KR102405788B1 - Location information-based disease field diagnosis method and related epidemiological information management system - Google Patents

Abstract

An epidemiological information management system related to a location information-based disease on-site diagnosis method according to an embodiment is provided. The epidemiological information management system associated with the location information-based disease site diagnosis method includes: a diagnosis device including at least one sensor tag and a communication unit; a mobile terminal configured to receive a guide message from the diagnosis device and generate a diagnosis message by including location information or estimated location information of the diagnosis device in the guide message; and a server configured to communicate with the mobile terminal, receiving the diagnosis message from the mobile terminal, and configuring a disease distribution map associated with the diagnosis message based on the location information, wherein the mobile terminal interworks with the server Check epidemiological information on disease spread, and the server can store and analyze disease-related information.

Description

Location information-based disease field diagnosis method and related epidemiological information management system

The present invention relates to a location information-based epidemiological information management system, and more particularly, to a location information-based disease site diagnosis method and an epidemiological information management system related thereto.

A diagnostic device is a device that determines whether a user is infected with a specific disease through an antigen-antibody reaction from saliva, blood, excrement, or other body fluid derived from a sample collected from the user. However, there is a problem in that the existing diagnostic device that determines a user's specific disease using body fluid derived from the user's saliva, blood, excrement or other specimen cannot efficiently predict and prevent disease transmission.

In this regard, the existing disease can be established by collecting a sample and checking it in a laboratory when a disease occurs in a certain area. These existing disease diagnosis methods, especially in the case of infectious diseases, take time to collect, transport, and test samples, and there is a risk of disease spread due to movement. Accordingly, a technology for diagnosing on-site, immediately transmitting the location of disease occurrence, and establishing quarantine measures is required.

Patent Document 1: Unexamined Patent Publication No. 10-2015-0102486 (2015.09.07.) Patent Document 2: Korean Patent Publication No. 10-2017-0108583 (2017.09.27.) Patent Document 3: Japanese Patent Laid-Open No. 2002-279076 (2002.09.27.)

An object of the present invention is to provide a location information-based disease on-site diagnosis method and an epidemiological information management system related thereto.

Furthermore, an object of the present invention is to provide a smart point-of-care diagnosis system using data related to infection, disease, and epidemiological information.

According to one aspect of the present invention, there is provided an epidemiological information management system associated with a location information-based disease on-site diagnosis method. The epidemiological information management system associated with the location information-based disease site diagnosis method includes: a diagnosis device including at least one sensor tag and a communication unit; a mobile terminal configured to receive a guide message from the diagnosis device and generate a diagnosis message by including location information or estimated location information of the diagnosis device in the guide message; and a server configured to communicate with the mobile terminal, receiving the diagnosis message from the mobile terminal, and configuring a disease distribution map associated with the diagnosis message based on the location information, wherein the mobile terminal interworks with the server Check epidemiological information on disease spread, and the server stores and analyzes disease-related information and stores the information in the database.

According to an embodiment, when the mobile terminal receives the guide message from the diagnostic device through the first wireless interface, the mobile terminal may generate the diagnostic message by including its own location information in the guide message. Meanwhile, when the mobile terminal receives the guide message from the diagnostic device through the second wireless interface, the mobile terminal may generate the diagnostic message by including the estimated location information of the diagnostic device in the guide message.

According to an embodiment, when the distance between the mobile terminal and the diagnosis device is equal to or greater than a threshold, an electronic device around the diagnosis device may receive the guide message through the first wireless interface. The mobile terminal may generate the diagnosis message by receiving the guide message from the electronic device through the second wireless interface.

According to an embodiment, the diagnosis device receives the entity information on the subject to be diagnosed on the spot of the disease from an electronic device attached to or possessed by the entity, and the mobile terminal responds to the information in the guide message. The diagnosis message may be generated by including the location information, the object information, and the location information of the object.

According to an embodiment, the server constructs the disease distribution map based on the time when the diagnosis message is generated and the estimated location information of the diagnosis device, and based on a change in the disease distribution map over time, the spread of the disease The direction of travel and the rate of diffusion can be estimated.

According to an embodiment, the server configures the disease distribution map based on the time when the diagnosis message is generated, the location information, the object information, and the location information of the object, and based on the change over time of the disease distribution map Thus, the direction and rate of spread of the disease can be estimated.

According to an embodiment, the mobile terminal includes a guide message related to a first disease received through a first sensor tag of the diagnosis device and a guide message related to a second disease received through a second sensor tag of the diagnosis device Based on the , a first diagnostic message and a second diagnostic message associated with the first disease and the second disease may be generated. The server may simultaneously configure a first disease distribution map and a second disease distribution map based on the first diagnosis message and the second diagnosis message. .

According to an embodiment, the server is configured to include the first disease distribution map and the second disease based on the generation time of the first diagnosis message, the generation time of the second diagnosis message, and the estimated location information of the diagnosis device. You can configure the distribution map at the same time.

According to an embodiment, the server estimates a spreading direction and a rate of spread of the first disease based on time-dependent changes in the first disease distribution map, and based on the time-dependent change in the second disease distribution map Thus, the direction and rate of spread of the second disease can be estimated.

According to an embodiment, the server estimates the correlation between the first disease and the second disease based on the direction and rate of spread of the first disease and the direction and rate of spread of the second disease, , when the correlation between the first disease and the second disease is equal to or greater than a threshold, the spreading direction and the rate of spread of the first disease may be re-estimated in consideration of the direction and rate of spread of the second disease. In addition, the server may re-estimate the spread direction and spread rate of the second disease in consideration of the re-estimated direction and rate of spread of the first disease.

