System for generating interpretation report including artificial intelligence-based analysis result, and method therefor

Abstract

The present disclosure provides a system for generating an interpretation report including an artificial intelligence-based analysis result in an electronic medical record environment, and a method therefor, the system comprising: a first receiver unit for obtaining and storing biometric data; an electronic medical record unit for providing an analysis result for the biometric data; and a middleware unit for connecting the receiver and the electronic medical record unit. The system for generating an interpretation report including an artificial intelligence-based analysis result, according to one embodiment of the present disclosure, comprises: an artificial intelligence server for analyzing the biometric data by using an artificial intelligence-based model so as to obtain an analysis result, and modifying the biometric data to include the analysis result; and a second receiver unit for obtaining the modified biometric data from the artificial intelligence server and transmitting the modified biometric data to the middleware unit.

Description

System and method for generating a reading including artificial intelligence-based analysis results

[0001] The present disclosure relates to deep learning technology in the medical field, and more particularly, to a system and method for reading biosignals based on artificial intelligence and generating a reading report including analysis results.

[0002] Electrocardiogram (ECG) data is a recording of electrocardiogram (ECG) signals generated by microcurrents within the heart. Accurate analysis of ECG data is essential in the medical field, as it can lead to early detection of cardiovascular disease or structural abnormalities in patients. Currently, most ECG analysis relies on the human eye. Specifically, doctors observe ECG data, analyze its waveform, or extract feature values ​​to diagnose patients. However, this method suffers from accuracy issues. Furthermore, the human eye alone has limitations in identifying potential features in ECG data, leading to the loss of valuable information that could otherwise be identified.

[0003] Meanwhile, recent advancements in information and communication technology have led to the utilization of artificial intelligence (AI) in various fields. In particular, various attempts are being made to integrate AI technology to improve efficiency in the medical field, which previously relied on specialized and limited resources such as doctors and researchers to diagnose patients' illnesses. Recently, AI technology has been actively utilized to analyze electrocardiogram data to predict potential heart disease in patients. However, hospitals face challenges in utilizing this technology due to fixed system resources that predate the adoption of AI technology.

[0004] The present disclosure is conceived in response to the aforementioned background technology, and aims to provide a system and method for reading biosignals based on artificial intelligence and generating a reading text including analysis results.

[0005] However, the problems to be solved in this disclosure are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood based on the description below.

[0006] In an electronic medical record environment including a first receiver unit for acquiring and storing biometric data and an electronic medical record unit for providing an analysis result for the biometric data according to an embodiment of the present disclosure for realizing the task as described above, a system for generating a reading including an artificial intelligence-based analysis result includes an artificial intelligence server for analyzing the biometric data using an artificial intelligence-based model to acquire an analysis result, modifying the biometric data to include the analysis result, and a second receiver unit for acquiring the modified biometric data from the artificial intelligence server and transmitting the obtained data to the electronic medical record unit.

[0007] Alternatively, the artificial intelligence server may input the biometric data into the artificial intelligence-based model to obtain a disease score associated with a biometric signal corresponding to the biometric data, and modify the biometric data to include information regarding the disease score.

[0008] Alternatively, when the artificial intelligence server receives an artificial intelligence result request from the electronic medical record unit, it may obtain the biometric data from the first receiver unit and analyze the biometric data using the artificial intelligence-based model to obtain an analysis result.

[0009] Alternatively, the artificial intelligence server may transmit the biometric data obtained from the first receiver unit and the biometric data modified to include the analysis result to the second receiver unit.

[0010] Alternatively, if the artificial intelligence server determines that new biometric data is stored in the first receiver unit, the artificial intelligence server may obtain the new biometric data from the first receiver unit and analyze the new biometric data using the artificial intelligence-based model to obtain an analysis result.

[0011] Alternatively, when biometric data is stored in the first receiver unit, the artificial intelligence server can compare the newly stored biometric data with existing biometric data already stored in the first receiver unit to determine whether the stored biometric data is new biometric data.

[0012] Alternatively, the biometric data may be acquired from the first receiver unit connected to the biometric signal measuring device, converted into first image data by the first receiver unit, and transmitted to the electronic medical record unit through a middleware unit connecting the first receiver unit and the electronic medical record unit, and the second receiver unit may convert the modified biometric data into second image data including information about the disease score, and transmitted to the electronic medical record unit through the middleware unit connecting the second receiver unit and the electronic medical record unit.

[0013] Alternatively, a waveform of a biosignal included in the biodata may be displayed in a first area of ​​a first image corresponding to the first image data, a waveform of a biosignal included in the modified biodata may be displayed in a first area of ​​a second image corresponding to the second image data, and a disease type and a disease score may be displayed in a second area of ​​the second image.

[0014] Alternatively, depending on the type of request transmitted from the electronic medical record unit to the middleware unit, either the first image data or the second image data may be transmitted to the electronic medical record by the middleware unit.

[0015] Alternatively, when an artificial intelligence result request is transmitted from the electronic medical record unit to the middleware unit, the second image data may be overwritten with the first image data as data corresponding to the first receiver unit, and then the second image data may be transmitted to the electronic medical record. Alternatively, the biometric data may be acquired in an XML format, and the artificial intelligence server may extract data related to a biometric signal from the biometric data, input the extracted data into the artificial intelligence-based model to acquire a disease score related to the biometric data, and insert data related to the disease score into the biometric data to modify the biometric data.

[0016] Alternatively, the artificial intelligence server may select only the biometric data related to the electrocardiogram signal from among the plurality of biometric data stored in the first receiver unit, and analyze the selected biometric data using the artificial intelligence-based model to obtain analysis results related to the electrocardiogram signal.

[0017] In an electronic medical record environment including a first receiver unit that acquires biometric data for realizing the task described above and stores the biometric data in a first folder, and an electronic medical record unit that provides an analysis result for the biometric data, a system for generating a reading statement including an artificial intelligence-based analysis result includes an artificial intelligence server that analyzes the biometric data using an artificial intelligence-based model to acquire an analysis result, modifies the biometric data to include the analysis result, and stores the modified biometric data in the second folder, and at least one of the biometric data and the modified biometric data is converted into image data at a request of the electronic medical record unit and transmitted to the electronic medical record unit.

