Computing device for setting medical treatment order on basis of patient's condition and method for controlling same

Abstract

The present disclosure provides a method and a computing device for setting the medical treatment order on the basis of the condition of emergency patients. A method for setting the medical treatment order on the basis of the condition of emergency patients is performed by a computing device including at least one processor according to an embodiment of the present disclosure. According to the method, if a new medical examination subject's reception data is acquired, the medical examination subject's biometric data is acquired through a sensing unit. The biometric data is analyzed by using a pretrained neural network model, thereby identifying the new medical examination subject's condition. The new medical examination subject's medical treatment order is set on the basis of the time at which the new medical examination subject's reception data has been acquired, and the new medical examination subject's condition. The step of setting the medical treatment order comprises a step of adjusting the medical treatment order of other medical examination subjects who have been received and are waiting for medical treatment on the basis of the medical examination subject's condition, thereby setting the new medical examination subject's medical treatment order.

Description

Computing device for setting treatment order based on patient's condition and control method thereof

[0001] The present disclosure relates to a computing device and a control method thereof that sets a treatment order based on a patient's condition, and more specifically, to a device and a control method thereof that analyzes biometric data obtained from a patient to determine the patient's condition and adjusts the treatment order of a plurality of patients received.

[0002] Currently, most hospital emergency rooms typically prioritize patient care based on the order in which patients are admitted. While medical staff initially assess patients to determine their severity, there is a lack of a system for continuously monitoring changes in their condition or adjusting treatment schedules in real time based on this assessment. This systemic limitation poses a greater risk to patients without caregivers. In particular, a recent case was reported where a patient classified as mildly ill and waiting for a long time died after their condition rapidly deteriorated, but the situation was not recognized in a timely manner.

[0003] These problems stem from structural limitations and the workload of medical staff in the emergency room (ER). When managing a large number of patients, medical staff often struggle to regularly monitor the condition of each patient. Furthermore, for patients without caregivers, medical staff are more likely to fail to recognize signs of deterioration. These limitations not only reduce the efficiency of emergency room operations but also seriously impact patient safety. Current technological approaches have focused on assessing patients' initial conditions or managing admissions, and lack sufficient means to detect changes in patient status in real time and adjust treatment schedules or respond appropriately based on these findings.

[0004] The present disclosure is conceived in response to the aforementioned background technology, and aims to provide a computing device and a control method thereof that sets a treatment order based on the condition of an emergency patient and adjusts the set treatment order.

[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] A method for setting a treatment order based on a patient's condition, performed by a computing device including at least one processor for realizing the task described above, comprises the steps of: when reception data of a new examination subject is obtained, obtaining biometric data of the examination subject through a sensing unit; analyzing the biometric data using a pre-learned neural network model to determine the condition of the new examination subject; setting a treatment order of the new examination subject based on the time at which the reception data of the new examination subject is obtained and the condition of the new examination subject; and providing waiting number information corresponding to the treatment order of the new examination subject based on the set treatment order, wherein the step of setting the treatment order includes the step of adjusting the treatment order of other examination subjects who have already been registered and are waiting for treatment based on the condition of the examination subject to set the treatment order of the new examination subject.

[0007] Alternatively, the step of setting the treatment order of the new examination subject includes a step of setting the treatment order of the new examination subject to a lower priority than other examination subjects waiting for treatment based on the reception time at which the reception data of the new examination subject was acquired.

[0008] Alternatively, the step of adjusting the treatment order of other examination subjects who have already been accepted and are waiting for treatment based on the status of the new examination subject to set the treatment order of the new examination subject includes the step of setting the treatment order of the new examination subject to a higher priority than the treatment order of the other examination subjects waiting for treatment based on the status of the new examination subject, if the risk of the new examination subject is determined to be higher than the risk of the other examination subjects waiting for treatment.

[0009] Alternatively, the risk of the new screening subject is classified into multiple status grades, and the step of setting the treatment order of the new screening subject to a higher priority than the treatment order of other screening subjects waiting for treatment includes, when there are multiple other screening subjects waiting for treatment, the step of identifying other screening subjects with a lower status grade than the risk of the new screening subject among the multiple other screening subjects, and setting the treatment order of the new screening subject to a higher priority than the treatment order of the identified other screening subjects.

[0010] Alternatively, the status of the new examination subject is classified into a plurality of status grades, and a re-measurement cycle for the biometric data is set corresponding to each status grade, and a step of identifying the status grade corresponding to the status of the new examination subject and providing re-measurement time information for the biometric data according to the re-measurement cycle corresponding to the status grade of the new examination subject is included.

[0011] Alternatively, when the re-measurement time arrives, the method includes displaying information requesting re-measurement of the biometric data of the new examination subject through a display of the computing device or transmitting information requesting re-measurement of the biometric data to a terminal device of the new examination subject through a communication interface.

[0012] Alternatively, if it is identified that the new examination subject does not re-measure the biometric data for a preset period of time after displaying or transmitting the information requesting the re-measurement, the method includes transmitting information of the new examination subject to the administrator's terminal device.

[0013] Alternatively, the method includes a step of re-determining the condition of the new examination subject based on the re-acquired biometric data of the new examination subject through the sensing unit according to the re-measurement of the new examination subject, and adjusting the treatment order of the examination subject based on the re-determined condition of the examination subject.