According to an embodiment, the server estimates movement path information of the entity from the time at which the diagnosis message is generated, the location information, the entity information, and the position information of the entity, and the time at which the diagnosis message is generated, the Perform real-time analysis and big data processing on the disease analysis data related to the disease distribution map based on the location information, the individual information, the location information of the individual, the movement path information, and the public environment data associated with the entity, and the Map-based visualization of analyzed and processed disease analysis data can be performed.

According to an embodiment, the server generates a learning model and learning result data associated with the disease analysis data, and accumulates the prediction data associated with the disease propagation prediction using the learning result data and machine learning data set, A disease propagation prediction related to the spread of the disease may be performed using the learning model and the prediction data, and a visualization may be performed on the performed disease propagation prediction result.

There is provided a location information-based disease site diagnosis method performed in an epidemiological information management system according to another aspect of the present invention. The location information-based disease site diagnosis method is performed by a server and includes: a diagnosis message receiving process of receiving a diagnosis message from a mobile terminal; a disease distribution map construction process of constructing a disease distribution map associated with a diagnosis message based on location information; and a process of estimating the direction and rate of disease spread for estimating the direction and rate of spread of the disease based on time-dependent changes in the distribution of the disease.

According to an embodiment, the location information-based on-site diagnosis method includes a correlation estimation process of estimating the correlation between the first and second diseases based on the spreading direction and the spreading rate of the first and second diseases; and if the correlation between the first and second disease is greater than or equal to a threshold, the disease spread direction and rate re-estimation process of reestimating the direction and rate of spread of the first disease in consideration of the direction and rate of spread of the second disease. can do. In the process of reestimating the direction and rate of disease spread, the direction and rate of spread of the second disease may be re-estimated in consideration of the re-estimated direction and rate of spread of the first disease.

According to an embodiment, the location information-based disease site diagnosis method estimates movement path information of an entity from the time when a diagnosis message is generated, location information, entity information, and location information of the entity, and includes the movement path information and public information associated with the entity. a disease real-time analysis and big data estimation process for performing real-time analysis and big data processing on disease analysis data related to a disease distribution map, further based on the environmental data; And it may further include a map-based visualization process for performing map-based visualization of the analyzed and processed disease analysis data.

According to an embodiment, the location information-based disease site diagnosis method generates a learning model and learning result data related to disease analysis data, and accumulates prediction data related to disease propagation prediction using the learning result data and machine learning data set. disease transmission prediction data accumulation process; and a disease propagation prediction and visualization process of performing disease propagation prediction related to disease propagation using the learning model and prediction data, and performing visualization on the performed disease propagation prediction result.

There is an advantage in that a system for preventing the spread of infection for an infectious disease can be built through the location information-based disease on-site diagnosis method and the related epidemiological information management system according to an embodiment of the present invention.

According to the present invention, the mobile terminal can check epidemiological information on the spread of a disease by interworking with the server, and the server can store and analyze disease-related information.

According to the present invention, the server can store and analyze real-time infection information, store and analyze epidemiologic information using location information, and store and analyze infection information and metadata according to epidemiological information.

According to the present invention, the server can operate a fast and efficient infection and disease management system using big data and artificial intelligence systems.

1 is an exemplary diagram illustrating a point-of-care diagnostic device for disease including a test module according to an embodiment of the present invention.
2A and 2B are exemplary views showing an inspection module according to different embodiments of the present invention.
3 is a diagram illustrating a body fluid pre-processing module derived from saliva, blood, excrement or other specimens combined with a terminal equipped with a test module according to an embodiment of the present invention.
4 is an exemplary view for explaining a process in which the test module is inserted into the body fluid pretreatment module derived from saliva, blood, excrement or other specimens.
5 is an exemplary diagram illustrating a state in which a point-of-care diagnosis device including a test module communicates with a mobile terminal according to an embodiment of the present invention.
6 is a diagram illustrating a configuration of an epidemiologic information management system in which a diagnostic device is connected to a server through a mobile terminal and includes the server according to an embodiment of the present invention.
7 shows the configuration of interworking with the system provider's server, hospital server, and research institute server associated with the smart point-of-care system according to the present invention.
8 shows a conceptual diagram of an epidemiological information management system implemented as an infection transmission prevention system using big data and artificial intelligence according to the present invention.
9 shows a detailed configuration of an infection transmission prevention system using big data and artificial intelligence according to the present invention.
10 is a flowchart of a location information-based disease site diagnosis method performed in the epidemiological information management system according to the present invention.
11 is a flowchart of a location information-based disease site diagnosis method performed based on big data and artificial intelligence according to another embodiment of the present invention.

Since the present invention can apply various transformations and can have various embodiments, specific embodiments are illustrated and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

The terms used in the present invention are only used to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present invention, terms such as 'comprising' or 'having' are intended to designate that the features, numbers, steps, operations, components, parts, or combinations thereof described in the specification exist, and one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In this case, it should be noted that in the accompanying drawings, the same components are denoted by the same reference numerals as much as possible. In addition, detailed descriptions of well-known functions and configurations that may obscure the gist of the present invention will be omitted. For the same reason, some components are exaggerated, omitted, or schematically illustrated in the accompanying drawings.

1 is an exemplary diagram illustrating a device for point-of-care diagnosis and diagnosis of diseases including a test module according to an embodiment of the present invention. 2A and 2B are exemplary views showing an inspection module according to the present invention. 3 is a diagram illustrating a body fluid pre-processing module derived from saliva, blood, excrement or other specimens combined with a terminal equipped with a test module according to an embodiment of the present invention. 4 is an exemplary view for explaining a process in which the test module is inserted into the body fluid pretreatment module derived from saliva, blood, excrement or other specimens.

A point-of-care diagnostic device (A) including a test module according to an aspect of the present invention includes a diagnostic device 100, a test module 200, and a body fluid pretreatment module 300 derived from saliva, blood, excrement or other specimens. ) may be included.

