WO2024049198A1 - Système et procédé pour fournir un service de négociation basé sur une chaîne de blocs pour des données d'électrocardiogramme - Google Patents

Système et procédé pour fournir un service de négociation basé sur une chaîne de blocs pour des données d'électrocardiogramme Download PDF

Info

Publication number
WO2024049198A1
WO2024049198A1 PCT/KR2023/012880 KR2023012880W WO2024049198A1 WO 2024049198 A1 WO2024049198 A1 WO 2024049198A1 KR 2023012880 W KR2023012880 W KR 2023012880W WO 2024049198 A1 WO2024049198 A1 WO 2024049198A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
ecg data
transaction
computing device
blockchain
Prior art date
Application number
PCT/KR2023/012880
Other languages
English (en)
Korean (ko)
Inventor
권준명
Original Assignee
주식회사 메디컬에이아이
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 주식회사 메디컬에이아이 filed Critical 주식회사 메디컬에이아이
Publication of WO2024049198A1 publication Critical patent/WO2024049198A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0613—Third-party assisted
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/333—Recording apparatus specially adapted therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0278—Product appraisal
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0281—Customer communication at a business location, e.g. providing product or service information, consulting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
    • 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
    • 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
    • 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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L2463/00—Additional details relating to network architectures or network communication protocols for network security covered by H04L63/00
    • H04L2463/041—Additional details relating to network architectures or network communication protocols for network security covered by H04L63/00 using an encryption or decryption engine integrated in transmitted data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L2463/00—Additional details relating to network architectures or network communication protocols for network security covered by H04L63/00
    • H04L2463/062—Additional details relating to network architectures or network communication protocols for network security covered by H04L63/00 applying encryption of the keys