[0014] Alternatively, the waiting number of the other examination subject waiting for treatment is set and issued at a preset interval corresponding to the treatment order of the other examination subject waiting for treatment, and when the treatment order of the new examination subject is set to a higher priority than the treatment order of the other examination subject waiting for treatment, the waiting number of the other examination subject adjusted to a lower priority is maintained, and the waiting number of the new examination subject is set and issued at a number that is earlier than the waiting number of the other examination subject adjusted to a lower priority existing in the preset interval.

[0015] Alternatively, the status of the other examination subjects waiting for the above treatment may be classified into multiple status grades, and the preset interval may be set to an interval corresponding to the status grade of the other examination subjects waiting for the above treatment.

[0016] A computing device for setting a treatment order based on a patient's condition to realize the task described above includes a memory including program codes, a sensing unit for acquiring biometric data of a subject for examination, and one or more processors for acquiring biometric data of a subject for examination through the sensing unit when reception data of a new subject for examination is acquired, analyzing the biometric data using a pre-learned neural network model to determine the condition of the new subject for examination, setting a treatment order of the new subject for examination based on the time at which the reception data of the new subject for examination was acquired and the condition of the new subject for examination, and providing waiting number information corresponding to the treatment order of the new subject for examination based on the set treatment order, wherein the one or more processors can set the treatment order of the new subject for examination by adjusting the treatment order of other subjects for examination who have already been accepted and are waiting for treatment based on the condition of the subject for examination.

[0017] A computer program stored in a computer-readable storage medium for realizing the task described above, wherein the computer program, when executed on one or more processors, performs operations for setting a treatment order based on a patient's condition, the operations including: when reception data of a new examination subject is obtained, an operation for obtaining biometric data of the examination subject through a sensing unit; an operation for analyzing the biometric data using a pre-learned neural network model to determine the condition of the new examination subject; an operation for setting a treatment order of the new examination subject based on the time at which the reception data of the new examination subject was obtained and the condition of the new examination subject; and an operation for providing waiting number information corresponding to the treatment order of the new examination subject based on the set treatment order, wherein the step of setting the treatment order includes an operation for setting a treatment order of the new examination subject by adjusting the treatment order of other examination subjects who have already been registered and are waiting for treatment based on the condition of the examination subject.

[0018] According to one embodiment of the present disclosure, a computing device that sets a treatment order based on a patient's condition can automatically detect changes in the patient's condition by monitoring the patient's biometric data in real time. This allows for the rapid identification of critical patients and dynamic adjustments to the treatment order, prompting timely response from medical staff. Consequently, this can prevent the deterioration of the patient's condition, contribute to the efficient use of medical resources, and improve patient survival rates.

[0019] FIG. 1 is an exemplary diagram of a computing device that sets a treatment order based on a patient's condition according to one embodiment of the present disclosure.

[0020] FIG. 2 is a schematic block diagram of a computing device (100) according to one embodiment of the present disclosure.

[0021] FIG. 3 is a flowchart schematically illustrating a method for setting a treatment order based on a patient's condition according to one embodiment of the present disclosure.

[0022] FIG. 4a and FIG. 4b are exemplary diagrams illustrating a method for setting a treatment order based on a patient's condition according to one embodiment of the present disclosure.

[0023] FIG. 5 is another exemplary diagram illustrating a method for setting a treatment order based on a patient's condition according to one embodiment of the present disclosure.

[0024] FIG. 6 is another exemplary diagram illustrating a method for setting a re-measurement cycle for a patient's condition according to one embodiment of the present disclosure.

[0025] FIG. 7 is a diagram illustrating a process in which a waiting number is issued to a subject for examination at preset intervals according to an embodiment of the present disclosure.

[0026] FIG. 8 is a diagram illustrating that a waiting number is issued to a person subject to examination at set intervals according to the condition grade of the examination target site according to one embodiment of the present disclosure.

[0027] FIG. 9 is a configuration diagram of a computing device according to another embodiment of the present disclosure.

[0028] 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.

[0029] Throughout the specification of this disclosure, identical or similar drawing numbers refer to identical or similar components. Furthermore, for clarity in the description of this disclosure, drawing numbers for parts unrelated to the description of this disclosure may be omitted in the drawings.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.”

[0034] The term "Nth (N is a natural number)" used in this disclosure can be understood as an expression used to distinguish components of this disclosure from each other 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] The term "data" used in this disclosure may include "images," signals, 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.

[0039] 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 present disclosure.

[0040] FIG. 1 is an exemplary diagram of a computing device that sets a treatment order based on a patient's condition according to one embodiment of the present disclosure.

[0041] A computing device (100) according to an embodiment of the present disclosure may be a hardware device or a part of a hardware device that performs comprehensive processing and calculation of data, or may be a software-based computing environment connected via a communication interface. For example, the computing device (100) may be a server that performs intensive data processing functions and shares resources, or may be a client that shares resources through interaction with a server. In addition, the computing device (100) may be a cloud system or a server in which multiple servers and clients interact to comprehensively process data. Since the above description is only one example related to the type of computing device (100), the type of computing device (100) may be configured in various ways within a category understandable to those skilled in the art based on the contents of the present disclosure. For example, the computing device (100) may be implemented as a device that identifies and manages patients who visit a medical facility (e.g., a hospital, a public health center, a regional emergency medical center, etc.). To this end, the computing device (100) may be implemented as various electronic devices, such as a desktop, laptop, smartphone, server device, smart band, smart watch, smart ring, kiosk device, or bio-signal measuring device installed in a medical facility. For example, the computing device (100) may be implemented as a kiosk device installed in a medical facility to receive patients, or may be implemented as a public smart watch (or bio-signal measuring device, etc.) installed in a medical facility to obtain bio-data of patients. However, in the following, for the sake of understanding of the present disclosure, the computing device (100) will be described assuming a kiosk device.

