 A1 WO 2021151326A1 CN 2020124219 W CN2020124219 W CN 2020124219W WO 2021151326 A1 WO2021151326 A1 WO 2021151326A1
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data
discrimination
discriminator
simulation
loss value
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PCT/CN2020/124219
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Chinese (zh)
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李彦轩
唐蕊
孙行智
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • This application relates to the field of machine learning, and in particular to a method, device, equipment, and medium for screening electronic medical records based on an adversarial network.
  • the medical record is used to record the treatment information generated by the patient during the diagnosis and treatment process, which is of great significance to the doctor's diagnosis and treatment.
  • medical records are gradually becoming electronic, and the formed electronic medical records are stored in the electronic medical record database of the hospital.
  • a screening method for electronic medical records based on a confrontation network including:
  • the comprehensive discriminator is used to process the medical record to be screened, and the processing result of the medical record to be screened is obtained.
  • An electronic medical record screening device based on a confrontation network including:
  • the first generating module is used to generate simulated misdiagnosis data through the first generator
  • the second generating module is used to generate simulated missed diagnosis data through the second generator
  • a training module configured to obtain real normal data, and use the real normal data, the simulated misdiagnosis data, and the simulated misdiagnosis data to train the initial comprehensive discriminator;
  • the determining discriminator module is used to determine the trained initial comprehensive discriminator as the comprehensive discriminator after the training is completed;
  • the screening module is configured to use the comprehensive discriminator to process the medical record to be screened, and obtain the processing result of the medical record to be screened.
  • a computer device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, and the processor implements the following steps when the processor executes the computer-readable instructions:
  • the comprehensive discriminator is used to process the medical record to be screened, and the processing result of the medical record to be screened is obtained.
  • One or more readable storage media storing computer readable instructions, when the computer readable instructions are executed by one or more processors, the one or more processors execute the following steps:
  • the comprehensive discriminator is used to process the medical record to be screened, and the processing result of the medical record to be screened is obtained.
  • the above-mentioned anti-network-based electronic medical record screening method, device, computer equipment and storage medium generate simulated misdiagnosis data through the first generator to generate a large amount of simulated misdiagnosis data close to real, and improve the initial comprehensive discriminator’s ability to discriminate misdiagnosis data .
  • the simulated missed diagnosis data is generated by the second generator to generate a large amount of simulated missed diagnosis data close to the real, and the initial comprehensive discriminator's ability to discriminate the missed diagnosis data is improved.
  • the discriminative ability of the model Huge improvements.
  • the trained initial comprehensive discriminator is determined as the comprehensive discriminator to obtain a discriminator that can screen medical records.
  • the comprehensive discriminator is used to process the medical records to be screened, and the processing results of the medical records to be screened are obtained.
  • the comprehensive discriminator is used to screen whether the medical records are normal, which can improve the accuracy and efficiency of medical record screening, and reduce the medical records The cost of screening.
  • This application can solve the screening problem of electronic medical records. This application can be applied to the smart medical field of smart cities, so as to promote the construction of smart cities.
  • FIG. 1 is a schematic diagram of an application environment of an electronic medical record screening method based on a confrontation network in an embodiment of the present application
  • FIG. 2 is a schematic flowchart of a method for screening electronic medical records based on a confrontation network in an embodiment of the present application
  • FIG. 3 is a schematic flowchart of an electronic medical record screening method based on a confrontation network in an embodiment of the present application
  • FIG. 4 is a schematic flowchart of an electronic medical record screening method based on a confrontation network in an embodiment of the present application
  • FIG. 5 is a schematic flowchart of a method for screening electronic medical records based on a confrontation network in an embodiment of the present application
  • FIG. 6 is a schematic flowchart of a method for screening electronic medical records based on a confrontation network in an embodiment of the present application
  • FIG. 7 is a schematic flowchart of an electronic medical record screening method based on a confrontation network in an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of an electronic medical record screening device based on a confrontation network in an embodiment of the present application.
  • Fig. 9 is a schematic diagram of a computer device in an embodiment of the present application.
  • the electronic medical record screening method based on the confrontation network can be applied in the application environment as shown in FIG. 1, in which the client communicates with the server.
  • the client includes, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
  • the server can be implemented with an independent server or a server cluster composed of multiple servers.
  • a method for screening electronic medical records based on a confrontation network is provided.
  • the method is applied to the server in FIG. 1 as an example for description, including the following steps:
  • the first generator refers to a simulation generator obtained after training by a Generative Adversarial Network (GAN), and is used to generate simulated misdiagnosis data.