[0018] In an electronic medical record environment including a first receiver unit including a first folder and a second folder in which biometric data is stored for realizing the aforementioned task, and an electronic medical record unit providing an analysis result for the biometric data, a system for generating a reading statement including an artificial intelligence-based analysis result includes an artificial intelligence server that obtains biometric data from a biometric signal measuring device, analyzes the biometric data using an artificial intelligence-based model to obtain an analysis result, modifies the biometric data to include the analysis result, and stores the biometric data and the modified biometric data in the first folder and the second folder, respectively, and at least one of the biometric data and the modified biometric data is converted into image data at a request of the electronic medical record unit and transmitted to the electronic medical record unit.

[0019] In an electronic medical record environment including a first receiver unit for obtaining image data corresponding to biometric data for realizing the task described above and an electronic medical record unit for providing an analysis result for the biometric data, a system for generating a reading statement including an artificial intelligence-based analysis result comprises an artificial intelligence server for obtaining biometric data in an XML file format from a biometric signal measuring device, analyzing the biometric data using an artificial intelligence-based model to obtain an analysis result, modifying the biometric data to include the analysis result, converting the biometric data and the modified biometric data into PDF file formats, and generating URL information that enables access to the PDF file and transmitting the URL information to the first receiver unit, wherein at least one of the biometric data and the modified biometric data is converted into the image data at the request of the electronic medical record unit that accessed the PDF file based on the URL information, and transmitted to the electronic medical record unit.

[0020] In an electronic medical record environment including a first receiver unit for acquiring and storing biometric data and an electronic medical record unit for providing an analysis result for the biometric data according to an embodiment of the present disclosure for realizing the task as described above, a method for generating a reading statement including an artificial intelligence-based analysis result is provided, the method comprising: a step in which an artificial intelligence server analyzes the biometric data using an artificial intelligence-based model to acquire an analysis result and modifies the biometric data to include the analysis result; and a step in which a second receiver unit acquires the modified biometric data from the artificial intelligence server and transmits the obtained data to the electronic medical record unit.

[0021] In an electronic medical record environment including a first receiver unit for acquiring and storing biometric data for realizing the aforementioned task and an electronic medical record unit for providing an analysis result for the biometric data, a computer program stored on a computer-readable storage medium according to an embodiment of the present disclosure is provided, wherein the computer program, when executed on one or more processors, performs an operation of generating a reading statement including an artificial intelligence-based analysis result, wherein the operation includes an operation of an artificial intelligence server analyzing the biometric data using an artificial intelligence-based model to acquire an analysis result and modifying the biometric data to include the analysis result, and an operation of a second receiver unit obtaining the modified biometric data from the artificial intelligence server and transmitting the same to the electronic record unit.

[0022] According to an artificial intelligence system and method for generating a diagnosis including an artificial intelligence-based analysis result according to an embodiment of the present disclosure, medical staff can identify the type of disease and the possibility of disease latent in the electrocardiogram waveform along with the electrocardiogram waveform.

[0023] FIG. 1 is a diagram illustrating an electronic medical record environment according to one embodiment of the present disclosure.

[0024] FIG. 2 is a diagram illustrating a method for generating an artificial intelligence-based reading result sheet in an electronic medical record environment according to one embodiment of the present disclosure.

[0025] FIG. 3 is a flowchart illustrating a method for generating an artificial intelligence-based reading result sheet of an artificial intelligence system (100) according to one embodiment of the present disclosure.

[0026] FIG. 4 is an example diagram of a reading text including an artificial intelligence-based reading result according to one embodiment of the present disclosure.

[0027] FIG. 5 is a diagram illustrating an electronic medical record environment according to another embodiment of the present disclosure.

[0028] FIG. 6 is a diagram illustrating an electronic medical record environment including an artificial intelligence server for obtaining biometric data according to another embodiment of the present disclosure.

[0029] FIG. 7 is a diagram illustrating an electronic medical record environment including an artificial intelligence server that generates URL information related to biometric data according to another embodiment of the present disclosure.

[0030] Below, embodiments of the present disclosure are described in detail with reference to the attached drawings so that those skilled in the art can easily implement the present disclosure. The embodiments presented in this disclosure are provided to enable those skilled in the art to utilize or implement the contents of the present 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 may be implemented in various different forms and is not limited to the embodiments described below.

[0031] Throughout the specification of this disclosure, identical or similar drawing numbers refer to identical or similar components. Furthermore, for the purpose of clearly describing the disclosure, drawing numbers for parts in the drawings that are not relevant to the description of the disclosure may be omitted.

[0032] The term "or" as used herein is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified herein or clear from context, "X employs A or B" should be understood to mean either of its natural inclusive permutations. For example, unless otherwise specified herein or clear from context, "X employs A or B" can be interpreted to mean either X employs A, X employs B, or X employs both A and B.

[0033] The term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the related concepts listed.

[0034] The terms "comprises" and / or "comprising" as used herein should be understood to mean the presence of certain features and / or components. However, it should be understood that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, other components, and / or combinations thereof.

[0035] Unless otherwise specified in this disclosure or unless the context makes it clear that the singular form is being referred to, the singular should generally be construed to include “one or more.”

[0036] The term "Nth (N is a natural number)" used in this disclosure can be understood as an expression used to mutually distinguish components of this disclosure based on a predetermined standard such as a functional perspective, a structural perspective, or convenience of explanation. For example, components performing different functional roles in this disclosure can be distinguished as a first component or a second component. However, components that are substantially the same within the technical spirit of this disclosure but must be distinguished for convenience of explanation may also be distinguished as a first component or a second component.

[0037] The term "acquisition" as used in this disclosure may be 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.