[0042] According to one embodiment of the present disclosure, a computing device (100) may perform a function of managing patients who have visited a medical facility in which the computing device (100) is installed. Specifically, the computing device (100) may obtain reception data regarding patients who have visited the medical facility. The reception data may include the patient's purpose of visiting the medical facility, the patient's personal information (name, gender, resident registration number, etc.), and the patient's biometric information (height, weight, etc.). In addition, based on the reception data, the computing device (100) may determine the number of other patients waiting in relation to the patient's purpose of visiting, such as for treatment, examination, or payment, to determine the patient's waiting order, and may generate and output a waiting number corresponding to the waiting order.

[0043] In particular, referring to FIG. 1, a computing device (100) according to an embodiment of the present disclosure can directly acquire biometric data (20) of a patient, particularly a patient visiting for the purpose of treatment (a subject for examination), by including a sensing unit (130). The biometric data (20) can include various data measured and acquired from the patient, such as the patient's body temperature, blood pressure, heart rate, electrocardiogram, electroencephalogram, etc., and the sensing unit (130) can include a sensing module for acquiring each biometric data (20). As an example, the computing device (100) can acquire electrocardiogram data of the patient by measuring the electrocardiogram of the patient using at least one electrode included in the sensing unit (130).

[0044] Furthermore, the computing device (100) can analyze the acquired electrocardiogram data to determine the patient's condition. In particular, the computing device (100) can analyze the electrocardiogram data and determine the patient's condition using a pre-trained neural network model (10). The patient's condition may be a health condition, such as the likelihood of developing a specific disease or the risk of the disease. For example, the specific disease may be an arrhythmia or left ventricular systolic dysfunction.

[0045] Referring to FIG. 1, according to one embodiment of the present disclosure, a computing device (100) may determine the order of patients based on the identified condition of the patient. In particular, the computing device (100) may determine the order of patients so that the more critical the condition of the patient, the earlier they are treated (or examined) before other patients waiting for treatment. For example, if the computing device (100) determines that the patient's condition is likely to develop a specific disease based on the analysis results of electrocardiogram data, the computing device (100) may determine that the patient's condition is likely to develop. When the computing device (100) classifies the patient's condition into multiple condition grades based on the degree of likelihood of developing the disease, if the condition grade corresponding to the patient's condition corresponds to a predetermined condition grade with a high probability among the multiple condition grades, the computing device (100) may determine that the patient's condition is likely to develop a critical condition. Accordingly, the order of treatment of other patients waiting for treatment may be adjusted. This is to identify critical patients requiring urgent treatment that are difficult to detect with the naked eye and adjust the order of treatment so that they can receive prompt treatment and care, thereby preventing the death of critical patients who may occur while waiting for treatment.

[0046] In addition, according to one embodiment of the present disclosure, the computing device (100) may set a re-measurement cycle for the patient based on the identified condition, and may request the patient to re-measure for acquiring biometric data (20) at each set re-measurement cycle. For example, the computing device (100) may request re-measurement using the patient's terminal device (e.g., the patient's smart phone, smart watch, etc.) based on the patient's terminal device information included in the reception data, or may request re-measurement through the speaker of the computing device (100) or a speaker installed in a medical facility.

[0047] This aims to monitor critically ill patients waiting in medical facilities without medical intervention, thereby identifying changes in their condition in real time. Furthermore, if a critical patient's condition worsens or they become unresponsive, the computing device can quickly dispatch medical personnel, thereby protecting the patient's life, minimizing the risk of worsening conditions, and enabling effective treatment intervention.

[0048] Hereinafter, embodiments of the present disclosure related thereto will be described in detail with reference to FIGS. 2 to 5.

[0049] FIG. 2 is a schematic block diagram of a computing device (100) according to one embodiment of the present disclosure.

[0050] Referring to FIG. 2, a computing device (100) according to an embodiment of the present disclosure may include one or more processors (110) (hereinafter, processor (110)), a memory (120), a sensing unit (130), and a display (140). However, FIG. 1 is only an example, and thus, the computing device (100) may further include other components for implementing a computing environment. In addition, only some of the disclosed components may be included in the computing device (100).

[0051] A processor (110) according to an embodiment of the present disclosure may be understood as a configuration unit including hardware and / or software for performing computing operations. For example, the processor (110) may read a computer program to perform data processing for machine learning. The processor (110) may process computational processes such as processing input data for machine learning, feature extraction for machine learning, and error calculation based on backpropagation. The processor (110) for performing such data processing may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), a tensor processing unit (TPU), an application specific integrated circuit (ASIC), or a field programmable gate array (FPGA). The above-described type of processor (110) is only one example, and thus, the type of processor (110) may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

[0052] The processor (110) is electrically connected to other components of the computing device (100) (i.e., memory (120), sensing unit (130), and display (140)) and controls the overall operation of the computing device (100).

