KR20200055293A - Healthcare monitoring system - Google Patents

Abstract

Disclosed is a computer program stored in a computer readable storage medium. To achieve the task described above, the computer program according to an embodiment of the present invention performs a method for healthcare monitoring by using network functions, when executed in one or more processors. The method can comprise the steps of: acquiring health data related to health of a user from a user terminal; generating analysis information on the health data by calculating the health data through a health care monitoring model including one or more network functions; and providing a message to at least one of a medical staff terminal and the user terminal based on the analysis information.

Description

Health monitoring system {HEALTHCARE MONITORING SYSTEM}

The present disclosure relates to a healthcare monitoring system, and more particularly, to a healthcare monitoring system that receives health-related data through a user terminal.

The number of diabetic patients worldwide is showing a rapid increase, and Korea is also experiencing a rapid increase in the diabetic population along with economic growth. According to regional studies conducted since 2000 and research on the National Health and Nutrition Survey, the prevalence of diabetes has increased to 8% in adults over the age of 20. Diabetes is an important cause of death, so its prevention and treatment are even more important.

In particular, type 2 diabetes occurs when the pancreas does not make a sufficient amount of insulin and somatic cells do not respond appropriately to insulin, resulting in high blood sugar levels. In order to prevent or treat type 2 diabetes, it is necessary for people at risk of diabetes to lead a healthy lifestyle. These include regular physical activity, healthy eating habits and maintaining normal weight.

In Korean Patent Publication No. KR2011-0059142, a blood glucose measurement unit and insulin are injected into a human body to emit radio waves through a blood sugar sensor inserted into a blood vessel of a human body and to measure a blood glucose level of an object to be measured using reflected radio waves reflected from an object to be measured. Disclosed is an insulin administration device comprising an insulin pump unit.

However, these inventions can only automate the administration of insulin, and can lead to rather bad results for people who have not yet developed diabetes or who need professional diagnosis and prescription from medical staff.

Therefore, there may be a demand in the art for a healthcare monitoring system that receives health-related data through a user terminal.

The present disclosure has been devised in response to the above-described background technology, and is to provide a health care monitoring system that receives health-related data through a user terminal.

Disclosed is a computer program stored in a computer readable storage medium in accordance with one embodiment of the present disclosure for realizing the above-described problems. When the computer program is executed on one or more processors, the following method for performing health care monitoring using a network function is performed. The method includes obtaining health data related to a user's health from a user terminal, and the health data Generating an analysis information for the health data by calculating through a healthcare monitoring model including one or more network functions, and providing a message to at least one of the medical staff terminal and the user terminal based on the analysis information. It can contain.

Alternatively, the health data may include at least one of a user's heart rate, blood pressure, blood glucose measurement, weight, height, age, gender, and user information.

Alternatively, the method may further include acquiring the health data through one or more sensors disposed on at least a portion of the user's body.

Alternatively, the one or more sensors may include at least one of a wearable device, a physical examination device, and a body measurement device authenticated in association with the user.

Alternatively, the step of obtaining health data related to the user's health from the user terminal may be performed in a manner of parsing the health data in the form of JavaScript Object Notation (JSON).

Alternatively, the step of providing a message to at least one of the medical staff terminal and the user terminal based on the analysis information may include publishing the message classified by topic based on the analysis information in the application, the application It may include the step of providing the message to the cluster formed inside the structure of the distributed message system, and at least one of the medical staff terminal and the user terminal (pulling) the message.

Alternatively, the step in which at least one of the medical staff terminal and the user terminal pulls the message may notify at least one of the medical staff terminal and the user terminal when the message related to a predetermined topic is issued. Can provide.

Alternatively, the healthcare monitoring model including the one or more network functions includes a diabetes type classification sub-model that is trained through learning health data labeled as the type of diabetes, and the diabetes type classification sub-model is the health data By calculating the may be configured to generate the analysis information to classify the diabetes type of the user.

Alternatively, the healthcare monitoring model including the one or more network functions includes a blood glucose measurement prediction sub-model that is trained to input learning health data to output a learning blood glucose measurement, and the blood glucose measurement prediction sub-model. May be configured to generate the analysis information that predicts the user's blood sugar measurement value for each time point by calculating the health data.

Alternatively, the method may further include storing at least some of the health data and the analysis information in an external database.

The present disclosure can provide a healthcare monitoring system that receives health-related data through a user terminal.

Various aspects are now described with reference to the figures, in which like reference numerals are used collectively to refer to similar elements. In the following embodiments, for illustrative purposes, a number of specific details are presented to provide a holistic understanding of one or more aspects. However, it will be apparent that such aspect (s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more aspects.
1 is a block diagram of a server according to an embodiment of the present disclosure.
2 is a structural diagram of a healthcare monitoring system according to an embodiment of the present disclosure.
3 is a real-time data processing processing process according to an embodiment of the present disclosure.
4 is a flowchart (flow-chart) of a healthcare monitoring method according to an embodiment of the present disclosure.
5 is a block diagram of a computing device according to an embodiment of the present disclosure.