[0038] Meanwhile, the term "module" or "unit" used in the present disclosure can be understood as a term referring to an independent functional unit that processes computing resources, such as a computer-related entity, firmware, software or a part thereof, hardware or a part thereof, or a combination of software and hardware. At this time, the "module" or "unit" may be a unit composed of a single element, or a unit expressed as a combination or set of multiple elements. For example, as a narrow concept, a "module" or "unit" may refer to a hardware element of a computing device or a set thereof, an application program that performs a specific function of software, a processing process implemented through software execution, or a set of instructions for program execution, etc. In addition, as a broad concept, a "module" or "unit" may refer to the computing device itself that constitutes the system, or an application running on the computing device, etc. However, since the above-described concept is only an example, the concept of “module” or “part” may be defined in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

[0039] The term "model" as used herein may be understood as a system implemented using mathematical concepts and language to solve a specific problem, a set of software units to solve a specific problem, or an abstract model of a processing process to solve a specific problem. For example, a neural network "model" may refer to the entire system implemented as a neural network that has problem-solving capabilities through learning. In this case, the neural network can have problem-solving capabilities by optimizing the parameters connecting nodes or neurons through learning. A neural network "model" may include a single neural network or a set of neural networks that are a combination of multiple neural networks.

[0040] The term "data" used in this disclosure may include "image," "signal," and the like. The term "image" used in this disclosure may refer to multidimensional data composed of discrete image elements. In other words, "image" may be understood as a term referring to a digital representation of an object visible to the human eye. For example, "image" may refer to multidimensional data composed of elements corresponding to pixels in a two-dimensional image. "Image" may refer to multidimensional data composed of elements corresponding to voxels in a three-dimensional image.

[0041] The explanation of the above terms is intended to aid understanding of the present disclosure. Therefore, unless explicitly stated as limiting the contents of the present disclosure, it should be noted that the above terms are not intended to limit the technical ideas of the contents of the present disclosure.

[0042] FIG. 1 is a diagram illustrating an electronic medical record environment (1000) according to one embodiment of the present disclosure. However, FIG. 1 is merely an example, and the electronic medical record environment (1000) may include other components for implementing an environment for recording, managing, and providing a patient's electronic medical information. Furthermore, only some of the disclosed components may be included in the electronic medical record environment (1000).

[0043] Referring to FIG. 1, an electronic medical record environment (1000) (Electronic Medical Record System, EMR) is an environment (1000) for electronically recording and managing medical information related to patient treatment in medical facilities such as hospitals and public health centers, and may also be referred to as a medical facility system.

[0044] An electronic medical record environment (1000) may include an artificial intelligence system (100), a first receiver unit (200), a middleware unit (300), and an electronic medical record unit (400).

[0045] FIG. 2 is a diagram illustrating a method for generating an artificial intelligence-based reading result sheet in an electronic medical record environment (1000) according to one embodiment of the present disclosure.

[0046] An artificial intelligence system (100) according to one embodiment of the present disclosure analyzes biometric data (10) based on artificial intelligence and transmits the analysis results to an electronic medical record unit (400) via a middleware unit (300). In particular, the artificial intelligence system (100) generates a reading result sheet including the artificial intelligence-based analysis results and transmits the sheet to the electronic medical record unit (400), thereby enabling medical staff to understand not only the biometric signals included in the reading result sheet but also the analysis results based on artificial intelligence. However, the present invention is not limited thereto, and the artificial intelligence system (100) may also directly transmit the analysis results to the electronic medical record unit (400).

[0047] A first receiver unit (200) according to one embodiment of the present disclosure acquires and stores a patient's biometric data (10). The first receiver unit (200) may be referred to as a first receiver device and may be implemented as at least one electronic device (e.g., a computer, a server, etc.).

[0048] Biometric data (10) may include biometric signal information of a patient (e.g., waveform of a biometric signal, characteristic information identified from the waveform of a biometric signal), patient information, measurement environment (1000) (setting information of a measurement device, measurement sampling rate information, etc.), and metadata (measurement time, measurement location, data format information, etc.). For example, if the biometric data (10) is electrocardiogram data acquired from an electrocardiogram measurement device, the electrocardiogram data may include electrocardiogram waveform information, electrode location and lead information, patient information, characteristic information such as P waves, etc.

[0049] The first receiver unit (200) is connected to a biometric device that measures a patient's biometric signal, and can obtain biometric data (10) obtained by the biometric device from the biometric device. The first receiver unit (200) can be connected to at least one biometric device by wire or wirelessly. For example, the first receiver unit (200) can obtain biometric data (10) of the patient from a biometric device that performs network communication with the first receiver unit (200) through a communication interface of the first receiver unit (200). In addition, the first receiver unit (200) can store the biometric data (10) obtained from the biometric device. For example, the first receiver unit (200) can store biometric data (10) in the memory of the first receiver unit (200) whenever biometric data (10) is obtained.

[0050] The first receiver unit (200) can be connected to various bio-signal measuring devices, which can include an electrocardiogram measuring device, an electroencephalogram measuring device, an electroencephalogram measuring device, an electroencephalogram measuring device, a blood pressure measuring device, an oxygen saturation measuring device, a blood sugar measuring device, a body temperature measuring device, etc. However, for the convenience of explanation of the present disclosure, the bio-signal measuring device will be described below assuming an electrocardiogram measuring device.

[0051] Meanwhile, referring to FIG. 2, the first receiver unit (200) can convert the acquired biometric data (10) into image data (20) corresponding to the biometric data (10). Specifically, the first receiver unit (200) can extract biometric signal information and biometric signal characteristics from the acquired biometric data (10), and convert the biometric signal information and biometric signal characteristics into image data (20) including a waveform of the biometric signal and text representing the biometric signal characteristics. For example, the biometric data (10) can be in an XML file format. The image data (20) can be a biometric signal reading result sheet. This will be described in detail with reference to FIG. 4.

[0052] Meanwhile, the first receiver unit (200) can transmit the image data (20) to the middleware unit (300) that connects the first receiver unit (200) and the electronic medical record unit (400). Alternatively, the first receiver unit (200) can directly transmit the image data (20) to the electronic medical record unit (400).

[0053] A middleware unit (300) according to one embodiment of the present disclosure connects a first receiver unit (200) and an electronic medical record unit (400). In addition, the middleware unit (300) connects an artificial intelligence system (100) and an electronic medical record unit (400). The middleware unit (300) performs a function of routing and transmitting medical data (e.g., image data (20) corresponding to biometric data (10) converted by the first receiver unit (200)) between the first receiver unit (200) and the electronic medical record unit (400), and between the artificial intelligence system (100) and the electronic medical record unit (400). For example, the middleware unit (300) can determine whether the prescription code issued by the medical staff corresponds to the data (e.g., a bio-signal reading result sheet) received from the first receiver unit (200) or the artificial intelligence system (100), and transmit the data corresponding to the prescription code to the electronic medical record unit (400). The prescription code can be received from the electronic medical record unit (400). Accordingly, the middleware unit (300) can select an electronic device that has transmitted the prescription code among various electronic devices included in the electronic medical record unit (400) and transmit the data. The middleware unit (300) can be implemented as an electronic device (e.g., a computer, a server, etc.) or can be implemented as software (e.g., a program, an application, etc.).

