KR20210151743A - Method for analyzing biometric image through image analysis model and method for predicting shock occurrence using the same - Google Patents

Abstract

Disclosed are a biometric information image analysis method through an image analysis model, a shock occurrence prediction method using the same, a device thereof, and a computer program. According to various embodiments of the present invention, the biometric information image analysis method through an image analysis model and the shock occurrence prediction method using the same are methods performed by a computing device. The method includes the steps of: collecting visualized biometric information data; generating a biometric information image using the visualized biometric data; analyzing the biometric information image; and detecting the occurrence of an event for the user according to the analysis result of the biometric information image.

Description

Biometric image analysis method through image analysis model and shock occurrence prediction method using the same

Various embodiments of the present invention relate to a biometric image analysis method using an image analysis model and a shock occurrence prediction method, apparatus, and computer program using the same.

The intensive care unit has a significant impact on the survival and death of patients, and in terms of quality and efficiency of care, such as a shortage of dedicated medical personnel for intensive care, heterogeneity between hospitals/regions, and high mortality during patient transport, even in important health care organizations that account for 25% of domestic medical expenses. in a very backward situation.

According to an article in the Hankook Ilbo on May 22, 2018, according to the '2014 (1st) intensive care unit adequacy evaluation result' published by the Health Insurance Review and Assessment Service in 2016, the average bed size of the intensive care unit per intensive care unit specialist in Korea The number of beds reaches a whopping 44.7, tertiary general hospitals have 40.4 beds and general hospitals have 48.9 beds. As such, since the average number of beds per specialist is high, there is a problem in that it is difficult for patients admitted to the intensive care unit to even see the specialist's face.

As a result, death is a reality in which sepsis is not detected at an early stage. According to a survey by the HIRA, the average mortality rate of adult patients admitted to the intensive care unit in Korea is 16.9%, 14.3% in tertiary hospitals and 17.4% in general hospitals.

The intensive care unit is a place for patients with very large problems such as breathing and heartbeat, which are essential functions for maintaining life, and is a place where intensive care is received 24 hours a day, 7 days a week, 365 days a year. In order not to miss out, it is necessary to measure and analyze the patient's biometric data in real time.

However, there is a problem in that it is difficult to extract valuable information from large-scale and complex data because the data generated by each medical device has many types and various formats and the amount of data generated is huge.

Korean Patent No. 10-1070389 (2011.09.28)

The problem to be solved by the present invention is through an image analysis model that provides real-time information on changes in a patient's condition by analyzing vast data measuring the patient's biometric information and detecting various events and event possibilities occurring in the patient. To provide a biometric image analysis method, a shock occurrence prediction method using the same, an apparatus, and a computer program.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

In a method performed by a computing device, a method for analyzing biometric information image using an image analysis model and a method for predicting shock occurrence using the same according to an embodiment of the present invention for solving the above problems, the visualized biometric data collecting, generating a biometric information image by using the visualized biometric information data, analyzing the biometric information image, and detecting the occurrence of an event for the user according to the analysis result of the biometric information image may include

A method for analyzing an image of biometric information through an image analysis model and an apparatus for predicting shock occurrence using the same according to another embodiment of the present invention for solving the above problems include a memory storing one or more instructions, and the one or more instructions stored in the memory and a processor executing A method of analyzing an image of biometric information and a method of predicting shock occurrence using the image analysis model, which includes analyzing the information image and detecting the occurrence of an event for the user according to the analysis result of the biometric image, can be performed. have.

A method for analyzing biometric information through an image analysis model according to another embodiment of the present invention for solving the above-described problems and a computer program for predicting shock occurrence using the same are combined with a computer that is hardware to collect visualized biometric data generating a biometric information image by using the visualized biometric information data, analyzing the biometric information image, and detecting the occurrence of an event for the user according to the analysis result of the biometric information image The biometric information image analysis method through the image analysis model and the shock occurrence prediction method using the same may be stored in a computer-readable recording medium.

Other specific details of the invention are included in the detailed description and drawings.

The problem to be solved by the present invention has the advantage of providing real-time information on a change in the patient's condition by analyzing a large amount of data for measuring the patient's biometric information and detecting various events and the possibility of occurrence of the event occurring in the patient.

