Instance search device, event occurrence prediction device, instance display device, instance search method, event occurrence prediction method, instance display method, program, and recording medium

Abstract

To present an instance which may occur to an individual in future on the basis of similarity of a set standard for an object to be analyzed.SOLUTION: An instance search device includes an information acquisition part, a similarity calculation part, an instance extraction part, and an output part. The information acquisition part acquires information on an object to be analyzed, and the similarity calculation part calculates the similarity of the object to be analyzed from the information on the object to be analyzed on the basis of a set standard; and the instance extraction part extracts an instance on the basis of the similarity, and the output part outputs at least one of the similarity and the instance.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a case search device, an event occurrence prediction device, a case display device, a case search method, an event occurrence prediction method, a case display method, a program, and a recording medium. [Background technology]

[0002] Patent Document 1 discloses a disease onset prediction method characterized by including the steps of receiving original data including multiple items from at least one external database, generating processed data based on the original data in accordance with predetermined criteria, the processed data representing one medical consultation or one health check as one event, inputting the processed data into a disease onset prediction model, and calculating the disease onset probability for at least one disease using the disease onset prediction model. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-008719 Summary of the Invention [Problem to be solved by the invention]

[0004] For example, when a user tries to understand their own health condition, it is difficult to form a concrete image using a method that presents the probability of disease onset, as in Patent Document 1. If the current state of the subject of analysis could be presented in a way that makes it easier to imagine, not just in the case of disease, but in any situation, it would be easier to improve the behavior and condition of the subject of analysis.

[0005] Therefore, the present disclosure aims to provide a case search device, an event occurrence prediction device, a case display device, a case search method, an event occurrence prediction method, a case display method, a program, and a recording medium for presenting cases that may occur in the future to an individual based on the similarity in the set criteria of the analysis target. [Means for solving the problem]

[0006] In order to achieve the above object, the case search device of the present disclosure includes: The system includes an information acquisition unit, a similarity calculation unit, a case extraction unit, and an output unit, the information acquisition unit acquires information to be analyzed, the similarity calculation unit calculates a similarity of the analysis target in a set criterion from information of the analysis target; the case extraction unit extracts cases based on the similarity; The output unit outputs at least one of the similarity and the case.

[0007] The event occurrence prediction device of the present disclosure includes: a similarity acquisition unit and a prediction unit, the similarity acquisition unit acquires a similarity of the analysis target in the set criterion; The prediction unit predicts the occurrence of a future event in the target individual based on the similarity.

[0008] The case display device of the present disclosure includes: Includes a display unit and a switching unit, the display unit displays at least one of the similarity and the case; The similarity is a similarity of the analysis target in the set criteria, the cases are cases extracted based on the similarity, The switching unit switches the display content of the similarity and the case.

[0009] The case search method of the present disclosure includes: The method includes an information acquisition step, a similarity calculation step, a case extraction step, and an output step, The information acquisition step acquires information to be analyzed, the similarity calculation step calculates a similarity of the analysis target in a set criterion from information of the analysis target; the case extraction step extracts cases based on the similarity; the output step outputs at least one of the similarity and the case. Each of the steps is a computer-implemented method.

[0010] The program of the present disclosure is a program for causing a computer to execute each step of the method of the present disclosure as a procedure.

[0011] The recording medium of the present disclosure is a computer-readable recording medium on which the program of the present disclosure is recorded. [Effects of the Invention]

[0012] According to the present disclosure, it is possible to present cases that may occur in the future to an individual based on the similarity in the set criteria of the analysis target. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a block diagram showing a configuration of an example of a case search device according to the present disclosure. [Figure 2] FIG. 2 is a block diagram illustrating an example of the hardware configuration of the case search device of the present disclosure. [Figure 3] FIG. 3 is a flowchart illustrating an example of processing in the case search device of the present disclosure. [Figure 4A] FIG. 4A is a block diagram illustrating a configuration of an example of an event occurrence prediction device according to the present disclosure. [Figure 4B] FIG. 4B is a block diagram showing a configuration of another example of an event occurrence prediction device according to the present disclosure. [Figure 5A] FIG. 5A is a block diagram illustrating an example of a hardware configuration of an event occurrence prediction device according to the present disclosure. [Figure 5B] FIG. 5B is a block diagram illustrating another example of the hardware configuration of the event occurrence prediction device of the present disclosure. [Figure 6A] FIG. 6A is a flowchart illustrating an example of processing in the event occurrence prediction device of the present disclosure. [Figure 6B]FIG. 6B is a flowchart showing another example of the processing in the event occurrence prediction device of the present disclosure. [Figure 7] FIG. 7 is a block diagram showing a configuration of an example of the case example display device of the present disclosure. [Figure 8] FIG. 8 is a block diagram showing an example of the hardware configuration of the case example display device of the present disclosure. [Figure 9] FIG. 9 is a flowchart showing an example of processing in the case display device of the present disclosure. [Figure 10] FIG. 10 shows an example of defining a relevance threshold in the case search device of the present disclosure. [Figure 11] FIG. 11 is a diagram illustrating an example of an analysis target and setting criteria in the case search device of the present disclosure. [Figure 12] FIG. 12 is a matrix that represents the relevance of the analysis targets in the case search device of the present disclosure using a similarity function. [Figure 13] FIG. 13 is a diagram illustrating an example of similarity graphed in the case search device of the present disclosure. [Figure 14] FIG. 14 is a diagram showing another example of similarity graphed in the case search device of the present disclosure. [Figure 15] FIG. 15 is a diagram illustrating yet another example of similarity graphed in the case search device of the present disclosure. [Figure 16] FIG. 16 is a table and graph showing an example of cases output by the case search device of the present disclosure. [Figure 17] FIG. 17 is a table showing another example of cases output by the case search device of the present disclosure. [Figure 18] FIG. 18 is a diagram showing the similarity and the display contents of the case displayed by the case display device of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0014] The present disclosure will be described. Note that the present disclosure is not limited to the following embodiments. Note that in the following drawings, the same parts are denoted by the same reference numerals. Furthermore, the descriptions of the embodiments can be used interchangeably unless otherwise specified. Furthermore, the configurations of the embodiments can be combined unless otherwise specified.

[0015] [Embodiment 1] First, an example of a case search device and a case search method according to the present disclosure will be described.

[0016] 1 is a block diagram showing an example of the configuration of a case search device 10 (hereinafter sometimes referred to as "the device 10") according to the present disclosure. As shown in FIG. 1, the device 10 includes an information acquisition unit 11, a similarity calculation unit 12, a case extraction unit 13, and an output unit 14.