Various embodiments are now described with reference to the drawings, and like reference numbers throughout the drawings are used to denote similar elements. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is clear that these embodiments can be practiced without these specific details. In other examples, well-known structures and devices are provided in block diagram form in order to facilitate describing the embodiments.

As used herein, the terms “component”, “module”, “system” and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or execution of software. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both the application and the computing device running on the computing device can be components. One or more components can reside within a processor and / or thread of execution, and one component can be localized within one computer, or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored therein. Components are, for example, signals having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and / or data via a network such as the Internet with other systems via a signal). ) Through local and / or remote processes.

Descriptions of the presented embodiments are provided to enable any person skilled in the art to use or practice the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art of the present disclosure, and the general principles defined herein can be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure should not be limited to the embodiments presented herein, but should be interpreted in the broadest scope consistent with the principles and novel features presented herein.

In one embodiment of the present disclosure, the server may include other configurations for performing the server environment of the server. The server can include any type of device. The server is a digital device, and may be a digital device equipped with a processor and having computing power, such as a laptop computer, a notebook computer, a desktop computer, a web pad, and a mobile phone. The server may be a web server that processes services. The above-described types of servers are only examples, and the present disclosure is not limited thereto.

Throughout this specification, models, neural networks, network functions, and neural networks may be used in the same sense. A neural network may consist of a set of interconnected computational units, which may generally be referred to as "nodes." These “nodes” may also be referred to as “neurons”. The neural network comprises at least one node. The nodes (or neurons) that make up the neural networks can be interconnected by one or more "links".

Throughout this specification, a model can be used to mean including one or more network functions. The model can perform data processing with other models. The models can be interconnected in series or in parallel with other models.

1 is a block diagram of a server according to an embodiment of the present disclosure.

The computing device 100 for performing healthcare monitoring of a user according to an embodiment of the present disclosure may be used to predict and grasp an abnormal symptom or the onset of diabetes, etc., from health data of an individual user.

The computing device 100 for performing healthcare monitoring of a user according to an embodiment of the present disclosure may include a network unit 110, a processor 120, and a memory 130.

The network unit 110 may obtain health data related to the user's health from the user terminal 200. Here, the health data may include at least one of a user's heart rate, blood pressure, blood sugar measurement value, weight, height, age, gender, and user information. Additionally, health data may refer to data related to a user's body.

The user terminal 200 may include a personal computer (PC), a notebook (note book), a mobile terminal (smart phone), a smart phone (smart phone), a tablet PC (tablet pc), VR (Virtual Reality) device, etc. In addition, it may include all types of terminals that can access the wired / wireless network. In addition, the network unit 110 may include a wired / wireless Internet module for network connection. Wireless Internet technology (Wireless LAN) (Wi-Fi), Wibro (Wireless broadband), Wimax (World Interoperability for Microwave Access), HSDPA (High Speed Downlink Packet Access) may be used. As a wired Internet technology, digital subscriber line (XDSL), fibers to the home (FTTH), and power line communication (PLC) may be used.

In addition, the user terminal 200 according to an embodiment of the present disclosure may communicate with one or more sensors in a Bluetooth Low Energy (BLE) method. Here, the one or more sensors may include at least one of a wearable device, a physical examination device, and a body measurement device that is authenticated in connection with a user.

More specifically, the one or more sensors may include at least one of a blood glucose meter, a blood pressure meter, a weight scale, and an extensometer for measuring blood sugar of the user. In addition, each device may transmit measurement data through BLE communication with the user terminal 200. For example, a blood glucose meter configured in a form attached to the human body or inserted into the human body may transmit the blood glucose measurement value to the user terminal 200 through the BLE communication module. The above description of the blood glucose meter is only an example, and the present disclosure is not limited thereto.

In addition, the network unit 110 may provide analysis information generated based on the health data as at least one of the medical staff terminal 300 and the user terminal 200. Here, the medical staff terminal 300 may include a terminal registered as a medical person such as a doctor or a nurse determined based on medical information registered by a user. Accordingly, the network unit 110 may provide analysis information to the medical staff terminal 300. For example, the network unit 110 may transmit analysis information about the user to the PC of the responsible physician and the nurse's PC registered as the medical staff terminal 300. The above description of the medical staff terminal 300 is only an example, and the present disclosure is not limited thereto.

In addition, the network unit 110 according to an embodiment of the present disclosure may transmit at least some of health data and analysis information to the database 400. More specifically, the network unit 110 may transmit at least some of the health data transmitted from the user terminal 200 and the analysis information generated by the processor 120 to the database 400. In addition, the database 400 (eg, Mongolia DB) may store and manage the health data and analysis information.

The processor 120 may generate analysis information on the health data by calculating the health data through a health care monitoring model including one or more network functions. Here, the healthcare monitoring model may include a diabetes model classification sub-model and a blood glucose measurement prediction sub-model.