  • GAN Generative Adversarial Network
  • the first generator can generate a large amount of simulated misdiagnosis data close to the real, ensuring that the initial comprehensive discriminator has enough misdiagnosis training data to improve the ability to discriminate misdiagnosed data.
  • Simulated misdiagnosis data is a type of medical record data, which refers to medical records that have misdiagnosis problems.
  • the second generator is also trained by a Generative Adversarial Network (GAN) to obtain a simulation generator for generating simulated missed diagnosis data.
  • GAN Generative Adversarial Network
  • the second generator can generate a large amount of simulated missed diagnosis data that is close to real, ensuring that the initial comprehensive discriminator has enough missed diagnosis training data to improve the ability to discriminate the missed diagnosis data.
  • the simulated missed diagnosis data is a type of medical record data, which refers to the medical records that have the problem of missed diagnosis.
  • the true normal data refers to the true medical record data without any missed diagnosis or misdiagnosis.
  • the initial comprehensive discriminator is a three-class classifier.
  • the discriminant data of the initial comprehensive discriminator can be returned to the first generator and the second generator to improve the correlation between the first generator, the second generator and the initial comprehensive discriminator (based on the loss Function) to further improve the discrimination ability of the initial comprehensive discriminator.
  • the discriminant data of the initial comprehensive discriminator converges.
  • the trained initial comprehensive discriminator can be determined as the comprehensive discriminator.
  • the comprehensive discriminator can be used to discriminate the type of medical record data.
  • the initial comprehensive discriminator combines the simulated data of the first generator and the second generator (including simulated missed diagnosis data and simulated misdiagnosis data), and the final comprehensive discriminator has good discrimination capabilities and can accurately distinguish the types of electronic medical records.
  • the medical record to be screened refers to the medical record that needs to be screened.
  • the comprehensive discriminator is used to process the medical records to be screened, and the processing results of the medical records to be screened can be obtained.
  • the simulated misdiagnosis data is generated by the first generator to generate a large amount of simulated misdiagnosis data close to the real, and the initial comprehensive discriminator's ability to discriminate the misdiagnosis data is improved.
  • the simulated missed diagnosis data is generated by the second generator to generate a large amount of simulated missed diagnosis data close to the real, and the initial comprehensive discriminator's ability to discriminate the missed diagnosis data is improved.
  • the trained initial comprehensive discriminator is determined as the comprehensive discriminator to obtain a discriminator that can screen medical records.
  • the comprehensive discriminator is used to process the medical records to be screened, and the processing results of the medical records to be screened are obtained.
  • the comprehensive discriminator is used to screen whether the medical records are normal, which can improve the accuracy and efficiency of medical record screening, and reduce the medical records The cost of screening.
  • step S10 that is, before generating simulated misdiagnosis data by the first generator, further includes:
  • the first initial generator receives the first random noise, and generates the first simulation data
  • the first discriminator receives the true misdiagnosis data with a tag of 1, and generates first real discrimination data; the first discriminator receives the first simulation data with a tag of 0, and generates first simulation discrimination data;
  • the first confrontation neural network includes a first initial generator and a first discriminator.
  • the first random noise can be generated by a random algorithm.
  • the generator will obtain a larger first generation loss value, and adjust the calculation parameters in the first initial generator according to the first generation loss value, so that the data generated by the first initial generator gradually approaches the true misdiagnosis data.
  • the first discriminator makes a misjudgment when discriminating the first analog data, it will also obtain a larger first discrimination loss value, and adjust the first discriminator according to the first discrimination loss value Calculating parameters in, so that it has a stronger ability to distinguish between the first simulated data and the real misdiagnosed data.
  • Repeating the step of updating the first discriminator refers to repeating the steps related to the first discriminator in steps S101-S104.
  • Repeating the step of updating the first generator refers to repeating the steps related to the first initial generator in steps S101-S104.
  • the update steps of the first discriminator and the first initial generator are performed simultaneously.
  • the first simulation judgment data is in the first preset range, it can be considered that the first adversarial neural network meets the first preset termination condition.
  • the first discrimination data of the first simulation data of the first discriminator is 0.5, it is difficult for the first discriminator to determine whether the first simulation data output by the first initial generator is true. That is, the first simulation data generated by the first generator is very similar to the real misdiagnosis data. At this time, the first adversarial neural network has reached convergence.
  • the first initial generator receives the first random noise and generates first simulation data, where the first initial generator continuously generates new first simulation data .
