CN115910255A - Diagnosis auxiliary system - Google Patents
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- CN115910255A CN115910255A CN202211194774.3A CN202211194774A CN115910255A CN 115910255 A CN115910255 A CN 115910255A CN 202211194774 A CN202211194774 A CN 202211194774A CN 115910255 A CN115910255 A CN 115910255A
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Abstract
The invention provides a diagnosis auxiliary system, which comprises a hospital image management subsystem, a hospital patient management subsystem, an interaction device, a recording device and an execution device, wherein the hospital patient management subsystem is used for managing and recording the information of individuals, medical records and attending doctors of patients; the hospital image management subsystem is used for managing and recording the medical image information of the patient according to the personal information of the patient and performing uplink storage on the medical image information of the patient; the interaction device is used for logging in and registering by a doctor and inputting a service requirement instruction to the artificial intelligence contract; the executing device is used for responding to the artificial intelligence contract to enable the trusted computing equipment arranged outside the chain to run the machine learning model so as to realize machine learning training and auxiliary diagnosis; and the recording device is used for assembling the hospitalizing files of the patient in the hospital into various types of patient medical data packets and performing uplink storage on the patient medical data packets.
Description
Technical Field
The invention relates to the technical field of medical auxiliary diagnosis, in particular to a diagnosis auxiliary system.
Background
In the prior art, medical staff usually use image data of patients as analysis basis to diagnose diseases, but because of the complexity of human diseases and the difference of professional abilities of medical staff, the accuracy of disease diagnosis is different, and various types of machine learning models can well help doctors to assist diagnosis of image data of patients such as CT, ultrasonic waves and the like, but the improvement of the diagnosis accuracy of various types of machine learning models requires massive medical data. At present, a system which can quickly and accurately help doctors to diagnose diseases of patients on the premise of having privacy of medical data is lacked in the prior art.
Disclosure of Invention
The present invention is directed to a diagnosis assistance system to solve the problems of the background art.
The invention is realized by the following technical scheme: a diagnosis assisting system comprises a hospital image management subsystem, a hospital patient management subsystem, an interaction device, a recording device and an execution device, wherein the interaction device is deployed on a protocol node a located in a hospital, the recording device is deployed on a protocol node b located in the hospital, the hospital image management subsystem is deployed on a protocol node c located in the hospital, the hospital patient management subsystem is deployed on a protocol node d located in the hospital, the execution device is deployed on a protocol node e located in a medical service company, a plurality of protocol nodes form a alliance chain, and an artificial intelligence contract is arranged in the alliance chain,
the hospital patient management subsystem is used for managing and recording the personal, medical record and attending physician information of the patient and performing uplink storage on the personal, medical record and attending physician information of the patient;
the hospital image management subsystem is used for managing and recording the medical image information of the patient according to the personal information of the patient and performing uplink storage on the medical image information of the patient;
the interaction device is used for the doctor to log in and register and input a service requirement instruction to the artificial intelligence contract;
the executing device is used for responding to the artificial intelligence contract to enable the trusted computing equipment arranged outside the chain to run the machine learning model so as to realize machine learning training and auxiliary diagnosis;
the recording device is used for assembling the hospitalizing files of the patients in the hospital into various types of patient medical data packets and performing uplink storage on the patient medical data packets.
Optionally, the interaction device includes a registration module, an authentication unit, an encryption module, and an instruction unit,
the registration module is used for recording the personal information of the doctor input by the doctor and generating a doctor ID and a doctor information recording document;
the authentication unit is used for inputting authentication information to the artificial intelligent contract, and the artificial intelligent contract issues a public key to the interaction device according to the authentication information;
the instruction unit is used for generating a first service demand instruction, a second service demand instruction and a third service demand instruction;
and the encryption sending module is used for encrypting the first service demand instruction, the second service demand instruction and the third service demand instruction according to the public key to form a first encryption calling instruction, a second encryption calling instruction and a second encryption calling instruction, and sending the first encryption calling instruction, the second encryption calling instruction and the third encryption calling instruction to the artificial intelligence contract.