A connector 150 is formed on one side of the diagnostic device 100 . Specifically, as shown in FIG. 1 , the connector 150 may be formed on the side of the diagnostic device 100 , and the connector 150 may be concavely formed in the body of the diagnostic device 100 . The test module 200 may be inserted into the connector 150 .

The point-of-care diagnostic device A for disease including the test module according to an embodiment of the present invention includes the test module 200 detachably attached to the diagnostic device 100, so that if the test module 200 is replaced, various There is an advantage that an antigen-antibody reaction is possible. Meanwhile, in the present invention, other tests and diagnostic methods other than the antigen-antibody reaction may be performed using the test module 200 . As an example, a PCR (Polymerase Chain Reaction) test using the test module 200 may be performed.

The connector 150 may be formed on one long side of the diagnostic device 100 . Specifically, the connector 150 may be formed on a side surface of the diagnostic device 100 in the thickness direction of the diagnostic device 100 . As shown in the figure, two or more connectors 150 may be formed to be spaced apart from each other. Also, unlike this, the number of connectors 150 may be three or more. This has the advantage of being able to quickly process a large amount of inspection.

The diagnostic device 100 further includes a speaker unit 130 disposed on the front or rear side, a battery cover 110 covering a battery inserted into the diagnostic device 100 , and an operation unit 120 for operating the diagnostic device 100 . may include

Specifically, as shown in FIG. 1 , the speaker unit 130 may be disposed on one side of the front side of the diagnostic device 100 . The speaker unit 130 may recognize an external sound or may emit a sound including an inspection result.

Also, the diagnostic device 100 may be disposed on a side surface of the diagnostic device 100 and include a manipulation unit 120 for operating the diagnostic device 100 . The operation unit 120 manipulates the power of the diagnostic device 100 or inserts the test module 200 and then contacts the sensor tag 210 disposed at the other end of the test module 200 - a result of the antigen-antibody reaction It is possible to perform operations such as deriving Meanwhile, the manipulation unit 120 may be formed on the front or rear surface of the diagnostic device 100 instead of the side.

On the other hand, the diagnostic device 100 may be formed with a ring groove 140 in which a ring or the like can be hung. There is an advantage in that the risk of loss or damage can be reduced by easily carrying the diagnostic device 100 through the ring groove 140 .

The test module 200 includes a sensor tag 210 having one end formed to be insertable into the connector 150 of the diagnostic device 100 and the other end in contact with saliva to enable antigen-antibody reaction. In addition, the test module 200 may include a long test rod 205 , and at one end of the test rod 205 , a terminal 220 that is electrically coupled to the diagnostic device 100 may be formed. .

Specifically, as shown in FIGS. 2A and 2B , a terminal 220 may be formed at one end of the test rod 205 of the test module 200 . The terminals 220 may be formed on both surfaces of the test rod 205 . The terminal 220 may transmit the result of the antigen-antibody reaction reacted from the sensor tags 210: 210a and 210b to the diagnostic device 100 .

A sensor tag 210 may be disposed on at least one surface of the other end of the test rod 205 . For example, as shown in FIG. 1 , in the sensor tag 210 , a front sensor tag 210a and a rear sensor tag 210b may be disposed on both surfaces of the test rod 205 .

In this case, the sensor tags 210 disposed on both sides of the test rod 205 may cause antigen-antibody reactions with respect to different antibodies, respectively. Meanwhile, the sensor tag 210 may be disposed only on one surface of the test rod 205 as shown in FIGS. 2A and 2B .

When the sensor tag 210 is disposed on both sides of the test rod 205, since antigen-antibody reactions can be performed against different antigens, there is an advantage in that results for various antigens can be quickly derived.

As described above, FIG. 3 is a diagram in which a body fluid pre-processing module derived from saliva, blood, excrement or other specimen is combined with a terminal having a test module according to an embodiment of the present invention. 4 is an exemplary view for explaining a process in which the test module is inserted into the body fluid pretreatment module derived from saliva, blood, excrement or other specimens.

Referring to FIG. 3 , it shows a configuration in which a body fluid pretreatment module derived from saliva, blood, excrement or other specimen (hereinafter, referred to as saliva pretreatment module 300 ) is combined with a diagnostic device 100 having a test module 200 . . Meanwhile, in the present invention, other tests and diagnostic methods other than the antigen-antibody reaction may be performed using the test module 200 . As an example, a PCR (Polymerase Chain Reaction) test using the test module 200 may be performed.

3 and 4 , the saliva pretreatment module 300 may include a tag insertion unit 312 in the main body 310 so that a plurality of sensor tags 210a and 210b may be inserted thereinto. Meanwhile, a bodily fluid accommodating part may be provided inside the saliva pretreatment module 300 to accommodate body fluids derived from saliva, blood, excrement, or other specimens. Some regions of the plurality of sensor tags 210a and 210b inserted into the tag insertion unit 312 may be configured to come into contact with body fluids derived from saliva, blood, excrement, or other specimens provided in the body fluid receiving unit. Accordingly, there is an advantage that various antigen-antibody reactions are possible through the plurality of sensor tags 210a and 210b inserted into the tag insertion unit.

Referring to FIGS. 3 and 4 , the saliva pretreatment module 300 has a space for accommodating saliva inside the main body 310 , and a tag insertion unit 312 in which a sensor tag 210 is insertable on one side. ) is formed. In addition, the saliva pretreatment module 300 includes a body 310 having a space for accommodating saliva therein, and an opening 312 into which at least a portion of the test module 200 is inserted is formed in the body 310 . can be Accordingly, the test module 200 inserted into the saliva pretreatment module 300 is not arbitrarily separated from the main body 310 of the saliva pretreatment module 300, and the coupled state can be stably maintained.