  • the first discriminator receives the true misdiagnosis data with a label of 1, and generates first real discrimination data; the first discriminator receives the first simulation data with a label of 0, and generates first simulation discrimination data, where, The first discriminator simultaneously discriminates the real misdiagnosed data and the first simulated data, which can improve the ability of the first discriminator to discriminate the misdiagnosed data.
  • the first discriminator is updated according to the first discriminating loss value, and the first initial generator is updated according to the first generation loss value.
  • the parameters of the respective models are gradually updated through the loss value to improve the model’s performance Accuracy.
  • the preset range is used to complete the training of the model.
  • the first initial generator that satisfies the first preset termination condition is determined as the first generator, so as to obtain a first generator that can be used to generate first simulation data.
  • step S103 the calculation of the first discrimination loss value of the first discriminator according to the first real discrimination data and the first simulation discrimination data includes:
  • x r1 represents the true misdiagnosis data
  • D 1 (x) represents the first true discrimination data
  • E is the expected calculation symbol
  • x f1 represents the first simulation data
  • z 1 represents the first random noise
  • G 1 (z 1 ) represents the first simulation data
  • D 1 (G 1 (z 1 )) represents the first discrimination loss value
  • step S103 the calculating the first generation loss value of the first initial generator according to the first simulation discrimination data includes:
  • D 1 (x f1 ) is the first simulation discrimination data
  • D 3 (x f1 ) is the discrimination data generated after the initial comprehensive discriminator discriminates the first simulation data
  • ⁇ is a hyperparameter
  • G 1 refers to the first initial generator
  • D 1 refers to the first discriminator.
  • the first initial generator is used to generate the first simulation data
  • the first discriminator is used to discriminate the first simulation data and generate the first simulation discrimination data; it is also used to discriminate the real misdiagnosis data and generate the first real discrimination data.
  • the training of the first initial generator and the first discriminator is a process of confrontation.
  • ⁇ is a hyperparameter, which can be set before model training.
  • a loss item ( ⁇ logD 3 (x f1 )) including the discriminant data of D 3 (initial comprehensive discriminator) can be added.
  • the addition of ⁇ logD 3 (x f1 ) can make the distribution of the misdiagnosis data generated by the first generator meet the requirements of the actual scene.
  • step S20 that is, before generating the simulated missed diagnosis data by the second generator, further includes:
  • the second initial generator receives the second random noise, and generates second simulation data
  • the second discriminator receives the real missed diagnosis data with a label of 1, and generates second real discrimination data; the second discriminator receives the second simulated data with a label of 0, and generates second simulated discrimination data;
  • the second adversarial neural network includes a second initial generator and a second discriminator.
  • the second random noise can be generated by a random algorithm.
  • the generator will obtain two larger second generation loss values, and adjust the calculation parameters in the second initial generator according to the second generation loss values, so that the data generated by the second initial generator gradually approaches the real Missed diagnosis data.
  • the second discriminator makes a misjudgment when discriminating the second analog data, it will also obtain two larger second discrimination loss values, and adjust the second discrimination according to the second discrimination loss value
  • the calculation parameters in the device make it more capable of distinguishing between the second simulated data and the real missed diagnosis data.
  • step of updating the second discriminator refers to repeating the steps related to the second discriminator in steps S201-S204.
  • step of updating the second generator refers to repeating the steps related to the second initial generator in steps S201-S204.
  • the update steps of the second discriminator and the second initial generator are performed simultaneously.
  • the second simulation judgment data is in the second preset range, it can be considered that the second adversarial neural network meets the second preset termination condition.
  • the second discriminator's second analog discrimination data for the second analog data is 0.5, it is difficult for the second discriminator to determine whether the second analog data output by the second initial generator is true. That is, the second simulation data generated by the second generator is very similar to the real missed diagnosis data. At this time, the second counter neural network has reached convergence.
  • the second initial generator receives the second random noise and generates second simulation data, where the second initial generator continuously generates new second simulation data .
  • the second discriminator receives the true missed diagnosis data with a label of 1, and generates second real discrimination data; the second discriminator receives the second simulated data with a label of 0, and generates second simulated discrimination data, where, The second discriminator simultaneously discriminates the real missed diagnosis data and the second simulated data, which can improve the ability of the second discriminator to discriminate the missed data.
  • the second discriminator is updated according to the second discriminant loss value, and the second initial generator is updated according to the second generation loss value.
  • the parameters of the respective models are gradually updated by the loss value to improve the performance of the model.