In addition, in order to accurately predict the patient's risk signs, the condition of patients is monitored 24 hours a day, 365 days a year, and AI-based prediction is performed. It is equipped with a visualization function so that the progress can be grasped at a glance.

In addition, it can contribute to improving the quality of care for the elderly critically ill, which is relatively weak, by eliminating manual input of patient vital information by nursing personnel and ensuring real-time processing of patient data.

In addition, as it is possible to achieve the effect of shortening the hospitalization period for critically ill patients, there is an advantage in that it is possible to reduce the overall national medical expenses for critically ill patients.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a block diagram of a biometric image analysis method using an image analysis model and a shock occurrence prediction system using the same according to an embodiment of the present invention.
2 is a hardware configuration diagram of a method for analyzing an image of biometric information through an image analysis model and an apparatus for predicting shock occurrence using the method according to another embodiment of the present invention.
3 is a flowchart of a method for analyzing an image of biometric information using an image analysis model and a method for predicting shock occurrence using the same according to another embodiment of the present invention.
4 is a diagram illustrating a configuration in which an event occurrence detecting device images biometric information data according to various embodiments of the present disclosure;
5 is a diagram illustrating a process in which an event occurrence detecting device learns a first artificial intelligence model and a second artificial intelligence model according to various embodiments of the present disclosure;
6 is a flowchart of a method for detecting occurrence of an event using an artificial intelligence model, according to various embodiments.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only the present embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

As used herein, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and “unit” or “module” performs certain roles. However, “part” or “module” is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside on an addressable storage medium or to reproduce one or more processors. Thus, as an example, “part” or “module” refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Components and functionality provided within “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules” or as additional components and “parts” or “modules”. can be further separated.

Spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe the correlation between a component and other components. Spatially relative terms should be understood as terms including different directions of components during use or operation in addition to the directions shown in the drawings. For example, when a component shown in the drawing is turned over, a component described as “beneath” or “beneath” of another component may be placed “above” of the other component. can Accordingly, the exemplary term “below” may include both directions below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

In this specification, a computer refers to all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood to include, but is not limited to, smart phones, tablet PCs, desktops, notebooks, and user clients and applications running on each device.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Each step described in this specification is described as being performed by a computer, but the subject of each step is not limited thereto, and at least a portion of each step may be performed in different devices according to embodiments.

1 is a configuration diagram of a biometric image analysis method using an image analysis model and a shock occurrence prediction system using the same according to an embodiment of the present invention.

Referring to FIG. 1 , a method for analyzing an image of biometric information through an image analysis model according to an embodiment of the present invention and a shock occurrence prediction system using the same are an event detection device 100 , a user terminal 200 and an external server 300 . ) may be included.

Here, the event occurrence detection system through artificial intelligence-based image analysis shown in FIG. 1 is according to an embodiment, and its components are not limited to the embodiment shown in FIG. 1, and may be added, changed or may be deleted.

In an embodiment, the event occurrence detecting apparatus 100 may collect biometric information data sensed from a user. For example, the event occurrence detection device 100 is installed on at least a part of the user's body and receives blood pressure from a plurality of sensors (eg, a blood pressure sensor, a body temperature sensor, a pulse sensor, and a respiration rate sensor) for detecting the user's biometric information. Vital sign data including at least one of data, body temperature data, pulse data, and respiration rate data may be collected.

In various embodiments, the event occurrence detecting device 100 collects biometric information data from the outside, but may collect biometric information data visualized like a graph.

In various embodiments, the event occurrence detecting apparatus 100 may collect biometric information data in a scalar form from the outside. However, the present invention is not limited thereto, and various types of data representing the user's biometric information may be collected.

In an embodiment, the event occurrence detecting apparatus 100 may generate a biometric information image by using the visualized biometric information data. For example, the event occurrence detecting apparatus 100 may generate a biometric information image by capturing the biometric information data in the form of a graph that is collected in real time every preset period.

In various embodiments, when the biometric data collected from the outside is in a scalar form, the scalar biometric data may be visualized in a graph form, and the visualized biometric data may be captured to generate a biometric image. However, the present invention is not limited thereto.

In an embodiment, the event occurrence detection apparatus 100 may determine the event occurrence detection for the user by analyzing the biometric information image. For example, the event occurrence detection apparatus 100 may determine the event occurrence detection for the user by analyzing the biometric information image using the image analysis model.