Optionally, the recording apparatus includes a classification module, a keyword extraction module, a first combination module, a patient information import module, and an image information import module,
the patient information importing module is used for executing a patient information importing request and importing the recorded discharged patient medical record data from the hospital patient management subsystem;
the image information import module is used for executing an image information import request and importing the recorded discharged patient medical image data from the hospital image management subsystem;
the first combination module is used for searching corresponding medical image data of the patient according to the medical record data of the patient and combining the medical record data of the patient and the medical image data of the patient into medical data of the patient;
the keyword extraction module is used for extracting disease keywords from the patient hospitalizing data;
and the classification module is used for comparing the similarity of the extracted disease keywords and generating the patient medical data packets corresponding to different diseases according to the comparison result.
Optionally, the authentication unit includes a hash value calculation module, a second combination module, and a code sending module, and the hash value calculation module calculates a hash value of the physician information recording document; the second combination module combines the physician ID with the hash value to obtain a first code; and the code sending module is used for sending the first code to the artificial intelligence contract, encrypting the first code through a public key of the artificial intelligence contract to form a digital signature, and sending the digital signature to the interaction device.
Optionally, the instruction unit combines the physician ID and the digital signature to form a first service requirement instruction; combining the doctor ID, the digital signature, the auxiliary diagnosis request, the patient name and the block mark information to form a second service requirement instruction; and combining the doctor ID, the digital signature and the training request to form a third service requirement instruction.
Optionally, after the artificial intelligence contract analyzes the first encryption calling instruction through the private key, the artificial intelligence contract obtains a plurality of pieces of patient personal information, medical record information, and patient image information, which are responsible for a primary physician and correspond to the physician ID, from the alliance chain, writes the obtained patient personal information, medical record information, and patient image information into the temporary block, and generates block marking information for recording the temporary block, where the block marking information is sent to the interaction device, and establishes an associative relationship with the physician ID.
Optionally, after the artificial intelligence contract analyzes the second encryption calling instruction through the private key, the image information and the medical record information of the specific patient are acquired from the temporary block according to the name and the block mark information, and the image information, the medical record information and the auxiliary diagnosis request of the specific patient are forwarded to the execution device.
Optionally, after the artificial intelligence contract analyzes the third encrypted call instruction through the private key, the artificial intelligence contract obtains a specific patient medical data packet from the alliance chain, and forwards the specific patient medical data packet and the training request to the execution device.
Optionally, the execution device includes a diagnosis unit, the diagnosis unit includes an auxiliary diagnosis request execution module, an auxiliary diagnosis feedback module,
the auxiliary diagnosis request execution module is used for forwarding image information and medical record information of a specific patient to the trusted computing equipment according to the auxiliary diagnosis request, and operating a machine diagnosis model in the trusted computing equipment to execute the auxiliary diagnosis request;
and the auxiliary diagnosis feedback module is used for feeding back an auxiliary diagnosis result obtained after the auxiliary diagnosis request is executed to the artificial intelligence contract.
Optionally, the executing device further comprises a training unit, the training unit comprises a training request executing module and a training feedback module,
the training request execution module is used for forwarding the patient medical data packet to the trusted computing equipment according to the training request and operating a machine diagnosis model in the trusted computing equipment to execute the training request;
and the training request feedback module feeds back training completion information to the artificial intelligence contract after the training of the machine diagnosis model is finished.
Compared with the prior art, the invention has the following beneficial effects:
the diagnosis auxiliary system provided by the invention has the advantages that a data island is opened through a alliance chain, a corresponding service request instruction is automatically sent to an artificial intelligence contract through an interaction device, personal information, medical record information and patient image information of a plurality of patients in charge of a main doctor are written into a temporary block through the artificial intelligence contract, identification information of the temporary block is generated, medical data of the patients can be quickly determined through the identification information, the searching efficiency is greatly improved, a patient medical data packet for different diseases is constructed through a recording device, a plurality of different machine learning models or deep learning models are trained through the patient medical data packet, the calculation cost and delay caused by calculation of intensive data of each block node on the chain are reduced, the consumption of storage resources caused by storage and operation results is reduced, the intelligence of an intelligent contract function is improved, in addition, the machine learning models are learned under more data samples, the model accuracy and generalization capability are improved, the model updating process can be traced, the safety, the model safety, the reliability and the reliability of the diagnosis auxiliary machine model can be more conveniently calculated by a doctor who selects the image information in the temporary block which is controllably conveyed to the credible auxiliary device, and the diagnosis equipment can be more accurately calculated.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a structural diagram of a diagnosis assistance system provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of embodiments of the invention and not all embodiments of the invention, with the understanding that the invention is not limited to the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the invention described herein without inventive step, shall fall within the scope of protection of the invention.