  • Accuracy Repeat the step of updating the second discriminator and the step of updating the second initial generator until a second preset termination condition is met, and the second preset termination condition is that the second simulation judgment data is in the second
  • the preset range is used to complete the training of the model.
  • the second initial generator that meets the second preset termination condition is determined as the second generator to obtain a second generator that can be used to generate second simulation data.
  • step S203 the calculation of the second discrimination loss value of the second discriminator according to the second real discrimination data and the second simulation discrimination data includes:
  • x r2 represents the real missed diagnosis data
  • D 2 (x) represents the second true discrimination data
  • E is the expected calculation symbol
  • x f2 represents the second simulation data
  • z 2 represents the second random noise
  • G 2 (z 2 ) represents the second simulation data
  • D 2 (G 2 (z 2 )) represents the second discrimination loss value
  • step S203 the calculating the second generation loss value of the second initial generator according to the second simulation discrimination data includes:
  • D 2 (x f2 ) is the second simulation discrimination data
  • D 3 (x f2 ) is the discrimination data generated after the initial comprehensive discriminator discriminates the second simulation data
  • ⁇ is a hyperparameter
  • G 2 refers to the second initial generator
  • D 2 refers to the second discriminator.
  • the second initial generator is used to generate second simulation data
  • the second discriminator is used to discriminate the second simulation data and generate the second simulation discrimination data; it is also used to discriminate the true missed diagnosis data and generate the second real discrimination data.
  • the training of the second initial generator and the second discriminator are two processes of confrontation.
  • ⁇ is a hyperparameter, which can be set before model training.
  • a loss item ( ⁇ logD 3 (x f2 )) including the discriminant data of D 3 (initial comprehensive discriminator) can be added.
  • the addition of ⁇ logD 3 (x f2 ) can make the distribution of the missed diagnosis data generated by the second generator meet the requirements of the actual scene.
  • step S30 that is, acquiring real normal data, using the real normal data, the simulated misdiagnosis data, and the simulated misdiagnosis data to train the initial comprehensive discriminator, includes :
  • S302 Calculate a comprehensive discrimination loss value of the initial comprehensive discriminator according to the missed diagnosis rate, the misdiagnosis rate, and the normal rate;
  • the missed diagnosis rate in the comprehensive discrimination data refers to the ratio of the number of missed diagnosed medical records determined by the initial comprehensive discriminator to the total number of discriminated medical records. For example, if the total number of discriminated medical records is 100, and the number of missed diagnoses determined by the initial comprehensive discriminator is 4, the missed diagnosis rate in the comprehensive discriminator data is 4%.
  • the misdiagnosis rate in the comprehensive discrimination data refers to the ratio of the number of misdiagnosed medical records determined by the initial comprehensive discriminator to the total number of discriminated medical records; the normal rate in the comprehensive discrimination data refers to the normal medical records judged by the initial comprehensive discriminator The ratio of the number to the total number of discriminative medical records.
  • the step of repeatedly updating the initial comprehensive discriminator refers to repeatedly performing steps S301-S303.
  • the preset convergence condition can mean that the comprehensive judgment loss value approaches a certain value.
  • step S302 that is, calculating the comprehensive discrimination loss value of the initial comprehensive discriminator according to the missed diagnosis rate, the misdiagnosed rate, and the normal rate includes:
  • the missed diagnosis rate, the misdiagnosis rate, and the normal rate are processed by a comprehensive loss function to generate the comprehensive discrimination loss value, and the comprehensive loss function is:
  • the comprehensive discrimination loss value can be calculated by the comprehensive loss function.
  • the comprehensive discriminant loss value approaches a certain value.
  • an electronic medical record screening device based on a confrontation network is provided, and the electronic medical record screening device based on the confrontation network corresponds to the electronic medical record screening method based on the confrontation network in the above-mentioned embodiment in a one-to-one correspondence.
  • the electronic medical record screening device based on the confrontation network includes a first generation module 10, a second generation module 20, a training module 30, a determination discriminator module 40 and a screening module 50.
  • the detailed description of each functional module is as follows:
  • the first generating module 10 is configured to generate simulated misdiagnosis data through the first generator
  • the second generating module 20 is used to generate simulated missed diagnosis data through the second generator
  • the training module 30 is configured to obtain real normal data, and use the real normal data, the simulated misdiagnosis data, and the simulated misdiagnosis data to train the initial comprehensive discriminator;
  • the determining discriminator module 40 is used to determine the trained initial comprehensive discriminator as a comprehensive discriminator after the training is completed;
  • the screening module 50 is configured to use the comprehensive discriminator to process the medical record to be screened, and obtain the processing result of the medical record to be screened.