[0053] The memory (120) according to one embodiment of the present disclosure may be understood as a configuration unit including hardware and / or software for storing and managing data processed in the computing device (100). That is, the memory (120) may store any type of data generated or determined by the processor (110) and any type of data received by the communication interface. For example, the memory (120) may include at least one type of storage medium among a flash memory (120) type, a hard disk type, a multimedia card micro type, a card type memory (120), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory (120), a magnetic disk, and an optical disk. Additionally, the memory (120) may include a database system that controls and manages data in a predetermined system. The types of memory (120) described above are merely examples, and thus, the types of memory (120) may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

[0054] The memory (120) can structure and organize and manage data, combinations of data, and program codes executable by the processor (110) required for the processor (110) to perform operations. For example, the memory (120) can store a neural network model (10) trained to predict heart disease based on biometric data (20). In addition, the memory (120) can include program codes and learning data (e.g., a plurality of biometric data (20) and a plurality of heart disease information matching each of the biometric data (20)) that operate to perform learning on the acquired neural network model (10). In addition, the memory (120) can store program codes that operate the neural network model (10) to receive biometric data (20) and perform inference according to the purpose of use of the computing device (100), and processed data generated as the program code is executed.

[0055] The sensing unit (130) can obtain biometric data (20) about the patient. For example, the sensing unit (130) can include at least one electrode. In this case, the processor (110) can obtain electrocardiogram data of the patient through the at least one electrode. In addition, the sensing unit (130) can include an image sensor or an optical sensor, and the processor (110) can obtain biometric data (20), such as an optical blood flow signal, through the image sensor (or optical sensor).

[0056] Meanwhile, the sensing unit (130) may be implemented as an external bio-signal measuring device capable of obtaining the patient's bio-data (20) and may be electrically connected to the computing device (100) or via a wired or wireless network.

[0057] The display (140) can display various images. Here, the images include both still images and moving images. The display (140) can output risk information or status information identified based on the patient's biometric data (20), or can output the patient's waiting number. The display (140) can be implemented as various types of displays (140) such as LCD (Liquid Crystal Display Panel), OLED (Organic Light Emitting Diodes), LCoS (Liquid Crystal on Silicon), DLP (Digital Light Processing), etc. In addition, the display (140) can also include a driving circuit, a backlight unit, etc. that can be implemented in a form such as a-si TFT, LTPS (low temperature poly silicon) TFT, OTFT (organic TFT), etc.

[0058] Meanwhile, the display (140) may be implemented as a touch screen by being combined with a touch panel. In this case, the display (140) may not only function as an output interface that outputs images through the touch screen, but also as an input interface that receives touch input from a patient. The display (140) may obtain patient reception data through touch input.

[0059] Figure 3 is a flowchart schematically illustrating a method for setting a treatment order based on a patient's condition according to one embodiment of the present disclosure. Steps S310 to S340 illustrated in Figure 3 may be further divided into additional steps or combined into fewer steps, depending on the embodiment of the present disclosure. Furthermore, some steps may be omitted as needed, and the order of steps may be changed.

[0060] Referring to FIG. 3, when the reception data of a new examination subject is acquired, the processor (110) can acquire the biometric data (20) of the new examination subject through the sensing unit (130) (S310). For example, the processor (110) can acquire the reception data of the new examination subject through the display (140) of the kiosk device. Here, the examination subject is a user who visited a medical facility for the purpose of treatment, and the processor (110) can identify the examination subject based on the user's reception data or the purpose of visit entered by the user during the process of acquiring the reception data.

[0061] When the reception data of a new examination subject is obtained, the processor (110) can obtain the biometric data (20) of the new examination subject through the sensing unit (130) of the computing device. To explain again with the above-described example, the processor (110) can obtain electrocardiogram data about the patient by measuring the electrocardiogram of the patient by attaching at least one electrode included in the sensing unit (130) to the user's body. At this time, the processor (110) can obtain a 1-lead electrocardiogram for the patient using one electrode, or can obtain various forms of electrocardiograms according to 3-lead, 6-lead, and 12-lead methods.

[0062] Meanwhile, the processor (110) may obtain the reception data of a new examination subject from an external computing device via a communication interface. For example, the processor (110) may obtain biometric data (20) from a wearable device (e.g., a smart watch, etc.) including a sensing unit (130) worn by the new examination subject. Alternatively, the processor (110) may obtain biometric data (20) from a server device that communicates with the computing device and the wearable device of the new examination subject. In this case, the processor (110) may obtain the status information of the new examination subject, which is the result of analyzing the biometric data (20), together with the biometric data (20).

[0063] When the biometric data (20) of a new examination subject is acquired, the processor (110) analyzes the biometric data (20) using a pre-learned neural network model (10) to determine the condition of the new examination subject (S320).

[0064] The processor (110) can identify the patient's condition using a pre-trained neural network model (10). The pre-trained neural network model (10) may be a model trained to identify the patient's condition based on training data including input data regarding specific biometric data (20) and label data in which the patient's condition is labeled. For example, if the biometric data (20) is electrocardiogram data and the disease type is left ventricular systolic dysfunction, the neural network model (10) may be trained based on input data including a plurality of electrocardiogram data and training data including label data in which the presence (or degree) of left ventricular systolic dysfunction is labeled for each input data, and may be trained to minimize the error between the output corresponding to the input data and the label data by a backpropagation algorithm. When electrocardiogram data is input, the neural network model (10) learned in this way can extract feature information (e.g., feature points) from the electrocardiogram data and output a score (or probability value) regarding the possibility of left ventricular systolic dysfunction in the subject of the examination.

[0065] The neural network model (10) may include multiple neural network models (10) depending on the type of biometric data (20) and the type of patient's condition to be identified through the biometric data (20) (e.g., disease type, type of information to be identified about the disease, etc.). For example, the neural network model (10) may include multiple neural network models (10) that each identify whether the subject of the examination has arrhythmia and left ventricular systolic dysfunction, and each neural network model (10) may output a score regarding the possibility of the subject of the examination having arrhythmia and left ventricular systolic dysfunction, respectively.