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.
It is to be understood that the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of the associated listed items.
In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of the present invention. Alternative embodiments of the invention are described in detail below, however, the invention can be practiced otherwise than as specifically described.
Referring to fig. 1, a diagnosis assistance system includes a hospital image management subsystem, a hospital patient management subsystem, an interaction device, a recording device, and an execution device, wherein the interaction device is disposed on a protocol node a located in a hospital, the recording device is disposed on a protocol node b located in a hospital, the hospital image management subsystem is disposed on a protocol node c located in a hospital, the hospital patient management subsystem is disposed on a protocol node d located in a hospital, the execution device is disposed on a protocol node e located in a medical service company, a plurality of the protocol nodes form a federation chain, and an artificial intelligence contract is set in the federation chain,
the hospital patient management subsystem is used for managing and recording the personal, medical record and attending physician information of the patient and performing uplink storage on the personal, medical record and attending physician information of the patient;
the hospital image management subsystem is used for managing and recording the medical image information of the patient according to the personal information of the patient and performing uplink storage on the medical image information of the patient;
the interaction device is used for the doctor to log in and register and input a service requirement instruction to the artificial intelligence contract;
the executing device is used for responding to the artificial intelligence contract to enable the trusted computing equipment arranged outside the chain to operate the machine learning model so as to realize machine learning training and auxiliary diagnosis;
the recording device is used for assembling the hospitalizing files of the patients in the hospital into various types of patient medical data packets and performing uplink storage on the patient medical data packets.
The invention discloses a diagnosis auxiliary system, which mainly provides doctors with image auxiliary diagnosis of inpatients in charge, wherein the requirement sending and data of the auxiliary diagnosis are interacted in a mode of a alliance chain, the alliance chain is constructed by a plurality of protocol nodes a, b, c, d and e, each protocol node can correspond to a subsystem with each data isolated island in a hospital, each item of data of the subsystem is synchronously uplinked and stored in the alliance chain, the data is stored on the block chain, the information is prevented from being tampered, the authenticity of the data is ensured, and meanwhile, artificial intelligence contracts are deployed in the alliance chain to realize the response of various types of request information;
in the training phase, the executing device selects the patient medical data packets stored in the chain of the recording device according to the instruction of the artificial intelligence contract, and forwards the patient medical data packets to the Trusted computing device outside the chain, wherein the Trusted computing device may be a computing device with a Trusted Execution Environment (TEE). The credible computing equipment can perform large-scale data calculation based on any machine learning model or deep learning model, train the machine learning model or deep learning model through the patient medical data packet, reduce the computing cost and delay caused by intensive data calculation of each block node on a chain, reduce the consumption of storage resources caused by storing operation results, improve the intelligence of intelligent contract functions, enable the machine learning model to learn under more data samples through the executing device, improve the model accuracy and generalization capability, and enable the model training updating process to be traceable, thereby realizing the safety, credibility and controllability of the model.
In the auxiliary diagnosis stage, a doctor registers through an interactive device, after the registration is successful, a corresponding service request instruction is automatically sent to an artificial intelligent contract, the artificial intelligent contract writes personal information, medical record information and patient image information of a plurality of patients for which the doctor is responsible into a temporary block and generates a recording text for the temporary block, and when the auxiliary diagnosis is specifically executed, an execution device selects image information of a specific patient in the temporary block according to the instruction of the artificial intelligent contract and transmits the image information to trusted computing equipment arranged outside a chain, and a machine learning model on the trusted computing equipment is operated to realize the auxiliary diagnosis.
Specifically, the recording device comprises a classification module, a keyword extraction module, a first combination module, a patient information import module and an image information import module,
the patient information import module is used for executing a patient information import request and importing the recorded discharged patient medical record data from the hospital patient management subsystem;
the image information import module is used for executing an image information import request and importing the recorded discharged patient medical image data from the hospital image management subsystem;
the first combination module is used for searching corresponding medical image data of the patient according to the medical record data of the patient and combining the medical record data of the patient and the medical image data of the patient into medical data of the patient;
the keyword extraction module is used for extracting disease keywords from the patient hospitalizing data;
and the classification module is used for comparing the similarity of the extracted disease keywords and generating the patient medical data packets corresponding to different diseases according to the comparison result.