[0017] The device 10 may be, for example, a single device including the above-described units, or a device in which the units can be connected via a communication network. The device 10 can also be connected to an external device (described later) via the communication network. The communication network is not particularly limited and any known network can be used, for example, a wired or wireless network. Examples of the communication network include the Internet, the World Wide Web (WWW), a telephone line, a Local Area Network (LAN), a Storage Area Network (SAN), a Delay Tolerant Networking (DTN), a Low Power Wide Area Network (LPWA), and a Local 5G (L5G). Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), Local 5G, and LPWA. Examples of the wireless communication include direct communication between devices (Ad Hoc communication), infrastructure communication, and indirect communication via an access point. The device 10 may be incorporated into a server as a system. Furthermore, the present device 10 may be, for example, a personal computer (PC, for example, desktop or notebook type) on which the program of the present disclosure is installed, a smartphone, a tablet terminal, etc. The present device 10 may be in the form of cloud computing or edge computing, for example, in which at least one of the above-mentioned units is located on a server and the other units are located on a terminal.

[0018] 2 shows a block diagram of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, a memory 102, a bus 103, a storage device 104, an input device 105, an output device 106, and a communication device 107. The components of the device 10 are connected to each other via the bus 103 and their respective interfaces (I / F).

[0019] The central processing unit 101 operates in cooperation with other components via a controller (such as a system controller or an I / O controller) and is responsible for overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program of the present disclosure and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 101 functions as an information acquisition unit 11, a similarity calculation unit 12, a case extraction unit 13, and an output unit 14. The central processing unit 101 may include, as a computing device, a CPU, a GPU (Graphics Processing Unit), an APU (Accelerated Processing Unit), or a combination of these.

[0020] The bus 103 can also be connected to, for example, an external device. Examples of the external device include an external storage device such as an external database, a printer, an external input device, an external display device, an external imaging device, etc. The device 10 can be connected to an external network (the communication line network) by, for example, a communication device 107 connected to the bus 103, and can also be connected to other devices via the external network.

[0021] The memory 102 may be, for example, a main memory (primary storage device). When the central processing unit 101 performs processing, the memory 102 reads various operating programs, such as the program of the present disclosure, stored in the storage device 104 (described later), and the central processing unit 101 receives data from the memory 102 and executes the programs. The main memory may be, for example, a RAM (random access memory). The memory 102 may also be, for example, a ROM (read only memory).

[0022] The storage device 104 is also referred to as an auxiliary storage device, for example, in contrast to the main memory (primary storage device). As described above, the storage device 104 stores an operating program including the program of the present disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive that reads and writes from and to the recording medium. The recording medium is not particularly limited and may be, for example, an internal or external type, such as a hard disk (HD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, or memory card. The storage device 104 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD) in which the recording medium and drive are integrated.

[0023] In the present device 10, the memory 102 and the storage device 104 can also store various information such as log information, information acquired from an external database (not shown) or an external device, information generated by the present device 10, and information used when the present device 10 executes processing. In this case, the memory 102 and the storage device 104 may store (record) information to be analyzed, which will be described later, for example. Note that at least a portion of the information may be stored, for example, in an external server other than the memory 102 and the storage device 104, or may be stored in a distributed manner across multiple terminals using blockchain technology or the like.

[0024] The device 10 further includes, for example, an input device 105 and an output device 106. Examples of the input device 105 include pointing devices such as a touch panel, track pad, and mouse; a keyboard; imaging means such as a camera and scanner; card readers such as an IC card reader and a magnetic card reader; and audio input means such as a microphone. Examples of the output device 106 include display devices such as an LED display and a liquid crystal display; audio output devices such as a speaker; a printer; and the like. In the present disclosure, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may also be configured as an integrated device, such as a touch panel display.

[0025] Next, an example of the case search method of the present disclosure will be described based on the flowchart of Fig. 3. The case search method of the present disclosure is implemented as follows, for example, using the device 10 of Fig. 1 or Fig. 2. Note that the case search method of the present disclosure is not limited to use of the device 10 of Fig. 1 or Fig. 2.

[0026] First, the information acquisition unit 11 acquires information about the analysis target (S11, information acquisition step). The analysis target may be a living thing or a non-living thing. The living thing is not particularly limited, and may be, for example, a human, a non-human animal, or a plant. The non-living thing is not particularly limited, and may be, for example, a machine. Examples of the machine include a car and an ATM. The information about the analysis target can be selected arbitrarily depending on the case to be searched, and examples include health-related information such as health checkup information, information about the progress of tasks such as exercise, study, and work, information about the growth and development of the analysis target, and information about the behavior of the analysis target.

[0027] Next, the similarity calculation unit 12 calculates the similarity of the analysis target in the set criteria from the information of the analysis target (S12, similarity calculation step). The set criteria may be, for example, a numerical criterion. The set criteria may be, for example, the time elapsed since the birth of the living thing, such as age, age in months, age in weeks, or age in days. The set criteria may also be, for example, the period of time spent working on the task, the performance of the task, an evaluation of the task, etc. Furthermore, the set criteria may include the period since the manufacture of a machine, an index related to use, etc. The index related to use may be, for example, the number of times a machine such as a car or an ATM is used, the mileage of a car, etc.

[0028] The similarity is calculated as the relevance of the analysis target, for example. The relevance may be determined by, for example, a graphical lasso. The similarity may be calculated by, for example, an arbitrary function. The similarity may be the inverse of the Euclidean distance, for example. The inverse of the Euclidean distance is expressed by, for example, the following formula 1 or 2. Here, the following formula 1 is used when the setting criterion is a numerical criterion and the numerical value is set to a pinpoint. Furthermore, the following formula 2 is used when the numerical value is set to have a range.

[0029]

number

number

[0030] In addition, in the above formula 1 and formula 2, x. k is the standardized value for information k, and x. kl is a standardized value for information k and information l, where i and j are the individuals to be analyzed, M is the number of variables, and L is the range of the set criteria.

[0031] The similarity may be calculated by setting a threshold value that determines whether there is a relevance or not. The threshold value is not particularly limited and can be set to any value. FIG. 10 shows an example of a defined threshold value for relevance. The threshold value may be set, for example, as an arbitrary percentile based on the distribution of similarities, as shown in FIG. 10. In this way, for example, using a threshold value as a criterion, those exceeding the threshold value may be determined as "relevant," and those below the threshold value may be determined as "unrelevant."

[0032] Next, the case extraction unit 13 extracts cases based on the similarity (S13, case extraction step). The cases are, for example, cases related to diseases, tasks, development, growth, or movements. Examples of the cases related to diseases include cases related to test results, medical interview information, lifestyle habits, medical history, etc. Examples of the cases related to tasks include cases related to the progress, results, evaluation, etc. of the tasks. Examples of the cases related to development and growth include cases related to the development and growth of living organisms. Examples of the cases related to movements include cases related to machine malfunctions, defects, etc.

[0033] Then, the output unit 14 outputs at least one of the similarity and the case (S14, output step). The output unit 14 may, for example, output cases of similar individuals that are similar to the target individual based on the similarity. The cases of similar individuals include, for example, at least one of past and present cases of the similar individuals. The form of the output similarity and case may be any form, such as text, a diagram, a table, etc. Furthermore, they may be output by the output device 106 of the present device 10, or by an output device of another device.