[0066] To this end, the neural network model (10) may be implemented as a multi-layer perceptron (MLP), a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network, etc. In this case, the neural network model (10) may include a plurality of residual blocks that extract latent features of the input biometric data (20) and a classifier that classifies heart disease based on the extracted latent features.

[0067] Meanwhile, the neural network model (10) may be stored in the memory (120) of the computing device (100), or may be stored in an external computing device connected to the computing device (100). When the computing device (100) uses the neural network model (10) stored in the external computing device, it transmits the acquired biometric data (20) to the external computing device through a communication interface, and obtains the result value of the neural network model (10) regarding the patient's condition (i.e., a score indicating the presence or absence of a specific disease or the degree of possibility of a specific disease) from the external computing device.

[0068] The processor (110) can determine the patient's condition based on the result values ​​obtained from the pre-trained neural network model (10). Specifically, the processor (110) can determine the presence or absence of a specific disease or the likelihood of a specific disease in the patient's condition. In particular, the processor (110) can classify the patient's condition into multiple condition grades based on the score indicating the likelihood of a specific disease obtained from the pre-trained neural network model (10). For example, when a score regarding the possibility of left ventricular systolic dysfunction is obtained from a pre-learned neural network model (10), the processor (110) may determine the condition of a new examination subject as a normal grade if the score is less than a first value, determine the condition of a new examination subject as a stable grade if the score is greater than or equal to the first value and less than a second value, determine the condition of a new examination subject as a caution grade if the score is greater than or equal to the second value and less than a third value, determine the condition of a new examination subject as a dangerous grade if the score is greater than or equal to the third value and less than a fourth value, and determine the condition of a new examination subject as a very dangerous grade if the score is greater than or equal to the fourth value. However, this is not limited thereto, and the number of condition grades and the reference values ​​of the scores for distinguishing the condition grades may be set in various ways.

[0069] Meanwhile, the processor (110) can also determine a patient's condition by combining scores for multiple diseases. For example, the processor (110) can determine a patient's condition by combining the status ratings of a new patient for left ventricular systolic dysfunction and arrhythmia.

[0070] According to one embodiment of the present disclosure, when the patient's condition is identified, the processor (110) may set the treatment order of the new examination subject based on the time at which the new examination subject's reception data was acquired and the condition of the new examination subject (S330). The processor (110) may set the treatment order of the new examination subject below that of other examination subjects waiting for treatment based on the reception time at which the new examination subject's reception data was acquired. In other words, the processor (110) may set the treatment order of the new examination subject below that of other examination subjects who visited and registered at the medical facility before the new examination subject.

[0071] At this time, the processor (110) may adjust the treatment order of other examination subjects who have already been registered and are waiting for treatment based on the condition of the new examination subject, thereby setting the treatment order of the new examination subject. That is, if the condition of the examination subject is determined to be critical, the processor (110) may postpone the treatment order of other examination subjects waiting for treatment to a lower priority than that of the new examination subject, and advance the treatment order of the new examination subject.

[0072] In particular, if the condition of a new examination subject is determined to be critical, the processor (110) may advance the order of treatment for the new examination subject. To this end, the processor (110) compares the risk of the new examination subject with the risk of other examination subjects waiting for treatment based on the condition of the new examination subject, and if the risk of the new examination subject is determined to be higher than that of other examination subjects waiting for treatment, the order of treatment for the new examination subject may be set to be higher than that of other examination subjects waiting for treatment.

[0073] FIG. 4a and FIG. 4b are exemplary diagrams illustrating a method for setting a treatment order based on a patient's condition according to one embodiment of the present disclosure.

[0074] Referring to FIG. 4a, the processor (110) can input electrocardiogram data acquired from a new examination subject (200) through the sensing unit (130) into a pre-trained neural network model (10) to obtain a score regarding the possibility of left ventricular systolic dysfunction of the new examination subject (200). At this time, the processor (110) can determine the condition of the new examination subject (200) based on the acquired score. The condition of the new examination subject (200) is divided into multiple condition grades, and in particular, in the case of the dangerous and very dangerous grades among the multiple condition grades, the processor (110) can determine that the condition of the new examination subject (200) is dangerous. If the processor (110) determines that the condition of the new examination subject (200) is normal (or stable, caution), the treatment order of the new examination subject (200) can be set to the fifth priority, lower than the treatment order of other examination subjects (210-1 to 210-4) waiting for treatment.

[0075] On the other hand, referring to FIG. 4b, if the condition of a new examination subject (200) is determined to be at a very high risk level based on the score obtained based on the examination data obtained through the sensing unit (130), the processor (110) may determine that the condition of the new examination subject (200) is critical, and may set the order of treatment of the new examination subject (200) to be higher priority, i.e., first, than the four other examination subjects (210-1 to 210-4) waiting for treatment.

[0076] At this time, according to one embodiment of the present disclosure, if there are multiple other examination subjects waiting for treatment, the processor (110) can identify a subject with a lower risk level than the new examination subject (200) among the multiple other examination subjects, and set the treatment order of the new examination subject (200) to a higher priority than the treatment order of the identified other examination subjects.

[0077] FIG. 5 is another exemplary diagram illustrating a method for setting a treatment order based on a patient's condition according to one embodiment of the present disclosure.

