WO2024049198A1

WO2024049198A1 - System and method for providing blockchain-based trading service for electrocardiogram data

Info

Publication number: WO2024049198A1
Application number: PCT/KR2023/012880
Authority: WO
Inventors: 권준명
Original assignee: 주식회사 메디컬에이아이
Priority date: 2022-09-01
Filing date: 2023-08-30
Publication date: 2024-03-07
Also published as: KR20240032657A

Abstract

The present disclosure relates to a system and method for providing a blockchain-based trading service for electrocardiogram data, the system comprising: at least one electrocardiogram measurement device that provides electrocardiogram data; and a computing device that stores, on the basis of blockchain, the electrocardiogram data provided from the at least one electrocardiogram measurement device, and provides a blockchain-based trading service for the stored electrocardiogram data, wherein the computing device provides sales list information about electrocardiogram data tradable at the request of a purchaser terminal, and if the purchaser terminal selects electrocardiogram data on the basis of the sales list information, provides the blockchain-based trading service between the purchaser terminal and a seller terminal with ownership of the selected electrocardiogram data.

Description

System and method for providing transaction services for blockchain-based electrocardiogram data

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.

An electrocardiogram (ECG) is a signal that measures electrical signals generated in the heart and checks for abnormalities in the conduction system from the heart to the electrodes to determine the presence or absence of disease.

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.

These 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.

Currently, research is continuing to quickly and accurately diagnose diseases using artificial intelligence based on electrocardiograms to reduce dependence on doctors. In addition, with the development of wearable and lifestyle electrocardiogram measurement devices, the possibility of diagnosing and monitoring not only heart disease but also various other diseases based on electrocardiogram is emerging.

Non-invasively assessing heart function by measuring electrocardiograms helps diagnose numerous heart diseases, including arrhythmia, myocardial infarction, and arterial disease. In all medical facilities, cardiac-related symptoms include electrocardiogram (ECG) measurements, which are stored in daily accumulation of ECG records. Additionally, due to the development of smart devices that include electrocardiogram measurement functions, users can easily measure and save their electrocardiograms even without visiting a medical institution.

In this way, 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. In addition, there is a widespread social perception that 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.

Therefore, in order to increase the competitiveness of the domestic medical data industry, ownership of medical data must be allowed to be owned by the examinee (or patient), and a fair price must be set according to the value of the ECG data. At this time, since medical data, including electrocardiogram data, deals with very sensitive personal information by its nature, a considerable level of reliability and security is required, so it is necessary to be able to trade secure, patient-led medical data in a transaction system.

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.

However, the problems to be solved by this disclosure are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood based on the description below.

In order to realize the above-mentioned tasks, 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. When 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.

Alternatively, 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.

Alternatively, the computing device encrypts the ECG data using an encryption key and then stores the encrypted ECG data based on blockchain.

Alternatively, 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.

Alternatively, 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.

Alternatively, 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.

Alternatively, the computing device provides transaction alarm information to a seller terminal that has ownership of the selected ECG data.

Alternatively, 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.

Alternatively, 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.

Meanwhile, according to an embodiment of the present disclosure, 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 according to an embodiment of the present disclosure 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.

Meanwhile, 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.

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.

Below, with reference to the attached drawings, embodiments of the present disclosure are described in detail so that those skilled in the art (hereinafter referred to as skilled in the art) can easily implement the present disclosure. The embodiments presented in this disclosure are provided to enable any person skilled in the art to use or practice the subject matter of this disclosure. Accordingly, various modifications to the embodiments of the present disclosure will be apparent to those skilled in the art. That is, the present disclosure can be implemented in various different forms and is not limited to the following embodiments.

The same or similar reference numerals refer to the same or similar elements throughout the specification of this disclosure. Additionally, in order to clearly describe the present disclosure, reference numerals in the drawings may be omitted for parts that are not related to the description of the present disclosure.

As used in this disclosure, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified in the present disclosure or the meaning is not clear from the context, “x uses a or b” should be understood to mean one of natural implicit substitutions. For example, unless otherwise specified in the present disclosure or the meaning is not clear from the context, “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.

The term “and/or” as used in this disclosure should be understood to refer to and include all possible combinations of one or more of the listed related concepts.

The terms “comprise” and/or “comprising” as used in this disclosure should be understood to mean that certain features and/or elements are present. However, the terms "comprise" and/or "including" should be understood as not excluding the presence or addition of one or more other features, other components, and/or combinations thereof.

Unless otherwise specified in this disclosure or the context is clear to indicate a singular form, the singular should generally be construed to include “one or more.”

The term "th nth (n is a natural number)" used in the present disclosure can be understood as an expression used to distinguish the components of the present disclosure according to a predetermined standard such as a functional perspective, a structural perspective, or explanatory convenience. there is. For example, in the present disclosure, components performing different functional roles may be distinguished as first components or second components. However, 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.

The term “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.

Meanwhile, the term "module" or "unit" used in this disclosure refers to a computer-related entity, firmware, software or part thereof, hardware or part thereof. , can be understood as a term referring to an independent functional unit that processes computing resources, such as a combination of software and hardware. At this time, 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. For example, 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. Additionally, as a broad concept, “module” or “unit” may refer to the computing device itself constituting the system, or an application running on the computing device. However, since the above-described concept is only an example, the concept of “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.

As used in this disclosure, the term "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. For example, 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.

The term “block” used in the present disclosure can be understood as a set of components divided based on various criteria such as type, function, etc. Accordingly, the configuration classified as one “block” can be changed in various ways depending on the standard. For example, 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.

The computing device 100 according to an embodiment of the present disclosure 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. For example, 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. Additionally, 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.

