KR102230239B1 - Prognosis management system based on measuring patient biometric data terminal and cloud server - Google Patents

Abstract

The present invention measures biometric data such as blood pressure or blood glucose level of patients with high risk of sudden deterioration of a sudden physical condition, such as a chronic disease person, in real time, and analyzes and recognizes the risk situation in real time in a prognostic analysis server using the measured data. In this case, it relates to a system that can warn patients or medical staff in advance. More specifically, the present invention measures biometric data for a patient to be monitored, and analyzes the received biometric data when receiving biometric data from a patient terminal, a medical staff terminal, and the patient terminal that transmits the measured biometric data to a server. Including a prognosis analysis server, wherein the prognosis analysis server transmits a notification to warn the predicted risk to the patient terminal or the medical staff terminal when a risk is predicted in the physical condition of the patient to be monitored as a result of the analysis. It characterized in that it relates to a prognosis management system.

Description

A prognostic management system using a mobile terminal and cloud server that measures patient's biometric data {PROGNOSIS MANAGEMENT SYSTEM BASED ON MEASURING PATIENT BIOMETRIC DATA TERMINAL AND CLOUD SERVER}

The present invention is a system for analyzing, predicting, and managing a patient's prognosis. More specifically, a patient's biometric data such as blood pressure or blood sugar level is measured using a mobile terminal, and the prognosis is analyzed in a cloud server based on artificial intelligence. It relates to a system that manages.

As the transmission and reception of information through online such as the Internet becomes free, new types of treatment methods such as telemedicine are being introduced. For telemedicine, medical examinations, examinations, and examinations that can be performed by doctors using remote medical equipment It is also possible to check the physical condition of the patient and send the test result to the patient through online. If periodic and continuous medical services are needed, such as in a poor environment where medical services are not available or a silver town composed of elderly people with disabilities and such elderly people, if remote medical treatment is used, the person treated is treated without space and time constraints. There may be an advantage of being able to receive it.

In addition, in addition to these cases, in the case of diabetes, heart disease, or patients after cancer surgery, who need to continuously manage the patient's life, the inconvenience of having to go to the hospital regularly through remote medical treatment can be solved. It has the advantage of being able to easily check the health status of the patient throughout.

In particular, in the case of chronic disease patients, such as diabetic patients, rapid deterioration of physical conditions such as hypoglycemic shock may occur. Such deterioration of the physical condition can be sufficiently predicted if the patient's real-time biometric data is continuously analyzed. If such a risk can be predicted, the patient can not only prescribe himself/herself before a dangerous situation occurs, but can also ask for help from nearby people or medical staff in advance, so that he or she can be fully prepared for the risk.

Accordingly, there is a need for research on a system capable of measuring and analyzing real-time biometric data for such chronically ill patients, and to warn patients or medical staff of risks to their physical condition in advance.

It is an object of the present invention to solve the above and other problems. Another object is to provide a system capable of predicting and preventing sudden physical risks that may occur to chronically ill patients through real-time biometric data measurement.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems that are not mentioned will be clearly understood by those of ordinary skill in the technical field to which the present invention belongs from the following description. I will be able to.

In order to achieve the above or other objects, according to an aspect of the present invention, there is provided a patient terminal that measures biometric data for a patient to be monitored and transmits the measured biometric data to a server; Medical staff terminal; And a prognostic analysis server that analyzes the received biometric data when receiving biometric data from the patient terminal, wherein the prognostic analysis server comprises: when a risk is predicted to the physical condition of the patient to be monitored as a result of the analysis, the patient It provides a prognosis management system, characterized in that it transmits a notification warning of the predicted risk to a terminal or the medical staff terminal.

The biometric data measured by the patient terminal is time-series data measured in real time.

The biometric data may include at least one of a blood glucose level, a blood pressure, and a body weight of the patient to be monitored.

In this case, the prognosis analysis server may store past measurement data of the patient to be monitored in a database, and compare the past measurement data with the received biometric data to analyze the prognosis.

In addition, the prognosis analysis server may use a long-short term memory (LSTM) cyclic neural network to analyze the prognosis.

In analyzing the prognosis, the prognosis analysis server may calculate the degree of risk of the physical condition as a Risky score, and provide the calculated Risky score to the patient terminal or the medical staff terminal.