5 is an exemplary diagram illustrating a state in which a point-of-care diagnosis device including a test module communicates with a mobile terminal according to an embodiment of the present invention. Referring to FIG. 5 , the on-site diagnosis and diagnosis apparatus (A, 100) including the examination module according to an embodiment of the present invention communicates with the mobile terminal 20, which is an external device, through communication with the sensor of the examination module 200. The tag 210 may further include a communication unit 160 that transmits the result of the antigen-antibody reaction.

The communication unit 160 may transmit a result value detected by the sensor tag 210 to the external device 20 . When a specific antigen-antibody reaction does not occur in the sensor tag 210 , a first guide message 21 indicating that no virus/bacterium is detected may be transmitted to the mobile terminal 20 .

In addition, when a specific antigen-antibody reaction occurs in the sensor tag 210 , a second guide message 22 indicating that a virus/bacterium has been detected may be transmitted to the mobile terminal 20 . At the same time, a third guide message 23 for an additional operation may be transmitted.

Through this, the user can more quickly receive the result transmitted to his/her terminal (external device 20) through the point-of-care diagnosis device A including the test module. Meanwhile, FIG. 6 shows the configuration of an epidemiologic information management system including a diagnosis device connected to a server through a mobile terminal and the server according to an embodiment of the present invention. The epidemiological information management system shown in FIG. 7 may be referred to as a smart on-site diagnosis system.

Meanwhile, a system provider that provides an epidemiological information management system through a diagnostic device through such a smart on-site diagnosis system can cooperate with hospitals (medical institutions) and research institutes (schools). In this regard, FIG. 7 shows the configuration of interworking with the server of the system provider, the hospital server, and the research institute server associated with the smart point-of-care system according to the present invention.

The smart point-of-care diagnosis system according to the present invention is a system that enables epidemiological disease management from an administrator by transmitting location information while diagnosing an infectious disease. This smart point-of-care diagnosis system can be used for early diagnosis of various legal livestock infectious diseases, and it is possible to detect two or more infectious diseases at the same time.

Referring to FIG. 6 , the epidemiological information management system associated with the location information-based on-site diagnosis method can be configured to include a diagnosis device 100 , a mobile terminal 20 , and a server 1000 .

Meanwhile, referring to FIG. 7 , the epidemiological information management system, that is, the smart on-site diagnosis system, may be configured to interwork with the system provider server 1000 , the hospital server 1100 , and the research institute server 1200 . Through data acquired through the hospital server 1100 and the research institute server 1200 in addition to the data accumulated by the server 1000 of the system provider itself, a non-invasive smart diagnosis system for infectious diseases can be implemented. In order to implement such a smart diagnosis system for infectious diseases, it is necessary to first check whether the disease is infected or not.

In relation to the confirmation of whether or not a disease is infected, the mobile terminal 20 may determine whether or not a disease is infected through sensor detection for disease diagnosis using the diagnostic device 100 . Meanwhile, the mobile terminal 20 may record setting information such as personal information/location information/environment information. In addition, the mobile terminal 20 may check the epidemiological information on the spread of the disease by interworking with the server 1000 . The server 1000 may store and analyze disease-related information. In this regard, the server 1000 may store and analyze real-time infection information, and store and analyze epidemiologic information using location information. Also, the server 1000 may store and analyze metadata according to infection information and epidemiological information.

In this regard, the diagnosis device 20 may automatically transmit infection, disease, epidemiological information and related data to the server 1000 in real time through the mobile terminal 20 . Meanwhile, the server 1000 may operate a fast and efficient infection and disease management system using big data and artificial intelligence systems.

To this end, the diagnostic device 100 may be configured to include at least one sensor tag 210 and a communication unit 160 . The mobile terminal 20 may be configured to receive a guide message from the diagnostic device 100 and generate a diagnostic message by including its own location information or estimated location information of the diagnostic device 100 in the guide message.

The server 1000 may be configured to communicate with the mobile terminal 20 and to receive a diagnostic message from the mobile terminal 20 . The server 1000 may configure a disease distribution map associated with the diagnosis message based on location information. In this regard, the location information may be at least one of location information of the mobile terminal 20 , estimated location information of the diagnostic device 100 , and location information of an object to be diagnosed on the spot of a disease. In this case, the subject subject to on-site diagnosis may be a human, livestock, or animal.

Meanwhile, the communication connection between the diagnostic device 100 and the mobile terminal 20 according to the present invention may be performed in different ways depending on the case of a short distance and a case of a long distance. In this regard, when the mobile terminal 20 receives the guide message from the diagnostic device 100 through the first wireless interface, the mobile terminal 20 may generate the diagnostic message by including its own location information in the guide message. In this case, the distance between the diagnostic device 100 and the mobile terminal 20 is short-range and a wireless interface such as blue-tooth low energy (BLE) may be used, but is not limited thereto.

Meanwhile, when the mobile terminal 20 receives the guide message from the diagnostic device 100 through the second wireless interface, the mobile terminal 20 may generate the diagnostic message by including the estimated location information of the mobile terminal 20 in the guide message. In this case, the distance between the diagnostic device 100 and the mobile terminal 20 is a long distance and a wireless interface such as WiFi may be used, but is not limited thereto. In this regard, the place where the on-site diagnosis of disease is performed may be a place where a sample is collected, and may be a place where the diagnostic device 100 is disposed. Meanwhile, the location of an electronic device attached to or possessed by the object may be estimated in order to estimate the location of the object subject to on-site diagnosis of disease.

The diagnosis device 100 may not support each other's wireless interfaces according to short-distance/long-distance. In this case, if the distance between the mobile terminal 20 and the diagnostic device 100 is equal to or greater than the threshold, the electronic device around the diagnostic device 100 may receive a guide message from the diagnostic device 100 through the first wireless interface. . Here, the first wireless interface may be a wireless interface such as BLE, but is not limited thereto. Accordingly, the mobile diagnostic device 100 may receive the guide message from the electronic device through the second wireless interface and generate the diagnostic message. Here, the second wireless interface may be a wireless interface such as WiFi, but is not limited thereto.