Referring to FIG. 1, a computing device 100 according to an embodiment of the present disclosure 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 according to an embodiment of the present disclosure may be understood as a structural unit including hardware and/or software for performing computing operations. For example, 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.

At this time, 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. To this end, the processor 110 can learn a neural network model that diagnoses heart disease based on electrocardiogram data. For example, 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. Specifically, 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. At this time, 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. For example, 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.

In addition to the examples described above, 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 according to an embodiment of the present disclosure 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. For example, 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. Additionally, 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. For example, 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 according to an embodiment of the present disclosure may be understood as a structural unit that transmits and receives data through any type of known wired or wireless communication system. For example, 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). broadband internet, 5th generation mobile communication (5g), ultra wide-band wireless communication, zigbee, radio frequency (RF) communication, wireless LAN, wireless fidelity ), data transmission and reception can be performed using a wired or wireless communication system such as near field communication (NFC), or Bluetooth. Since the above-described communication systems are only examples, the wired and wireless communication systems for data transmission and reception of the network unit 130 may be applied in various ways other than the above-described examples.

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, and FIG. 3 is a detailed configuration of a computing device according to an embodiment of the present disclosure. This is a drawing explaining.

Referring to FIG. 2, 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.

Meanwhile, 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.

If the electrocardiogram meter 10 is implemented as an intelligent device based on artificial intelligence technology, the computing device 100 may be operated independently or integrated with the electrocardiogram meter 10.

As shown in FIG. 3, 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. At this time, the biological information may include information such as gender, age, weight, and height about the subject of ECG data measurement, and the reading information may include ECG reading information using a neural network model or expert reading information.

Here, 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. Specifically, 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.

For example, 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. 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.

Meanwhile, since the structure and type of neural network of the above-described neural network model are only examples, 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.

In general, 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. However, since 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.

Each module described above is only an example for explaining the present invention, but is not limited thereto and may be implemented in various modifications. In addition, 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.

Referring to FIG. 2 again, 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. In addition, 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. At this time, 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.

Referring to FIG. 4 , 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.

The step (S40) of providing a transaction service for ECG data according to an embodiment of the present invention 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.

Additionally, when registering ownership of ECG data, 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.

In addition, 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.

For example, 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. In addition, 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. .

That is, 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.

When the buyer terminal selects ECG data based on the sales list information (S42), 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). When 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).

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)

Meanwhile, when the computing device 100 determines transaction rejection of the transaction alarm information based on the user input of the seller terminal (S44), the computing device 100 may transmit a transaction impossibility status for the ECG data selected by the buyer terminal to the buyer terminal (S48). .

In this disclosure, 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.

In addition, in this disclosure, the ownership of 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.

The various embodiments of the present disclosure described above may be combined with additional embodiments and may be changed within the scope understandable to those skilled in the art in light of the above detailed description. The embodiments of the present disclosure should be understood in all respects as illustrative and not restrictive. For example, each component described as unitary may be implemented in a distributed manner, and similarly, components described as distributed may also be implemented in a combined form. Accordingly, all changes or modified forms derived from the meaning and scope of the claims of the present disclosure and their equivalent concepts should be construed as being included in the scope of the present disclosure.

Claims

A system that provides transaction services for blockchain-based electrocardiogram data,

At least one electrocardiogram meter providing electrocardiogram data; and

A computing device that stores ECG data provided from the ECG meter on a blockchain basis and provides a blockchain-based transaction service for the stored ECG data,

The computing device is,

At the request of the buyer terminal, sales list information for tradable ECG data is provided, and when the buyer terminal selects ECG data based on the sales list information, the seller terminal and the buyer who have ownership of the selected ECG data Providing blockchain-based transaction services between terminals,

system.
According to paragraph 1,

The computing device is,

For ECG data for which transactions have been completed, identification information is provided to access ECG data stored on a blockchain basis.

system.
According to paragraph 1,

The computing device is,

After encrypting the ECG data using an encryption key, the encrypted ECG data is stored on a blockchain basis,

system.
According to paragraph 3,

The computing device is,

For ECG data for which transactions have been completed, identification information for accessing ECG data stored on a blockchain basis and a decryption key for decrypting the encrypted ECG data are provided,

system.
According to paragraph 1,

The computing device is,

Additional collection and storage of at least one related information among biological information linked to the ECG data, characteristic values for ECG characteristics, reading information, inspection information, or supporting data,

system.
According to clause 5,

The computing device is,

Providing a matching data list by matching the electrocardiogram data and the related information to the sales list information, and differentially setting a transaction price of the electrocardiogram data according to the type of related information matched to the matching data list,

system.
According to paragraph 1,

The computing device is,

Providing transaction alarm information to a seller terminal that has ownership of the selected ECG data,

system.
In clause 7,

The computing device is,

Based on the user input of the seller terminal, when transaction approval of the transaction alarm information is determined, completion of the transaction with the buyer terminal is confirmed,

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 buyer terminal is transmitted to the buyer terminal,

system.
According to paragraph 1,

The computing device is,

Performing user de-identification processing on the ECG data for which the transaction has been completed and providing the user de-identified ECG data to the purchaser terminal,

system.
A method of providing transaction services for electrocardiogram data based on blockchain, performed by a computing device including at least one processor, comprising:

Collecting electrocardiogram data for each user;

Storing the collected electrocardiogram data based on blockchain; and

At the request of the buyer terminal, sales list information for tradable ECG data is provided, and when the buyer terminal selects ECG data based on the sales list information, the seller terminal and the buyer who have ownership of the selected ECG data Providing blockchain-based transaction services between terminals;

Including,

method.