  • the first generation module 10 includes:
  • the first initial generator receives the first random noise, and generates the first simulation data
  • the first discriminating unit is used for the first discriminator to receive the true misdiagnosis data with a label of 1, and generate first real discriminating data; the first discriminator receives the first simulation data with the label of 0, and generates a first simulation Discriminate data;
  • the first loss value calculating unit is configured to calculate the first discriminant loss value of the first discriminator according to the first true discriminant data and the first simulation discriminant data; calculate the first discriminant loss value of the first discriminator according to the first simulation discriminant data The first generation loss value of the first initial generator;
  • a first update unit configured to update the first discriminator according to the first discrimination loss value, and update the first initial generator according to the first generation loss value
  • the first iterative update unit is configured to repeat the step of updating the first discriminator and the step of updating the first initial generator until the first preset termination condition is satisfied, and the first preset termination condition is the The first simulation judgment data is in the first preset range;
  • a determining first generator unit is configured to determine the first initial generator meeting a first preset termination condition as the first generator.
  • the unit for calculating the first loss value includes:
  • a unit for calculating a first discriminant loss value is used to process the first true discriminant data and the first simulated discriminant data through a first discriminant loss function to generate the first discriminant loss value, and the first discriminant loss function is :
  • x r1 represents the true misdiagnosis data
  • D 1 (x) represents the first true discrimination data
  • E is the expected calculation symbol
  • x f1 represents the first simulation data
  • z 1 represents the first random noise
  • G 1 (z 1 ) represents the first simulation data
  • D 1 (G 1 (z 1 )) represents the first discrimination loss value
  • a unit for calculating a first generation loss value is configured to process the first simulation discrimination data through a first generation loss function to generate the first generation loss value, and the first generation loss function is:
  • D 1 (x f1 ) is the first simulation discrimination data
  • D 3 (x f1 ) is the discrimination data generated after the initial comprehensive discriminator discriminates the first simulation data
  • ⁇ is a hyperparameter
  • the second generation module 20 includes:
  • the second initial generator receives the second random noise, and generates the second simulation data
  • the second discriminating unit is used for the second discriminator to receive the true missed diagnosis data with the tag of 1, and to generate second real discriminating data; the second discriminator receives the second simulation data with the tag of 0, and generates the second simulation Discriminate data;
  • the second loss value calculation unit is configured to calculate the second discriminant loss value of the second discriminator according to the second true discriminant data and the second simulation discriminant data; calculate the second discriminant loss value of the second discriminator according to the second simulation discriminant data The second generation loss value of the second initial generator;
  • a second update unit configured to update the second discriminator according to the second discrimination loss value, and update the second initial generator according to the second generation loss value
  • the second iterative update unit is configured to repeat the step of updating the second discriminator and the step of updating the second initial generator until a second preset termination condition is satisfied, and the second preset termination condition is the
  • the second simulation discrimination data is in the second preset range
  • a determining second generator unit is configured to determine the second initial generator meeting a second preset termination condition as the second generator.
  • the unit for calculating the second loss value includes:
  • a unit for calculating a second discriminant loss value is used to process the second true discriminant data and the second simulated discriminant data through a second discriminant loss function to generate the second discriminant loss value, and the second discriminant loss function is :
  • x r2 represents the real missed diagnosis data
  • D 2 (x) represents the second true discrimination data
  • E is the expected calculation symbol
  • x f2 represents the second simulation data
  • z 2 represents the second random noise
  • G 2 (z 2 ) represents the second simulation data
  • D 2 (G 2 (z 2 )) represents the second discrimination loss value
  • the second generation loss value calculation unit is configured to process the second simulation discrimination data through a second generation loss function to generate the second generation loss value, and the second generation loss function is:
  • D 2 (x f2 ) is the second simulation discrimination data
  • D 3 (x f2 ) is the discrimination data generated after the initial comprehensive discriminator discriminates the second simulation data
  • ⁇ is a hyperparameter
  • the training module 30 includes:
  • Generating a comprehensive discrimination data unit configured to use the initial comprehensive discriminator to discriminate the true normal data, the simulated misdiagnosis data and the simulated missed diagnosis data, and generate comprehensive discrimination data, the comprehensive discrimination data including missed diagnosis rate, Misdiagnosis rate and normal rate;
  • Updating the initial comprehensive discriminator unit configured to update the initial comprehensive discriminator according to the comprehensive discrimination loss value
  • the iterative update of the initial comprehensive discriminator unit is used to repeat the steps of updating the initial comprehensive discriminator until the comprehensive discrimination loss value meets the preset convergence condition.