The diabetes type classification sub-model according to an embodiment of the present disclosure may classify the diabetes type through machine learning. Here, the diabetes type classification sub-model may be trained through learning health data labeled as the type of diabetes. That is, the diabetes type classification sub-model can be learned by iteratively calculating learning data comprising learning health data of diabetic patients as an input value and diabetes type of the patients as an output value. In this process, learning data is added and the errors that occur can be corrected by changing the weight of machine learning.

In addition, the blood glucose measurement value prediction sub-model according to an embodiment of the present disclosure may predict a change pattern of the blood sugar measurement value through deep learning. Here, the blood glucose measurement prediction sub-model may be trained to input learning health data to output the learning blood glucose measurement. That is, the blood glucose measurement prediction sub-model can be learned by repeatedly calculating learning data comprising learning health data measured for the general public and diabetes patients as input values and blood glucose measurement values of the general public and diabetes patients as output values. . Here, the blood sugar measurement value may be measured by being distinguished by time point (eg, before / after meal, before / after sleep, etc.).

The blood glucose measurement prediction sub-model according to an embodiment of the present disclosure may predict blood glucose measurement values for each time point using a Recurrent Neural Network (RNN). Here, RNN may mean a deep learning model for learning a pattern based on health data including one or more items. That is, the processor 120 may repeatedly learn health data as input data and blood glucose measurement values as output data based on the RNN. Accordingly, as the health data received through the network unit 110 accumulates, and the blood glucose measurement value information for the health data accumulates, the processor 120 may perform more accurate prediction of the blood glucose measurement value.

In addition, the RNN according to an embodiment of the present disclosure may solve a problem of vanishing gradient due to continuous learning through at least one of a Long Short-Term Memory Units (LSTM) method and a Gated Recurrent Units (GRU) method. . More specifically, the processor 120 may prevent gradient loss by using an LSTM method of adding input, output, and oblivion gates having respective weights to the basic RNN structure. In addition, in another embodiment, the processor 120 may prevent loss of gradient by a GRU method (eg, dynamic Gated Recurrent Units (GRU) method) that additionally includes only input and forget gates in the RNN structure. The detailed description of the structure of the RNN described above is merely an example, and the present disclosure is not limited thereto.

The processor 120 may provide a message to at least one of the medical staff terminal 300 and the user terminal 200 based on the analysis information.

More specifically, the processor 120 may cause an application to publish a message classified by topic based on analysis information. In addition, the processor 120 may provide the message in an application to a cluster formed inside the structure of the distributed message system. In addition, the processor 120 may cause at least one of the medical staff terminal 300 and the user terminal 200 to take a message. Additionally, the processor 120 according to an embodiment of the present disclosure may provide a notification to at least one of the medical staff terminal 300 and the user terminal 200 when a message related to a predetermined topic is issued. A detailed description of this will be described later with reference to FIG. 3.

Accordingly, the healthcare monitoring system according to an embodiment of the present disclosure can accurately predict future blood sugar measurement values by using data measured during a user's daily life, thereby providing feedback from medical staff in advance. I can receive it. In addition, as data increases through prediction of blood sugar measurement values or learning of diabetes type classification, there is an advantage that precise prediction and classification are possible.

And, in this process, by providing a message in a publish-subscribe manner, a message can be obtained according to the performance of a user terminal or a medical staff terminal. That is, it is possible to minimize degradation of performance of each terminal while performing fast messaging according to the analysis information of the healthcare monitoring model.

2 is a structural diagram of a healthcare monitoring system according to an embodiment of the present disclosure.

Healthcare monitoring according to an embodiment of the present disclosure may be performed by the interaction of the user terminal 200, the server, the medical staff terminal 300, and the database 400.

The user terminal 200 may acquire health data through one or more sensors disposed on at least a part of the user's body. Here, the one or more sensors may include at least one of a wearable device, a physical examination device, and a body measurement device that is authenticated in connection with a user.

More specifically, one or more sensors may include a blood glucose meter that measures a user's blood sugar. For example, the blood glucose meter includes a short-range communication module such as Bluetooth, and may transmit the blood glucose measurement value to the user terminal 200 through Bluetooth. As another example, the blood glucose meter may be configured to be attached to the human body or inserted into the human body. The above description of the blood glucose meter is only an example, and the present disclosure is not limited thereto.

In addition, the blood glucose meter according to an embodiment of the present disclosure, when a user does not measure blood sugar within a waiting time (eg, 15 minutes) after a predetermined time or a predetermined period has elapsed, the blood sugar measuring time with the user terminal 200 The alarm can be output in at least one of audible and visual.

In addition, the one or more sensors may include a blood pressure meter, a blood pressure meter, a height meter, and a measuring device such as a weight scale. In addition, each device may transmit one item of health data measured through the wired / wireless communication with the user terminal 200.

