DECISION SUPPORT DEVICE, DECISION SUPPORT METHOD, AND DECISION SUPPORT PROGRAM

Abstract

To provide a decision-making support device, a decision-making support method and a decision-making support program capable of easily selecting an improvement effect.SOLUTION: A decision-making branch device 100 comprises a processor 101 for executing a program and a storage device 102. The storage device 102 stores a score which is a relative index among a plurality of intervention measures for each intervention measure. The processor 101 executes: selection processing of receiving selection of a first improvement effect among a plurality of improvement effects related to the plurality of intervention measures; first output processing of outputting adjustable information on a first score within an adjustment range based on the first score of the first intervention measure for which the first improvement effect is selected by the selection processing; input processing of receiving a score range set by the first score after adjustment within the adjustment range; and second output processing of outputting a second improvement effect of a second intervention measure having a second score included in the score range received by the input processing among the plurality of improvement effects.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a decision-making support device, a decision-making support method, and a decision-making support program for supporting decision-making. [Background technology]

[0002] Patent Document 1 discloses a decision support system that presents combinations of multiple values ​​and shows which changes to make to the current situation will lead to an improvement or a deterioration, allowing the user to decide on specific measures to improve the current situation. This decision support system includes: a setting means that acquires, from a terminal, variables whose values ​​are changeable among the variables used in an estimation model; a combination generation means that generates, from the variables acquired from the terminal, multiple combinations of values ​​to be used for estimation; an estimation means that performs a simulation using the multiple combinations of values ​​generated by the generation means to obtain an estimation result; and a means that outputs the estimation result and the combinations of values ​​used by the estimation means to the terminal, and visually displays which value changes will change which estimation result. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-81878 Summary of the Invention [Problem to be solved by the invention]

[0004] In Patent Document 1, the number of estimation results displayed on the terminal is equal to the total number of combinations of values ​​of changeable input variables. Therefore, when there are a large number of changeable input variables and their values, a large number of improvement effects are displayed. Therefore, the more changeable input variables and their values ​​there are, the more difficult it becomes to narrow down the improvement effects of intervention measures from the predicted improvement effects of intervention measures.

[0005] An object of the present invention is to facilitate the selection of an improvement effect. [Means for solving the problem]

[0006] A decision-making support device according to one aspect of the invention disclosed in the present application is a decision-making support device having a processor that executes a program and a storage device that stores the program, wherein the storage device stores, for each intervention measure, a score that serves as a relative indicator between multiple intervention measures, and the processor executes the following steps: a selection process that accepts a selection of a first improvement effect from multiple improvement effects related to the multiple intervention measures; a first output process that outputs adjustable information for the first score within an adjustment range based on the first score of the first intervention measure for which the first improvement effect has been selected by the selection process; an input process that accepts a score range set by the first score after adjustment within the adjustment range; and a second output process that outputs a second improvement effect of a second intervention measure from the multiple improvement effects, the second improvement effect being included in the score range accepted by the input process. [Effects of the Invention]

[0007] According to the exemplary embodiment of the present invention, it is possible to facilitate the selection of the improvement effect. Problems, configurations, and effects other than those described above will become clear from the following description of the examples. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram showing an example of the hardware configuration of a decision-making support device. [Figure 2] FIG. 2 is a block diagram illustrating an example of a functional configuration of the decision-making support device. [Figure 3] FIG. 3 is an explanatory diagram illustrating an example of the test campaign DB. [Figure 4] FIG. 4 is a flowchart showing an example of a decision-making processing procedure performed by the decision-making support device. [Figure 5] FIG. 5 is an explanatory diagram illustrating an example of processing in step S402 and step S403 according to the first embodiment. [Figure 6] FIG. 6 is an explanatory diagram showing an example of differential data in a group of outcome prediction values. [Figure 7] FIG. 7 is an explanatory diagram showing an example of the time-varying information. [Figure 8] FIG. 8 is an explanatory diagram showing an example of the prediction information display screen. [Figure 9] FIG. 9 is an explanatory diagram showing a display example 1 of the prediction result display area. [Figure 10] FIG. 10 is an explanatory diagram showing a display example 1 of the result display adjustment area. [Figure 11] FIG. 11 is an explanatory diagram illustrating an example of processing in step S402 and step S403 according to the second embodiment. [Figure 12] FIG. 12 is an explanatory diagram showing an example of grouping K groups of outcome predicted values. [Figure 13] FIG. 13 is an explanatory diagram illustrating an example of differential data in the second embodiment. [Figure 14] FIG. 14 is an explanatory diagram showing a display example 2 of the prediction result display area. [Figure 15] FIG. 15 is an explanatory diagram showing a display example 2 of the result display adjustment area. [Figure 16] FIG. 16 is an explanatory diagram illustrating an example of processing in steps S403 and S404 according to the third embodiment. [Figure 17] FIG. 17 is an explanatory diagram showing a display example 3 of the prediction result display area. [Figure 18] FIG. 18 is an explanatory diagram showing a display example 3 of the result display adjustment area. [Figure 19] FIG. 19 is a diagram illustrating an example of prediction result data by the decision support device according to the fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION [Example]