The recording device mainly assembles various types of patient medical data packets aiming at a hospital hospitalizing file of a patient who is discharged or finishes treatment, when in assembly, firstly, recorded discharged patient medical record data are imported from a hospital patient management subsystem through a patient information import module and an image information import module, and recorded discharged patient medical image data are imported from a hospital image management subsystem.
Specifically, the interactive device comprises a registration module, an authentication unit, an encryption module and an instruction unit,
the registration module is used for recording the personal information of the doctors input by the doctors and generating a doctor ID and a doctor information recording document, and a plurality of personal information of the doctors are recorded in the doctor information recording document;
the authentication unit is used for inputting authentication information into the artificial intelligent contract, and the artificial intelligent contract issues a public key to the interaction device according to the authentication information;
the instruction unit is used for generating a first service demand instruction, a second service demand instruction and a third service demand instruction;
the encryption sending module is used for encrypting the first service demand instruction, the second service demand instruction and the third service demand instruction according to the public key to form a first encryption calling instruction, a second encryption calling instruction and a second encryption calling instruction, sending the first encryption calling instruction, the second encryption calling instruction and the third encryption calling instruction to the artificial intelligence contract, and achieving interaction between the interaction device and the artificial intelligence contract through the encryption calling instruction, so that the safety of a communication process is greatly improved.
When the interactive device is involved in auxiliary diagnosis, the interactive device needs to be authenticated on an artificial intelligence contract, authentication information is input to the artificial intelligence contract through an authentication unit, the authentication information can represent the unique identification of the specific interactive device under a specific hospital, the artificial intelligence contract generates a public and private key pair based on the authentication information, forms a unique digital signature of the interactive device based on a public key, and sends the digital signature to the interactive device to serve as the unique identification of the interactive device, so that the authentication of the interactive device is realized.
Specifically, the authentication unit comprises a hash value calculation module, a second combination module and a code sending module, wherein in the authentication execution process, the hash value of the physician information recording document is calculated through the hash value calculation module, then the physician ID is combined with the hash value through the second combination module to obtain a first code, the first code is the authentication information, and the first code is sent to the artificial intelligence contract through the code sending module. And encrypting the first code through a public key in the artificial intelligence contract to obtain a digital signature, and issuing the digital signature to the interactive device to be used as the unique identifier of the interactive device.
In the auxiliary diagnosis stage, a doctor registers through a registration port provided by a registration module, personal information of the doctor needs to be input for verification in the registration process, after the registration is successful, the registration module automatically generates a unique doctor ID, the personal information of the doctor is recorded through a doctor information recording document, while the registration is successful, an instruction unit combines the doctor ID and a digital signature to form a first service requirement instruction, the first service requirement instruction is automatically sent to an artificial intelligence contract, the artificial intelligence contract acquires a plurality of pieces of patient personal information, medical record information and patient image information which are responsible for an attending doctor and correspond to the doctor ID from a union chain, the acquired patient personal information, medical record information and patient image information are written into a temporary block, block mark information for recording the temporary block is generated, the temporary block can be set to facilitate quick search of the patient information, and the block mark information is sent to the interaction device and is in a united relationship with the doctor ID;
specifically, the execution device comprises a diagnosis unit and a training unit, the diagnosis unit comprises an auxiliary diagnosis request execution module and an auxiliary diagnosis feedback module, the training unit comprises a training request execution module and a training feedback module,
the auxiliary diagnosis request execution module is used for forwarding image information and medical record information of a specific patient to the trusted computing equipment according to the auxiliary diagnosis request, and operating a machine diagnosis model in the trusted computing equipment to execute the auxiliary diagnosis request;
and the auxiliary diagnosis feedback module is used for feeding back an auxiliary diagnosis result obtained after the auxiliary diagnosis request is executed to the artificial intelligence contract.
The training request execution module is used for forwarding the patient medical data packet to the trusted computing device according to the training request and operating a machine diagnosis model in the trusted computing device to execute the training request;
and the training request feedback module feeds back training completion information to the artificial intelligence contract after the training of the machine diagnosis model is finished.