[0054] In addition, the middleware unit (300) can relay connections to various electronic devices (e.g., bio-signal measuring devices, etc.) and systems (e.g., PACS (Picture Archiving and Communication System), Laboratory Information System (LIS), etc.) within a medical institution connected to the electronic medical record unit (400), and can convert or integrate the format of transmitted medical data. In addition, the middleware unit (300) can perform encryption on medical data or restrict access by external electronic devices (or external systems).

[0055] An electronic medical record unit (400) according to one embodiment of the present disclosure records and manages a patient's medical information. Specifically, the electronic medical record unit (400) stores the patient's medical records, prescription details, test results, etc. entered by medical staff. Data related thereto may be transmitted from the middleware unit (300). In addition, the electronic medical record unit (400) transmits prescription codes, medical information, etc. entered by medical staff to the middleware unit (300). To this end, the electronic medical record unit (400) may be implemented as an electronic device (e.g., a computer, etc.) including a processor, and may include a storage unit (e.g., a database, etc.), a display, etc. The electronic medical record unit (400) may display medical data (e.g., image data (20) corresponding to biometric data (10) acquired by the first receiver unit (200)) transmitted from the middleware unit (300) to the first receiver unit (200) or the artificial intelligence system (100) on a display. Meanwhile, the electronic medical record unit (400) may be referred to as an electronic medical record system.

[0056] Below, the operation of an artificial intelligence system (100) according to one embodiment of the present disclosure will be described in detail.

[0057] FIG. 3 is a flowchart illustrating a method for generating an artificial intelligence-based reading result sheet of an artificial intelligence system (100) according to one embodiment of the present disclosure.

[0058] Referring back to FIGS. 1 and 2, the artificial intelligence system (100) includes an artificial intelligence server (110) and a second receiver unit (120). However, the present invention is not limited thereto and may include other configurations for implementing an electronic medical record environment (1000).

[0059] An artificial intelligence server (110) according to one embodiment of the present disclosure can analyze biometric data (10) using an artificial intelligence-based model to obtain analysis results, and modify the biometric data (10) to include the analysis results (S210).

[0060] Specifically, when the first receiver unit (200) obtains and stores biometric data (10) from the biometric signal measuring device, the artificial intelligence server (110) can obtain the stored biometric data (10) from the first receiver unit (200). Then, the artificial intelligence server (110) can input the biometric data (10) into an artificial intelligence-based model to obtain an analysis result of the biometric data (10). For example, the artificial intelligence server (110) can extract data related to a biometric signal (specifically, data related to a biometric signal waveform or data related to a feature identified in a biometric signal waveform) from the biometric data (10) in XML format, and input the extracted information into the artificial intelligence-based model to obtain an analysis result. As a more specific example, when the biosignal is an electrocardiogram, the artificial intelligence server (110) can extract data related to the electrocardiogram waveform from the electrocardiogram data and / or data related to features identified in the electrocardiogram waveform, such as P waves (and Q waves, QRS intervals, etc.), and input the extracted and acquired data into an artificial intelligence-based model to obtain analysis results regarding the electrocardiogram waveform.

[0061] When the artificial intelligence server (110) obtains analysis results on biometric data (10) through an artificial intelligence-based model, it can modify the biometric data (10) to include the obtained analysis results. That is, if the biometric data (10) obtained by the first receiver unit (200) from the biometric signal measuring device is original data, the artificial intelligence unit can modify the original data by including the analysis results in the original data.

[0062] Meanwhile, the artificial intelligence-based model may be a pre-trained neural network model that analyzes biosignals and outputs analysis results. For example, the artificial intelligence-based model may be a neural network model, and the neural network model may be implemented as a multi-layer perceptron (MLP), a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network, etc. Meanwhile, the artificial intelligence-based model may output the likelihood of a disease predicted from the biosignal as an analysis result. Accordingly, the artificial intelligence server (110) may modify the biodata (10) by including information on the likelihood of a disease.

[0063] According to one embodiment of the present disclosure, the artificial intelligence server (110) inputs biometric data (10) into an artificial intelligence-based model to obtain a disease score related to a biometric signal corresponding to the biometric data (10), and may modify the biometric data (10) to include information about the disease score. That is, the neural network model may output the disease probability predicted from the biometric signal in the form of a score. To this end, the neural network model may be trained in advance with a plurality of biometric data (10) and a heart disease score corresponding to each biometric data (10) as learning data so as to calculate the probability of the disease existence as a score. For example, if the biometric data (10) is electrocardiogram data, the disease may be heart disease, and heart disease may include various diseases and symptoms expressed in the heart, such as heart failure symptoms and hypotension symptoms. Meanwhile, when the artificial intelligence server (110) obtains a disease score from the artificial intelligence-based model, it may modify the biometric data (10) to include the obtained disease score.

[0064] In particular, the artificial intelligence server (110) can extract data related to a biosignal from the biodata (10), input the extracted data into an artificial intelligence-based model to obtain a disease score related to the biodata (10), and insert data related to the disease score into the biodata (10) to modify the biodata (10). For example, the artificial intelligence server (110) can extract data related to a biosignal from the biodata (10) in XML format. Specifically, the artificial intelligence server (110) can identify a tag related to the biosignal and extract data related to the biosignal as a sub-element of the identified tag. In addition, the artificial intelligence server (110) can input the extracted data into an artificial intelligence-based model to obtain a disease score that quantifies the possibility of a disease related to the biodata (10). In addition, the artificial intelligence server (110) can modify the biodata (10) to include disease score information by inserting a new tag related to the disease score and information related to the disease score.