[0009] <Example of hardware configuration for decision support device> FIG. 1 is a block diagram showing an example of the hardware configuration of a decision-making support apparatus. The decision-making support apparatus 100 includes a processor 101, a storage device 102, an input device 103, an output device 104, and a communication interface (communication IF) 105. The processor 101, the storage device 102, the input device 103, the output device 104, and the communication IF 105 are connected via a bus 106. The processor 101 controls the decision-making support apparatus 100. The storage device 102 serves as a working area for the processor 101. The storage device 102 is a non-transitory or temporary recording medium that stores various programs and data. Examples of the storage device 102 include a read-only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), and a flash memory. The input device 103 inputs data. Examples of the input device 103 include a keyboard, a mouse, a touch panel, a numeric keypad, a scanner, a microphone, and a sensor. The output device 104 outputs data. The output device 104 includes, for example, a display, a printer, and a speaker. The communication IF 105 connects to a network and transmits and receives data.

[0010] <Example of functional configuration of decision support device> 2 is a block diagram showing an example of the functional configuration of the decision-making support device 100. The decision-making support device 100 includes an acquisition unit 201, a duplication unit 202, a prediction unit 203, a calculation unit 204, a display control unit 205, a test campaign DB 211, and a prediction model DB 212. The acquisition unit 201, the duplication unit 202, the prediction unit 203, the calculation unit 204, and the display control unit 205 are specifically realized by, for example, having the processor 101 execute a program stored in the storage device 102 shown in FIG. 1. The test campaign DB 211 and the prediction model DB 212 are specifically stored in, for example, the storage device 102 shown in FIG. 1 or the storage device 102 of a computer that can communicate with the decision-making support device 100, and are accessible by the processor 101.

[0011] The test action DB 211 stores test action data for a certain repetition. Details of the test action DB 211 will be described later with reference to FIG.

[0012] The prediction model DB212 stores a group of prediction models. The group of prediction models consists of M (M is an integer equal to or greater than 2) identical prediction models PMa1 to PMaM. When the prediction models PMa1 to PMaM are not to be distinguished from one another, they are referred to as prediction model PMam. m is an integer satisfying 1≦m≦M, and corresponds to the number of iterations m, which will be described later. The prediction model PMam is, for example, a neural network model. Furthermore, it is assumed that the prediction model PMam has completed learning in advance using training data having the same type of input variables as the test measures. The outcome prediction value, which is the numerical value of the outcome output by the prediction model PMam, ranges from 0% to 100%.

[0013] The acquisition unit 201 acquires test campaign data from the test campaign DB 211. The duplication unit 202 duplicates the test campaign data acquired by the acquisition unit 201. The prediction unit 203 inputs each of the duplicated test campaign data into the prediction models PMa1 to PMaM and outputs a prediction result. The calculation unit 204 calculates difference data and auxiliary information. The display control unit 205 generates prediction information and outputs the prediction information in a displayable manner. The prediction information is output to a display device, which is an example of the output device 104, or a display device of a computer that can communicate with the decision-making support apparatus 100. The display control unit 205 also adjusts the prediction information based on an operation input using the input device 103 or the input device 103 of a computer that can communicate with the decision-making support apparatus 100, and outputs the adjusted prediction information in a displayable manner. Specific processing by the acquisition unit 201, duplication unit 202, prediction unit 203, calculation unit 204, and display control unit 205 will be described later with reference to FIG. 4.

[0014] <Test Measure DB> FIG. 3 is an explanatory diagram showing an example of a test measure DB. The test measure DB 211 is a database that specifies input variables for test measures, and is prepared in advance. The test measure DB 211 stores test measure data 300-3 having a test measure 301 and an input variable group 302 as fields. The test measure 301 includes a baseline measure P0 and T (T is an integer equal to or greater than 1) intervention measures P1 to PT. The baseline measure P0 is a test measure that serves as a reference for the intervention measures P1 to PT. Specifically, for example, the intervention measures P1 to PT are test measures 301 in which the value of the input variable group 302 is changed with respect to the baseline measure P0.

[0015] When there is no distinction between the baseline measure P0 and the intervention measures P1, P2, ..., PT, they are written as measure Pt (t is an integer that satisfies 0≦t≦T). When there is no distinction between the intervention measures P1, P2, ..., PT, they are written as intervention measure Pt (t is an integer that satisfies 1≦t≦T).