[0034] [Embodiment 2] Next, an example of a case search method using the case search device 10 of the present disclosure will be described with reference to Fig. 11 to Fig. 15. Note that, although the present embodiment describes a case search related to diseases, the present disclosure is not limited thereto.

[0035] First, the device 10 acquires, as information about the analysis target, for example, health checkup information of the analysis target. From the health checkup information, the similarity of the analysis target is calculated. Here, the similarity may be calculated using, for example, age as a set criterion. FIG. 11 is a diagram showing an example of analysis targets and set criterion. FIG. 11 includes analysis targets A to D. In FIG. 11, analysis target A is a target individual for which cases of similar individuals are to be searched, and analysis targets B to D are analysis targets for which similarity with analysis target A is to be calculated. For example, when searching for cases related to a disease of analysis target A, if analysis target A is 50 years old, the similarity may be calculated from the health checkup information of analysis targets A to D at the age of 50. Furthermore, as shown in FIG. 11, the set criterion may not be limited to a specific age, but may be set to a range, for example, an age frame of 2. That is, the similarity may be calculated from the health checkup information of analysis targets A to D at the ages of 49 to 50. In this disclosure, four analysis targets, A to D, are exemplified, but the number of analysis targets is not particularly limited.

[0036] The similarity is calculated as the relevance of the analysis targets A to D, for example. FIG. 12 is a matrix that expresses the relevance of the analysis targets using a similarity function. As shown in FIG. 12, the relevance r of the analysis targets A to D can be expressed as a matrix. Here, as shown in FIG. 10 above, a threshold value may be set to calculate the relevance of the analysis targets A to D. For example, when the relevance between the analysis targets A and D is low (below the set threshold), r is calculated as shown in FIG. 12. AD , and r DA may be set to "0" to indicate no relevance.

[0037] Next, the device 10 extracts cases related to the diseases of the analysis targets based on the similarity. For example, if the correlation between analysis targets A and D is low as described above, cases related to the diseases of only analysis targets A to C are extracted.

[0038] Then, at least one of the similarity and the case is output. FIG. 13 is a diagram showing an example of graphed similarity. As shown in FIG. 12 above, if analysis objects A to C are related but analysis objects A and D are not related, a graph showing similarity as shown in FIG. 13 may be output based on the matrix. In FIG. 13, if the nodes representing the analysis objects are connected by an edge, the analysis objects connected by the edge are related. On the other hand, if the analysis objects are not connected by an edge, the analysis objects are not related. Furthermore, the level of similarity may be indicated by the nodes representing analysis objects A to D and the length of the edge connecting the nodes (the distance between the nodes). Furthermore, the magnitude of the time difference between the analysis objects (the current ages of analysis objects A to D in FIG. 13) may be indicated by the direction of the arrow of the edge, etc. FIG. 14 is a diagram showing another example of graphed similarity. When there are a plurality of analysis targets, the similarity between the analysis targets can be expressed as shown in the left diagram of Fig. 14, and by enlarging the analysis target (target individual) of interest as a reference, it is possible to output those analysis targets with higher similarity from among the plurality of analysis targets with priority. Fig. 15 is a diagram showing yet another example of graphed similarity. The similarity may be output by other display methods such as those shown in Fig. 15, and is not limited to these.

[0039] FIG. 16 is a table and graph showing an example of an output case. When analysis subjects A to C are related, cases related to these diseases may be output in a table and graph as shown in FIG. 16. In FIG. 16, test values ​​from health checkups of analysis subjects A to C aged 48 to 60 are shown in a table and graph. FIG. 17 is a table showing another example of an output case. While only test values ​​are output in the above-mentioned FIG. 16, this is not limitative, and for example, medical history and interview information of similar analysis subjects (analysis subjects A to C in FIG. 17) may also be output as an example, as shown in FIG. 17.

[0040] Although the above description has been given regarding case search related to diseases, the present device 10 can also be used for applications other than case search related to diseases. For example, information regarding the progress of a task, such as exercise, study, or work, of an analysis target may be acquired, and a similarity of the analysis target may be calculated from the acquired information using set criteria, such as the period the analysis target spent on the task, the performance of the task, and an evaluation of the task. Cases related to the progress, results, and evaluation of the task, such as exercise, study, or work, of the analysis target may be extracted based on the similarity, and at least one of the similarity and the case may be output. Furthermore, information regarding the development and growth of an organism to be analyzed may be acquired, and a similarity of the analysis target may be calculated from the acquired information using set criteria, such as the age, age in months, weeks, or days, of the analysis target. Cases related to the development and growth of the analysis target may be extracted based on the similarity, and at least one of the similarity and the case may be output. Furthermore, information regarding the operation of the machine to be analyzed may be acquired, and from the acquired information, the similarity of the object to be analyzed may be calculated using set criteria such as the period since the manufacture of the machine and indicators related to its use, and examples regarding the operation of the object to be analyzed may be extracted based on the similarity, and at least one of the similarity and the examples may be output.

[0041] As described above, the device 10 outputs at least one of the similarity and case examples based on the similarity in the set criteria of the analysis target, making it possible to present cases that may occur in the future to the individual. By referring to the output cases, it is possible to change habits, behaviors, usage methods, etc., and to use them to avoid or achieve events that may occur in the future.

[0042] [Embodiment 3] Next, an example of an event occurrence prediction device and an event occurrence prediction method according to the present disclosure will be described.

[0043] 4A is a block diagram showing an example of the configuration of an event occurrence prediction device 20 (hereinafter, sometimes referred to as "the device 20") according to the present disclosure. As shown in FIG. 4A, the device 20 includes a similarity acquisition unit 21 and a prediction unit 22.

[0044] The device 20 may be, for example, a single device including the above-described units, or a device in which the units can be connected via a communication network. The device 20 can also be connected to an external device (described later) via the communication network. The communication network is not particularly limited and may be any known network, for example, wired or wireless. Examples of the communication network include the Internet, the World Wide Web (WWW), a telephone line, a Local Area Network (LAN), a Storage Area Network (SAN), a Delay Tolerant Networking (DTN), a Low Power Wide Area Network (LPWA), and a Local 5G (L5G). Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), Local 5G, and LPWA. Examples of the wireless communication include direct communication between devices (Ad Hoc communication), infrastructure communication, and indirect communication via an access point. The device 20 may be incorporated into a server as a system. Furthermore, the present device 20 may be, for example, a personal computer (PC, for example, a desktop or notebook type) on which the program of the present disclosure is installed, a smartphone, a tablet terminal, etc. The present device 20 may be, for example, in the form of cloud computing or edge computing, in which at least one of the above-mentioned units is located on a server and the other units are located on a terminal.

[0045] 5A shows a block diagram of the hardware configuration of the device 20. The device 20 includes, for example, a central processing unit (CPU, GPU, etc.) 201, a memory 202, a bus 203, a storage device 204, an input device 205, an output device 206, and a communication device 207. The components of the device 20 are connected to each other via the bus 203 and their respective interfaces (I / F).