In addition, the one or more sensors may include an activity amount meter that measures the amount of activity of the user. In addition, the activity meter can grasp the user's motion level or the number of steps by using a combination of a motion sensor, an acceleration sensor, and a GPS. Additionally, a heart rate may be provided from a user's wearable device (eg, a heart rate check module in the form of clothes) to accurately calculate the amount of activity. In addition, the activity amount meter may transmit the activity amount measured through short-range wireless communication such as Bluetooth to the user terminal 200.

The user terminal 200 may generate health data based on blood sugar measurement values and activity amounts, and transmit the health data to a server. In addition, it is possible to increase an item of health data by acquiring additional items about the user's health from one or more sensors.

Then, the server, that is, the computing device 100 may generate analysis information based on the health data received from the user terminal 200. More specifically, the computing device 100 may generate analysis information by calculating health data through a healthcare monitoring model including one or more network functions. For example, when the computing device 100 receives the blood glucose measurement value, activity amount, and diet information from the user terminal 200, the blood glucose measurement value for each time point is predicted based on three health data, or the user's diabetes It is possible to generate analysis information classified by type. Additionally, the computing device 100 may generate a message including feedback on at least one of blood sugar, diet, and exercise to the user based on the analysis information. In addition, the computing device 100 may provide the message to at least one of the user terminal 200 and the medical staff terminal 300.

For example, when the expected blood glucose measurement value included in the analysis information is within a certain range, the computing device 100 may send a relief message indicating that the user terminal 200 is well managed. In addition, as another example, when the expected blood sugar measurement value included in the analysis information is within the hypoglycemia or hyperglycemia range, the computing device 100 may use a warning message and a user terminal 200 indicating that medical examination is required to the medical staff terminal 300. It can provide a cautionary message that management is required. Additionally, the same message as the user terminal 200 may be provided to the guardian terminal of the guardian linked to the user. The above-described message is merely an example, and the present disclosure is not limited thereto.

Also, the computing device 100 may parse the health data in the form of JavaScript Object Notation (JSON). More specifically, the user terminal 200 may generate health data obtained from one or more sensors in JSON format. In addition, the user terminal 200 may transmit the health data to the computing device 100. Accordingly, the computing device 100 analyzes the health data transmitted from the user terminal 200 and data for at least one of blood glucose measurement values, blood pressure, heart rate, weight, height, age, gender, and user information for each user Can be obtained. The data format of JSON will be described later in detail with reference to FIG. 3.

In addition, the computing device 100 according to an embodiment of the present disclosure may acquire blood glucose measurement values, blood pressure, heart rate, and additional information for a user based on health data. Here, the additional information may include a user's diet information, activity amount information, and the like.

In addition, the computing device 100 may predict blood glucose measurement values based on health data or generate analysis information that classifies diabetes types. More specifically, the computing device 100 may output analysis information including at least one of predicted blood glucose measurement values and diabetes type classification by calculating health data through a healthcare monitoring model. Here, the healthcare monitoring model may include a blood glucose measurement prediction sub-model and a diabetes type classification sub-model composed of one or more network functions. Then, the computing device 100 may calculate at least a portion of the health data into each sub-model, and calculate diabetes type classification and predicted blood glucose measurement values.

The computing device 100 may determine the type of diabetes to which the user belongs by calculating the user's health data through the diabetes type classification sub-model. Then, the computing device 100 may generate analysis information including information on the type of diabetes.

Here, the type of diabetes may be classified as type 1 diabetes and type 2 diabetes, depending on whether insulin is secreted, and may also be classified as general diabetes and pregnancy diabetes based on the cause of diabetes. Type 1 diabetes is insulin-dependent diabetes in which insulin is not secreted from the body, and type 2 diabetes may be insulin-independent diabetes in which insulin resistance and insulin deficiency occur in which cells in the body do not respond appropriately to insulin. In addition, type 1 diabetes and type 2 diabetes can be classified as general diabetes, and the case of developing high blood sugar in a pregnant woman without a history of diabetes can be classified as pregnancy diabetes.

Additionally, the computing device 100 may be classified into a group in which diabetes does not develop, a group in which a risk of diabetes exists, and a group in which diabetes is to be treated. In addition, in addition to this, diabetes types may be classified and managed in various groups according to embodiments.

In addition, the computing device 100 may predict the user's blood glucose measurement value for each future time point by calculating the user's health data through the blood glucose measurement value prediction sub-model. Then, the computing device 100 may generate analysis information including information on the predicted blood sugar measurement value.

Here, the predicted blood glucose measurement value may mean an estimated blood glucose measurement value when a user predicts a change pattern of the blood glucose measurement value from the current time point (that is, when the last health data is received). That is, the computing device 100 may estimate whether or not the user's blood glucose measurement value will rise or fall through the health data, or determine how much to increase or decrease. Additionally, the computing device 100 may issue a message to be provided to at least one of the medical staff terminal 300 and the user terminal 200 based on the analysis information including the predicted blood sugar measurement value.