[0065] Meanwhile, according to one embodiment of the present disclosure, the artificial intelligence server (110) may include a plurality of neural network models depending on the type of biosignal corresponding to the biodata (10). At this time, the artificial intelligence server (110) may identify the type of biosignal of the acquired biodata (10), select a neural network model corresponding to the type of the identified biosignal from among the plurality of neural network models, and obtain an analysis result. For example, the artificial intelligence server (110) may select only biodata (10) related to an electrocardiogram signal from among the plurality of biodata (10) stored in the first receiver unit (200), and analyze the selected biodata (10) using an artificial intelligence-based model to obtain an analysis result related to the electrocardiogram signal.

[0066] In addition, according to one embodiment of the present disclosure, the artificial intelligence server (110) may include multiple neural network models for the same biosignal according to the disease type. At this time, the artificial intelligence server (110) may identify a set disease type or a disease type requested from the electronic medical record unit (400), select a neural network model corresponding to the type of the identified disease signal from among the multiple neural network models, and obtain an analysis result. Meanwhile, the artificial intelligence server (110) may obtain multiple disease scores corresponding to each of the multiple diseases associated with the biodata (10) using the multiple neural network models, and may modify the biodata (10) to include the multiple disease scores. At this time, the artificial intelligence server (110) may select a neural network model corresponding to the type of biosignal and the disease type according to a request and prescription code of a medical professional transmitted through the electronic medical record unit (400).

[0067] Meanwhile, according to one embodiment of the present disclosure, when the artificial intelligence server (110) receives an artificial intelligence result request from the electronic medical record unit (400), it may obtain biometric data (10) from the first receiver unit (200), and analyze the biometric data (10) using an artificial intelligence-based model to obtain an analysis result. Specifically, the artificial intelligence server (110) may receive an artificial intelligence result request from the electronic medical record unit (400). In particular, the artificial intelligence server (110) may receive an artificial intelligence result request of the electronic medical record unit (400) from the middleware unit (300). In addition, when the artificial intelligence server (110) receives an artificial intelligence result request from the electronic medical record unit (400), it may obtain biometric data (10) stored in the first receiver unit (200) and obtain an analysis result regarding the obtained biometric data (10).

[0068] Meanwhile, the first receiver unit (200) may receive a request for biometric data (10) from the electronic medical record unit (400). Here, the request for biometric data (10) may be a request for transmission of biometric data (10) that does not include an artificial intelligence result. That is, when the first receiver unit (200) is requested to transmit biometric data (10) that does not include an artificial intelligence result, the first receiver unit (200) may convert the biometric data (10) into image data (20) and transmit it to the middleware unit (300). Then, the middleware unit (300) may transmit the image data (20) to the electronic medical record unit (400).

[0069] Referring again to FIG. 2, according to one embodiment of the present disclosure, the second receiver unit (120) obtains modified biometric data (11) from the artificial intelligence server (110) and transmits it to the middleware unit (300) (S220). Specifically, the artificial intelligence server (110) may modify the biometric data (10) by including the analysis results obtained through the artificial intelligence-based model in the biometric data (10), and then transmit the modified biometric data (11) to the second receiver unit (120).

[0070] At this time, referring back to FIG. 2, the second receiver unit (120) can convert the received modified biometric data (11) into image data (30). In particular, if the analysis result corresponds to a disease score, the second receiver unit (120) can convert it into second image data (30) containing information about the disease score and transmit it to the middleware unit (300).

[0071] Meanwhile, image data corresponding to the acquired biometric data and image data corresponding to the modified biometric data can be selectively transmitted by the electronic medical record unit (400) according to a request received from the electronic medical record unit (400) by the middleware unit (300).

[0072] In this regard, according to one embodiment of the present disclosure, the middleware unit (300) may receive image data (20) corresponding to biometric data (10) acquired from the first receiver unit (200) and may receive image data (30) corresponding to modified biometric data (11) from the second receiver unit (120). In addition, the middleware unit (300) may be requested to transmit the image data (20 and 30) from the electronic medical record unit (400). At this time, when the middleware unit (300) receives a request for an artificial intelligence result from the electronic medical record unit (400), the middleware unit (300) may transmit the image data (30) corresponding to the modified biometric data (11) transmitted from the second receiver unit (120) to the electronic medical record unit (300). And, when the middleware unit (300) receives a request for general bio-signal measurement results from the electronic medical record unit (300), it can transmit image data (20) corresponding to the acquired bio-data (10) received from the first receiver unit (200) to the electronic medical record unit (400).

[0073] Meanwhile, according to one embodiment of the present disclosure, when the middleware unit (300) receives a request for an artificial intelligence result from the electronic medical record unit (400), the middleware unit (300) may overwrite the image data (20) received from the first receiver unit (200) with the image data received from the second receiver unit (120). That is, the middleware unit (300) may store the image data (20) received from the first receiver unit (200) by overwriting the image data (30) received from the second receiver unit (120). In addition, the middleware unit (300) may identify the biometric data corresponding to the prescription code included in the request for the artificial intelligence result as the image data (30) received from the second receiver unit (120), and transmit it to the electronic medical record unit (400).

[0074] In addition, according to one embodiment of the present disclosure, the artificial intelligence server (110) can transmit the biometric data (10) acquired from the first receiver unit (200) and the biometric data (11) modified to include the analysis result to the second receiver unit (120). That is, the artificial intelligence server (110) can transmit the biometric data (10) (i.e., original data) acquired from the first receiver unit (200) together with the modified biometric data (11) to the second receiver unit (120). At this time, the second receiver unit (120) can convert both biometric data (10) (i.e., original data and modified biometric data (11)) into image data (20 and 30) and transmit them to the middleware unit (300). Through this, medical staff using the electronic medical record unit (400) can compare and review the interpretation result sheet including the artificial intelligence-based analysis result and the original interpretation result sheet.

[0075] At this time, the artificial intelligence server (110) obtains image data (20) corresponding to the biometric data (10) (i.e., original data) converted by the first receiver unit (200), and transmits the obtained image data (20) to the second receiver unit (120), and transmits it to the middleware unit (300) together with the image data (30) corresponding to the modified biometric data (11). This can save resources of the second receiver unit (120).