[0016] The input variable group 302 is N (N is an integer equal to or greater than 1) input variables X1 to XN that characterize the test campaign 301, and the values ​​of the input variables X1 to XN are stored in the entries of each test campaign 301. The input variables X1 to XN include variables related to time. For example, one of the input variables X1 to XN (e.g., X1) is the repetition number m. The repetition number m is a numerical value indicating the current number of times the test campaign 301 repeatedly encounters the same situation or the same intervention. In FIG. 3, the repetition number m=3 as an example. The value "3" of the input variable X1 of the test campaign data 300-3 stored in the test campaign DB 211 is referred to as the base number.

[0017] <Decision-making process> FIG. 4 is a flowchart showing an example of a decision-making process procedure performed by the decision-making support device 100.

[0018] Step S400: The decision support apparatus 100 receives an input instruction from the user, which indicates the start of outcome prediction analysis, via the input device 103, and starts data processing.

[0019] Step S401: The decision support device 100 acquires (T+1) test measures 301, i.e., entry data of the baseline measure P0 and T intervention measures P1 to PT, from the test measure DB 211, using the acquiring unit 201. Next, the decision support device 100 executes a serialization process for the test measures 301, using the acquiring unit 201, to input the acquired (T+1) test measures 301 to the prediction model PMam. For example, the decision support device 100 uses the acquiring unit 201 to shape the test measure data 300 so that the test measures are input to the prediction model in a time-division manner in the order of baseline measure P0, intervention measures P1, P2, ..., PT. This will be specifically described below with reference to FIG. 5.

[0020] FIG. 5 is an explanatory diagram illustrating an example of processing in step S402 and step S403 according to the first embodiment.

[0021] Step S402: The decision support device 100 causes the copying unit 202 to copy all entries of the serialized test measures 301 (P0 to PT) by (M-1) copies, and sets the copies as test measure data 300-1, 300-2, 300-4, . . . , 300-M.

[0022] For each of the (M-1) replicated test action data 300-1, 300-2, 300-4, ..., 300-M, the decision-making support device 100 changes only the value m of the input variable X1 (repetition number) in all entries of the test action 301 (P0 to PT). For example, for each of the (M-1) test action data 300-1, 300-2, 300-4, ..., 300-M, the decision-making support device 100 changes the value of the input variable X1 in the test action 301 to a value up to M in ascending order from 1 (excluding the base number "3").

[0023] Specifically, for example, the decision support device 100 uses the duplication unit 202 to change the value of the input variable X1 of the first test measure 301 out of the (M-1) test measures 301 excluding the test measure 301 before the change shown in Figure 3 to "1", the value of the input variable X1 of the second test measure 301 to "2", the value of the input variable X1 of the fourth test measure 301 to "4", ..., the value of the input variable X1 of the ith test measure 301 to "i", ..., the value of the input variable X1 of the Mth test measure 301 to "M".

[0024] Step S403: The decision-making support device 100, using the prediction unit 203, acquires M prediction models PMa1 to PMaM from the prediction model DB 212, and sequentially inputs the M test policy data 300-1 to 300-M into each prediction model PMa1 to PMaM. That is, the test policy data 300-1 to 300-M having different values ​​for the number of repetitions of the input variable X1 are input to each of the M prediction models PMa1 to PMaM. The decision-making support device 100 then acquires M groups of outcome predicted values ​​Ra1 to RaM sequentially predicted from the M prediction models PMa1 to PMaM using the prediction unit 203.

[0025] Each of the outcome prediction value groups Ra1 to RaM is a set of (T+1) outcome prediction values. For example, the outcome prediction value group Ra1 includes the outcome prediction value y10 of the baseline measure P0, the outcome prediction value y11 of the intervention measure P1, ..., the outcome prediction value y1T of the intervention measure P1 at the iteration m=1.

[0026] The outcome prediction value group Ra2 includes the outcome prediction value y20 of the baseline measure P0, the outcome prediction value y21 of the intervention measure P1, ..., the outcome prediction value y2T of the intervention measure P1 at iteration m=2. The outcome prediction value group Ra3 includes the outcome prediction value y30 of the baseline measure P0, the outcome prediction value y31 of the intervention measure P1, ..., the outcome prediction value y3T of the intervention measure P1 at iteration m=3 (base iteration). The same applies to the outcome prediction value groups Ra4 and beyond, and the outcome prediction value group RaM includes the outcome prediction value yM0 of the baseline measure P0, the outcome prediction value yM1 of the intervention measure P1, ..., the outcome prediction value yMT of the intervention measure P1 at iteration m=M.