For example, when the predicted blood glucose measurement value calculated by the computing device 100 by calculating health data through the blood sugar measurement value prediction sub-model is 320 or more, the computing device 100 analyzes the predicted blood sugar measurement value of 320 Information can be generated. In addition, the computing device 100 may issue an emergency warning message by recognizing that blood sugar may be increased to a dangerous level based on the analysis information. Accordingly, the computing device 100 may provide a notification so that the medical staff terminal 300 obtains the emergency warning message. Additionally, the computing device 100 may provide a notification to obtain an emergency warning message to a guardian terminal (not shown) registered in association with the user terminal 200. The above-described operation of the computing device 100 is only an example, and the present disclosure is not limited thereto.

As another example, when the predicted blood glucose measurement value calculated by the computing device 100 through calculation of health data through the blood glucose measurement value prediction sub-model is 70 or less, the computing device 100 includes a predicted blood glucose measurement value of 70 Analysis information can be generated. In addition, the computing device 100 may issue a sugar supplement message by recognizing that blood sugar may be lowered to a dangerous level based on the analysis information. Accordingly, the computing device 100 may provide a notification so that the medical staff terminal 300 obtains the sugar supplementary message. Additionally, a notification may be provided to obtain a supplementary message to a guardian terminal (not shown) registered in association with the user terminal 200 of the computing device 100. The above-described operation of the computing device 100 is only an example, and the present disclosure is not limited thereto.

In addition, the computing device 100 according to an embodiment of the present disclosure may provide a notification to at least one of the medical staff terminal 300 and the user terminal 200 when a message related to a predetermined topic is issued. Additionally, the computing device 100 may provide a notification to a guardian terminal (not shown) linked with the user terminal 200. More specifically, the computing device 100 may determine a topic based on the analysis information, and may issue a message matching the topic to the cluster. And, each topic may be a user's current or future state based on the analysis information. For example, the computing device 100 may issue a message corresponding to “topic 1: hyperglycemia risk” based on the fact that the predicted blood glucose measurement value of the analysis information is 300 mg / dL. Also, the computing device 100 may provide a notification to the consumer corresponding to the topic 1, that is, the medical staff terminal 300. The operation of providing the aforementioned notification is merely an example, and the present disclosure is not limited thereto.

Also, the computing device 100 may store at least some of health data and analysis information in the database 400. Here, the database may be, for example, a Mongo DB. More specifically, the computing device 100 may transmit and store health data obtained from the user terminal 200 to the database 400 in real-time. In addition, the computing device 100 may store the analysis information output by calculating the health data through the healthcare monitoring model in the database 400. In addition, the computing device 100 may delegate authority to selectively access data to the medical staff terminal 300 and the user terminal 200. Additionally, the computing device 100 may distribute and store the database 400 for each item of health data.

Accordingly, the computing device 100 according to an exemplary embodiment of the present disclosure may safely store the user's health data, and may assist the medical staff to check the change pattern and history at any time.

Accordingly, the healthcare monitoring system according to an embodiment of the present disclosure can accurately predict future blood sugar measurement values by using data measured during a user's daily life, thereby providing feedback from medical staff in advance. I can receive it. In addition, as data increases through prediction of blood sugar measurement values or learning of diabetes type classification, there is an advantage that precise prediction and classification are possible.

And, in this process, by providing a message in a publish-subscribe manner, a message can be obtained according to the performance of a user terminal or a medical staff terminal. That is, it is possible to minimize degradation of performance of each terminal while performing fast messaging according to the analysis information of the healthcare monitoring model.

3 is a real-time data processing processing process according to an embodiment of the present disclosure.

The user terminal 200 according to an embodiment of the present disclosure may acquire user health data from one or more sensors. In addition, the user terminal 200 may transmit health data to the data processing structure 500.

More specifically, the user terminal 200 may store health data (eg, blood pressure, blood sugar measurement value, etc.) for each item obtained from one or more sensors as JSON format data. Here, the JSON (JavaScript Object Notation) format may mean a lightweight data format that is frequently used to exchange data through a network.

For example, the user terminal 200 may generate a name / value pair of {“blood sugar measurement value”: “160 mg / dL”} using the blood sugar measurement value as one object. As another example, the user terminal 200 may generate a part of health data of {"Name": "Hong Gil-dong", "Age": 25, "Gender": "Female"} for user information. The detailed description of the generation of health data of the user terminal 200 described above is merely an example, and the present disclosure is not limited thereto.

And, the data processing structure 500 may be composed of an application and a distributed messaging system. Here, as illustrated in FIG. 3, the application may be composed of a REST (Representational State Transfer) API to include a producer. Here, the producer may be an object that issues a message.

More specifically, the data processing structure 500 may include an application in the form of a server including one or more processors 120, such as the computing device 100 of FIG. 2. In addition, the application may obtain necessary item values by parsing the health data transmitted from the user terminal 200. In addition, the application may generate distributed information by calculating each health data item value through a healthcare monitoring model. And, it can serve to issue it to a distributed messaging system.