Definitions

  • the present disclosure relates to a system and method for providing transaction services for electrocardiogram data based on blockchain, and specifically relates to a system capable of providing transaction services for electrocardiogram data based on blockchain.
  • ECG electrocardiogram
  • Such an electrocardiogram can be detected through a bipolar lead, which records the potential difference between two parts, and a unipolar lead, which records the potential of the area where the electrode is attached.
  • Methods for measuring an electrocardiogram include the bipolar lead. There is a standard limb lead, a unipolar limb lead, and a unipolar thoracic lead (precordial lead).
  • the electrical activity stage of the heart is largely divided into atrial depolarization, ventricular depolarization, and ventricular repolarization, and each of these stages is reflected in the form of several waves called P, Q, R, S, and T waves, as shown in Figure 1.
  • These waves must have a standard shape for the heart's electrical activity to be considered normal. In order to determine whether it is a standard form or not, it is necessary to check whether characteristics such as the time each wave is maintained, the interval between each wave, the amplitude of each wave, and kurtosis are within the normal range.
  • electrocardiograms are measured with expensive measuring equipment and used as an auxiliary tool to measure the patient's health status. In general, electrocardiogram measuring equipment only displays measurement results and diagnosis is entirely up to the doctor.
  • Non-invasively assessing heart function by measuring electrocardiograms helps diagnose numerous heart diseases, including arrhythmia, myocardial infarction, and arterial disease.
  • cardiac-related symptoms include electrocardiogram (ECG) measurements, which are stored in daily accumulation of ECG records.
  • ECG electrocardiogram
  • users can easily measure and save their electrocardiograms even without visiting a medical institution.
  • ECG data is highly valuable data that can be used in a variety of ways, such as medical treatment, health management, and heart disease-related research, but the lack of a transaction system for medical data makes it impossible to clearly evaluate its value.
  • medical data is a public good, making it difficult to discuss the price of medical data. Due to this, domestic medical-related AI companies are unable to use their own high-quality medical data as the data needed to train AI models, and either purchase and use medical data from overseas or use data sets collected in small quantities as samples. The reality is that it is being artificially created and used by imitating statistical characteristics.
  • the present disclosure was developed in response to the above-mentioned background technology, and its purpose is to provide a system that can provide transaction services for electrocardiogram data based on blockchain.
  • the present disclosure seeks to provide a system that provides a transaction service for ECG data based on blockchain according to an embodiment of the present disclosure.
  • the system includes at least one electrocardiogram meter providing electrocardiogram data; And a computing device that stores the ECG data provided from the ECG meter on a blockchain basis and provides a blockchain-based transaction service for the stored ECG data, wherein the computing device performs a transaction at the request of the purchaser terminal.
  • sales list information for available ECG data is provided, and the buyer terminal selects ECG data based on the sales list information, a blockchain-based transaction occurs between the seller terminal with ownership of the selected ECG data and the buyer terminal. It is about providing services.
  • the computing device provides identification information for accessing the ECG data stored on a blockchain based on the ECG data for which the transaction has been completed.
  • the computing device encrypts the ECG data using an encryption key and then stores the encrypted ECG data based on blockchain.
  • the computing device provides, for the ECG data for which the transaction has been completed, identification information for accessing the ECG data stored on a blockchain basis and a decryption key for decrypting the encrypted ECG data.
  • the computing device additionally collects and stores at least one of biological information linked to the ECG data, characteristic values for ECG characteristics, reading information, inspection information, or supporting information.
  • the computing device provides a matching data list by matching the ECG data and the related information to the sales list information, and sets a transaction price of the ECG data according to the type of related information matched to the matching data list. is set differentially.
  • the computing device provides transaction alarm information to a seller terminal that has ownership of the selected ECG data.
  • the computing device determines the completion of a transaction with the buyer terminal when transaction approval of the transaction alarm information is determined based on the user input of the seller terminal, and based on the user input of the seller terminal, When the transaction rejection of the transaction alarm information is determined, a transaction impossibility status for the ECG data selected by the purchaser terminal is transmitted to the purchaser terminal.
  • the computing device performs user de-identification on the ECG data for which the transaction has been completed and provides the user de-identified ECG data to the purchaser terminal.
  • an object is to provide a method of providing a blockchain-based transaction service for electrocardiogram data, which is performed by a computing device including at least one processor.
  • the method includes collecting electrocardiogram data for each user; Storing the collected electrocardiogram data based on blockchain; and providing sales list information for tradable ECG data at the request of the buyer terminal, and when the buyer terminal selects ECG data based on the sales list information, a seller terminal with ownership of the selected ECG data and the It may include providing a blockchain-based transaction service between buyer terminals.
  • the system that provides a transaction service for ECG data based on blockchain can store and manage ECG data based on blockchain, thereby ensuring the integrity of ECG data. It can increase the reliability of data transactions, and has the effect of ensuring the stability of transactions by automatically saving transaction details for ECG data.