When a doctor needs to provide auxiliary diagnosis service, an instruction unit of the interaction device combines a doctor ID, a digital signature, an auxiliary diagnosis request input by the doctor, a patient name input by the doctor and block mark information to form a second business requirement instruction, and sends the second business requirement instruction to an artificial intelligence contract, the artificial intelligence contract acquires image information and medical record information of a specific patient from a temporary block, and forwards the image information, the medical record information and the auxiliary diagnosis request of the specific patient to an execution device, an auxiliary diagnosis request execution module forwards the image information and the medical record information of the specific patient to a trusted computing device according to the auxiliary diagnosis request, and operates a machine diagnosis model in the trusted computing device to execute the auxiliary diagnosis request, so that an auxiliary diagnosis result is obtained, wherein the auxiliary diagnosis result is analysis information of the image and the medical record of the patient, and can include disease risk and probability, and finally, the auxiliary diagnosis result obtained after the auxiliary diagnosis request is executed is fed back to the artificial intelligence contract and is transmitted to the interaction device through the interaction device to be displayed to the doctor through the interaction device.
In the training stage, an instruction unit of the interaction device combines the doctor ID, the digital signature and the training request to form a third service requirement instruction, and sends the third service requirement instruction to an artificial intelligence contract, the artificial intelligence contract acquires a specific patient medical data packet from a alliance chain, forwards the specific patient medical data packet and the training request to an execution device, and a training request execution module in the execution device forwards the patient medical data packet to a trusted computing device according to the training request, runs a machine diagnosis model in the trusted computing device to execute the training request, and feeds training completion information back to the artificial intelligence contract through a training request feedback module after training is completed.
Specifically, the training request comprises a disease keyword, the training request execution module selects a specific machine diagnosis model in the trusted computing device according to the disease keyword to achieve training of a specific disease, and in addition, the trusted computing device can achieve parallel training of a plurality of machine diagnosis models, so that training efficiency is greatly improved.
Specifically, in some embodiments of the present invention, after the patient is discharged, the hospital patient management subsystem generates a discharge voucher for the patient, and stores the discharge voucher in an uplink manner, when the discharge voucher is monitored by the artificial intelligence contract, the personal information, medical record information, and patient image information of the patient are deleted from the corresponding temporary block, and when the discharge voucher is monitored by the recording device, the patient hospitalization data of the patient is imported from the hospital patient management subsystem, and the patient hospitalization data is imported into a patient medical data packet corresponding to a different disease, so as to be used for next training.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A diagnosis assisting system is characterized in that the system comprises a hospital image management subsystem, a hospital patient management subsystem, an interaction device, a recording device and an execution device, wherein the interaction device is arranged on a protocol node a located in a hospital, the recording device is arranged on a protocol node b located in the hospital, the hospital image management subsystem is arranged on a protocol node c located in the hospital, the hospital patient management subsystem is arranged on a protocol node d located in the hospital, the execution device is arranged on a protocol node e located in a medical service company, a plurality of protocol nodes form a federation chain, and an artificial intelligence contract is arranged in the federation chain,
the hospital patient management subsystem is used for managing and recording the personal, medical record and attending physician information of the patient and performing uplink storage on the personal, medical record and attending physician information of the patient;
the hospital image management subsystem is used for managing and recording the medical image information of the patient according to the personal information of the patient and performing uplink storage on the medical image information of the patient;
the interaction device is used for the doctor to log in and register and input a service requirement instruction to the artificial intelligence contract;
the executing device is used for responding to the artificial intelligence contract to enable the trusted computing equipment arranged outside the chain to operate the machine learning model so as to realize machine learning training and auxiliary diagnosis;
the recording device is used for assembling the hospitalizing files of the patients in the hospital into various types of patient medical data packets and performing uplink storage on the patient medical data packets.
2. The system of claim 1, wherein the interaction means comprises a registration module, an authentication unit, an encryption module, and an instruction unit,
the registration module is used for recording the personal information of the doctor input by the doctor and generating a doctor ID and a doctor information recording document;
the authentication unit is used for inputting authentication information into the artificial intelligent contract, and the artificial intelligent contract issues a public key to the interaction device according to the authentication information;
the instruction unit is used for generating a first service demand instruction, a second service demand instruction and a third service demand instruction;
and the encryption sending module is used for encrypting the first service demand instruction, the second service demand instruction and the third service demand instruction according to the public key to form a first encryption calling instruction, a second encryption calling instruction and a second encryption calling instruction, and sending the first encryption calling instruction, the second encryption calling instruction and the third encryption calling instruction to the artificial intelligence contract.