In this process, the application according to an embodiment of the present disclosure is composed of a REST API, so that state information for work is not separately stored and managed. Accordingly, the application simply processes incoming requests and has advantages such as statelessness, high degree of freedom, and simplification.

And, the data processing structure 500 may include a distributed messaging system. Here, the distributed messaging system may be, for example, kafka from Apache. Accordingly, the data processing structure 500 may store a message issued by the application in an internal cluster.

More specifically, the application may publish a message classified by topic based on at least one of health data and analysis information. And, the issued message can be stored and managed in a cluster of a distributed message system. Here, the topic is a type of message, and may include, for example, an emergency message generated based on the predicted blood glucose measurement value, or a sugar supplement message. The application may issue a message corresponding to each topic, that is, a user's health condition, based on the analysis information. The description of the above-mentioned topics is only examples, and the present disclosure is not limited thereto.

Further, the distributed message system may store messages issued for each topic through one or more brokers in one or more clusters. And, a consumer group registered in the distributed message system can take the message. Here, the consumer group may include subjects consuming health data and analysis information, including a medical staff terminal 300, a user terminal 200, a guardian terminal (not shown), and a database 400. That is, each object included in the consumer group 600 may be taken in a manner of pulling a desired time point and a desired message stored in a cluster of a distributed messaging system.

The method for healthcare monitoring according to an embodiment of the present disclosure, unlike a method of pushing a message to a consumer in an existing messaging system, stores a message in a cluster and the consumer included in the consumer group 600 directly sends the message. It operates in a pull method. Consumers can get the best performance because they only get messages as much as their processing power.

Additionally, a distributed message system according to an embodiment of the present disclosure may be formed in a publish-subscribe model. More specifically, a distributed message system may include a cluster composed of one or more partitions. In addition, the distributed message system may store messages issued for each topic from an application in a corresponding partition. Further, each partition may be allowed access only to one consumer (eg, medical staff terminal 300 or user terminal 200) of the consumer group 600. Accordingly, the distributed message system according to an embodiment of the present disclosure issues a message while allocating different partitions to the medical staff terminal 300 and the user terminal 200, and based on the user's health data. The message, for example, the medical staff terminal 300 may issue a message of the content that requires medical examination, and the user terminal 200 may issue a message of the content that the improvement of lifestyle is required. The description of the foregoing message is merely an example, and the present disclosure is not limited now.

4 is a flowchart (flow-chart) of a healthcare monitoring method according to an embodiment of the present disclosure.

The computing device 100 for health care monitoring according to an embodiment of the present disclosure may acquire (701) health data related to a user's health from a user terminal. And, prior to that, the computing device 100 according to an embodiment of the present disclosure may acquire the health data through one or more sensors disposed on at least a part of the user's body.

Then, the computing device 100 may generate 702 analysis information about the health data by calculating the health data through a health care monitoring model including one or more network functions.

More specifically, the computing device 100 generates the analysis information that classifies the diabetes type of the user by calculating the health data through a diabetes type classification sub-model learned through learning health data labeled as the type of diabetes. Can be. In addition, the computing device 100 calculates the health data through the blood sugar measurement value prediction sub-model, which is learned to input learning health data and outputs the learning blood sugar measurement value, thereby predicting the user's blood sugar measurement value for each time point. Information can be generated.

In addition, the computing device 100 may provide a message 703 to at least one of the medical staff terminal 300 and the user terminal 200 based on the analysis information. More specifically, the computing device 100 according to an embodiment of the present disclosure may publish the message classified by topic based on the analysis information in an application. In addition, the computing device 100 may provide the message in an application to a cluster formed inside the structure of the distributed message system. In addition, at least one of the medical staff terminal 300 and the user terminal 200 may pull the message of the computing device 100. Further, according to an embodiment, the computing device 100 may provide a notification to at least one of the medical staff terminal 300 and the user terminal 200 when the message related to a predetermined topic is issued.

In addition, the computing device 100 according to an embodiment of the present disclosure may store at least some of health data and the analysis information in the external database 400.

Accordingly, the healthcare monitoring system according to an embodiment of the present disclosure can accurately predict future blood sugar measurement values by using data measured during a user's daily life, thereby providing feedback from medical staff in advance. I can receive it. In addition, as data increases through prediction of blood sugar measurement values or learning of diabetes type classification, there is an advantage that precise prediction and classification are possible.

And, in this process, by providing a message in a publish-subscribe manner, a message can be obtained according to the performance of a user terminal or a medical staff terminal. That is, it is possible to minimize degradation of performance of each terminal while performing fast messaging according to the analysis information of the healthcare monitoring model.

5 is a block diagram of a computing device according to an embodiment of the present disclosure.

5 shows a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure can be implemented.

Although the present disclosure has been described above in general with respect to computer-executable instructions that may be executed on one or more computers, those skilled in the art appreciate that the present disclosure can be implemented in combination with other program modules and / or as a combination of hardware and software. will be.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those of ordinary skill in the art may appreciate that the methods of the present disclosure include single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like (each of which is It will be appreciated that it may be implemented with other computer system configurations, including those that may operate in conjunction with one or more associated devices.