[0078] Referring to FIG. 5, if the status of a new screening subject (200) is determined to be at risk based on the acquired score, the processor (110) can set the treatment order of the new screening subject (200) to be higher than that of the other screening subjects (210-2 to 210-4) among the four other screening subjects (210-1 to 210-4) waiting for treatment and whose status is lower than the risk level. That is, the processor (110) can set the treatment order of the new screening subject (200) to be lower than that of the other screening subject (210-1) who is at the first very risk level, but to be higher than that of the remaining screening subjects (210-2 to 210-4).

[0079] Meanwhile, if the condition grade of a new examination subject (200) judged to be in a dangerous state matches the condition grade of another examination subject waiting for treatment, the processor (110) can sequentially set the treatment order according to the time of acquisition of the reception data.

[0080] Referring back to FIG. 3, the processor (110) may provide waiting number information corresponding to the treatment order of a new examinee (200) based on the established treatment order (S340). For example, the processor (110) may issue a waiting number corresponding to the treatment order of the new examinee (200) and display the issued waiting number through the display (140). In addition, the processor (110) may issue a waiting number corresponding to the treatment order of the new examinee (200) and transmit the issued waiting number to the terminal device (e.g., a smart watch, etc.) of the new examinee (200). Thereafter, the processor (110) may display the waiting number of the examinee whose turn has arrived on the display (140) or output it through a speaker, thereby calling the examinee to the examination room. In addition, information notifying that the examination turn has arrived may be transmitted to the terminal device of the new examinee (200).

[0081] FIG. 6 is another exemplary diagram illustrating a method for setting a re-measurement cycle for a patient's condition according to one embodiment of the present disclosure.

[0082] According to one embodiment of the present disclosure, when the status of a subject for examination is classified into a plurality of status grades, the processor (110) may set a re-measurement cycle for biometric data (20) corresponding to each status grade. At this time, the processor (110) may determine the status grade corresponding to the status of a new subject for examination (200) and provide re-measurement time information for biometric data (20) according to the re-measurement cycle corresponding to the status grade of the new subject for examination (200) through the display (140). The above-described description applies equally to the method of providing the re-measurement time information. The re-measurement cycle may be set shorter as the risk level according to the status grade of the new subject for examination (200) is higher.

[0083] For example, referring to FIG. 6, the processor (110) may pre-set a corresponding re-measurement cycle according to the condition grade of the examination subject. Specifically, the processor (110) may set the re-measurement cycle to 20 minutes for a normal grade, 15 minutes for a stable grade, 10 minutes for a caution grade, 5 minutes for a risk grade, and 2 minutes for a very risk grade. In addition, the processor (110) may provide re-measurement time information calculated as a re-measurement cycle corresponding to the condition of the examination subject. For example, if the condition of a new examination subject (200) is identified as a risk grade, the processor (110) may display re-measurement time information according to a 5-minute re-measurement cycle on the display (140) for the new examination subject (200).

[0084] When the re-measurement time arrives, the processor (110) may display information requesting re-measurement of the biometric data (20) of the new examination subject (200) through the display (140) or transmit information requesting re-measurement of the biometric data (20) to the terminal device (400) of the new examination subject (200) through the communication interface. Specifically, the processor (110) may manage whether to re-measure a plurality of examination subjects received, including the new examination subject (200). To this end, the processor (110) may display a message requesting re-measurement of the biometric data (20) on the display (140) or transmit it to the terminal device (400) of the examination subject through the communication interface for the examination subject whose re-measurement time has arrived. In addition, the processor (110) may output a voice message requesting re-measurement of the biometric data (20) through the speaker of the computing device.

[0085] The processor (110) can determine whether each examination subject has performed re-measurement through the sensing unit (130) at the re-measurement time set for each examination subject. If the processor (110) identifies that a new examination subject (200) has not re-measured the biometric data (20) for a preset time after displaying or transmitting information requesting re-measurement, the processor (110) can transmit information of the new examination subject (200) to a terminal device of an administrator (e.g., a management server, etc.). In particular, if the processor (110) determines that a high-risk examination subject (e.g., when the condition level of the examination subject is dangerous or very dangerous) has not re-measured within the preset re-measurement time or within a preset time range based on the re-measurement time, the processor (110) can notify the terminal device of the administrator of the medical facility of the failure to perform re-measurement of the examination subject through a communication interface and provide information of the examination subject (e.g., name, gender, etc. based on reception data). At this time, the processor (110) may obtain the location information of the subject of the examination from the terminal device (e.g., smart phone, smart watch, etc.) of the subject of the examination, and provide the obtained location information to the administrator so that the administrator can quickly find the subject of the examination.

[0086] Meanwhile, according to one embodiment of the present disclosure, when the biometric data (20) of a new examination subject (200) is re-acquired through the sensing unit (130) based on the re-measurement of the new examination subject (200), the processor (110) may re-determine the condition of the examination subject based on the re-acquired biometric data (20), and adjust the examination order of the examination subject based on the re-determined condition of the examination subject. Specifically, when the condition grade of the examination subject who has undergone the re-measurement has changed and the condition of the examination subject is determined to be at a higher risk than other examination subjects in the waiting line, the processor (110) may adjust the examination order of the examination subject whose condition has changed. In this regard, since the description of the present disclosure described above is equally applicable, a detailed description thereof will be omitted.

[0087] FIG. 7 is a diagram illustrating a process in which a waiting number is issued to a subject for examination at preset intervals according to an embodiment of the present disclosure.