3. The diagnosis assistance system according to claim 2, wherein the recording device comprises a classification module, a keyword extraction module, a first combination module, a patient information import module, and an image information import module,
the patient information import module is used for executing a patient information import request and importing the recorded discharged patient medical record data from the hospital patient management subsystem;
the image information import module is used for executing an image information import request and importing the recorded discharged patient medical image data from the hospital image management subsystem;
the first combination module is used for searching corresponding medical image data of the patient according to the medical record data of the patient and combining the medical record data of the patient and the medical image data of the patient into medical data of the patient;
the keyword extraction module is used for extracting disease keywords from the patient hospitalizing data;
and the classification module is used for comparing the similarity of the extracted disease keywords and generating the patient medical data packets corresponding to different diseases according to the comparison result.
4. The system of claim 3, wherein the authentication unit comprises a hash value calculation module, a second combination module, and a code transmission module, wherein the hash value calculation module is used for calculating the hash value of the physician information record document; the second combination module combines the physician ID with the hash value to obtain a first code; and the code sending module is used for sending the first code to the artificial intelligence contract, encrypting the first code through a public key of the artificial intelligence contract to form a digital signature, and sending the digital signature to the interaction device.
5. The system of claim 4, wherein the command unit combines a physician ID and a digital signature to form a first business requirement command; combining the doctor ID, the digital signature, the auxiliary diagnosis request, the patient name and the block mark information to form a second service requirement instruction; and combining the doctor ID, the digital signature and the training request to form a third service requirement instruction.
6. The diagnosis assistance system according to claim 5, wherein the artificial intelligence contract obtains a plurality of pieces of patient personal information, medical record information, and patient image information for which an attending physician is responsible, which correspond to a physician ID, from a federation chain after analyzing the first encrypted call instruction by using a private key, writes the obtained pieces of patient personal information, medical record information, and patient image information into a temporary block, and generates block marking information for recording the temporary block, and the block marking information is sent to the interaction device and establishes a simultaneous relationship with the physician ID.
7. The diagnosis assistance system according to claim 6, wherein the artificial intelligence contract obtains the image information and the medical record information of the specific patient from the temporary block according to the name and the block mark information after analyzing the second encrypted call instruction by the private key, and forwards the image information, the medical record information and the diagnosis assistance request of the specific patient to the execution device.
8. The system of claim 7, wherein the artificial intelligence contract obtains the specific patient medical data packet from the federation chain after parsing the third encrypted call instruction by the private key, and forwards the specific patient medical data packet and the training request to the execution device.
9. The system of claim 8, wherein the execution device comprises a diagnostic unit, the diagnostic unit comprises a diagnostic aid request execution module, a diagnostic aid feedback module,
the auxiliary diagnosis request execution module is used for forwarding image information and medical record information of a specific patient to the trusted computing equipment according to the auxiliary diagnosis request, and operating a machine diagnosis model in the trusted computing equipment to execute the auxiliary diagnosis request;
and the auxiliary diagnosis feedback module is used for feeding back an auxiliary diagnosis result obtained after the auxiliary diagnosis request is executed to the artificial intelligence contract.
10. The diagnostic support system of claim 9, wherein said execution means further comprises a training unit, said training unit comprising a training request execution module, a training feedback module,
the training request execution module is used for forwarding the patient medical data packet to the trusted computing device according to the training request and operating a machine diagnosis model in the trusted computing device to execute the training request;
and the training request feedback module feeds back training completion information to the artificial intelligence contract after the training of the machine diagnosis model is finished.
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| CN114822735A (en) * | 2022-03-30 | 2022-07-29 | 西安华力国盾信息技术有限公司 | Safe and reliable electronic medical record signing management system and application |
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| CN111095256A (en) * | 2019-04-26 | 2020-05-01 | 阿里巴巴集团控股有限公司 | Securely executing intelligent contract operations in a trusted execution environment |
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