Here, the image analysis model may be an artificial intelligence model (eg, a first artificial intelligence model and a second artificial intelligence model) that has been pre-learned using a specific image and information pointed to by the specific image as learning data. However, the present invention is not limited thereto.

Also, here, the event means an abnormal state generated by the user. For example, the event may include any one or more of hypovolemic shock, cardiogenic shock, obstructive shock, and distributional shock.

In an embodiment, the event occurrence detecting apparatus 100 may provide a user interface (UI) for outputting information related to an event, such as a user's biometric information data, a biometric information image, and whether an event has occurred.

In an embodiment, the user terminal 200 may include a display on at least a portion of the user terminal 200 , and various information (eg, event occurrence) provided from the event occurrence detection device 100 through the network 400 . information about whether or not and information about the possibility of event occurrence) can be output. For example, the user terminal 200 may output a UI (eg, a UI for outputting user's biometric information and information related to the occurrence of an event) provided from the event occurrence detection device 100 , and an event is generated through the UI Various types of information provided from the sensing device 100 may be output.

In various embodiments, the user terminal 200 may include at least one of a smart phone, a tablet PC, a notebook desktop, and a kiosk. However, the present invention is not limited thereto.

In one embodiment, the external server 300 may be connected to the event occurrence detection device 100 via wire or wireless connection through the network 400 , and various information (eg, event occurrence information and information on the possibility of occurrence of an event) can be provided and stored.

Here, the biometric information image analysis method through the image analysis model shown in FIG. 1 and the shock occurrence prediction system using the same are described in a form in which various data generated by the event occurrence detection device 100 are stored in the external server 300 , However, the present invention is not limited thereto, and the event occurrence detecting device 100 may include a separate storage device to store various data in the separately provided storage device.

In various embodiments, the external server 300 may store various patient-related information (eg, patient's personal information, patient's disease information, etc.) (100) can be provided. For example, the external server 300 may be a hospital server, and when receiving a request for information on a specific patient from the event occurrence detection device 100 , selects various pieces of information related to the specific patient to select the event occurrence detection device 100 . ) can be provided. Hereinafter, a hardware configuration of the event occurrence detecting apparatus 100 will be described with reference to FIG. 2 .

2 is a hardware configuration diagram of a method for analyzing an image of biometric information through an image analysis model and an apparatus for predicting shock occurrence using the method according to another embodiment of the present invention.

Referring to FIG. 2 , an event occurrence detecting device 100 (hereinafter, “computing device 100”) according to another embodiment of the present invention may include a processor 110 and a memory 120 . In various embodiments, the computing device 100 may further include a network interface (or communication interface) (not shown), storage (not shown), and a bus (not shown).

In an embodiment, the processor 110 may control the overall operation of each component of the computing device 100 . The processor 110 may include a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), or any type of processor well known in the art.

In various embodiments, the processor 110 may perform an operation on at least one application or program for executing the method according to the embodiments of the present invention. In various embodiments, the processor 110 includes one or more cores (not shown) and a graphic processing unit (not shown) and/or a connection path (eg, a bus, etc.) for transmitting and receiving signals to and from other components. can do.

In various embodiments, the processor 110 temporarily and/or permanently stores a signal (or data) processed inside the processor 110 , a random access memory (RAM) and a read access memory (ROM). -Only Memory, not shown) may be further included. In addition, the processor 110 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, a RAM, and a ROM.

In one embodiment, the processor 110 executes one or more instructions stored in the memory 120, and thus a method to be described in relation to FIGS. shock occurrence prediction method) can be performed. For example, the processor 110 executes one or more instructions stored in the memory 120 to collect visualized biometric information data, generate a biometric image using the visualized biometric data data, biometric information The operation of analyzing the image and the operation of detecting the occurrence of an event to the user according to the analysis result of the biometric information image may be performed.

In one embodiment, the memory 120 may store various data, commands, and/or information. The memory 120 may store programs (one or more instructions) for processing and controlling the processor 110 . Programs stored in the memory 120 may be divided into a plurality of modules according to functions.

In various embodiments, steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, as a software module executed by hardware, or by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software components, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , may be implemented in a programming or scripting language such as Java, assembler, or the like. Functional aspects may be implemented in an algorithm running on one or more processors. Hereinafter, a method for analyzing an image of biometric information through an image analysis model performed by the computing device 100 and a method for predicting shock occurrence using the method will be described with reference to FIGS. 3 to 8 .