[0088] According to one embodiment of the present disclosure, the waiting numbers of other examination subjects (210-2 to 210-5) waiting for treatment may be issued at preset intervals corresponding to the treatment order of other examination subjects waiting for treatment. Referring to FIG. 7, when the treatment order for the first to fifth examination subjects (210-` to 210-5) is sequentially set, the waiting numbers 1, 5, 9, 13, and 17 may be issued to the first to fifth examination subjects, respectively. That is, the interval set for the waiting numbers may be 4. At this time, when the processor (110) sets the treatment order of the new examination subject (200) to be higher than the treatment order of other examination subjects waiting for treatment, the waiting numbers of other examination subjects (210-2 to 210-5) adjusted to a lower priority are maintained, and the waiting number of the new examination subject (200) can be issued by setting it to a number that is earlier than the waiting numbers of other examination subjects adjusted to a lower priority within a preset interval.

[0089] That is, if the processor (110) determines that the condition of the sixth examination subject (200) is at a higher risk than the conditions of the third to fifth examination subjects, the processor (110) may set the treatment order of the sixth examination subject (200) to be higher than that of the second to fifth examination subjects, and may issue the waiting number by setting it to 3, which is one of the numbers existing in the gap between the first examination subject and the second examination subject. Through this, time and resources can be saved by not repeatedly issuing waiting numbers even if the treatment order of the examination subjects changes, and confusion can also be prevented from occurring to the examination subjects.

[0090] FIG. 8 is a diagram illustrating that a waiting number is issued to a person subject to examination at set intervals according to the condition grade of the examination target site according to one embodiment of the present disclosure.

[0091] Meanwhile, the processor (110) may set the interval between the issued waiting numbers to an interval corresponding to the status grade of other examination subjects waiting for treatment. At this time, the processor (110) may set the interval wider as the risk level according to the status grade is lower, and may set the interval narrower as the risk level is higher. For example, referring to FIG. 8, the processor (110) may not set a preset interval when the status grade is a very dangerous grade, may set the preset interval to 2 when the status grade is a dangerous grade, may set the preset interval to 3 when the status grade is a caution grade, may set the preset interval to 4 when the status grade is a stable grade, and may set the preset interval to 5 when the status grade is a normal grade.

[0092] The processor (110) can set intervals not only based on the condition rating of the screening subject, but also by considering the level of congestion within the medical facility. Congestion can be identified by considering factors such as the number of patients visiting the medical facility, the number of medical staff on duty, and the presence of infectious diseases. Depending on the level of congestion, the processor (110) can adjust the intervals by weighting them based on the condition rating of the screening subject.

[0093] FIG. 9 is a configuration diagram of a computing device (900) according to another embodiment of the present disclosure.

[0094] Referring to FIG. 9, a computing device (900) according to another embodiment of the present disclosure may include one or more processors (910) (hereinafter, processor (910)), memory (920), sensing unit (930), display (940), communication interface (950), camera (960), user interface (970), speaker (980), and output unit (990). Among the configurations illustrated in FIG. 9, the processor (910), memory (920), sensing unit (930), and display (940) correspond to the configurations of the processor (110), memory (120), sensing unit (130), and display (140) (130) of the computing device (100) illustrated in FIG. 2, and thus a detailed description thereof will be omitted. In addition, the description of the present disclosure described above with reference to FIGS. 1 to 8 may be equally applied to the computing device (900) illustrated in FIG. 9.

[0095] A communication interface (950) according to an embodiment of the present disclosure may be understood as a component that transmits and receives data through any type of known wired or wireless communication system. For example, the communication interface (950) may perform data transmission and reception using a wired or wireless communication system such as a local area network (LAN), wideband code division multiple access (WCDMA), long term evolution (LTE), wireless broadband internet (WiBro), fifth generation mobile communication (5G), ultrawide-band, ZigBee, radio frequency (RF) communication, wireless LAN, wireless fidelity, near field communication (NFC), or Bluetooth. Since the above-described communication systems are only examples, the wired and wireless communication system for data transmission and reception of the communication interface (950) may be applied in various ways other than the above-described examples.

[0096] The communication interface (950) can receive data required for the processor (110) to perform calculations through wired or wireless communication with any system or any client, etc. In addition, the communication interface (950) can transmit data generated through calculations of the processor (110) through wired or wireless communication with any system or any client, etc. For example, the communication interface (950) can receive patient data and medical data through communication with a database within a medical facility (and environment), a cloud server that performs tasks such as standardization of medical data, or a computing device, etc. The communication interface (950) can transmit output data of the neural network model (10), and intermediate data, processed data, etc. derived from the calculation process of the processor (110) through communication with the aforementioned database, server, or computing device, etc. As an example, the processor (110) can obtain result data of a pre-learned neural network model (10) from an external computing device (e.g., an external server device) through the communication interface (950). Additionally, the processor (110) may obtain biometric data (20) from an external computing device (e.g., a biometric signal measuring device) through a communication interface (950), or may obtain learning data of a neural network model (10).

[0097] The camera (960) captures images of objects around the patient, including the patient. Specifically, the camera (960) can capture images of the patient. At this time, the processor (910) analyzes the captured patient image to identify the patient and capture previously stored reception data or determine the patient's condition. To this end, the camera (960) may be implemented with an imaging device such as a CMOS image sensor (CIS) having a CMOS structure or a charge-coupled device (CCD) having a CCD structure. However, the present invention is not limited thereto, and the camera (960) may be implemented with a camera module of various resolutions capable of capturing an object. Meanwhile, the camera (960) may be implemented with a depth camera (e.g., an IR depth camera), a stereo camera, an RGB camera, or the like.