  • this disclosure can effectively record and manage ECG data using NFT, and can reduce the possibility of personal information leakage by making it impossible to forge or falsify ECG data when trading, making medical innovation a reality.
  • FIG. 1 is a block diagram of a computing device according to an embodiment of the present disclosure.
  • Figure 2 is a block diagram illustrating the configuration of a system that provides a blockchain-based transaction service for ECG data according to an embodiment of the present disclosure.
  • FIG. 3 is a diagram illustrating the detailed configuration of a computing device according to an embodiment of the present disclosure.
  • Figure 4 is a flowchart explaining a method of providing a transaction service for blockchain-based ECG data according to an embodiment of the present disclosure.
  • Figure 5 is a flowchart illustrating in detail the steps of providing a transaction service for ECG data according to an embodiment of the present invention.
  • x uses a or b should be understood to mean one of natural implicit substitutions.
  • x uses a or b means that x uses a, x uses b, or x uses a and It can be interpreted as one of the cases where both b are used.
  • th nth (n is a natural number)
  • n is a natural number
  • a predetermined standard such as a functional perspective, a structural perspective, or explanatory convenience.
  • components performing different functional roles may be distinguished as first components or second components.
  • components that are substantially the same within the technical spirit of the present disclosure but must be distinguished for convenience of explanation may also be distinguished as first components or second components.
  • acquisition used in this disclosure is understood to mean not only receiving data through a wired or wireless communication network with an external device or system, but also generating data in an on-device form. It can be.
  • module refers to a computer-related entity, firmware, software or part thereof, hardware or part thereof.
  • the “module” or “unit” can be understood as a term referring to an independent functional unit that processes computing resources, such as a combination of software and hardware.
  • the “module” or “unit” may be a unit composed of a single element, or may be a unit expressed as a combination or set of multiple elements.
  • a “module” or “part” in the narrow sense is a hardware element or set of components of a computing device, an application program that performs a specific function of software, a process implemented through the execution of software, or a program. It can refer to a set of instructions for execution, etc.
  • module or “unit” may refer to the computing device itself constituting the system, or an application running on the computing device.
  • module or “unit” may be defined in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.
  • model refers to a system implemented using mathematical concepts and language to solve a specific problem, a set of software units to solve a specific problem, or a process to solve a specific problem. It can be understood as an abstract model of a process.
  • a neural network “model” may refer to an overall system implemented as a neural network that has problem-solving capabilities through learning. At this time, the neural network can have problem-solving capabilities by optimizing parameters connecting nodes or neurons through learning.
  • a neural network “model” may include a single neural network or a neural network set in which multiple neural networks are combined.
  • a neural network “block” can be understood as a set of neural networks containing at least one neural network. At this time, it can be assumed that the neural networks included in the neural network “block” perform the same specific operation.
  • the explanation of the foregoing terms is intended to aid understanding of the present disclosure. Therefore, if the above-mentioned terms are not explicitly described as limiting the content of the present disclosure, it should be noted that the content of the present disclosure is not used in the sense of limiting the technical idea.
  • FIG. 1 is a block diagram of a computing device according to an embodiment of the present disclosure.
  • the computing device 100 may be a hardware device or part of a hardware device that performs comprehensive processing and calculation of data, or may be a software-based computing environment connected to a communication network.
  • the computing device 100 may be a server that performs intensive data processing functions and shares resources, or it may be a client that shares resources through interaction with the server.
  • the computing device 100 may be a cloud system in which a plurality of servers and clients interact to comprehensively process data. Since the above description is only an example related to the type of computing device 100, the type of computing device 100 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.
  • a computing device 100 may include a processor 110, a memory 120, and a network unit 130. there is. However, since FIG. 1 is only an example, the computing device 100 may include other components for implementing a computing environment. Additionally, only some of the configurations disclosed above may be included in computing device 100.
  • the processor 110 may be understood as a structural unit including hardware and/or software for performing computing operations.
  • the processor 110 may read a computer program and perform data processing for machine learning.
  • the processor 110 may process computational processes such as processing input data for machine learning, extracting features for machine learning, and calculating errors based on backpropagation.
  • the processor 110 for performing such data processing includes a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), a tensor processing unit (TPU), and a custom processing unit (TPU). It may include a semiconductor (ASICc: application specific integrated circuit), or a field programmable gate array (FPGA: field programmable gate array). Since the type of processor 110 described above is only an example, the type of processor 110 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.
  • the processor 110 stores ECG data based on blockchain and can provide a transaction service between a seller terminal and a buyer terminal that have ownership of the ECG data.