  • a unit for generating a comprehensive discrimination loss value is further configured to process the missed diagnosis rate, the misdiagnosis rate, and the normal rate through a comprehensive loss function to generate the comprehensive discrimination loss value, and the comprehensive loss function is:
  • the various modules in the above-mentioned anti-network-based electronic medical record screening device can be implemented in whole or in part by software, hardware, and a combination thereof.
  • the above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 9.
  • the computer equipment includes a processor, a memory, a network interface, and a database connected through a system bus.
  • the processor of the computer device is used to provide calculation and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system, computer readable instructions, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the non-volatile storage medium.
  • the database of the computer equipment is used to store the data involved in the above-mentioned electronic medical record screening method.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a method for screening electronic medical records based on a counter-network is realized.
  • a computer device including a memory, a processor, and computer-readable instructions stored on the memory and capable of running on the processor, and the processor implements the following steps when the processor executes the computer-readable instructions:
  • the comprehensive discriminator is used to process the medical record to be screened, and the processing result of the medical record to be screened is obtained.
  • one or more computer-readable storage media storing computer-readable instructions are provided.
  • the readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage media. Storage medium.
  • the readable storage medium stores computer readable instructions, and when the computer readable instructions are executed by one or more processors, the following steps are implemented:
  • the comprehensive discriminator is used to process the medical record to be screened, and the processing result of the medical record to be screened is obtained.
  • Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

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Abstract

Procédé et appareil de criblage de dossier médicaux électroniques sur la base d'un réseau adverse, dispositif et support. Le procédé comprend les étapes suivantes : un premier générateur génère des données de diagnostic erroné simulées (S10) ; un second générateur génère des données de diagnostic manqué simulées (S20) ; l'acquisition de données normales réelles, et l'entraînement d'un discriminateur synthétique initial à l'aide des données normales réelles, des données de diagnostic erroné simulées et des données de diagnostic manqué simulées (S30) ; après la fin de l'entraînement, la détermination du fait que le discriminateur synthétique initial entraîné est un discriminateur synthétique (S40) ; et le traitement, à l'aide du discriminateur synthétique, de dossiers médicaux à soumettre au criblage pour obtenir un résultat de traitement concernant lesdits dossiers médicaux (S50). Le problème du criblage de dossiers médicaux électroniques est résolu, et les coûts de criblage de dossiers médicaux sont réduits.
PCT/CN2020/124219 2020-09-09 2020-10-28 Procédé et appareil de criblage de dossiers médicaux électroniques sur la base d'un réseau adverse, dispositif et support WO2021151326A1 (fr)

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US20160180022A1 (en) * 2014-12-18 2016-06-23 Fortinet, Inc. Abnormal behaviour and fraud detection based on electronic medical records
CN109003678A (zh) * 2018-06-12 2018-12-14 清华大学 一种仿真文本病历的生成方法及系统
CN110060774A (zh) * 2019-04-29 2019-07-26 赵蕾 一种基于生成式对抗网络的甲状腺结节识别方法
CN110808095A (zh) * 2019-09-18 2020-02-18 平安科技（深圳）有限公司 诊断结果识别、模型训练的方法、计算机设备及存储介质
CN110910976A (zh) * 2019-10-12 2020-03-24 平安国际智慧城市科技股份有限公司 病历检测方法、装置、设备和存储介质

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KR20170099737A (ko) * 2016-02-23 2017-09-01 노을 주식회사 접촉식 염색 패치 및 이를 이용하는 염색 방법

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US20160180022A1 (en) * 2014-12-18 2016-06-23 Fortinet, Inc. Abnormal behaviour and fraud detection based on electronic medical records
CN109003678A (zh) * 2018-06-12 2018-12-14 清华大学 一种仿真文本病历的生成方法及系统
CN110060774A (zh) * 2019-04-29 2019-07-26 赵蕾 一种基于生成式对抗网络的甲状腺结节识别方法
CN110808095A (zh) * 2019-09-18 2020-02-18 平安科技（深圳）有限公司 诊断结果识别、模型训练的方法、计算机设备及存储介质
CN110910976A (zh) * 2019-10-12 2020-03-24 平安国际智慧城市科技股份有限公司 病历检测方法、装置、设备和存储介质

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