[0076] According to one embodiment of the present disclosure, when the artificial intelligence server (110) determines that new biometric data is stored in the first receiver unit (200), the artificial intelligence server (110) can obtain new biometric data from the first receiver unit (200) and analyze the new biometric data using an artificial intelligence-based model to obtain an analysis result. Specifically, the artificial intelligence server (110) can monitor the first receiver unit (200) to detect an event in which new biometric data is stored in the first receiver unit (200). Then, when the event in which new biometric data is stored is detected, the artificial intelligence server (110) can obtain new biometric data from the first receiver unit (200). Then, using the artificial intelligence-based model, the artificial intelligence server can obtain an analysis result regarding the new biometric data.

[0077] In particular, when biometric data (10) is stored in the first receiver unit (200), the artificial intelligence server (110) can compare the existing biometric data already stored in the first receiver unit (200) with the newly stored biometric data (10) to determine whether the stored biometric data (10) is new biometric data. Specifically, when the artificial intelligence server (110) detects an event in which new biometric data is stored in the first receiver unit (200) while monitoring the first receiver unit (200), the artificial intelligence server (110) can distinguish whether the newly stored biometric data (10) is newly measured and acquired biometric data (10) or whether existing biometric data has been modified or copied and stored. To this end, the artificial intelligence server (110) can compare the newly stored biometric data (10) with the existing biometric data to determine the similarity, or compare metadata to determine whether the newly stored biometric data (10) is new biometric data.

[0078] FIG. 4 is an example diagram of a reading including an artificial intelligence-based analysis result according to one embodiment of the present disclosure.

[0079] Referring to FIG. 4, this is an example of a reading result sheet output from an electronic medical record unit (400).

[0080] According to one embodiment of the present disclosure, a waveform of a biosignal included in the biodata (10) may be displayed in a first area of ​​a first image corresponding to image data (20) (hereinafter, first image data (20)) obtained by converting original biodata (10), a waveform of a biosignal included in the biodata (10) may be displayed in a first area of ​​a second image corresponding to image data (30) (hereinafter, second image data (30)) obtained by converting modified biodata (11), and a disease type and a disease score may be displayed in a second area of ​​the second image.

[0081] For example, referring to FIG. 4, if the bio-signal is an electrocardiogram, the first image data (20) may display an electrocardiogram waveform in the central region (31), and the second image data (30) may display the disease type and disease score in the upper right region (32) together with the electrocardiogram waveform displayed in the central region. Through this, the medical staff may display the electrocardiogram waveform together with the electrocardiogram waveform.

[0082] FIG. 5 is a diagram illustrating an electronic medical record environment according to another embodiment of the present disclosure.

[0083] Referring to FIG. 5, an electronic medical record environment (2000) according to another embodiment of the present disclosure includes a first receiver unit (2100), an artificial intelligence system (2200), and an electronic medical record unit (2300). However, FIG. 5 is merely an example, and the electronic medical record environment (2000) may include other components for implementing an environment for recording, managing, and providing electronic medical information of a patient. In addition, only some of the disclosed components may be included in the electronic medical record environment (2000).

[0084] The first receiver unit (2100) is connected to a biosignal measuring device (2400) and acquires the patient's biodata (10) from the biosignal measuring device (2400). In this regard, the description of the first receiver unit (200) in FIGS. 1 to 4 applies equally, so a detailed description thereof is omitted.

[0085] Meanwhile, the first receiver unit (2100) stores the acquired biometric data (10) in the first folder (21). Here, the first folder (21) may be a storage created in the computing environment of the first receiver unit (2100) or a cloud computing environment, where biometric data is stored.

[0086] The electronic medical record unit (2300) records and manages patient medical information. Since the description of the electronic medical record unit (400) in FIGS. 1 to 4 applies equally to this, a detailed description will be omitted.

[0087] Meanwhile, the artificial intelligence system (2200) includes an artificial intelligence server (2210). The artificial intelligence server (2210) analyzes biometric data (10) using an artificial intelligence-based model to obtain analysis results, and can modify the biometric data (10) to include the analysis results. In this regard, the description of the artificial intelligence server (110) in FIGS. 1 to 4 applies equally, so a detailed description thereof will be omitted.

[0088] The artificial intelligence server (2210) modifies the biometric data (10) and then stores the modified biometric data (11) in the second folder (22) of the first receiver unit (400). Here, the second folder (21) may be a storage where biometric data is stored, created in the computing environment or cloud computing environment of the first receiver unit (2100), similar to the first folder (21).

[0089] At this time, the first receiver unit (2100) may receive a request for a bio-signal measurement result measured for the patient from the electronic medical record unit (2300). Then, when the first receiver unit (2100) receives a request for a general bio-signal measurement result from the electronic medical record unit (2300), the first receiver unit (2100) may extract the acquired bio-data (10) stored in the first folder (21) and transmit the extracted bio-data (10) as image data (20) (hereinafter, first image data) to the electronic medical record unit (400). And, when the first receiver unit (2100) receives a request for a bio-signal measurement result including an artificial intelligence result from the electronic medical record unit (2300), the first receiver unit (2100) can extract the modified bio-data (11) stored in the second folder (22), convert the modified bio-data (11) into image data (30) (hereinafter, second image data), and transmit the second image data (30) to the electronic medical record unit (2300).

[0090] Meanwhile, the electronic medical record environment (2000) illustrated in FIG. 5 may further include a middleware unit that relays the first receiver unit (2100) and the electronic medical record unit (2300). In this case, the description of the middleware unit (300) in FIGS. 1 to 4 may be equally applicable to the middleware unit.

[0091] FIG. 6 is a diagram illustrating an electronic medical record environment including an artificial intelligence server for obtaining biometric data according to another embodiment of the present disclosure.

[0092] Referring to FIG. 6, an electronic medical record environment (3000) including an artificial intelligence server for acquiring biometric data according to another embodiment of the present disclosure includes a first receiver unit (3100), an artificial intelligence system (3200), and an electronic medical record unit (3300). However, FIG. 6 is only an example, and the electronic medical record environment (3000) may include other components for implementing an environment for recording, managing, and providing electronic medical information of a patient. In addition, only some of the disclosed components may be included in the electronic medical record environment (3000).