  • the processor 110 may provide the results of artificial intelligence analysis, including the presence or absence of disease, likelihood of disease occurrence, noise score, etc., for ECG data using a pre-trained neural network model as ECG reading information.
  • the processor 110 can learn a neural network model that diagnoses heart disease based on electrocardiogram data.
  • the processor 110 may train a neural network model to estimate arrhythmia and other heart diseases based on biological information including information such as gender, age, weight, height, etc., along with electrocardiogram data.
  • the processor 110 may input electrocardiogram data and various biological information into the neural network model and train the neural network model to detect changes in the electrocardiogram due to arrhythmia or other heart diseases.
  • the neural network model can perform learning based on an ECG dataset that includes features extracted from ECG data and diagnostic data for arrhythmia and other heart diseases.
  • the processor 110 may perform an operation representing at least one neural network block included in the neural network model during the learning process of the neural network model.
  • the processor 110 may estimate ECG reading information based on ECG data using a neural network model generated through the above-described learning process.
  • the processor 110 inputs electrocardiogram data and biological information including information such as gender, age, weight, height, etc. into a neural network model learned through the above-described process and inferred data representing the result of estimating the probability of heart disease. can be created.
  • the processor 110 can input electrocardiogram data into a trained neural network model to predict the presence or progression of arrhythmia or other heart disease.
  • the types of medical data and the output of the neural network model may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.
  • the memory 120 may be understood as a structural unit including hardware and/or software for storing and managing data processed in the computing device 100. That is, the memory 120 can store any type of data generated or determined by the processor 110 and any type of data received by the network unit 130.
  • the memory 120 may be a flash memory type, hard disk type, multimedia card micro type, card type memory, or random access memory (RAM). ), SRAM (static random access memory), ROM (read-only memory), EEPROM (electrically erasable programmable read-only memory), PROM (prom: programmable read-only memory), magnetic memory , may include at least one type of storage medium among a magnetic disk and an optical disk.
  • the memory 120 may include a database system that controls and manages data in a predetermined system. Since the type of memory 120 described above is only an example, the type of memory 120 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.
  • the memory 120 can structure, organize, and manage data necessary for the processor 110 to perform operations, combinations of data, and program codes executable on the processor 110.
  • the memory 120 may store ECG data received through the network unit 130, which will be described later.
  • the memory 120 includes program code that operates the neural network model to receive medical data and perform learning, program code that operates the neural network model to receive medical data and perform inference according to the purpose of use of the computing device 100, and Processed data generated as the program code is executed can be saved.
  • the network unit 130 may be understood as a structural unit that transmits and receives data through any type of known wired or wireless communication system.
  • the network unit 130 may be connected to a local area network (LAN), wideband code division multiple access (WCDMA), long term evolution (LTE), or wireless (WIBRO).
  • LAN local area network
  • WCDMA wideband code division multiple access
  • LTE long term evolution
  • WIBRO wireless
  • broadband internet 5th generation mobile communication
  • 5g ultra wide-band wireless communication
  • zigbee radio frequency (RF) communication
  • RF radio frequency
  • wireless LAN wireless fidelity
  • NFC near field communication
  • Bluetooth Bluetooth
  • the network unit 130 may receive data necessary for the processor 110 to perform calculations through wired or wireless communication with any system or client. Additionally, the network unit 130 may transmit data generated through the calculation of the processor 110 through wired or wireless communication with any system or any client. For example, the network unit 130 may receive medical data through communication with a database in a hospital environment, a cloud server that performs tasks such as standardization of medical data, or a computing device. The network unit 130 may transmit output data of the neural network model, intermediate data derived from the calculation process of the processor 110, processed data, etc. through communication with the above-described database, server, or computing device.
  • FIG. 2 is a block diagram illustrating the configuration of a system that provides a transaction service for blockchain-based ECG data according to an embodiment of the present disclosure
  • FIG. 3 is a detailed configuration of a computing device according to an embodiment of the present disclosure. This is a drawing explaining.
  • the system may include an electrocardiogram measuring device 10, a computing device 100, and an expert terminal 200 for in-depth reading or inspection by an expert.
  • the electrocardiogram measuring device 10 is worn on the user's body and can not only measure the electrocardiogram, but also measure various biosignals such as blood pressure and pulse rate.
  • This electrocardiogram measuring device 10 may include wearable devices such as electronic accessories and smartwatches, and medical devices such as massage chairs, which have received medical device approval from the Ministry of Food and Drug Safety.
  • the electrocardiogram measuring device 10 can measure the electrocardiogram using various electrode combinations, such as a 12-lead method and a 6-lead method, as well as a single-guide method using a wearable device. It is desirable that the electrocardiogram measurement time is also set by adding or subtracting depending on the signal to be obtained.
  • the computing device 100 may be connected to the ECG meter 10 by wire or wirelessly, obtain ECG data from the ECG meter 10, and provide ECG reading information obtained by analyzing the ECG data using a neural network model.