[0046] The central processing unit 201 cooperates with other components via a controller (such as a system controller or an I / O controller) and is responsible for overall control of the device 20. In the device 20, the central processing unit 201 executes, for example, the program of the present disclosure and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 201 functions as a similarity acquisition unit 21 and a prediction unit 22. The central processing unit 201 may include, as a computing device, a CPU, a GPU (Graphics Processing Unit), an APU (Accelerated Processing Unit), or a combination of these.

[0047] The bus 203 can also be connected to, for example, an external device. Examples of the external device include an external storage device such as an external database, a printer, an external input device, an external display device, an external imaging device, etc. The device 20 can be connected to an external network (the communication line network) by, for example, a communication device 207 connected to the bus 203, and can also be connected to other devices via the external network.

[0048] The memory 202 is, for example, a main memory (primary storage device). When the central processing unit 201 performs processing, the memory 202 reads various operating programs, such as the program of the present disclosure, stored in the storage device 204 (described later), and the central processing unit 201 receives data from the memory 202 and executes the programs. The main memory is, for example, a RAM (random access memory). The memory 202 may also be, for example, a ROM (read only memory).

[0049] The storage device 204 is also referred to as an auxiliary storage device, for example, in contrast to the main memory (primary storage device). As described above, the storage device 204 stores an operating program including the program of the present disclosure. The storage device 204 may be, for example, a combination of a recording medium and a drive that reads and writes from and to the recording medium. The recording medium is not particularly limited and may be, for example, an internal or external type, such as a hard disk (HD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, or memory card. The storage device 204 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD) in which the recording medium and drive are integrated.

[0050] In the present device 20, the memory 202 and the storage device 204 can also store various information such as log information, information acquired from an external database (not shown) or an external device, information generated by the present device 20, and information used when the present device 20 executes processing. In this case, the memory 202 and the storage device 204 may store (record), for example, the acquired similarity. Note that at least a portion of the information may be stored, for example, in an external server other than the memory 202 and the storage device 204, or may be stored in a distributed manner across multiple terminals using blockchain technology or the like.

[0051] The device 20 further includes, for example, an input device 205 and an output device 206. Examples of the input device 205 include pointing devices such as a touch panel, track pad, and mouse; a keyboard; imaging means such as a camera and scanner; card readers such as an IC card reader and a magnetic card reader; and audio input means such as a microphone. Examples of the output device 206 include display devices such as an LED display and a liquid crystal display; audio output devices such as a speaker; a printer; and the like. In the present disclosure, the input device 205 and the output device 206 are configured separately, but the input device 205 and the output device 206 may also be configured as an integrated device, such as a touch panel display.

[0052] Next, an example of the event occurrence prediction method of the present disclosure will be described with reference to the flowchart of Fig. 6A. The event occurrence prediction method of the present disclosure is implemented as follows, for example, using the device 20 of Fig. 4A or Fig. 5A. Note that the event occurrence prediction method of the present disclosure is not limited to use of the device 20 of Fig. 4A or Fig. 5A.

[0053] First, the similarity obtaining unit 21 obtains the similarity of the analysis target in the set criteria (S21, similarity obtaining step). The similarity may be calculated by the case search device of the present disclosure, or may be calculated by another device, for example.

[0054] Then, the prediction unit 22 predicts the occurrence of a future event in the subject individual based on the similarity (S22, prediction step). The occurrence of the event may be, for example, the onset of a disease, achievement of the task goal, achievement of the growth or development goal of the subject to be analyzed, malfunction of the subject to be analyzed, or the occurrence of a defect. The prediction may be performed, for example, by a conventionally known method. For example, when the prediction is for the onset of coronary artery disease, the onset of the disease can be predicted by the Suita score.

[0055] Furthermore, the prediction unit 22 may predict the occurrence of a future event in the target individual, for example, as the probability that a case of the target individual will transition to a case of a similar individual that is similar to the target individual, based on the similarity. The probability of transition can be calculated, for example, using the following Equation 3.

[0056]

number

[0057] In the above formula 3, i is the target individual, j is the similar individual, and P ij is the transition probability from the case of target individual i to the case of similar individual j, and r ij indicates the similarity. The similarity may also be the inverse of the Euclidean distance, where i≠j.

[0058] As described above, the present device 20 predicts the occurrence of a future event in a subject individual based on the similarity of the analysis subject in the set criteria, thereby enabling future prediction with high accuracy.

[0059] [Embodiment 4] Next, another example of the event occurrence prediction device and the event occurrence prediction method of the present disclosure will be described.

[0060] FIG. 4B is a block diagram showing the configuration of another example of the event occurrence prediction device 20 of the present disclosure. As shown in FIG. 4B, the device 20 further includes, for example, a prediction result output unit 23. Except for including the prediction result output unit 23, the device 20 is similar to the above-described event occurrence prediction device 20. FIG. 5B further shows an example of a block diagram of the hardware configuration of the device 20. The central processing unit 201 further functions as, for example, the prediction result output unit 23. Except for functioning as the prediction result output unit 23, the device 20 is similar to the above-described event occurrence prediction device 20.

[0061] Next, another example of the event occurrence prediction method of the present disclosure will be described with reference to the flowchart of Fig. 6B. The event occurrence prediction method of the present disclosure is implemented as follows, for example, using the device 20 of Fig. 4B or Fig. 5B. Note that the event occurrence prediction method of the present disclosure is not limited to use of the device 20 of Fig. 4B or Fig. 5B.

[0062] First, the explanation regarding the similarity obtaining unit 21 and the prediction unit 22 is the same as that described above.

[0063] Next, the prediction result output unit 23 outputs the predicted result. The output prediction result may be output in the form of, for example, text, a diagram, a table, or the like, and is not particularly limited. The prediction result may be output by the output device 206 included in the device 20, or by an output device included in another device.

[0064] As described above, the device 20 outputs a prediction result of the occurrence of a future event in a target individual, which is predicted based on the similarity of the analysis target in the set criteria. Note that, although the device 10 and the device 20 have been described as separate devices in the present disclosure, they may be integrated into one device to predict the occurrence of an event.

[0065] [Embodiment 5] Next, an example of a case display device and a case display method according to the present disclosure will be described.

[0066] 7 is a block diagram showing an example of the configuration of a case example display device 30 (hereinafter, sometimes referred to as "the device 30") according to the present disclosure. As shown in FIG. 7, the device 30 includes a display unit 31 and a switching unit 32.