[0098] The user interface (970) is a component used by the computing device (100) to interact with the patient, and may include, but is not limited to, at least one of a touch sensor, a motion sensor, a button, a jog dial, and a switch. The processor (910) can receive patient information and biological information (occupation, age, gender, etc.) related to the patient's reception data through the user interface (970).

[0099] The speaker (980) is a component that outputs various audio data that have undergone various processing operations, such as decoding, amplification, and noise filtering, by an audio processing unit (not shown). The speaker (980) can output various notification sounds or voice messages. According to one embodiment of the present disclosure, the processor (910) can convert an electrical signal into a user's (1) voice and output it through the speaker (980). For example, the speaker (980) can output the waiting number of a patient whose turn for treatment has arrived or output a voice message for calling a patient whose biometric data (20) re-measurement time has arrived.

[0100] The output unit (990) performs a function of outputting various information generated by the computing device (100). For example, the output unit (990) includes a printer device, and may output a paper on which the waiting number of a patient (i.e., a person subject to examination) is written through the printer device.

[0101] The various embodiments of the present disclosure described above can be combined with additional embodiments and modified within the scope understood by those skilled in the art in light of the detailed description 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. A method for setting a treatment order based on a patient's condition, performed by a computing device including at least one processor, When the reception data of a new examination subject is obtained, a step of obtaining the biometric data of the new examination subject through a sensing unit; A step of analyzing the biometric data using a pre-learned neural network model to determine the condition of the new examination subject; and A step of setting the treatment order of the new examination subject based on the time at which the reception data of the new examination subject is acquired and the status of the new examination subject; including; The steps for setting the above treatment order are: A step of setting the treatment order of the new examination subject by adjusting the treatment order of other examination subjects who have already been accepted and are waiting for treatment based on the status of the new examination subject; including; method.

2. In paragraph 1, The step of setting the order of treatment for the above new examination subjects is: A method comprising: a step of setting the treatment order of the new examination subject to a lower priority than other examination subjects waiting for treatment based on the reception time at which the reception data of the new examination subject was acquired.

3. In paragraph 2, The step of setting the treatment order of the new examination subject by adjusting the treatment order of other examination subjects who have already been accepted and are waiting for treatment based on the status of the new examination subject is as follows: A step of setting the treatment order of the new examination subject to a higher priority than the treatment order of the other examination subjects waiting for treatment, if the risk of the new examination subject is determined to be higher than the risk of the other examination subjects waiting for treatment based on the condition of the new examination subject; including; method.

4. In paragraph 3, The risk level of the above new screening subjects is classified into multiple status levels, The step of setting the order of treatment for the above new examination subject to be given priority over the order of treatment for other examination subjects waiting for treatment is as follows: In the case where there are multiple other examination subjects waiting for the above treatment, a step of identifying a subject of another examination with a lower risk status grade than that of the new examination subject among the multiple other examination subjects, and setting the treatment order of the new examination subject to a higher priority than that of the other examination subjects identified; including; method.

5. In paragraph 1, The condition of the above new examination subject is classified into multiple condition grades, and a re-measurement cycle for the biometric data is set corresponding to each condition grade. A step of identifying a condition grade corresponding to the condition of the new examination subject and providing re-measurement time information on the biometric data according to a re-measurement cycle corresponding to the condition grade of the new examination subject; including; method.

6. In paragraph 5, When the re-measurement time arrives, a step of displaying information requesting re-measurement of the biometric data of the new examination subject through the display of the computing device or transmitting information requesting re-measurement of the biometric data to the terminal device of the new examination subject through the communication interface; including; method.

7. In paragraph 6, A step of transmitting information of the new examination subject to the terminal device of the administrator, if it is identified that the new examination subject does not re-measure the biometric data for a preset period of time after displaying or transmitting the information requesting the re-measurement; method.

8. In paragraph 6, A step of re-determining the condition of the new examination subject based on the re-acquired biometric data of the new examination subject through the sensing unit according to the re-measurement of the new examination subject, and adjusting the examination order of the examination subject based on the re-determined condition of the examination subject; including; method.

9. In paragraph 3, The waiting number of the other examinee waiting for the above treatment is set and issued at preset intervals in response to the order of treatment of the other examinee waiting for the above treatment. In the case where the order of treatment of the new examination subject is set to be higher than the order of treatment of other examination subjects waiting for treatment, the waiting number of the other examination subjects adjusted to a lower priority is maintained, and the waiting number of the new examination subject is set to be a number that is earlier than the waiting number of the other examination subjects adjusted to a lower priority existing in the preset interval, and issuing the same, method.

10. In paragraph 9, The status of the other examinee waiting for the above treatment is classified into multiple status grades, and the preset interval is set to an interval corresponding to the status grade of the other examinee waiting for the above treatment. method.

11. In a computing device that sets the treatment order based on the condition of an emergency patient, Memory containing program codes; A sensing unit that obtains the biometric data of the subject of examination; and When the reception data of a new examination subject is obtained, the biometric data of the examination subject is obtained through a sensing unit, the biometric data is analyzed using a pre-learned neural network model to determine the condition of the new examination subject, and one or more processors are included to set the treatment order of the new examination subject based on the time at which the reception data of the new examination subject is obtained and the condition of the new examination subject. One or more of the above processors, Based on the status of the above-mentioned examination subject, the examination order of other examination subjects who have already been accepted and are waiting for treatment is adjusted to set the examination order of the new examination subject. Computing device.

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