  • These computing devices 100 include mobile phones, smart phones, laptop computers, digital broadcasting terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), navigation, slate PCs, This may include tablet PCs, ultrabooks, etc.
  • the computing device 100 may be operated independently or integrated with the electrocardiogram meter 10.
  • this computing device 100 includes a data collection module 111, an encryption module 112, a blockchain execution module 113, a transaction mediation module 114, and a de-identification module 115. ), but is not limited to this.
  • the data collection module 111 is capable of acquiring electrocardiogram data from the electrocardiogram measuring device 10, and includes biological information linked to each electrocardiogram data, characteristic values for electrocardiogram characteristics, reading information, inspection information, or supporting materials along with the electrocardiogram data. At least one additional piece of related information can be collected.
  • the biological information may include information such as gender, age, weight, and height about the subject of ECG data measurement
  • the reading information may include ECG reading information using a neural network model or expert reading information.
  • the neural network model uses a deep learning algorithm to learn ECG data by the characteristics of the ECG, and using the learned model, ECG reading information including classification information related to heart disease can be derived.
  • the neural network model may be learned based on a learning dataset including electrocardiograms and diagnostic results of heart disease, and based on correlations between various factors in the learning dataset.
  • the neural network model includes at least one convolutional neural network (CNN), batch normalization, and ReLU activation function layer, and may include a dropout layer.
  • CNN convolutional neural network
  • the neural network model may include a fully connected layer in which biological information such as age, gender, height, and weight is input as auxiliary information.
  • the neural network model may include a neural network corresponding to each of a plurality of leads of ECG data. That is, the neural network model may include an individual neural network into which electrocardiograms measured with individual leads are input.
  • the neural network model according to an embodiment of the present invention may be configured in various ways based on the above-described examples.
  • the encryption module 112 may generate an encryption key using a preset encryption algorithm and encrypt the ECG data using the generated encryption key.
  • the blockchain execution module 113 stores and manages encrypted ECG data in the blockchain, and can be stored in the blockchain, including related information linked to the ECG data.
  • blockchain-based medical data management technology is virtually impossible to store medical data including CT images and Medical data is stored in a separate database, and only the token value of the location where the medical data is stored is stored in the blockchain.
  • the blockchain execution module 113 of the present disclosure can easily store ECG data, which has a smaller capacity than other medical data, directly in the blockchain, compared to the existing method, the ECG data stored on the blockchain has It can effectively prevent forgery and falsification.
  • the transaction brokerage module 114 can broker a transaction on electrocardiogram data between a buyer terminal and a seller terminal through a web-based platform. That is, the transaction brokerage module 114 provides a matching data list by matching ECG data and related information to sales list information, and can differentially set the transaction price of ECG data according to the type of related information matched to the matching data list. . In addition, the transaction brokering module 114 may provide identification information for accessing ECG data stored on a blockchain basis for ECG data for which the transaction has been completed and a decryption key for decrypting the encrypted ECG data, and may provide the seller terminal with a decryption key to decrypt the encrypted ECG data. Transaction alarm information can be provided for ECG data selected by the buyer terminal.
  • the de-identification module 115 may perform user de-identification processing on the ECG data for which the transaction has been completed and provide the user de-identified ECG data to the purchaser terminal. In this way, the de-identification module 115 performs de-identification processing on information that may infringe personal information, such as personal identification ID, to prevent personal information from being collected or used by a third party without user consent. You can.
  • modules described above are only an example for explaining the present invention, but is not limited thereto and may be implemented in various modifications.
  • the above-described modules are stored in the memory 120 as a computer-readable recording medium that can be controlled by the processor 110. Additionally, at least some of the above-described modules may be implemented as software, firmware, hardware, or a combination of at least two of them, and may include a module, program, routine, instruction set, or process to perform one or more functions.
  • the expert terminal 200 may be an electrocardiogram reading center capable of collaborating with external experts and providing expert in-depth reading services, or a terminal providing diagnostic services by medical experts. This expert terminal 200 can generate expert reading information based on in-depth analysis of ECG data and provide it to the computing device 100.
  • the expert terminal 200 may be a terminal owned by a cardiologist or emergency medicine specialist with expertise in ECG reading in order to inspect the reading information of ECG data.
  • the computing device 100 may provide a platform that serves as an online market to connect seller terminals and buyer terminals.
  • the expert terminal 200 such as an ECG reading center or a medical institution, can read or inspect ECG data listed for sale through the platform.
  • the expert terminal 200 may request user consent from the terminal that has ownership of the ECG data to be read or inspected, and may read or inspect only the ECG data for which user consent has been granted.