[0067] The device 30 may be, for example, a single device including the above-described units, or a device in which the units can be connected via a communication network. The device 30 can also be connected to an external device (described later) via the communication network. The communication network is not particularly limited and may be any known network, for example, wired or wireless. Examples of the communication network include the Internet, the World Wide Web (WWW), a telephone line, a Local Area Network (LAN), a Storage Area Network (SAN), a Delay Tolerant Networking (DTN), a Low Power Wide Area Network (LPWA), and a Local 5G (L5G). Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), Local 5G, and LPWA. Examples of the wireless communication include direct communication between devices (Ad Hoc communication), infrastructure communication, and indirect communication via an access point. The device 30 may be incorporated into a server as a system. Furthermore, the present device 30 may be, for example, a personal computer (PC, for example, desktop or notebook type) on which the program of the present disclosure is installed, a smartphone, a tablet terminal, etc. The present device 30 may be in the form of cloud computing or edge computing, for example, in which at least one of the above-mentioned units is located on a server and the other units are located on a terminal.

[0068] 8 shows a block diagram of the hardware configuration of the device 30. The device 30 includes, for example, a central processing unit (CPU, GPU, etc.) 301, a memory 302, a bus 303, a storage device 304, an input device 305, an output device 306, and a communication device 307. The components of the device 30 are connected to each other via the bus 303 and their respective interfaces (I / F).

[0069] The central processing unit 301 cooperates with other components via a controller (such as a system controller or an I / O controller) and is responsible for overall control of the device 30. In the device 30, the central processing unit 301 executes, for example, the program of the present disclosure and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 301 functions as a display unit 31 and a switching unit 32. The central processing unit 301 may include, as a computing device, a CPU, a GPU (Graphics Processing Unit), an APU (Accelerated Processing Unit), or a combination of these.

[0070] The bus 303 can also be connected to, for example, an external device. Examples of the external device include an external storage device such as an external database, a printer, an external input device, an external display device, an external imaging device, etc. The device 30 can be connected to an external network (the communication line network) by, for example, a communication device 307 connected to the bus 303, and can also be connected to other devices via the external network.

[0071] The memory 302 may be, for example, a main memory (primary storage device). When the central processing unit 301 performs processing, the memory 302 reads various operating programs, such as the program of the present disclosure, stored in the storage device 304 (described later), and the central processing unit 301 receives data from the memory 302 and executes the programs. The main memory may be, for example, a RAM (random access memory). The memory 302 may also be, for example, a ROM (read only memory).

[0072] The storage device 304 is also referred to as an auxiliary storage device, for example, in contrast to the main memory (primary storage device). As described above, the storage device 304 stores an operating program including the program of the present disclosure. The storage device 304 may be, for example, a combination of a recording medium and a drive that reads and writes from and to the recording medium. The recording medium is not particularly limited and may be, for example, an internal or external type, such as a hard disk (HD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, or memory card. The storage device 304 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD) in which the recording medium and drive are integrated.

[0073] In the present device 30, the memory 302 and the storage device 304 can also store various information such as log information, information acquired from an external database (not shown) or an external device, information generated by the present device 30, and information used when the present device 30 executes processing. In this case, the memory 302 and the storage device 304 may store (record), for example, similarities and cases described below. Note that at least a portion of the information may be stored, for example, in an external server other than the memory 302 and the storage device 304, or may be stored in a distributed manner across multiple terminals using blockchain technology or the like.

[0074] The device 30 further includes, for example, an input device 305 and an output device 306. Examples of the input device 305 include pointing devices such as a touch panel, track pad, or mouse; a keyboard; imaging means such as a camera or scanner; card readers such as an IC card reader or a magnetic card reader; and audio input means such as a microphone. Examples of the output device 306 include display devices such as an LED display or a liquid crystal display; audio output devices such as a speaker; a printer; etc. In the present disclosure, the input device 305 and the output device 306 are configured separately, but the input device 305 and the output device 306 may also be configured as an integrated device, such as a touch panel display.

[0075] Next, an example of the case display method of the present disclosure will be described based on the flowchart of Fig. 9. The case display method of the present disclosure is implemented as follows, for example, using the device 30 of Fig. 7 or Fig. 8. Note that the case display method of the present disclosure is not limited to use of the device 30 of Fig. 7 or Fig. 8.

[0076] First, the display unit 31 displays at least one of the similarity and the case (S31, display step). The similarity is the similarity of the analysis target in the set criteria. The case is a case extracted based on the similarity. The similarity may be, for example, a similarity calculated by the case search device of the present disclosure, or may be a similarity calculated by another device. The case may be, for example, a case extracted by the case search device of the present disclosure, or may be a case extracted by another device.

[0077] The display unit 31 displays, for example, the similarity of the analysis objects using nodes and edges. The similarity is represented, for example, by the length of the edge between the analysis objects represented by the nodes. The edge represents, for example, a temporal difference between the nodes. The temporal difference can be, for example, a difference in the time elapsed since the birth of the organism, such as age, age in months, weeks, or days. The edge can also represent, for example, a difference in the period spent working on the task, a difference in the performance of the task, or a difference in the evaluation of the task. The edge can also represent, for example, a difference in the period since the manufacture of a machine, a difference in indicators related to use (such as the number of times the machine is used or the mileage of a car). The display of the temporal difference using the edge is not particularly limited, and can, for example, be the direction of an arrow. For example, when a node is selected, the display unit displays an example of the analysis object corresponding to the node. When a node is selected, for example, it displays an example of a similar individual similar to the target individual corresponding to the node. The display unit may further display a prediction result of the occurrence of an event in the target individual predicted based on the similarity, for example. The prediction result may be a prediction result predicted by the event occurrence prediction device of the present disclosure or a prediction result predicted by another device.

[0078] Then, the switching unit 32 switches the similarity and the display content of the case (S32, switching unit step). The switching unit may, for example, switch the number of the display content. The display content of the case may, for example, be content related to a disease, a task, growth, development, movement, etc. Examples of the content related to the disease may include content related to test results, medical interview information, lifestyle habits, medical history, etc. Examples of the content related to the task may include content related to the progress, results, evaluation, etc. of the task. Examples of the content related to growth and development may include content related to the development and development of a living organism, etc. Examples of the content related to movement may include content related to machine malfunctions, defects, etc.

[0079] [Embodiment 6] Next, an example of a case display method using the case display device of the present disclosure will be described with reference to Fig. 18. Note that, although the present embodiment describes the display of cases related to diseases, the present invention is not limited to this.

[0080] FIG. 18 is a diagram illustrating the display contents of similarities and cases displayed by the case display device of the present disclosure. The present device 30 displays at least one of the similarities and cases. In FIG. 18, similarities are displayed using nodes and edges, and cases are displayed using a graph. The display method using nodes and edges is the same as that described for the case search device of the present disclosure. As shown in (a) to (c) of FIG. 18, the displayed similarities can be enlarged or reduced based on the analysis object of interest (target individual) to increase or decrease the number of analysis objects, which are the display contents. For example, as shown in FIG. 18, the number of cases can be changed by sliding the displayed "magnification ratio." If the similarity is displayed on a touch panel, the number can be changed by pinching in or out, for example. Furthermore, as shown in (c) of FIG. 18, for example, by selecting a node of a similar individual similar to the target individual, cases such as the test values ​​and lifestyle habits of the similar individual can be added to the case of the target individual displayed in a graph. Also, as shown in (c) of FIG. 18, for example, by selecting the node of the similar individual, the name of the disease that the similar individual has developed can be displayed at the selected node. Furthermore, as shown in (c) and (d) of FIG. 18, for example, the display content of the case can be switched. In (c) and (d) of FIG. 18, the display content of the case is switched between the display of test values ​​and lifestyle habits. As shown in (c) and (d) of FIG. 18, the display content of the case may be switched by switching the displayed toggle. Furthermore, although not shown in FIG. 18, the occurrence of a predicted event may be displayed by any method.