As described above, the place where the on-site diagnosis is performed may be a place where the sample is collected, and may be a place where the diagnostic device 100 is disposed or a place where an object to be diagnosed on the spot of a disease is located. Meanwhile, the location of an electronic device attached to or possessed by the object may be estimated in order to estimate the location of the object subject to on-site diagnosis of disease. In this regard, the diagnosis device 20 may receive individual information about an object to be diagnosed on the spot of a disease from an electronic device attached to the object or possessed by the object. In this case, the mobile terminal 20 may generate the diagnosis message by including its own location information (or estimated location information of the diagnostic device), object information, and location information of the object in the guide message.

The server 1000 may configure the disease distribution map based on the time when the diagnosis message is generated and the estimated location information of the diagnosis device 100 . The server 1000 may estimate the progression direction and the rate of spread of the disease based on time-dependent changes in the disease distribution map.

According to another embodiment, the server 1000 may configure a disease distribution map by further considering the entity information and the position information of the entity, and estimate the direction and rate of spread of the disease. The server 1000 may configure a disease distribution map based on the time when the diagnosis message is generated, location information, object information, and location information of the object. In addition, the server 1000 may estimate the progression direction and the rate of spread of the disease based on time-dependent changes in the disease distribution map. In this regard, the epidemiological information management system related to the location information-based disease on-site diagnosis method according to the present invention can construct an infection transmission prevention system using big data and artificial intelligence. In relation to the above, FIG. 8 shows a conceptual diagram of an epidemiological information management system implemented as an infection transmission prevention system using big data and artificial intelligence according to the present invention.

5 to 8 , an epidemiological information management system implemented as an infection propagation prevention system can perform 1) location information analysis, 2) recommendation/prediction of a safe section for infection propagation, and 3) user service provision. .

1) In relation to location information analysis, location information analysis of an infection propagating entity, environment information analysis, movement section path analysis, and infection frequency section analysis can be performed.

2) With respect to recommendation/prediction of safe section for infection transmission, it is possible to perform safe section recommendation function, risk section personalization recommendation, and big data/artificial intelligence analysis.

3) In relation to the provision of user services, notification of surrounding infected areas, prediction of safe sections, and user notification services can be performed. In addition, it is possible to perform risk section prediction and related organization notification service.

5 to 8 , through an epidemiological information management system implemented as an infection propagation prevention system, an algorithm development and advancement of an infectious disease prevention system through artificial intelligence analysis can be performed. In this regard, it is possible to perform location and path analysis when diagnosing an infection based on a discrete probability distribution algorithm. In addition, it is possible to provide a safe area and safe section prediction service by applying an artificial intelligence recommendation algorithm. Meanwhile, the configuration of the infection transmission prevention system using big data and artificial intelligence can be structured as follows. In this regard, FIG. 9 shows a detailed configuration of an infection propagation prevention system using big data and artificial intelligence according to the present invention.

5 to 9 , in relation to the epidemiological information management system implemented as an infection propagation prevention system, the server 1000 determines the object from the time when the diagnosis message is generated, the location information, the object information, and the location information of the object. can estimate the movement path information of The server 1000 performs real-time analysis and big data analysis on disease analysis data related to disease distribution based on the time at which the diagnosis message is generated, location information, object information, location information of the object, movement route information, and public environment data associated with the object. Data processing can be performed. In this case, the public environment data may be obtained by the server 1000 in conjunction with the hospital server 1100 and the research institute server 1200 of FIG. 7 , but is not limited thereto.

On the other hand, in the case of a system for preventing the spread of infection to humans, individual information may be replaced with personal information. In addition, even in the case of a system for preventing the spread of infection to livestock and animals other than humans, the personal information may be personal information for managing the individual in addition to the individual information. Alternatively, the personal information may be personal information about an individual who manages an object that may be an infection target or an individual who may be an infection target. Also, the server 1000 may perform map-based visualization on the analyzed and processed disease analysis data.

On the other hand, the server 1000 of the epidemiological information management system implemented as an infection transmission prevention system may perform disease transmission prediction with a learning-based platform such as artificial intelligence. To this end, the server 1000 may generate a learning model and learning result data related to the disease analysis data, and accumulate the prediction data related to the disease propagation prediction using the learning result data and the machine learning data set. In addition, the server 1000 may perform disease propagation prediction related to disease spread using the learning model and prediction data, and may perform visualization on the performed disease propagation prediction result.

In this regard, referring to FIG. 9 , the server 1000 may be configured with a big data management and monitoring module and an infectious disease propagation prediction module. The big data management and monitoring module and the infectious disease propagation prediction module may be configured by a server or a server and a mobile terminal.

The data collection, analysis and visualization process performed in the big data management and monitoring module is as follows. In this regard, in addition to the diagnostic message, a plurality of data related to the generation of the learning result data and the prediction data may be utilized. Considering the sources of the plurality of data, learning result data and prediction data can be generated by using public environment data, personal information, location information, movement route information, personal detailed information, and diagnostic institution information.

The raw data corresponding to the plurality of data collected in this way may be subjected to a data storage operation for real time data analysis and statistical analysis. Meanwhile, analysis result data by real-time data analysis may be stored in a data storage unit and transmitted for batch analysis. On the other hand, data stored in the data storage unit and batch-analyzed data can be utilized for big data visualization.

Meanwhile, in the infectious disease propagation prediction module, the learning model and the learning result data learned through the learning module are stored in the data storage unit together with the machine learning dataset. It can be delivered to the prediction service model and prediction module so that the learning result data can be applied to the prediction service. In addition, the learning result data may be transferred to the visualization module to visualize the learning result. Meanwhile, prediction visualization data to which the prediction service is applied may be transmitted to the visualization module.