  • the value of the ECG data read or inspected by the expert terminal 200 increases, and as the value of the ECG data increases, the transaction price may also increase. Accordingly, the expert terminal 200 can share the ownership stake in the ECG data of increased value with the seller terminal in a certain ratio or receive compensation for reading and inspection.
  • Figure 4 is a flowchart explaining a method of providing a transaction service for blockchain-based ECG data according to an embodiment of the present disclosure.
  • the computing device 100 may obtain electrocardiogram data for each user from the electrocardiogram measuring device 10 (S10).
  • the computing device 100 may encrypt ECG data using an encryption key generated by a preset encryption algorithm (S20) and store the encrypted ECG data in the blockchain (S30).
  • the computing device 100 may enable ECG data to be traded using ownership and related information about the ECG data stored based on blockchain.
  • the computing device 100 provides sales list information for tradable ECG data at the request of the buyer terminal, and when the buyer terminal selects ECG data based on the sales list information, the seller terminal with ownership of the selected ECG data A transaction can be made between the buyer terminal and the purchaser terminal (S40).
  • the computing device 100 allows the purchaser terminal to select purchase options based on related information linked to electrocardiogram data, and provides a matching data list filtered from the sales list information to the purchaser terminal according to the purchase options selected by the purchaser terminal. can be provided. For example, the buyer terminal may select only ECG data showing myocardial infarction as a purchase option and then set additional options such as gender and age.
  • Figure 5 is a flowchart illustrating in detail the steps of providing a transaction service for ECG data according to an embodiment of the present invention.
  • the step (S40) of providing a transaction service for ECG data may include steps for brokering a transaction between the seller terminal and the purchaser terminal in detail.
  • the computing device 100 may provide sales list information including tradable electrocardiogram data to the buyer terminal through a web-based platform (S41). At this time, the computing device 100 may provide a matching data list by matching the ECG data and related information to the sales list information, and differentially set the transaction price of the ECG data according to the type of related information matched to the matching data list. can be provided. At this time, in the matching data list, not only the ECG but also related information such as characteristic values for ECG characteristics, ECG reading information resulting from artificial intelligence analysis, or reading information including expert reading information can be selected as purchase options.
  • the computing device 100 may set a transaction price differentially according to the number of related information and a weight according to the type of related information added with the ECG data.
  • the computing device 100 may differentially raise the transaction price based on a preset disease list or a disease list that can be read by electrocardiogram, depending on whether at least one disease in the disease list is present in the biological information. For example, the computing device 100 sets a low weight for user information such as age and gender among biological information, and sets a high weight for disease-related information such as myocardial infarction and heart failure, and sets the transaction price according to the weight of each information.
  • the value of ECG data can be increased by differentially setting the price.
  • the computing device 100 highly evaluates the value of the ECG data depending on whether the reading information or inspection information includes expert authentication information that has been completed by expert in-depth reading or inspection and the number of expert authentication information included, thereby raising the transaction price. can be set.
  • the computing device 100 sets the weight low if the reading information is ECG reading information using a neural network model, and sets the weight high if the reading information is expert reading information through the expert terminal 200 including expert authentication information. Pricing can be differentiated.
  • the computing device 100 may highly evaluate the value of the ECG data if the inspection information includes expert certification information, such as a cardiologist or emergency medicine specialist with expertise in ECG reading, and increase the transaction price. .
  • the computing device 100 sets the transaction price higher as the weight according to the type of related information linked to the ECG data increases and the number of related information increases compared to the transaction price when only ECG data is traded. I do it.
  • the computing device 100 sends transaction alarm information including transaction approval and transaction rejection to the seller terminal that has ownership of the ECG data selected by the buyer terminal. can be provided (S43).
  • transaction approval of the transaction alarm information is determined based on the user input of the seller terminal (S44)
  • the computing device 100 may confirm the completion of the transaction with the buyer terminal (S45).
  • the computing device 100 When payment for the transaction price of the ECG data is confirmed, the computing device 100 provides identification information for accessing the ECG data stored on a blockchain for the ECG data for which the transaction has been completed and a decryption key to decrypt the encrypted ECG data. can be provided to the buyer terminal to complete the transaction for the corresponding ECG data (S46, S47)
  • the computing device 100 may transmit a transaction impossibility status for the ECG data selected by the buyer terminal to the buyer terminal (S48). .
  • encrypted ECG data can be stored and managed directly on the blockchain, which not only ensures the integrity of ECG data, but also increases the reliability of ECG data transactions, and allows transaction details for ECG data to be stored and managed. The stability of transactions can also be guaranteed by being automatically saved.
  • ECG data is clearly owned by the user (or seller), allowing many users to be interested in and participate in creating ownership for medical data, including their ECG data, and trading or utilizing the data. As medical data transactions become easier, active communication between sellers and buyers can occur.