[0081] As described above, the device 30 can switchably display at least one of the similarity and the case examples depending on the purpose. Note that, although the device 10, the device 20, and the device 30 have been described as separate devices in the present disclosure, the cases may be displayed as a unified device.

[0082] [Embodiment 7] The program of the present disclosure is a program for causing a computer to execute each step of the present disclosure as a procedure.

[0083] Specifically, the case search program of the present disclosure is a program for causing a computer to execute an information acquisition procedure, a similarity calculation procedure, a case extraction procedure, and an output procedure. The case search program of the present disclosure can also be said to be a program for causing a computer to function as the information acquisition procedure, the similarity calculation procedure, the case extraction procedure, and the output procedure, and the case search program of the present disclosure can cite the descriptions of the case search device and the case search method of the present disclosure.

[0084] The event occurrence prediction program of the present disclosure is a program for causing a computer to execute a similarity acquisition procedure and a prediction procedure. The event occurrence prediction program of the present disclosure may further cause a computer to execute a prediction result output procedure. The event occurrence prediction program of the present disclosure can also be said to be a program for causing a computer to function as the similarity acquisition procedure and the prediction procedure, and the descriptions of the event occurrence prediction device and event occurrence prediction method of the present disclosure can be used for the event occurrence prediction program of the present disclosure.

[0085] Furthermore, the case display program of the present disclosure is a program for causing a computer to execute a display procedure and a switching procedure. The case display program of the present disclosure can also be said to be a program for causing a computer to function as the display procedure and the switching procedure, and the case display program of the present disclosure can cite the descriptions of the case display device and case display method of the present disclosure.

[0086] For example, the "procedure" in each of the steps can be read as a "process." The program of the present disclosure may be recorded on a computer-readable recording medium. The recording medium is, for example, a non-transitory computer-readable storage medium. The recording medium is not particularly limited, and examples thereof include random access memory (RAM), read-only memory (ROM), hard disk (HD), flash memory (e.g., USB flash memory, SD / SDHC card, etc.), optical disk (e.g., CD-R / CD-RW, DVD-R / DVD-RW, BD-R / BD-RE, etc.), magneto-optical disk (MO), and floppy disk (FD). The program of the present disclosure (also referred to as a programming product or program product) may be distributed from an external computer. The "distribution" may be, for example, distribution via a communication network or via a device connected via a wire. The program of the present disclosure may be installed and executed on the device to which it is distributed, or may be executed without being installed.

[0087] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure.