In this case, the medical staff terminal may arrange a plurality of monitored patients based on the provided Riskey score based on the received Riskey score, and may preferentially examine patients with a high Riskey score.

The prognosis management server may classify or predict a disease for the patient to be monitored by analyzing the biometric data.

The effect of the prognosis management system according to the present invention will be described as follows.

According to at least one of the embodiments of the present invention, there is an advantage in that it is possible to predict and prevent a sudden physical risk of a person with chronic disease such as a diabetic patient in advance.

In addition, according to at least one of the embodiments of the present invention, there is an advantage that it is possible to provide a system that can easily and simply escape from obesity by systematically managing obesity and the like on a daily basis.

Further scope of the applicability of the present invention will become apparent from the detailed description below. However, since various changes and modifications within the spirit and scope of the present invention can be clearly understood by those skilled in the art, it should be understood that the detailed description and specific embodiments such as preferred embodiments of the present invention are given by way of example only.

1 is a diagram showing a configuration diagram of a prognosis management system 1000 according to an embodiment of the present invention.
2 is a block diagram showing a patient terminal 100-1, a prognosis analysis server 100-2, and a medical staff terminal 100-3 according to an embodiment of the present invention.
3 is a diagram illustrating a flow chart of a system control method according to an embodiment of the present invention.
4 is a diagram illustrating a flow chart of a first example of step S303 in which the prognosis analysis server 100-2 analyzes biometric data according to an embodiment of the present invention.
5 is a diagram illustrating a second example flow chart of step S303 in which the prognosis prediction server 100-2 analyzes biometric data according to an embodiment of the present invention.
6 shows a table for explaining analysis of biometric data using body weight according to an embodiment of the present invention.

Hereinafter, exemplary embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but identical or similar elements are denoted by the same reference numerals regardless of reference numerals, and redundant descriptions thereof will be omitted. The suffixes "module" and "unit" for constituent elements used in the following description are given or used interchangeably in consideration of only the ease of writing the specification, and do not themselves have a distinct meaning or role from each other. In addition, in describing the embodiments disclosed in the present specification, when it is determined that a detailed description of related known technologies may obscure the subject matter of the embodiments disclosed in the present specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are for easy understanding of the embodiments disclosed in the present specification, and the technical idea disclosed in the present specification is not limited by the accompanying drawings, and all changes included in the spirit and scope of the present invention , It should be understood to include equivalents or substitutes.

Terms including ordinal numbers such as first and second may be used to describe various elements, but the elements are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another component.

When a component is referred to as being "connected" or "connected" to another component, it is understood that it may be directly connected or connected to the other component, but other components may exist in the middle. It should be. On the other hand, when a component is referred to as being "directly connected" or "directly connected" to another component, it should be understood that there is no other component in the middle.

Singular expressions include plural expressions unless the context clearly indicates otherwise.

In this application, terms such as "comprises" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof does not preclude in advance.

1 is a diagram showing a configuration diagram of a prognosis management system 1000 according to an embodiment of the present invention.

According to the illustrated drawing, the prognosis management system 1000 may include a patient terminal 100-1, a prognosis analysis server 100-2, and a medical staff terminal 100-3. In the illustrated example, the patient terminal 100-1 is illustrated as a mobile terminal and the medical staff terminal 100-3 is illustrated as a fixed terminal, but the type of terminal is not limited thereto.

The patient terminal 100-1, the prognosis analysis server 100-2, and the medical staff terminal 100-3 may be connected through a wired or wireless network.

The patient terminal 100-1 may be a terminal possessed by a patient to be monitored, such as a chronically ill patient. For example, such a terminal may be a device such as a smartphone. That is, for the operation of the system, the device is not separately installed on the patient, but may be operated as a device that can be used daily, such as a smartphone.

The prognosis analysis server 100-2 may analyze the physical condition of the patient to be monitored based on data transmitted from the patient terminal 100-1. In particular, the prognosis management server may classify or predict a disease for the patient to be monitored by analyzing the biometric data using a cyclic neural network of a long-short term memory (LSTM) method.

The medical staff terminal 100-3 may provide the medical staff with measurement data for a patient to be monitored or an analysis result by the prognosis analysis server 100-2.