The described embodiments of the present disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer readable media. Any computer-accessible medium can be any computer-readable medium, such computer-readable media being volatile and non-volatile media, transitory and non-transitory media, removable and non- Includes removable media. By way of example, and not limitation, computer readable media may include computer readable storage media and computer readable transmission media. Computer-readable storage media are volatile and non-volatile media, temporary and non-transitory media, removable and non-removable media implemented in any method or technology for storing information such as computer readable instructions, data structures, program modules or other data Includes media. Computer storage media includes RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, Or any other medium that can be accessed by a computer and used to store desired information.

Computer readable transmission media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and Includes all information delivery media. The term modulated data signal means a signal in which one or more of the characteristics of the signal are set or changed to encode information in the signal. By way of example, and not limitation, computer readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above-described media are also intended to be included within the scope of computer-readable transmission media.

An exemplary environment 1100 is shown that implements various aspects of the present disclosure, including a computer 1102, and the computer 1102 includes a processing device 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 connects system components, including, but not limited to, system memory 1106 to processing device 1104. The processing device 1104 can be any of a variety of commercial processors. Dual processor and other multiprocessor architectures may also be used as the processing unit 1104.

The system bus 1108 can be any of several types of bus structures that can be further interconnected to a memory bus, peripheral bus, and local bus using any of a variety of commercial bus architectures. System memory 1106 includes read-only memory (ROM) 1110 and random access memory (RAM) 1112. A basic input / output system (BIOS) is stored in a non-volatile memory 1110 such as ROM, EPROM, EEPROM, etc., and this BIOS is basic to help transfer information between components in the computer 1102 at the same time as during startup. Contains routines. The RAM 1112 may also include high-speed RAM, such as static RAM for caching data.

Computer 1102 also has an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) —this internal hard disk drive 1114 can also be configured for external use within a suitable chassis (not shown). Yes—, magnetic floppy disk drive (FDD) 1116 (eg, for reading from or writing to removable diskette 1118), and optical disk drive 1120 (eg, CD-ROM Disc 1122, or for reading from or writing to other high-capacity optical media such as DVDs). The hard disk drive 1114, the magnetic disk drive 1116, and the optical disk drive 1120 are the system bus 1108 by the hard disk drive interface 1124, the magnetic disk drive interface 1126, and the optical drive interface 1128, respectively. ). The interface 1124 for implementing an external drive includes at least one of USB (Universal Serial Bus) and IEEE 1394 interface technology, or both.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the above description of computer readable media refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those of ordinary skill in the art can use a zip drive, magnetic cassette, flash memory card, cartridge, etc. It will be appreciated that other types of media readable by a computer, etc., may also be used in the exemplary operating environment and any such media may include computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in the drive and RAM 1112, including the operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or part of the operating system, applications, modules, and / or data may also be cached in RAM 1112. It will be appreciated that the present disclosure can be implemented in various commercially available operating systems or combinations of operating systems.

The user may input commands and information to the computer 1102 through one or more wired / wireless input devices, for example, pointing devices such as a keyboard 1138 and a mouse 1140. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, etc. These and other input devices are often connected to the processing unit 1104 through an input device interface 1142 connected to the system bus 1108, but the parallel port, IEEE 1394 serial port, game port, USB port, IR interface, And other interfaces.

The monitor 1144 or other type of display device is also connected to the system bus 1108 through an interface such as a video adapter 1146. In addition to the monitor 1144, the computer generally includes other peripheral output devices (not shown) such as speakers, printers, and the like.

The computer 1102 can operate in a networked environment using logical connections to one or more remote computers, such as remote computer (s) 1148 via wired and / or wireless communication. The remote computer (s) 1148 can be a workstation, computing device computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, typically on the computer 1102. It includes many or all of the components described with respect to, but for simplicity, only the memory storage device 1150 is shown. The illustrated logical connections include wired / wireless connections to a local area network (LAN) 1152 and / or a larger network, such as a wide area network (WAN) 1154. Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to computer networks around the world, such as the Internet.

When used in a LAN networking environment, the computer 1102 is connected to the local network 1152 via a wired and / or wireless communication network interface or adapter 1156. The adapter 1156 may facilitate wired or wireless communication to the LAN 1152, which also includes a wireless access point installed therein to communicate with the wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158, connect to a communication computing device on the WAN 1154, or establish communication over the WAN 1154, such as through the Internet. Have other means. The modem 1158, which may be an internal or external and wired or wireless device, is connected to the system bus 1108 through a serial port interface 1142. In a networked environment, program modules described for the computer 1102 or portions thereof may be stored in the remote memory / storage device 1150. It will be appreciated that the illustrated network connection is exemplary and other means of establishing a communication link between computers can be used.