Abstract

La présente divulgation concerne un système et un procédé pour fournir un service de négociation basé sur une chaîne de blocs pour des données d'électrocardiogramme, le système comprenant : au moins un dispositif de mesure d'électrocardiogramme qui fournit des données d'électrocardiogramme ; et un dispositif informatique qui stocke, sur la base de la chaîne de blocs, les données d'électrocardiogramme fournies par ledit ou lesdits dispositifs de mesure d'électrocardiogramme, et fournit un service de négociation basé sur une chaîne de blocs pour les données d'électrocardiogramme stockées, le dispositif informatique fournissant des informations de liste de ventes concernant des données d'électrocardiogramme pouvant être négociées à la demande d'un terminal d'acheteur, et si le terminal d'acheteur sélectionne des données d'électrocardiogramme sur la base des informations de liste de ventes, fournit le service de négociation basé sur une chaîne de blocs entre le terminal d'acheteur et un terminal de vendeur avec la propriété des données d'électrocardiogramme sélectionnées.
PCT/KR2023/012880 2022-09-01 2023-08-30 Système et procédé pour fournir un service de négociation basé sur une chaîne de blocs pour des données d'électrocardiogramme WO2024049198A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR10-2022-0110538 2022-09-01
KR20220110538 2022-09-01
KR10-2023-0114280 2023-08-30
KR1020230114280A KR20240032657A (ko) 2022-09-01 2023-08-30 블록체인 기반의 심전도 데이터에 대한 거래 서비스를 제공하는 시스템 및 그 방법

Publications (1)

Publication Number Publication Date
WO2024049198A1 true WO2024049198A1 (fr) 2024-03-07

Family

ID=90098306

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2023/012880 WO2024049198A1 (fr) 2022-09-01 2023-08-30 Système et procédé pour fournir un service de négociation basé sur une chaîne de blocs pour des données d'électrocardiogramme

Country Status (2)

Country Link
KR (1) KR20240032657A (fr)
WO (1) WO2024049198A1 (fr)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20200040019A (ko) * 2018-10-08 2020-04-17 (주)허니냅스 의료 데이터 거래 시스템 및 그 방법
CN113066552A (zh) * 2021-04-20 2021-07-02 江苏省人民医院（南京医科大学第一附属医院） 基于区块链技术的监护数据管理系统及方法
KR20220004081A (ko) * 2019-04-30 2022-01-11 레즈메드 아이엔씨. 데이터 동의 저장 및 관리 시스템 및 방법
KR20220096995A (ko) * 2020-12-31 2022-07-07 ㈜에셈블 블록체인 기반의 데이터 커머스 플랫폼 방법 및 장치