3 is a flowchart of a method for analyzing an image of biometric information through an image analysis model and a method for predicting shock occurrence using the same according to another embodiment of the present invention.

Referring to FIG. 3 , in step S110 , the computing device 100 may collect biometric information data from the outside. For example, the computing device 100 may generate blood pressure data, body temperature data, and pulse data generated from a plurality of sensors (eg, blood pressure sensor, body temperature sensor, pulse sensor, and respiration sensor) for detecting biometric information of a patient. And it is possible to collect vital sign data including at least one data of the respiratory rate data.

In various embodiments, the computing device 100 generates blood pressure data, body temperature data, pulse data, and respiration rate generated from a plurality of sensors (eg, blood pressure sensor, body temperature sensor, pulse sensor, and respiration sensor) for detecting biometric information of a patient. Visualized vital sign data may be collected from a biometric information output device (eg, a bed-side monitoring device in an intensive care unit) that visualizes and outputs vital sign data including at least one of the data.

In various embodiments, the computing device 100 directly collects vital sign data including at least one of blood pressure data, body temperature data, pulse data, and respiration rate data from a plurality of sensors that detect biometric information of a patient, The directly collected vital sign data can be visualized in graph form.

In various embodiments, the computing device 100 performs outlier analysis (eg, when there is a standardized outlier pattern, removes an outlier, Mahalanobis distance, etc.) on biometric information data collected from the outside using standard deviation using a filter to remove noise), and a biometric image may be generated using only biometric data from which noise has been removed through outlier analysis in step S120 to be described later.

In step S120 , the computing device 100 may generate a biometric information image by using the visualized biometric information data collected in step S110 .

In various embodiments, the computing device 100 generates a biometric information image by using the visualized biometric data data, and divides the biometric information image into a preset time unit to obtain a plurality of unit biometric information images for learning an image analysis model. can create Hereinafter, a method of generating a biometric information image performed by the computing device 100 will be described with reference to FIG. 4 .

4 is a diagram illustrating a configuration in which an event occurrence detecting device images biometric information data according to various embodiments of the present disclosure;

Referring to FIG. 4 , in various embodiments, the computing device 100 may generate a biometric information image using biometric information data collected from the outside, and generate a plurality of unit biometric information images by dividing the biometric information image. can do.

First, the computing device 100 collects externally visualized biometric information data (eg, vital sign data) in a graph form, or collects scalar form biometric data from the outside and graphs the collected scalar biometric data data. It is possible to generate visualized biometric data by visualization in the form.

In various embodiments, the computing device 100 may collect the user's biometric information data every preset first period T.

In various embodiments, when the periods at which biometric data are sensed from each of the plurality of sensors are different, the computing device 100 performs the biometric data detection based on the sensor having the shortest period among the biometric data detection periods set in each of the plurality of sensors. Information data may be collected. However, the present invention is not limited thereto.

Thereafter, the computing device 100 may generate a biometric information image by using the biometric information data visualized in a graph form. For example, the computing device 100 may generate a bio-information image by capturing the bio-information data in the form of a graph collected in real time every second preset period.

Here, the computing device 100 may set the second period to be the same as the first period or shorter than the first period, but is not limited thereto.

Thereafter, the computing device 100 may generate a plurality of unit biometric information images by dividing the captured biometric information image.

In various embodiments, the computing device 100 may generate a plurality of unit biometric information images by dividing the biometric information image based on a preset time unit.

In various embodiments, the computing device 100 may generate a plurality of unit biometric information images by dividing the biometric information image into a preset number.

In various embodiments, the computing device 100 may adjust a time unit of each of the plurality of unit biometric information images based on whether an event has occurred and the possibility of occurrence of the event, or may combine two or more unit biometric information images into one.

Here, the computing device 100 may use a plurality of unit biometric information images as an input value of image analysis performed in the image analysis model, and may use it as learning data for learning the image analysis model after image analysis. However, the present invention is not limited thereto.