2 is a block diagram showing a patient terminal 100-1, a prognosis analysis server 100-2, and a medical staff terminal 100-3 according to an embodiment of the present invention. In FIG. 2, the patient terminal 100-1, the prognosis analysis server 100-2, and the medical staff terminal 100-3 are collectively referred to as the terminal 100, and will be described as a block diagram.

The terminal 100 may include a wireless communication unit 110, a sensing unit 140, an output unit 150, a memory 170, a control unit 180, a power supply unit 190, and the like. Since the components shown in FIG. 2 are not essential for implementing the terminal 100, the terminal 100 described herein may have more or fewer components than the components listed above.

More specifically, among the components, the wireless communication unit 110 includes wireless communication between the terminal 100 and the wireless communication system, between the terminal 100 and other terminals 100, or between the terminal 100 and an external server. It may include one or more modules that enable it. In addition, the wireless communication unit 110 may include one or more modules that connect the terminal 100 to one or more networks.

The wireless communication unit 110 may include at least one of a mobile communication module 112, a wireless Internet module 113, and a short-range communication module 114.

The wireless Internet module 113 refers to a module for wireless Internet access, and may be built-in or external to the terminal 100. The wireless Internet module 113 is configured to transmit and receive wireless signals in a communication network according to wireless Internet technologies.

The mobile communication module 112 includes technical standards or communication methods for mobile communication (for example, GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), CDMA2000 (Code Division Multi Access 2000)), EV -DO (Enhanced Voice-Data Optimized or Enhanced Voice-Data Only), WCDMA (Wideband CDMA), HSDPA (High Speed Downlink Packet Access), HSUPA (High Speed Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), etc.), transmits and receives radio signals with at least one of a base station, an external terminal, and a server.

Examples of wireless Internet technologies include WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), Long Term Evolution (LTE), Long Term Evolution-Advanced (LTE-A), and the like, and the wireless Internet module ( 113) transmits and receives data according to at least one wireless Internet technology in a range including Internet technologies not listed above.

From the point of view that wireless Internet access by WiBro, HSDPA, HSUPA, GSM, CDMA, WCDMA, LTE, LTE-A, etc. is made through a mobile communication network, the wireless Internet module 113 performs wireless Internet access through the mobile communication network. ) May be understood as a kind of the mobile communication module 112.

The short range communication module 114 is for short range communication, and includes Bluetooth™, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, and NFC. Near field communication may be supported using at least one of (Near Field Communication), Wi-Fi (Wireless-Fidelity), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus) technologies. Such, short-range communication module 114, the terminal 100 and the wireless communication system, the terminal 100 and the other terminal 100, or the terminal 100 and another terminal through a wireless area network (Wireless Area Networks) It is possible to support wireless communication between networks in which (100, or external servers) are located. The local area wireless communication network may be a wireless personal area network (Wireless Personal Area Networks).

The sensing unit 140 may include one or more sensors for sensing at least one of information in the terminal 100, information on a surrounding environment surrounding the terminal 100, and user information. For example, the sensing unit 140 according to an embodiment of the present invention may further include a sensor for measuring biometric data of a patient to be monitored. The biometric data is data on various bio signals that can be obtained from a target human body, and may include, for example, blood pressure, blood sugar, or body weight. Meanwhile, the terminal 100 disclosed in the present specification may combine and utilize information sensed by at least two or more of these sensors.

The output unit 150 is for generating an output related to time and may include a display unit. The display unit forms a mutual layer structure with the touch sensor or is integrally formed to implement a touch screen. Such a touch screen may function as a user input unit that provides an input interface between the terminal 100 and a user, and may provide an output interface between the terminal 100 and a user.

In addition, the memory 170 stores data supporting various functions of the terminal 100. The memory 170 may store a plurality of application programs or applications driven by the terminal 100, data for operation of the terminal 100, and commands. At least some of these application programs may be downloaded from an external server through wireless communication. In addition, at least some of these application programs may exist on the terminal 100 from the time of shipment for basic functions of the terminal 100 (eg, incoming calls, outgoing functions, message reception, and outgoing functions). Meanwhile, the application program may be stored in the memory 170, installed on the terminal 100, and driven by the controller 180 to perform an operation (or function) of the terminal 100.

In particular, the memory 170 according to an embodiment of the present invention may include a database for systematically storing data of a large amount. Furthermore, such a database stores measured biometric data to be described later in a big data format, and uses it for prognosis prediction through machine learning or deep learning.