[0093] The first receiver unit includes a plurality of folders in which biometric data is stored. Specifically, the first receiver unit includes a folder (hereinafter, referred to as a third folder) in which original biometric data obtained from a biometric signal measuring device (3400) is stored, and a folder (hereinafter, referred to as a third folder) in which biometric data modified to include artificial intelligence analysis results is stored. Here, the third folder (31) and the fourth folder (32) may be repositories created in the computing environment of the first receiver unit (3100) or a cloud computing environment, and in which biometric data is stored.

[0094] The electronic medical record unit (3300) records and manages patient medical information. Since the description of the electronic medical record unit (400) in FIGS. 1 to 4 applies equally to this, a detailed description will be omitted.

[0095] Meanwhile, the artificial intelligence system (3200) includes an artificial intelligence server (3210). The artificial intelligence server (3210) is connected to a bio-signal measuring device (3400) and can obtain the patient's bio-data (i.e., original bio-data) from the bio-signal measuring device (3400). The artificial intelligence server (3210) can be connected to at least one bio-signal measuring device (3400) by wire or wirelessly. For example, the artificial intelligence server (3210) can obtain the patient's bio-data (10) from at least one bio-signal measuring device (3400) that performs network communication with the artificial intelligence server (3210) through a communication interface of the artificial intelligence server (3210).

[0096] In addition, the artificial intelligence server (3210) can store biometric data (10) acquired from the biometric signal measuring device (3400) in the third folder (31) of the first receiver unit (3100). For example, the artificial intelligence server (3210) can store biometric data (10) in the third folder (31) of the first receiver unit (3100) every time it is acquired.

[0097] The artificial intelligence server (3210) can be connected to various bio-signal measuring devices (3400), which can include an electrocardiogram measuring device, an electroencephalogram measuring device, an brainwave signal measuring device, a blood pressure measuring device, an oxygen saturation measuring device, a blood sugar measuring device, a body temperature measuring device, etc.

[0098] The artificial intelligence server (3210) can analyze acquired biometric data (10) using an artificial intelligence-based model to obtain analysis results, and modify the biometric data (10) to include the analysis results. In this regard, the description of the artificial intelligence server (110) in FIGS. 1 to 4 applies equally, so a detailed description is omitted.

[0099] Meanwhile, the artificial intelligence server (3210) may store the modified biometric data (10) in the fourth folder (32) of the first receiver unit (3100). The biometric data (10) and modified biometric data (11) acquired by the artificial intelligence server (3210) may be in XML file format. In this regard, the descriptions of the biometric data (10) and modified biometric data (11) described in FIGS. 1 to 4 may be equally applicable.

[0100] At this time, the first receiver unit (3100) may receive a request for the bio-signal measurement results measured for the patient from the electronic medical record unit (3300), and depending on the request type, may convert either the original bio-data (10) stored in the third folder (31) or the modified bio-data (11) stored in the fourth folder (32) into image data (i.e., the first and second image data (20 and 30)) and transmit the converted data to the electronic medical record unit (3300). In this regard, the description of the first receiver unit (2100) in FIG. 5 is equally applicable, and therefore, a detailed description thereof will be omitted.

[0101] FIG. 7 is a diagram illustrating an electronic medical record environment including an artificial intelligence server that generates URL information related to biometric data according to another embodiment of the present disclosure.

[0102] Referring to FIG. 7, an electronic medical record environment (4000) including an artificial intelligence server that generates URL information related to biometric data according to another embodiment of the present disclosure includes a first receiver unit (4100), an artificial intelligence system (4200), and an electronic medical record unit (4300). However, FIG. 7 is only an example, and the electronic medical record environment (4000) may include other components for implementing an environment for recording, managing, and providing electronic medical information of a patient. In addition, only some of the disclosed components may be included in the electronic medical record environment (4000).

[0103] The first receiver unit (4100) and electronic medical record unit (4300) illustrated in FIG. 7 can be equally applied to the above-described description, so a detailed description thereof will be omitted.

[0104] The artificial intelligence system (4200) includes an artificial intelligence server (4210). The artificial intelligence server (4210) is connected to a biosignal measuring device (4400) and can obtain a patient's biometric data (i.e., original biometric data) from the biosignal measuring device (4400). In this regard, the description of the artificial intelligence server (3210) in FIG. 6 applies equally, so a detailed description thereof is omitted.

[0105] Meanwhile, the artificial intelligence server (4210) can modify the original biometric data (10) acquired from the biometric signal measuring device (4400) so that the original biometric data (10) includes an analysis result based on artificial intelligence, and then convert the original biometric data (10) and the modified biometric data (11) into PDF file formats, respectively. In addition, the artificial intelligence server (4210) can store the original biometric data (10) and the modified biometric data (11) in PDF file formats. For example, the artificial intelligence server (4210) can store the original biometric data (10) and the modified biometric data (11) in PDF file formats in a storage unit (e.g., memory, etc.) within the computing environment of the artificial intelligence server (4210) or in a cloud system. In addition, the artificial intelligence server (4210) can generate link information that enables access to the stored original biometric data (10) and the modified biometric data (11) in PDF file formats. Here, the link information may be URL information of the original biometric data (10) and the modified biometric data (11). The artificial intelligence server (4210) may generate URL information and transmit the generated URL information to the first receiver unit (4100). At this time, the first receiver unit (4100) may access a PDF file (i.e., the original biometric data (10) and the modified biometric data (11) in PDF file format) based on the URL information and then convert either the original biometric data (10) or the modified biometric data (11)) into image data.

[0106] In particular, the first receiver unit (4100) can select either the original biometric data (10) or the modified biometric data (11) according to a request received from the electronic medical record unit (4300), convert it into image data, and then transmit it to the electronic medical record unit (4300). In this regard, the description of the first receiver unit (2100) in FIG. 5 applies equally, so a detailed description thereof is omitted.

[0107] Meanwhile, a non-transitory computer readable medium may be provided in which a program is stored that sequentially performs a method of generating a reading text including an artificial intelligence-based analysis result according to the present disclosure.

[0108] A non-transitory readable medium refers to a medium that permanently stores data and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, the various applications or programs described above may be stored and provided on non-transitory readable media, such as a CD, DVD, hard disk, Blu-ray disc, USB, memory card, or ROM.