Meanwhile, the epidemiological information management system according to the present invention, that is, the smart on-site diagnosis system, can diagnose one or more diseases. In this regard, African swine fever, avian influenza, foot-and-mouth disease, cattle disease, swine fever, swine blister disease, etc. can be considered as the type 1 infectious diseases related to smart point-of-care diagnosis. First, African swine fever is a kind of ASF DNA virus and is characterized by fever, bleeding, lethargy, and 100% mortality, and there is no vaccine treatment. On the other hand, avian influenza is a kind of AIV RNA virus, which is characterized by H1-H16, N1-N9 mutants, cyanosis of the face, and facial edema, and there is no preventive drug. On the other hand, foot-and-mouth disease virus is a kind of virus of the genus Apthovirus of the family Picornaviridae, and has 7 serotypes (A, O, C, Asia1, SAT1, SAT2, SAT3), body temperature increase in cows, pigs, sheep, goats, etc. It is characterized by blistering, myocarditis.

With respect to the smart point-of-care disease described above, African swine fever and foot-and-mouth disease can be simultaneously detected using samples extracted from pigs. In this regard, referring to FIG. 5 , a guide message 21 related to the first disease may be transmitted through the first sensor tag 210a of the diagnostic device 100 . Also, the guide message 22 associated with the second disease may be transmitted through the second sensor tag 210b of the diagnostic device 20 . In this regard, the mobile terminal 20 receives the guide message 21 associated with the first disease through the first sensor tag 210a of the diagnosis device 100 and the second sensor tag 210b of the diagnosis device 20 . ), based on the guide message 22 associated with the second disease, a first diagnosis message and a second diagnosis message associated with the first disease and the second disease may be generated. Accordingly, the server 1000 may simultaneously configure the first disease distribution map and the second disease distribution map based on the first diagnostic message and the second diagnostic message. For example, the server 1000 may simultaneously configure a first disease distribution map for African swine fever and a second disease distribution map for foot-and-mouth disease.

On the other hand, the epidemiological information management system according to the present invention, that is, the smart point-of-care diagnosis system can be used for examination and diagnosis of other diseases in addition to African swine fever, avian influenza, foot-and-mouth disease, cattle disease, swine fever, swine blister disease, etc. as described above. have. In this regard, the epidemiological information management system according to the present invention, that is, the smart point-of-care diagnosis system, can be applied to human disease diagnosis in addition to animal disease diagnosis. Therefore, the epidemiological information management system according to the present invention, ie, the smart point-of-care diagnosis system, can be applied to any disease test and diagnosis that can be tested and diagnosed through antigen-antibody reaction or PCR test using the test module.

Meanwhile, the results of inspection and analysis through the first sensor tag 210a and the second sensor tag 210b are not limited to diseases of the same individual. For example, examination and analysis are performed for different diseases for different individuals, and accordingly, the server 1000 may configure a disease distribution map for different diseases and perform disease spread trend/rate and prediction. .

The server 1000 may simultaneously configure the first disease distribution map and the second disease distribution map based on the time when the first diagnostic message is generated, the time when the second diagnostic message is generated, and the estimated location information of the diagnostic device 100 . have.

Meanwhile, the server 1000 may perform analysis on other diseases in consideration of the associated disease distribution and disease spread trend/rate. To this end, the server 1000 may estimate the spreading direction and the spreading speed for each disease. That is, based on the time-dependent change of the distribution of the first disease, the spreading direction and the spreading rate of the first disease may be estimated. The server 1000 may estimate the spreading direction and the spreading speed of the second disease based on the change over time of the distribution of the second disease.

Next, the server 1000 may estimate the correlation between the first disease and the second disease based on the direction and rate of spread of the first disease and the direction and rate of spread of the second disease. Meanwhile, if the correlation between the first disease and the second disease is greater than or equal to the threshold, the server 1000 may re-estimate the direction and rate of spread of the first disease in consideration of the direction and rate of spread of the second disease. . In addition, the server 1000 may re-estimate the spread direction and spread rate of the second disease in consideration of the re-estimated direction and rate of spread of the first disease.

In the above, the epidemiological information management system for performing the location information-based disease on-site diagnosis method according to the present invention has been described. Hereinafter, a location information-based disease site diagnosis method according to another aspect of the present invention will be described. In this regard, FIG. 10 is a flowchart of a location information-based disease site diagnosis method performed in the epidemiological information management system according to the present invention. 5 to 10 , the disease site diagnosis method may be performed by the server 1000 . Referring to FIG. 10 , the disease site diagnosis method may include a process of receiving a diagnosis message ( S100 ), a process of constructing a disease distribution map ( S200 ), and a process of estimating the direction and speed of disease spread ( S300 ). Meanwhile, the disease site diagnosis method may further include a disease correlation estimation process ( S400 ) and a disease spread direction and speed re-estimation process ( S500 ).

Meanwhile, the location information-based disease site diagnosis method according to another embodiment of the present invention may be performed based on big data and artificial intelligence. 11 is a flowchart of a location information-based disease site diagnosis method performed based on big data and artificial intelligence according to another embodiment of the present invention. 5 to 11 , as described above, the big data and AI-based disease site diagnosis method includes the process of receiving a diagnosis message (S100), the process of constructing a disease distribution map (S200), and the process of estimating the direction and speed of disease spread (S300). ) may be included. Meanwhile, the big data and AI-based disease field diagnosis method may further include a disease real-time analysis and big data estimation process (S400a) and a map-based visualization process (S500a). In addition, the big data and artificial intelligence-based disease field diagnosis method may further include a disease transmission prediction data accumulation process (S600a) and a disease spread prediction and visualization process (S700a).