In various embodiments, the computing device 100 generates a biometric information image using a plurality of biometric data (eg, vital sign data in graph form), but visualizes it in different shapes and patterns depending on the type of biometric data. Alternatively, biometric information images can be created by visualizing them in different colors. For example, when the biometric data is blood pressure data, the computing device 100 may visualize it in the form of a bar graph representing the diastolic and maximal blood pressure values, and when the biometric data is body temperature data, the body temperature at the time of detection. Values can be visualized in the form of a line graph.

In addition, the computing device 100 may visualize a plurality of biometric information in the form of a line graph, set the blood pressure value to blue, set the body temperature value to red, and set the pulse rate value to orange. That is, the computing device In 100, by different visualization or color difference for each type of biometric data, it is possible to check numerical changes and pattern changes for each biometric data, as well as check the correlation between each biometric data, , it can be used to determine whether an event has occurred and the possibility of an event occurring by using it.

In various embodiments, the computing device 100 generates a biometric image using a plurality of biometric data (eg, vital sign data in graph form), but uses each biometric data individually to obtain each biometric information A plurality of biometric information images corresponding to the data may be generated.

Referring back to FIG. 3 , in step S130 , the computing device 100 may perform image analysis on the biometric information image generated in step S120 .

In various embodiments, the computing device 100 may image-analyze the bio-information image itself or perform image-analysis for each of a plurality of unit bio-information images generated by dividing the bio-information image.

In various embodiments, the computing device 100 analyzes the biometric information image using the pre-learned artificial intelligence model, and may selectively use the artificial intelligence model according to the type of analysis result to be derived by analyzing the biometric information image. have.

For example, when the type of analysis result to be derived by analyzing the biometric information image is whether or not an event occurs, the computing device 100 uses the pre-learned first artificial intelligence model to obtain a biosignal image and a plurality of unit biometric information. Images can be image-analyzed. Here, the pre-learned first artificial intelligence model may refer to a model obtained by learning as learning data a plurality of biosignal images on which occurrence of an event is labeled.

In various embodiments, when the type of analysis result to be derived by analyzing the biometric information image is the possibility of occurrence of an event, the computing device 100 uses the pre-learned second artificial intelligence model to obtain a biosignal image and a plurality of unit biometrics. Information images can be image-analyzed. Here, based on the first time point at which the event occurs, it may refer to a model in which the biometric information image at the second time point before the first time point is learned as learning data.

Here, the first artificial intelligence model and the second artificial intelligence model are models learned using different learning data (eg, a plurality of biosignal images labeled with or without event occurrence, biometric information images before the event occurrence). described, but is not limited thereto, and one artificial intelligence model (eg, CNN model, NN model, RNN model) learns all of a plurality of biosignal images labeled whether an event occurs or not, and a biometric information image before the event occurs Thus, one AI model can perform both the functions of the first AI model and the second AI model.

Here, the first artificial intelligence model and the second artificial intelligence model are models learned using different learning data (eg, a plurality of biosignal images labeled with or without event occurrence, biometric information images before the event occurrence). described, but is not limited thereto, and one artificial intelligence model (eg, CNN model, NN model, RNN model) learns all of a plurality of biosignal images labeled whether an event occurs or not, and a biometric information image before the event occurs Thus, one AI model can perform both the functions of the first AI model and the second AI model. Hereinafter, a process in which the computing device 100 learns the first artificial intelligence model and the second artificial intelligence model will be described with reference to FIG. 5 .

In various embodiments, the computing device 100 analyzes biometric data using a third artificial intelligence model previously learned using the scalar biometric data as learning data when biometric data is collected in a scalar form from the outside. can do.

In various embodiments, the computing device 100 analyzes the biometric information image generated in step S120, and determines the type of the biometric data based on the visualization form and color of the biometric data included in the biometric image, Depending on the type of the determined biometric data, different AI models may be applied to analyze the image.

5 is a diagram illustrating a process in which an event occurrence detecting device learns a first artificial intelligence model and a second artificial intelligence model according to various embodiments of the present disclosure;

In various embodiments, the computing device 100 may train the first artificial intelligence model and the second artificial intelligence model by using the biometric information image and the plurality of unit biometric information images.

First, the computing device 100 may generate a plurality of unit biometric information images by dividing the biometric information image into a preset time unit or by dividing the biometric information image into a preset number.