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20230069373A (ko) 2021-11-12 2023-05-19 주식회사 빅스터 블록체인의 nft를 이용한 바이오 데이터 중개 시스템 및 그 방법

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20200040019A (ko) * 2018-10-08 2020-04-17 (주)허니냅스 의료 데이터 거래 시스템 및 그 방법
KR20220004081A (ko) * 2019-04-30 2022-01-11 레즈메드 아이엔씨. 데이터 동의 저장 및 관리 시스템 및 방법
KR20220096995A (ko) * 2020-12-31 2022-07-07 ㈜에셈블 블록체인 기반의 데이터 커머스 플랫폼 방법 및 장치
CN113066552A (zh) * 2021-04-20 2021-07-02 江苏省人民医院（南京医科大学第一附属医院） 基于区块链技术的监护数据管理系统及方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
CHOWDHURY MOHAMMAD JABED MORSHED; FERDOUS MD SADEK; BISWAS KAMANASHIS; CHOWDHURY NIAZ; KAYES ASM; WATTERS PAUL; NG ALEX: "Trust Modeling for Blockchain-Based Wearable Data Market", 2019 IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGY AND SCIENCE (CLOUDCOM), IEEE, 11 December 2019 (2019-12-11), pages 411 - 417, XP033694531, DOI: 10.1109/CloudCom.2019.00070 *

Also Published As

Publication number Publication date
KR20240032657A (ko) 2024-03-12

Similar Documents

Publication Publication Date Title
US10572626B2 (en) Advanced telemedicine system with virtual doctor
US9254092B2 (en) Systems and methods for processing and analyzing medical data
Smys et al. Internet of things and big data analytics for health care with cloud computing
Guidi et al. A multi-layer monitoring system for clinical management of Congestive Heart Failure
US20230414151A1 (en) Mobile electrocardiogram system
Spinsante et al. Technological approaches to remote monitoring of elderly people in cardiology: a usability perspective
CN113990482A (zh) 健康数据处理系统及方法
US20140330580A1 (en) System and method for unlocking an over-the-counter biosignal recorder
US11529106B1 (en) Noninvasive tool for assessing hydration status
Vodička et al. The role of telecardiology in dealing with patients with cardiac rhythm disorders in family medicine–systematic review
Gómez-García et al. Platform for healthcare promotion and cardiovascular disease prevention
CN112735607A (zh) 一种全数据化的罕见病病例库及mdt讨论平台
WO2024049198A1 (fr) Système et procédé pour fournir un service de négociation basé sur une chaîne de blocs pour des données d'électrocardiogramme
WO2024049193A1 (fr) Procédé, programme et dispositif de fourniture de service de transaction de données d'électrocardiogramme
Raheja et al. A study of telecardiology-based methods for detection of cardiovascular diseases
Boulos et al. Opportunistic atrial fibrillation screening and detection in “self-service health check-up stations”: a brief overview of current technology potential and possibilities
WO2024005348A1 (fr) Système et procédé pour fournir un service de lecture d'ecg
Talha et al. Paving the way to cardiovascular health monitoring using Internet of Medical Things and Edge-AI
Rakhmatullov et al. Technologies for optimization and digitalization of cardiological care
Hwang et al. Comparison of mobile application-based ECG consultation by collective intelligence and ECG interpretation by conventional system in a tertiary-level hospital
WO2024014864A1 (fr) Clavier fournissant une fonction de mesure d'électrocardiogramme, système le comprenant et procédé associé
Chandana et al. A Hybrid E-Prescription Management System using IoT
WO2024019584A1 (fr) Procédé, programme et dispositif pour diagnostiquer un infarctus du myocarde à l'aide d'un électrocardiogramme
WO2024014840A1 (fr) Procédé, programme et dispositif pour fournir des services de transport pour un patient en situation d'urgence et surveiller un patient hospitalisé sur la base d'électrocardiogrammes
WO2024025326A1 (fr) Procédé, programme et dispositif pour fournir un service d'interprétation d'ecg

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23860874

Country of ref document: EP

Kind code of ref document: A1