Referring to FIG. 10 , a diagnostic message may be received from the mobile terminal in the process of receiving the diagnostic message ( S100 ). Meanwhile, the diagnostic message may be generated by including location information of the mobile terminal and the diagnostic device entity in the guidance message. In the disease distribution configuration process ( S200 ), a disease distribution map associated with the diagnosis message may be configured based on location information. In this case, the disease distribution map may be constructed based on the time when the diagnosis message is generated and the estimated location information of the diagnosis device. Alternatively, the disease distribution map may be configured based on the time at which the diagnosis message is generated, location information, object information, and location information of the object. Alternatively, the first and second disease distribution maps may be configured based on the time and estimated location information at which the first and second diagnostic messages generated based on the guide message received through the first and second sensor tags are generated. have.

In the disease spread direction and velocity estimation process ( S300 ), the disease spread direction and spread rate may be estimated based on time-dependent changes in the disease distribution map. In this case, the spreading direction and spreading rate of the first and second diseases may be estimated based on changes in the distribution of the first and second diseases with time.

In the association estimation process ( S400 ), the association between the first and second diseases may be estimated based on the direction and rate of spread of the first and second diseases. In the process of re-estimating the direction and rate of disease spread ( S500 ), if the correlation between the first and second disease is greater than or equal to the threshold, the direction and rate of spread of the first disease are calculated in consideration of the direction and rate of spread of the second disease. can be re-estimated. In addition, the diffusion direction and the rate of spread of the second disease may be re-estimated in consideration of the re-estimated direction and rate of spread of the first disease.

Referring to FIG. 11 , in the real-time disease analysis and big data estimation process ( S400a ), movement path information of the entity may be estimated from the time at which a diagnosis message is generated, location information, object information, and location information of the object. In addition, real-time analysis and big data processing of disease analysis data related to the disease distribution map is performed based on the time when the diagnostic message is generated, location information, object information, location information of the object, movement route information, and public environment data associated with the object. can be done In the map-based visualization process ( S500a ), map-based visualization may be performed on the analyzed and processed disease analysis data.

In the disease transmission prediction data accumulation process ( S600a ), a learning model and learning result data related to the disease analysis data may be generated, and prediction data related to the disease transmission prediction may be accumulated using the learning result data and the machine learning data set. Meanwhile, in the disease propagation prediction and visualization process ( S700a ), a disease propagation prediction related to the spread of the disease may be performed using the learning model and the prediction data, and visualization may be performed on the performed disease propagation prediction result.

Meanwhile, the location information-based disease site diagnosis method according to the present invention and the related epidemiological information management system and the existing livestock infectious disease inspection system are compared as follows. Diagnosis of existing diseases is carried out centered on a specific laboratory, which is expensive and takes a relatively long time. In addition, there is a problem in that it takes time to confirm the test result, so that the transmission spreads. On the other hand, a potential market for on-site diagnostic devices can be developed through the location information-based disease on-site diagnosis method and the related epidemiological information management system according to the present invention. Specifically, 1) a field diagnosis system for livestock infectious diseases can be established. In this regard, by establishing a cooperative system with domestic and foreign companies, certification and public institution procurement are possible. In addition, 2) it is possible to make regular infectious disease diagnosis mandatory for livestock farms and companies. In this regard, it is possible to promote B2G marketing and legalization. In addition, 3) it is possible to establish a management system that can be controlled by computers and networks, and create an industry that provides services for other human and material resources. Accordingly, it is possible to create a next-generation disease management system.

There is an advantage in that a system for preventing the spread of infection for an infectious disease can be built through the location information-based disease on-site diagnosis method and the related epidemiological information management system according to an embodiment of the present invention.

According to the present invention, the mobile terminal can check epidemiological information on the spread of a disease by interworking with the server, and the server can store and analyze disease-related information.

According to the present invention, the server can store and analyze real-time infection information, store and analyze epidemiologic information using location information, and store and analyze infection information and metadata according to epidemiological information.

According to the present invention, the server can operate a fast and efficient infection and disease management system using big data and artificial intelligence systems.

As described above, the epidemiological information management system according to the present invention, that is, the smart point-of-care diagnosis system, is used for the examination and diagnosis of other diseases in addition to African swine fever, avian influenza, foot-and-mouth disease, cattle disease, swine fever, swine blister disease, etc. as described above. can be utilized. In this regard, the epidemiological information management system according to the present invention, that is, the smart point-of-care diagnosis system, can be applied to human disease diagnosis in addition to animal disease diagnosis. Therefore, the epidemiological information management system according to the present invention, ie, the smart point-of-care diagnosis system, can be applied to any disease test and diagnosis that can be tested and diagnosed through antigen-antibody reaction or PCR test using the test module.

In the above, an embodiment of the present invention has been described, but those of ordinary skill in the art can add, change, delete or add components within the scope that does not depart from the spirit of the present invention described in the claims. It will be possible to variously modify and change the present invention by, etc., which will also be included within the scope of the present invention.

Claims (15)