Thereafter, the computing device 100 may select a unit biometric information image to be used as learning data from among a plurality of unit biometric information images. For example, the computing device 100 may generate an event bar based on a unit biometric information image including a time point at which an event is determined to have occurred through image analysis among a plurality of unit biometric information images and a time point at which the event occurs. The previous unit biometric information image may be selected as learning data.

In various embodiments, the computing device 100 may provide a UI for receiving a selection of a biometric information image related to an event from among a plurality of unit biometric information images to the user terminal 200, and may provide a UI from among the plurality of unit biometric information images through the UI. A biometric information image related to the event may be selected.

Thereafter, the computing device 100 may perform labeling on the unit biometric information image selected as the learning data. For example, the computing device 100 labels each correct answer of the unit biometric information image selected as the learning data (eg, labels the unit biometric information image including the time when the event occurs as class 1, and is not related to the occurrence of the event. The unit biometric information image can be labeled as class 0).

In various embodiments, the computing device 100 may directly receive a labeling input for an event-related biometric information image from a user through a UI for receiving a selection of the event-related biometric information image. For example, the computing device 100 may provide a UI for selecting an event-related biometric information image to a specialist's terminal. Labeling information of the biometric information image may be input together.

According to various embodiments, the computing device 100 may differently label the unit biometric information image including the time point at which the event occurs according to the cause of the event and the type of the event. However, the present invention is not limited thereto.

In various embodiments, when it is determined that an event is generated based on the user's biometric information image, the computing device 100 automatically labels the biometric information images prior to a predetermined time from the event occurrence time. can do. In this case, the computing device 100 may also label the information indicating how many hours before the event is the biometric information. For example, the computing device 100 may label biometric information images 6 hours ago, 12 hours ago, and 24 hours ago into different classes from the event occurrence time.

Thereafter, the computing device 100 may supervised learning the first artificial intelligence model and the second artificial intelligence model using the labeled unit biometric information image. For example, the computing device 100 may learn the first artificial intelligence model and the second artificial intelligence model by performing supervised learning using the learning data (the biometric information image related to the event) obtained according to the labeling result. However, the present invention is not limited thereto, and various methods for learning the artificial intelligence model using the biometric information image may be applied.

Referring back to FIG. 3 , in step S140 , the computing device 100 may detect the occurrence of an event using the analysis result derived through the image analysis performed in step S130 . Hereinafter, an event occurrence detection method performed by the computing device 100 will be described with reference to FIG. 6 .

6 is a flowchart of a method for detecting occurrence of an event using an artificial intelligence model, according to various embodiments.

Referring to FIG. 6 , in step S210 , the computing device 100 may determine whether an event has occurred using the analysis result derived through the image analysis performed in step S130 .

In various embodiments, the computing device 100 inputs a biometric information image to determine whether an event occurs, a plurality of biosignal images labeled with whether an event occurs or not, as learning data, into a pre-learned first artificial intelligence model, It may be determined whether an event has occurred based on the analysis result derived using the first artificial intelligence model.

According to various embodiments of the present disclosure, the computing device 100 may determine whether an event has occurred by comparing the biometric information image to be determined whether the event has occurred and the biometric information image sensed when the event has occurred.

In step S220, when it is determined that the event has occurred through step S210, the computing device 100 may determine the cause of the occurrence of the event.

In various embodiments, the computing device 100 may determine the cause of the occurrence of the event by using the first artificial intelligence model.

For example, in response to determining that an event has occurred according to the analysis result derived through the first artificial intelligence model, the computing device 100 analyzes each of the plurality of unit biometric information images to obtain a biometric information image from the analysis result. of image patterns can be detected.

Thereafter, the computing device 100 may determine the type and cause of the generated event by using the detected image pattern. For example, when the detected image pattern has the first pattern, the computing device 100 may determine that the generated event is hypovolemic shock, and when the detected image pattern has the second pattern, the generated event is psychogenic shock can be judged to be However, the present invention is not limited thereto.

In step S230 , when it is determined that the event has not occurred through step S210 , the computing device 100 may determine the possibility of occurrence of the event.

In various embodiments, the computing device 100 sets the biometric information image for determining the possibility of occurrence of the event, the biometric information image at a second time point before the first time point based on the first time point when the event occurs. may be input as learning data into the previously learned second artificial intelligence model, and the possibility of occurrence of an event may be determined based on the analysis result analyzed using the second artificial intelligence model. Here, steps S210 to S230 may correspond to steps S140 of FIG. 3 .