In addition to the operation related to the application program, the controller 180 generally controls the overall operation of the terminal 100. The controller 180 may provide or process appropriate information or functions to a user by processing signals, data, information, etc. input or output through the above-described components or by driving an application program stored in the memory 170.

In addition, in order to drive the application program stored in the memory 170, the controller 180 may control at least some of the components discussed with reference to FIG. 2. Furthermore, in order to drive the application program, the controller 180 may operate by combining at least two or more of the components included in the terminal 100 with each other.

The power supply unit 190 receives external power and internal power under the control of the controller 180 and supplies power to each of the components included in the terminal 100. The power supply unit 190 includes a battery, and the battery may be a built-in battery or a replaceable battery.

At least some of the respective components may operate in cooperation with each other to implement the operation, control, or control method of the terminal 100 according to various embodiments described below. In addition, the operation, control, or control method of the terminal 100 may be implemented on the terminal 100 by driving at least one application program stored in the memory 170.

3 is a diagram illustrating a flow chart of a system control method according to an embodiment of the present invention.

In S301-1, the patient terminal 100-1 may measure biometric data from a patient to be monitored and transmit the measured biometric data to the prognosis analysis server 100-2 (step S302).

Here, the biometric data measured by the patient terminal 100-1 may be time-series data measured in real time. In addition, the biometric data may include at least one of a blood glucose level, a blood pressure, and a body weight of the patient to be monitored.

In S302-2, the patient terminal 100-1 may receive user data from the user and transmit it to the prognosis analysis server 100-2 similarly.

Here, the user data refers to various information that can affect changes in the user's biometric data, such as information on the user's lifestyle or food consumed.

The prognosis analysis server 100-2 analyzes the current physical state of the patient to be monitored based on the received biometric data or user data (step S303).

In particular, in the present invention, the biometric data measured from a large number of patients are continuously converted into big data and stored in a database, and the data stored in the database and the biometric data measured in real time are compared with each other. It is suggested to predict the prognosis and prevent risk conditions.

In addition, in an embodiment of the present invention, the prognosis analysis server may use a long-short term memory (LSTM) cyclic neural network to be suitable for analysis of signals measured in time series in analyzing the prognosis. have.

As described above, the analysis method in step S303 will be described with reference to FIGS. 4 and 5.

4 is a diagram illustrating a flow chart of a first example of step S303 in which the prognosis analysis server 100-2 analyzes biometric data according to an embodiment of the present invention.

In the first example, a blood glucose level measured from a patient to be monitored is received (step S401), and an analysis thereof is performed.

In step S402, the prognosis analysis server 100-2 performs an analysis using the past blood glucose level change value previously stored in the database. In particular, the database may include not only data measured from a patient to be monitored, but also data measured from other people.

The prognosis analysis server 100-2 analyzes the prognosis of the current monitoring target patient by using the past blood glucose level analysis (step S403). Based on the analyzed prognosis, it is determined whether the current state of the patient is in a dangerous state (step S404), and if it is not dangerous, the general analysis result is returned to the patient terminal 100-1 or the medical staff terminal 100-3 (S404). And, if it is dangerous, the analysis result including the warning may be returned (step S405).

In FIG. 4, an analysis based on blood sugar has been described, but an analysis based on blood pressure may also be possible, as will be described later.

5 is a diagram illustrating a second example flow chart of step S303 in which the prognosis prediction server 100-2 analyzes biometric data according to an embodiment of the present invention.

In the second example, a blood pressure value (SBP, DBP, etc.) measured from a patient to be monitored is received (step S501), and an analysis thereof is performed. With systolic blood pressure (SBP), systolic blood pressure. It is the blood pressure in the state where the heart is contracted, which means the maximum blood pressure, and the diastolic blood pressure (DBP) is the diastolic blood pressure. This is the blood pressure when the heart expands, and it refers to the lowest blood pressure.

In step S502, the prognosis analysis server 100-2 performs an analysis using the past blood pressure change value previously stored in the database. In particular, the database may include not only data measured from a patient to be monitored, but also data measured from other people.