In the epidemiological information management system related to the location information-based disease on-site diagnosis method,
a diagnostic device including at least one sensor tag and a communication unit;
a mobile terminal configured to receive a guide message from the diagnosis device and generate a diagnosis message by including location information or estimated location information of the diagnosis device in the guide message; and
a server configured to communicate with the mobile terminal, receiving the diagnostic message from the mobile terminal, and configuring a disease distribution map associated with the diagnostic message based on the location information,
The mobile terminal,
A second sensor tag according to a first guide message related to a first disease received through a first sensor tag according to a first response related to the first disease of the diagnostic device and a second response related to a second disease of the diagnostic device generating a first diagnosis message and a second diagnosis message related to the first disease and the second disease,
The server is
simultaneously configuring a first disease distribution map and a second disease distribution map based on the first diagnostic message and the second diagnostic message;
simultaneously configuring the first disease distribution map and the second disease distribution map based on the generation time of the first diagnosis message, the generation time of the second diagnosis message, and the estimated location information of the diagnostic device;
Generating a learning model and learning result data associated with the disease analysis data, and using the learning result data and a machine learning data set to predict data associated with disease propagation prediction related to the propagation of a disease including the first disease and the second disease accumulate,
Performing the disease propagation prediction including the first disease and the second disease using the learning model and the prediction data, predicting a safe section and a risk section for the spread of the disease,
A dynamics information management system that delivers the learning result data and the prediction data to a visualization module. According to claim 1,
The mobile terminal,
When the guide message is received from the diagnostic device through the first wireless interface, the diagnostic message is generated by including the location information of the user in the guide message;
and when the guide message is received from the diagnostic device through a second wireless interface, the guide message includes estimated location information of the diagnostic device to generate the diagnostic message. 3. The method of claim 2,
When the distance between the mobile terminal and the diagnostic device is greater than or equal to a threshold, an electronic device around the diagnostic device receives the guide message through the first wireless interface;
The mobile terminal receives the guide message from the electronic device through the second air interface, and generates the diagnosis message. 3. The method of claim 2,
The diagnosis device receives individual information on the subject to be diagnosed on the spot of the disease from an electronic device attached to the subject or possessed by the subject;
The mobile terminal generates the diagnosis message by including the location information, the object information, and the location information of the object in the guide message. 3. The method of claim 2,
The server is
constructing the disease distribution map based on the time when the diagnosis message is generated and the estimated location information of the diagnosis device;
An epidemiological information management system for estimating the direction and rate of spread of the disease based on time-dependent changes in the disease distribution map. 5. The method of claim 4,
The server is
constructing the disease distribution map based on the time when the diagnosis message is generated, the location information, the object information, and the location information of the object;
An epidemiological information management system for estimating the direction and rate of spread of the disease based on time-dependent changes in the disease distribution map. delete delete According to claim 1,
The server is
estimating the direction and rate of spread of the first disease based on time-dependent changes in the distribution of the first disease,
An epidemiologic information management system for estimating a spreading direction and a rate of spread of the second disease based on a change over time of the second disease distribution map. 10. The method of claim 9,
The server is
estimating a correlation between the first disease and the second disease based on the direction and rate of spread of the first disease and the direction and rate of spread of the second disease;
If the correlation between the first disease and the second disease is greater than or equal to a threshold, re-estimating the direction and rate of spread of the first disease in consideration of the direction and rate of spread of the second disease;
An epidemiological information management system for re-estimating the spread direction and rate of spread of the second disease in consideration of the re-estimated direction and rate of spread of the first disease. 5. The method of claim 4,
The server is
estimating movement path information of the object from the time when the diagnosis message is generated, the location information, the object information, and the location information of the object;
Based on the generation time of the diagnosis message, the location information, the object information, the location information of the object, the movement route information, and public environment data associated with the object, real-time analysis of the disease analysis data associated with the disease distribution map and big data processing;
An epidemiological information management system for performing map-based visualization of the analyzed and processed disease analysis data. delete In the location information-based disease site diagnosis method, the method is performed by a server, the method comprising:
a diagnostic message receiving process of receiving a diagnostic message from the mobile terminal; and
and a disease distribution map configuration process of configuring a disease distribution map associated with the diagnosis message based on location information,
The diagnosis message is generated by the mobile terminal receiving a guide message from a diagnostic device, including its own location information or estimated location information of the diagnostic device in the guide message, and the mobile terminal determines the first disease of the diagnostic device A second disease received through a second sensor tag according to a first guide message related to a first disease received through a first sensor tag according to a first response associated with a first response and a second response related to a second disease of the diagnostic device generating a first diagnostic message and a second diagnostic message associated with the first disease and the second disease based on a second guide message associated with
In the process of constructing the disease distribution map, a disease distribution map is constructed based on the time at which the diagnosis message is generated, location information, object information of an object to be diagnosed, and location information of the object,
In the process of constructing the disease distribution map, the server simultaneously configures a first disease distribution map and a second disease distribution map based on the first diagnostic message and the second diagnostic message,
simultaneously configuring the first disease distribution map and the second disease distribution map based on the generation time of the first diagnosis message, the generation time of the second diagnosis message, and the estimated location information of the diagnostic device;
Generating a learning model and learning result data associated with the disease analysis data, and using the learning result data and a machine learning data set to predict data associated with disease propagation prediction related to the propagation of a disease including the first disease and the second disease accumulate,
Performing the disease propagation prediction including the first disease and the second disease using the learning model and the prediction data, predicting a safe section and a risk section for the spread of the disease,
A location information-based disease site diagnosis method for transferring the learning result data and the prediction data to a visualization module. 14. The method of claim 13,
Based on the change over time of the disease distribution map, further comprising a disease spread direction and rate estimation process for estimating the spread direction and rate of spread of the disease,
In the process of constructing the disease distribution map, the first and second diagnosis messages generated based on the guide messages received through the first and second sensor tags are generated based on time and estimated location information, the first and second diseases The distribution chart is constructed,
In the process of estimating the direction and rate of disease spread, based on the change over time of the first and second disease distribution maps, the first and second disease spread direction and spread rate are estimated, location information-based disease site diagnosis Way. 15. The method of claim 14,
a correlation estimation process of estimating a correlation between the first and second diseases based on a spreading direction and a rate of spread of the first and second diseases; and
If the correlation between the first disease and the second disease is greater than or equal to a threshold, the diffusion direction and rate of the first disease are re-estimated in consideration of the direction and rate of spread of the second disease, and The location information-based disease on-site diagnosis method further comprising a process of reestimating the direction and rate of disease spread for reestimating the direction and rate of spread of the second disease in consideration of the direction and rate of spread.

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