In various embodiments, the computing device 100 may determine the event occurrence probability by analyzing the user's biometric information image, but if the determined event occurrence probability is greater than or equal to a reference value, the computing device 100 may classify the event occurrence risk group. Thereafter, the computing device 100 may monitor the user's status more frequently by setting a short cycle for collecting biometric information data and generating a biometric information image for a user classified as an event occurrence risk group.

In various embodiments, the computing device 100 may train the first artificial intelligence model and the second artificial intelligence model by using the biometric information image of the user classified into the risk group and whether the user's actual event has occurred as learning data. have. Through this, the computing device 100 may determine whether an actual event has occurred according to the possibility of occurrence of the event using the artificial intelligence model.

In various embodiments, the computing device 100 provides a user interface (UI) that visualizes user-related information, user's biometric information data, and whether an event for the user has occurred and configured as a single screen to output. can Through this, the computing device 100 provides a dashboard where information about the patient and status information can be viewed at a glance, so that a user who monitors the patient's status can more easily monitor the patient's status, and according to the occurrence of an event It can motivate you to react quickly.

In various embodiments, when an event occurs, the computing device 100 may output a guide message and a warning signal for guiding whether the event has occurred and information related to the generated event through the UI. For example, when it is determined that the event has occurred through image analysis of the biometric information image, the computing device 100 outputs a guide message for guiding information related to the event through the UI and a warning signal in the form of a voice together. , it can enable a user who monitors the patient's condition in real time to quickly recognize whether an event has occurred.

In various embodiments, the computing device 100 may set a form in which a warning signal is output based on the type of event determined through the biometric information image. For example, the computing device 100 sets the type of voice data to be output according to the type of event (eg, outputs the first voice data when the event is hypovolemic shock, and outputs the second voice data when the event is psychogenic shock) output) or set the output time of voice data. However, the present invention is not limited thereto.

In various embodiments, the computing device 100 may provide a UI for outputting information about a plurality of users and biometric information and event-related information for each of the plurality of users, and requesting information for a specific user through the UI When this is input, only information about a specific user, biometric information about a specific user, and information related to an event may be configured on one screen and outputted through the UI. For example, the computing device 100 may configure only information about a specific user, biometric information about a specific user, and information related to an event on one screen and output the information in the form of a pop-up window. However, the present invention is not limited thereto.

The biometric image analysis method using the above-described image analysis model and the shock occurrence prediction method using the same have been described with reference to the flowchart shown in the drawings. For a brief description, the biometric image analysis method through the image analysis model and the shock occurrence prediction method using the same have been described with a series of blocks, but the present invention is not limited to the order of the blocks, and some blocks are described herein. It may be performed in an order different from that shown and described, or may be performed concurrently. In addition, new blocks not described in the present specification and drawings may be added, or some blocks may be deleted or changed.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains know that the present invention may be embodied in other specific forms without changing the technical spirit or essential features thereof. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

100: event occurrence detection device (computing device)
200: user terminal
300 : external server
400: network

Claims (3)

A method performed by a computing device, comprising:
collecting biometric information data visualized in the graph form from a biometric information output device that visualizes and outputs the user's biometric information in a graph form;
generating a bio-information image by capturing the bio-information data visualized in the graph form every preset period;
analyzing the biometric information image; and
Detecting the occurrence of an event for the user according to the analysis result of the biometric information image,
The step of detecting the occurrence of the event is
Based on the first time point when the event occurred, the biometric information image at the second time point before the first time point is analyzed using the second artificial intelligence model previously learned as the learning data. Comprising the step of determining the possibility of occurrence of an event for the user based on,
The step of generating the biometric information image comprises:
When the probability of occurrence of the event is greater than or equal to a reference value, classifying the user into an event occurrence risk group and adjusting the period for capturing the biometric information data visualized in the graph form to be shortened;
Biometric image analysis method through image analysis model and shock occurrence prediction method using the same. a memory storing one or more instructions; and
a processor executing the one or more instructions stored in the memory;
The processor by executing the one or more instructions,
An apparatus for performing the method of claim 1 . A computer program stored in a computer-readable recording medium in combination with a computer, which is hardware, to perform the method of claim 1.

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