The computer 1102 is associated with any wireless device or object that is deployed and operates in wireless communication, such as a printer, scanner, desktop and / or portable computer, a portable data assistant (PDA), communication satellite, or wireless detectable tag. It operates to communicate with any equipment or place and telephone. This includes at least Wi-Fi and Bluetooth wireless technology. Accordingly, the communication may be a predefined structure as in a conventional network or simply ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) enables a connection to the Internet or the like without a wired connection. Wi-Fi is a wireless technology such as a cell phone that allows a computer, for example, a computer to transmit and receive data indoors and outdoors, ie anywhere within the base station's coverage area. Wi-Fi networks use a wireless technology called IEEE 802.11 (a, b, g, etc.) to provide a secure, reliable and high-speed wireless connection. Wi-Fi can be used to connect computers to each other, to the Internet, and to a wired network (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz radio bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band). have.

Those of ordinary skill in the art of the present disclosure will understand that information and signals can be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols and chips that can be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields Or particles, or any combination thereof.

Those of ordinary skill in the art of the present disclosure may use various examples of logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein in electronic hardware, (convenience For this, it will be understood that it may be implemented by various forms of program or design code (referred to herein as “software”) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art of the present disclosure may implement the functions described in various ways for each particular application, but such implementation decisions should not be interpreted as being outside the scope of the present disclosure.

Various embodiments presented herein can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term “manufactured article” includes a computer program, carrier, or media accessible from any computer-readable device. Here, the medium may include a storage medium and a transmission medium. For example, computer-readable storage media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flashes. Memory devices (eg, EEPROMs, cards, sticks, key drives, etc.), but are not limited to these. In addition, various storage media presented herein include one or more devices and / or other machine-readable media for storing information. In addition, transmission media includes, but is not limited to, wireless channels and various other media capable of carrying command (s) and / or data.

It is understood that the specific order or hierarchy of steps in the processes presented is an example of exemplary approaches. Based on design priorities, it is understood that within the scope of the present disclosure, a specific order or hierarchy of steps in the processes may be rearranged. The accompanying method claims provide elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

Descriptions of the presented embodiments are provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art of the present disclosure, and the general principles defined herein can be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure should not be limited to the embodiments presented herein, but should be interpreted in the broadest scope consistent with the principles and novel features presented herein.

100: computing device, server
110: network unit
120: processor
130: memory
200: user terminal
300: medical staff terminal
400: database
500: data processing structure
600: consumer group

Claims (10)

A computer program stored in a computer-readable storage medium, and when the computer program is executed on one or more processors, performs the following method for health care monitoring using a network function.
Obtaining health data related to a user's health from a user terminal;
Generating analysis information on the health data by calculating the health data through a health care monitoring model including one or more network functions; And
Providing a message to at least one of the medical staff terminal and the user terminal based on the analysis information;
Containing,
A computer program stored on a computer readable storage medium.
According to claim 1,
The health data,
Comprising at least one of a user's heart rate, blood pressure, blood glucose measurement, weight, height, age, gender and user information,
A computer program stored on a computer readable storage medium.
According to claim 1,
Acquiring the health data through one or more sensors disposed on at least a portion of the user's body;
Further comprising,
A computer program stored on a computer readable storage medium.
The method of claim 3,
The one or more sensors,
And at least one of a wearable device, a physical examination device, and a body measurement device authenticated in association with the user,
A computer program stored on a computer readable storage medium.
According to claim 1,
The step of obtaining health data related to the user's health from the user terminal,
Is performed in a manner of parsing the health data in the form of JSON (JavaScript Object Notation),
A computer program stored on a computer readable storage medium.
According to claim 1,
Providing a message to at least one of the medical staff terminal and the user terminal based on the analysis information,
Publishing the message classified by topic based on the analysis information in an application;
Providing the message from the application to a cluster formed inside the structure of the distributed message system; And
At least one of the medical staff terminal and the user terminal pulling the message;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 6,
At least one of the medical staff terminal and the user terminal (pulling) step of the message,
When the message related to a predetermined topic is issued, providing a notification to at least one of the medical staff terminal and the user terminal,
A computer program stored on a computer readable storage medium.
According to claim 1,
The healthcare monitoring model including the one or more network functions,
Diabetes type classification sub-models learned through learning health data labeled as the type of diabetes;
And
The diabetes type classification sub-model,
Configured to generate the analysis information that classifies the diabetes type of the user by calculating the health data,
A computer program stored on a computer readable storage medium.
According to claim 1,
The healthcare monitoring model including the one or more network functions,
A blood glucose measurement prediction sub-model, which is trained to input learning health data to output a learning blood glucose measurement;
And
The blood glucose measurement prediction sub-model,
Configured to generate the analysis information that predicts the user's blood sugar measurement value for each time point by calculating the health data,
A computer program stored on a computer readable storage medium.
According to claim 1,
Storing at least some of the health data and the analysis information in an external database;
Further comprising,
A computer program stored on a computer readable storage medium.

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