[0109] The various embodiments of the present disclosure described above can be combined with additional embodiments and modified within a scope that would be understood by those skilled in the art in light of the detailed description set forth above. It should be understood that the embodiments of the present disclosure are illustrative in all respects and not restrictive. For example, each component described as a single component may be implemented in a distributed manner, and likewise, components described as distributed may be implemented in a combined manner. Accordingly, all changes or modifications derived from the meaning, scope, and equivalent concepts of the claims of the present disclosure should be construed as being included within the scope of the present disclosure.

Claims

1. In an electronic medical record environment including a first receiver unit that acquires and stores biometric data and an electronic medical record unit that provides analysis results for the biometric data, a system for generating a reading statement including an artificial intelligence-based analysis result, An artificial intelligence server that analyzes the biometric data using an artificial intelligence-based model to obtain analysis results and modifies the biometric data to include the analysis results; and A second receiver unit that obtains the modified biometric data from the artificial intelligence server and transmits it to the electronic medical record unit; System.

2. In paragraph 1, The above artificial intelligence server, Inputting the biometric data into the artificial intelligence-based model to obtain a disease score related to a biometric signal corresponding to the biometric data, and modifying the biometric data to include information about the disease score. System.

3. In paragraph 1, The above artificial intelligence server, When receiving an artificial intelligence result request from the electronic medical record unit, the biometric data is obtained from the first receiver unit, and the biometric data is analyzed using the artificial intelligence-based model to obtain an analysis result. System.

4. In paragraph 3, The above artificial intelligence server, Transmitting the biometric data obtained from the first receiver unit and the biometric data modified to include the analysis result to the second receiver unit; System.

5. In paragraph 1, The above artificial intelligence server, When it is confirmed that new biometric data is stored in the first receiver unit, the new biometric data is obtained from the first receiver unit, and the new biometric data is analyzed using the artificial intelligence-based model to obtain an analysis result. System.

6. In paragraph 2, The above biometric data is, After being acquired from the first receiver unit connected to the biosignal measuring device, the first image data is converted by the first receiver unit and transmitted to the electronic medical record unit through a middleware unit connecting the first receiver unit and the electronic medical record unit. The above second receiver unit, The modified biometric data including information about the disease score is converted into second image data and transmitted to the electronic medical record unit through a middleware unit connecting the first receiver unit and the electronic medical record unit. System.

7. In paragraph 6, In the first area of ​​the first image corresponding to the first image data, the waveform of the biosignal included in the biodata is displayed, In the first area of ​​the second image corresponding to the second image data, the waveform of the biosignal included in the modified biodata is displayed, and in the second area of ​​the second image, the disease type and the disease score are displayed. System.

8. In paragraph 6, Depending on the type of request transmitted from the electronic medical record unit to the middleware unit, either the first image data or the second image data is transmitted to the electronic medical record by the middleware unit. System.

9. In paragraph 7, When an artificial intelligence result request is transmitted from the electronic medical record unit to the middleware unit, the second image data is overwritten with the first image data as data corresponding to the first receiver unit, and then the second image data is transmitted to the electronic medical record. System 10. In paragraph 2, The above biometric data is, Obtained in XML format, The above artificial intelligence server, Extracting data related to a biosignal from the biometric data, inputting the extracted data into the artificial intelligence-based model to obtain a disease score related to the biometric data, and inserting data related to the disease score into the biometric data to modify the biometric data. System.

11. In an electronic medical record environment including a first receiver unit that acquires biometric data and stores it in a first folder and an electronic medical record unit that provides analysis results for the biometric data, a system for generating a reading statement including an artificial intelligence-based analysis result, An artificial intelligence server that analyzes the biometric data using an artificial intelligence-based model to obtain an analysis result, modifies the biometric data to include the analysis result, and stores the modified biometric data in a second folder of the first receiver unit; At least one of the above biometric data and the above modified biometric data is converted into image data by the first receiver unit at the request of the electronic medical record unit and transmitted to the electronic medical record unit. System.

12. In an electronic medical record environment including a first receiver unit including a third folder and a fourth folder where biometric data is stored and an electronic medical record unit providing analysis results for the biometric data, a system for generating a reading statement including an artificial intelligence-based analysis result, An artificial intelligence server that obtains biometric data from a biometric signal measuring device, analyzes the biometric data using an artificial intelligence-based model to obtain an analysis result, modifies the biometric data to include the analysis result, and stores the biometric data and the modified biometric data in the third folder and the fourth folder, respectively; At least one of the above biometric data and the above modified biometric data is converted into image data by the first receiver unit at the request of the electronic medical record unit and transmitted to the electronic medical record unit. System.

13. In an electronic medical record environment including a first receiver unit for acquiring image data corresponding to biometric data and an electronic medical record unit for providing analysis results for the biometric data, a system for generating a reading statement including an artificial intelligence-based analysis result, An artificial intelligence server that obtains biometric data in XML file format from a biometric signal measuring device, analyzes the biometric data using an artificial intelligence-based model to obtain an analysis result, modifies the biometric data to include the analysis result, converts the biometric data and the modified biometric data into PDF file formats, and generates URL information that enables access to the PDF file and transmits the URL information to the first receiver unit; At least one of the biometric data and the modified biometric data is converted into the image data at the request of the electronic medical record unit that accessed the PDF file based on URL information and transmitted to the electronic medical record unit. System.

14. A method for generating a reading statement including an artificial intelligence-based analysis result in an electronic medical record environment including a first receiver unit that acquires and stores biometric data and an electronic medical record unit that provides an analysis result for the biometric data, A step in which an artificial intelligence server analyzes the biometric data using an artificial intelligence-based model to obtain an analysis result and modifies the biometric data to include the analysis result; and A step in which the second receiver unit obtains the modified biometric data from the artificial intelligence server and transmits it to the electronic medical record unit; including, How to create.

15. A computer program stored in a computer-readable storage medium in an electronic medical record environment including a first receiver unit that acquires and stores biometric data and an electronic medical record unit that provides analysis results for the biometric data, wherein the computer program, when executed on one or more processors, performs an operation of generating a reading statement including an artificial intelligence-based analysis result. The above action is, An operation in which an artificial intelligence server analyzes the biometric data using an artificial intelligence-based model to obtain an analysis result and modifies the biometric data to include the analysis result; and An operation of the second receiver unit obtaining the modified biometric data from the artificial intelligence server and transmitting it to the electronic medical record unit; including, Computer program.

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