[0088] <Additional Notes> Some or all of the above embodiments can be described as, but not limited to, the following supplementary notes. (Appendix 1) The system includes an information acquisition unit, a similarity calculation unit, a case extraction unit, and an output unit, the information acquisition unit acquires information to be analyzed, the similarity calculation unit calculates a similarity of the analysis target in a set criterion from information of the analysis target; the case extraction unit extracts cases based on the similarity; the output unit outputs at least one of the similarity and the case. Case search device. (Appendix 2) 2. The case search device according to claim 1, wherein the output unit outputs cases of similar individuals that are similar to the target individual based on the similarity. (Appendix 3) 3. The case search device according to claim 2, wherein the cases of the similar individuals include at least one of past and present cases of the similar individuals. (Appendix 4) 4. The case search device according to any one of appendices 1 to 3, wherein the information to be analyzed is health checkup information. (Appendix 5) 5. A case search device according to any one of appendices 1 to 4, wherein the set criterion is age. (Appendix 6) 6. The case search device according to claim 1, wherein the similarity is calculated as a relevance of the analysis target. (Appendix 7) 7. A case search device according to any one of appendices 1 to 6, wherein the case is a case related to a disease. (Appendix 8) a similarity acquisition unit and a prediction unit, the similarity acquisition unit acquires a similarity of the analysis target in the set criterion; the prediction unit predicts the occurrence of a future event in the target individual based on the similarity. Event occurrence prediction device. (Appendix 9) 9. An event occurrence prediction device according to claim 8, wherein the similarity is calculated by a case search device according to any one of claims 1 to 7. (Appendix 10) 10. The event occurrence prediction device according to claim 8 or 9, wherein the occurrence of the event is the onset of a disease. (Appendix 11) 11. An event occurrence prediction device according to any one of appendices 8 to 10, wherein the prediction unit predicts the occurrence of a future event in the target individual as a probability that an instance of the target individual will transition to an instance of a similar individual that is similar to the target individual, based on the similarity. (Appendix 12) Further, a prediction result output unit is included, 12. The event occurrence prediction device according to claim 8, wherein the prediction result output unit outputs the predicted result. (Appendix 13) Includes a display unit and a switching unit. the display unit displays at least one of the similarity and the case; The similarity is a similarity of the analysis target in the set criteria, the cases are cases extracted based on the similarity, the switching unit switches the display content of the similarity and the case. Case display device. (Appendix 14) The similarity is calculated by a case search device according to any one of Supplementary Notes 1 to 7, The cases are cases extracted by a case search device described in any one of Supplementary Notes 1 to 7. 14. The case display device of claim 13. (Appendix 15) 15. The case display device according to claim 14, wherein the display unit displays the similarity of the analysis object using nodes and edges. (Appendix 16) the similarity is represented by the length of the edge between the analysis objects represented by the nodes; 16. The case display device of claim 15. (Appendix 17) 17. The case display device of claim 15 or 16, wherein the edges represent temporal differences between the nodes. (Appendix 18) 18. The case display device according to any one of appendices 15 to 17, wherein the display unit displays a case to be analyzed that corresponds to a node when the node is selected. (Appendix 19) 19. The case display device according to any one of appendices 13 to 18, wherein the switching unit switches the number of displayed contents. (Appendix 20) 20. The case display device according to any one of appendices 13 to 19, wherein the display content of the case includes content related to a disease. (Appendix 21) 21. The case display device according to any one of appendices 13 to 20, wherein the display unit further displays a prediction result of an occurrence of an event in a target individual predicted based on the similarity. (Appendix 22) The case display device according to claim 21, wherein the prediction result is a prediction result predicted by an event occurrence prediction device according to any one of claims 8 to 12. (Appendix 23) The method includes an information acquisition step, a similarity calculation step, a case extraction step, and an output step, The information acquisition step acquires information to be analyzed, the similarity calculation step calculates a similarity of the analysis target in a set criterion from information of the analysis target; the case extraction step extracts cases based on the similarity; the output step outputs at least one of the similarity and the case. A case search method in which each of the steps is executed by a computer. (Appendix 24) 24. The case search method according to claim 23, wherein the output step outputs cases of similar individuals that are similar to the target individual based on the similarity. (Appendix 25) 25. The case search method according to claim 24, wherein the cases of the similar individuals include at least one of past and present cases of the similar individuals. (Appendix 26) 26. A case search method according to any one of appendices 23 to 25, wherein the information to be analyzed is health checkup information. (Appendix 27) 27. A case search method according to any one of appendices 23 to 26, wherein the set criterion is age. (Appendix 28) 28. A case search method according to any one of appendices 23 to 27, wherein the similarity is calculated as the relevance of the analysis target. (Appendix 29) A case search method according to any one of appendices 23 to 28, wherein the case is a case related to a disease. (Appendix 30) The method includes a similarity obtaining step and a prediction step, the similarity obtaining step obtains a similarity of the analysis target in the set criterion, the prediction step predicts the occurrence of a future event in a target individual based on the similarity. An event occurrence prediction method, wherein each of the steps is executed by a computer. (Appendix 31) An event occurrence prediction method according to Supplementary Note 30, wherein the similarity is calculated by a case search method according to any one of Supplementary Note 23 to 28. (Appendix 32) 32. The event occurrence prediction method according to claim 30 or 31, wherein the occurrence of the event is the onset of a disease. (Appendix 33) 33. The event occurrence prediction method according to any one of Appendices 30 to 32, wherein the prediction step predicts the occurrence of a future event in the target individual as a probability that an instance of the target individual will transition to an instance of a similar individual that is similar to the target individual, based on the similarity. (Appendix 34) Further, a prediction result output step is included, 34. The event occurrence prediction method according to any one of appendices 30 to 33, wherein the prediction result output step outputs the predicted result. (Appendix 35) Including a display process and a switching process, the display step displays at least one of the similarity and the case; The similarity is a similarity of the analysis target in the set criteria, the cases are cases extracted based on the similarity, the switching step switches the display content of the similarity and the case. A method for displaying cases, wherein each of the steps is executed by a computer. (Appendix 36) The similarity is calculated by a case search method according to any one of Supplementary Notes 23 to 29, The case is extracted by a case search method described in any one of Supplementary Notes 23 to 29. The method for displaying cases is described in Appendix 35. (Appendix 37) 37. The case display method according to claim 36, wherein the display step displays the similarity of the analysis object using nodes and edges. (Appendix 38) the similarity is represented by the length of the edge between the analysis objects represented by the nodes; The method for displaying cases described in Appendix 37. (Appendix 39) 39. The method of claim 37 or 38, wherein the edges represent temporal differences between the nodes. (Appendix 40) 40. The case display method according to any one of appendices 37 to 39, wherein the display step displays a case to be analyzed that corresponds to a node selected by the node. (Appendix 41) 41. The case display method according to any one of appendices 35 to 40, wherein the switching step switches the number of items to be displayed. (Appendix 42) 42. A case display method according to any one of Appendices 35 to 41, wherein the display content of the case includes content relating to a disease. (Appendix 43) 43. The case display method according to any one of appendices 35 to 42, wherein the display step further displays a prediction result of the occurrence of an event in the target individual predicted based on the similarity. (Appendix 44) A case display method according to claim 43, wherein the prediction result is a prediction result predicted by an event occurrence prediction method according to any one of claims 30 to 34. (Appendix 45) It includes an information acquisition procedure, a similarity calculation procedure, a case extraction procedure, and an output procedure, The information acquisition step acquires information to be analyzed, the similarity calculation step calculates a similarity of the analysis target in a set criterion from information of the analysis target; The case extraction step extracts cases based on the similarity, the output step outputs at least one of the similarity and the case. A case search program for causing a computer to execute each of the above procedures. (Appendix 46) 46. ​​The case search program according to claim 45, wherein the output step outputs cases of similar individuals that are similar to the target individual based on the similarity. (Appendix 47) 47. The case search program according to claim 46, wherein the cases of the similar individuals include at least one of past and present cases of the similar individuals. (Appendix 48) 48. A case search program according to any one of appendices 45 to 47, wherein the information to be analyzed is health checkup information. (Appendix 49) A case search program described in any of appendices 45 to 48, wherein the set criterion is age. (Appendix 50) 50. A case search program according to any one of appendices 45 to 49, wherein the similarity is calculated as the relevance of the analysis target. (Appendix 51) A case search program described in any of Appendices 45 to 50, wherein the case is a case related to a disease. (Appendix 52) The method includes a similarity acquisition step and a prediction step, the similarity obtaining step obtains a similarity of the analysis target in the set criterion; the prediction step predicts the occurrence of a future event in a target individual based on the similarity. An event occurrence prediction program for causing a computer to execute each of the above procedures. (Appendix 53) An event occurrence prediction program according to appendix 52, wherein the similarity is calculated by a case search program according to any one of appendices 45 to 51. (Appendix 54) 54. An event occurrence prediction program according to claim 52 or 53, wherein the occurrence of the event is the onset of a disease. (Appendix 55) 55. An event occurrence prediction program according to any one of Appendices 52 to 54, wherein the prediction procedure predicts the occurrence of a future event in the target individual as a probability that an instance of the target individual will transition to an instance of a similar individual that is similar to the target individual, based on the similarity. (Appendix 56) Further, a prediction result output step is included, 56. An event occurrence prediction program according to any one of appendices 52 to 55, wherein the prediction result output step outputs the predicted result. (Appendix 57) Including display procedures and switching procedures, the display step displays at least one of the similarity and the case; The similarity is a similarity of the analysis target in the set criteria, the cases are cases extracted based on the similarity, the switching step switches the display content of the similarity and the case. A case display program for causing a computer to execute each of the above procedures. (Appendix 58) The similarity is calculated by a case search program described in any one of Supplementary Notes 45 to 51, The case is extracted by a case search program described in any one of appendices 45 to 51. The case display program described in Appendix 57. (Appendix 59) 59. The case display program according to claim 58, wherein the display step displays the similarity of the analysis object using nodes and edges. (Appendix 60) the similarity is represented by the length of the edge between the analysis objects represented by the nodes; The example display program described in Appendix 59. (Appendix 61) 61. The case display program of claim 59 or 60, wherein the edges represent temporal differences between the nodes. (Appendix 62) 62. The case display program according to any one of appendices 59 to 61, wherein the display step displays a case to be analyzed corresponding to a node by selecting the node. (Appendix 63) 63. The case display program according to any one of appendices 57 to 62, wherein the switching step switches the number of displayed contents. (Appendix 64) A case display program according to any one of appendices 57 to 63, wherein the display content of the case includes content related to a disease. (Appendix 65) 65. The case display program according to any one of Appendices 57 to 64, wherein the display step further displays a prediction result of an event occurrence in the target individual predicted based on the similarity. (Appendix 66) The case display program according to claim 65, wherein the prediction result is a prediction result predicted by an event occurrence prediction program according to any one of claims 52 to 56. (Appendix 67) It includes an information acquisition procedure, a similarity calculation procedure, a case extraction procedure, and an output procedure, The information acquisition step acquires information to be analyzed, the similarity calculation step calculates a similarity of the analysis target in a set criterion from information of the analysis target; The case extraction step extracts cases based on the similarity, the output step outputs at least one of the similarity and the case. A computer having a program recorded thereon for causing the computer to execute each of the above procedures. A readable recording medium. (Appendix 68) 68. The recording medium according to claim 67, wherein the output step outputs examples of similar individuals that are similar to the target individual based on the similarity. (Appendix 69) 69. The recording medium of claim 68, wherein the examples of the similar individuals include at least one of past and present examples of the similar individuals. (Appendix 70) 70. The recording medium according to any one of appendices 67 to 69, wherein the information to be analyzed is health checkup information. (Appendix 71) 71. The recording medium of any one of Appendices 67 to 70, wherein the set criterion is age. (Appendix 72) 72. The recording medium according to any one of appendices 67 to 71, wherein the similarity is calculated as the relevance of the analysis objects. (Appendix 73) 73. A recording medium according to any one of appendices 67 to 72, wherein the case is a disease-related case. (Appendix 74) The method includes a similarity acquisition step and a prediction step, the similarity obtaining step obtains a similarity of the analysis target in the set criterion; the prediction step predicts the occurrence of a future event in a target individual based on the similarity. A computer having a program recorded thereon for causing the computer to execute each of the above procedures. A readable recording medium. (Appendix 75) 75. The recording medium of claim 74, wherein the similarity is calculated by a recording medium of any one of claims 67 to 73. (Appendix 76) 76. The recording medium of claim 74 or 75, wherein the occurrence of the event is the onset of a disease. (Appendix 77) 77. A recording medium described in any of Appendices 74 to 76, wherein the prediction procedure predicts the occurrence of a future event in the target individual as the probability that an instance of the target individual will transition to an instance of a similar individual that is similar to the target individual, based on the similarity. (Appendix 78) Further, a prediction result output step is included, 78. The recording medium according to any one of appendices 74 to 77, wherein the prediction result output step outputs the predicted result. (Appendix 79) Including display procedures and switching procedures, the display step displays at least one of the similarity and the case; The similarity is a similarity of the analysis target in the set criteria, the cases are cases extracted based on the similarity, the switching step switches the display content of the similarity and the case. A computer having a program recorded thereon for causing the computer to execute each of the above procedures. A readable recording medium. (Appendix 80) the similarity is calculated by a recording medium according to any one of Supplementary Notes 67 to 73; The case is a case extracted by a recording medium described in any one of appendices 67 to 73. 79. The recording medium according to claim 79. (Appendix 81) 81. The recording medium according to claim 80, wherein the display step displays the similarity of the analysis object using nodes and edges. (Appendix 82) the similarity is represented by the length of the edge between the analysis objects represented by the nodes; 82. The recording medium according to claim 81. (Appendix 83) 83. The storage medium of claim 81 or 82, wherein the edges represent temporal differences between the nodes. (Appendix 84) 84. The recording medium according to any one of appendices 81 to 83, wherein the display step displays an example of an analysis target corresponding to a node by selecting the node. (Appendix 85) 85. The recording medium according to any one of appendices 79 to 84, wherein the switching procedure switches the number of displayed contents. (Appendix 86) A recording medium described in any one of Appendices 79 to 85, wherein the displayed content of the case includes content related to a disease. (Appendix 87) 87. The recording medium of any one of appendices 79 to 86, wherein the display step further displays a prediction result of an event occurrence in the target individual predicted based on the similarity. (Appendix 88) 88. The recording medium of claim 87, wherein the prediction result is a prediction result predicted by a recording medium of any one of claims 74 to 78. [Industrial Applicability]