The prognosis analysis server 100-2 analyzes the prognosis of the currently monitored patient using the past blood pressure change numerical analysis (step S503). Based on the analyzed prognosis, it is determined whether the current state of the patient is in a dangerous state (step S504), and if it is not dangerous, a general analysis result is returned to the patient terminal 100-1 or the medical staff terminal 100-3 (S504). And, if it is dangerous, the analysis result including the warning may be returned (step S505).

Returning to FIG. 3 again, the prognostic analysis server 100-2 transmits the analyzed result to the patient terminal 100-1 (S304-1) or to the medical staff terminal 100-3 (S304). -2) You can.

The patient terminal 100-1 may output the patient analysis result (S305-1) based on the analysis result received in step S304-1. Similarly, the medical staff terminal 100-3 may output the patient analysis result (S305-2) based on the analysis result received in step S304-2.

On the other hand, the prognostic analysis server 100-2 does not simply output a warning, but can quantify the patient's risk level as a score, and deliver the numerical value to the terminals 100-1 and 100-2. will be. This is because it is possible to determine the degree of risk, and especially when the medical staff judges, it is possible to quickly determine who is the patient who needs the most urgent action among many patients.

Accordingly, the prognosis analysis server 100-2 calculates a Risky score that quantifies the patient's risk based on the biometric data received in the step S302, and then calculates the patient terminal 100-1 or It can be delivered to the medical staff terminal 100-3.

In receiving this, the medical staff terminal 100-3 outputs the analysis result (S305-2), and sorts in the order of risk using a plurality of received Riskey scores, and can give priority to emergency patients. will be.

6 shows a table for explaining analysis of biometric data using body weight according to an embodiment of the present invention.

According to the drawing, BMI (body mass index) is measured based on the patient's weight measurement data, and the patient's body is'UNDER WEIGHT','NORMAL', and'OVER WEIGHT' based on the measured BMI. And'OBESE'. This classification is only an example, and the present invention will not be limited to specific classification items.

For each category, different words of advice may be printed to the patient. In particular, in the present invention, it is proposed to apply a different method of calculating the'normal weight' according to the classification (additional formula), and to calculate it according to the method and include it in the advice phrase to the user.

In the example of FIG. 6, the Riskey score may be calculated using BMI by Equation 1 below.

As described above, the Riskey score calculated as described above is transmitted to the medical staff terminal 100-3 or the patient terminal 100-1, so that the present dangerous state can be guided.

The embodiment of the prognosis management system according to the present invention has been described above, but this is described as at least one embodiment, and the technical idea of the present invention and its configuration and operation are not limited thereto. The scope of the idea is not limited/limited by the drawings or the description referring to the drawings. In addition, the concepts and embodiments of the invention presented in the present invention may be used by those of ordinary skill in the art as a basis for modifying or designing a different structure in order to perform the same object of the present invention. , Modified or changed equivalent structure by a person skilled in the art to which the present invention belongs is bound by the technical scope of the present invention described in the claims, and does not depart from the spirit or scope of the invention described in the claims. Various changes, substitutions and changes are possible within the limit.

Claims (8)

A patient terminal that measures biometric data on a patient to be monitored and transmits the measured biometric data to a server;
Medical staff terminal; And
When receiving the biometric data from the patient terminal, comprising a prognosis analysis server for analyzing the received biometric data,
The prognosis analysis server, when a risk is predicted in the physical condition of the patient to be monitored as a result of the analysis, transmits a notification warning of the predicted risk to the patient terminal or the medical staff terminal, and the living body measured by the patient terminal The data is characterized in that it is time-series data measured in real time,
The prognostic analysis server,
Analyzing the prognosis by storing past measurement data of the patient to be monitored in a database, and comparing the past measurement data with the received biometric data,
In analyzing the prognosis, a long-short term memory (LSTM) circulatory neural network is used, and the degree of risk of the physical condition is calculated as a Risky score, and the calculated li A ski score is provided to the patient terminal or the medical staff terminal,
The medical staff terminal,
A prognosis management system, characterized in that by arranging a plurality of monitored patients based on the received Riskey score, patients with a high Riskey score can be examined preferentially.
delete The method of claim 1, wherein the biometric data,
It characterized in that it comprises at least one of the blood glucose level, blood pressure, and body weight of the patient to be monitored,
Prognosis management system. delete delete delete delete The method of claim 1,
The prognosis management server, characterized in that by analyzing the biometric data to classify or predict a disease for the patient to be monitored,
Prognosis management system.

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