[0089] According to the present disclosure, it is possible to present cases that may occur in the future to an individual based on the similarity in the set criteria of the analysis target. The fields to which the present disclosure can be applied are not limited, and the present disclosure can be applied to a wide range of fields using a case search device, an event occurrence prediction device, or a case display device. [Explanation of symbols]

[0090] 11 Information acquisition department 12 Similarity calculation unit 13 Case extraction part 14 Output section 21 Similarity acquisition unit 22 Prediction Department 23 Prediction result output section 31 Display section 32 Switching section 101, 201, 301 CPUs 102, 202, 302 memory Buses 103, 203, and 303 104, 204, 304 storage device 105, 205, 305 Input devices 106, 206, 306 output devices 107, 207, 307 Communication devices

Claims

1. The system includes an information acquisition unit, a similarity calculation unit, a case extraction unit, and an output unit, the information acquisition unit acquires information to be analyzed, the similarity calculation unit calculates a similarity of the analysis target in a set criterion from information of the analysis target; the case extraction unit extracts cases based on the similarity; the output unit outputs at least one of the similarity and the case. Case search device.

2. The case search device according to claim 1 , wherein the output unit outputs cases of similar individuals that are similar to the target individual based on the similarity.

3. 3. The case search device according to claim 2, wherein the cases of the similar individuals include at least one of past and present cases of the similar individuals.

4. 3. The case search device according to claim 1, wherein the information to be analyzed is health checkup information.

5. 3. The case search device according to claim 1, wherein the set criterion is age.

6. The case search device according to claim 1 , wherein the similarity is calculated as a relevance of the analysis target.

7. a similarity acquisition unit and a prediction unit, the similarity acquisition unit acquires a similarity of the analysis target in the set criterion; the prediction unit predicts the occurrence of a future event in the target individual based on the similarity. Event occurrence prediction device.

8. Includes a display unit and a switching unit. the display unit displays at least one of the similarity and the case; The similarity is a similarity of the analysis target in the set criteria, the cases are cases extracted based on the similarity, the switching unit switches the display content of the similarity and the case. Case display device.

9. The method includes an information acquisition step, a similarity calculation step, a case extraction step, and an output step, The information acquisition step acquires information to be analyzed, the similarity calculation step calculates a similarity of the analysis target in a set criterion from information of the analysis target; the case extraction step extracts cases based on the similarity; the output step outputs at least one of the similarity and the case. A case search method in which each of the steps is executed by a computer.

10. It includes an information acquisition procedure, a similarity calculation procedure, a case extraction procedure, and an output procedure, The information acquisition step acquires information to be analyzed, the similarity calculation step calculates a similarity of the analysis target in a set criterion from information of the analysis target; The case extraction step extracts cases based on the similarity, the output step outputs at least one of the similarity and the case. A case search program for causing a computer to execute each of the above procedures.

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