[0027] When the groups of outcome prediction values ​​Ra1 to RaM are not distinguished, they are simply referred to as the group of outcome prediction values ​​Ram. Similarly, each of the (T+1) outcome prediction values ​​in the group of outcome prediction values ​​Ram is referred to as the outcome prediction value ym0 of the baseline measure P0, the outcome prediction value ym1 of the intervention measure P1, ..., the outcome prediction value ymT of the intervention measure P1 at the repetition number m. Furthermore, when the group of outcome prediction values ​​ym0 of the baseline measure P0, the outcome prediction value ym1 of the intervention measure P1, ..., the outcome prediction value ymT of the intervention measure P1 at the repetition number m=M are not distinguished, they are simply referred to as the outcome prediction value ymt.

[0028] Step S404: The decision support device 100, using the calculation unit 204, calculates the differences dm0 to dmT between the outcome predicted value ym0 of the baseline measure P0 and each of the (T+1) outcome predicted values ​​ym0 of the baseline measure P0, the outcome predicted value ym1 of the intervention measure P1, ..., the outcome predicted value ymT of the intervention measure P1 for the group of outcome predicted values ​​Ram at the repetition number m.

[0029] FIG. 6 is an explanatory diagram showing an example of difference data in the group of outcome predicted values ​​Ram. The difference data Dm in the group of outcome predicted values ​​Ram includes differences dm0 to dmT. When differences dm0 to dmT are not distinguished, they are referred to as difference dm. The difference data Dm is calculated for the group of outcome predicted values ​​Ram. The difference data Dm is used in the improvement effect ranking, which will be described later.

[0030] Step S405: Returning to Fig. 4, the decision support apparatus 100 calculates attribute information for each group of outcome prediction values ​​Ram based on the group of outcome prediction values ​​Ram using the calculation unit 204. The attribute information includes time-varying information for the group of outcome prediction values ​​Ram and a time-varying score for the group of outcome prediction values ​​Ram.

[0031] Figure 7 is an explanatory diagram showing an example of time-course variation information. Time-course variation information 700 can be displayed as a graph, as shown in Figure 7. The horizontal axis of graph 701 represents the iteration number m (input variable X1), and the vertical axis represents the outcome prediction value ymt at the iteration number m.

[0032] The base time-course variation data 702 is data that indicates the characteristics of the baseline measure P0 over time. Specifically, for example, the base time-course variation data 702 is data obtained by plotting the outcome predicted values ​​y10, y20, y30, ..., yM0 (indicated as M=7 in FIG. 7) of each baseline measure P0 in the outcome predicted value groups Ra1 to RaM with different repetition times m in the order of the repetition times (indicated by ▲ in FIG. 7).

[0033] The intervention time-course variation data 703 is data showing the characteristics of the intervention measure Pt over time. Specifically, for example, the intervention time-course variation data 703 is data obtained by plotting the outcome prediction values ​​y1t, y2t, y3t, ..., yMt (indicated as M=7 in FIG. 7) of each intervention measure Pt in the outcome prediction value groups Ra1 to RaM with different repetition times m in the order of the repetition times (indicated by ● in FIG. 7). The intervention time-course variation data 703 exists for each of the intervention measures P1 to PT.

[0034] The time-varying score is calculated, for example, for an intervention measure Pt, using the outcome prediction value y1t when the number of repetitions is m=1 and the outcome prediction value yMt when the number of repetitions is m=M, according to the following formula (1): SCt is the time-varying score (unit: [%]) for the intervention measure Pt.

[0035] SCt=(|y1t-yMt| / y1t)×100...(1)

[0036] The temporal variation score SCt is calculated for each of the intervention measures P1 to PT. The temporal variation score SCt increases as |y1t-yMt| increases.

[0037] 4, the decision-making support apparatus 100 generates and displays prediction information through the display control unit 205. The prediction information is displayed on a display device, which is an example of the output device 104.

[0038] 8 is an explanatory diagram showing an example of a prediction information display screen. The prediction information display screen 800 is a display screen for displaying prediction information, and has an improvement effect ranking display area 801, a selected intervention measure breakdown display area 802, a prediction result display area 803, and a result display adjustment area 804.

[0039] The improvement effect ranking display area 801 is an area for displaying the improvement effect ranking in the base round (m=3) as prediction information.

[0040] The improvement effect ranking is a set of improvement effects in which intervention measures Pt are ranked in descending order of improvement effect in the base number of times (m=3). Specifically, for example, the decision support device 100 ranks the intervention measures Pt from 1st place to Tth place in descending order of the difference dm in the base number of times (m=3). For example, the difference dm of the intervention measure Pt in 1st place is "+35%".

[0041] In addition, for each ranking, the number in parentheses is the predicted outcome value ymt of the intervention measure Pt in the base round (m=3). For example, the predicted outcome value ymt of the first-place intervention measure Pt is "55%." Note that the improvement effect of the intervention measure Pt requires at least one of the difference dm and the predicted outcome value ymt.

[0042] The "current state" on the left side of the improvement effect ranking display area 801 includes the difference dm0 (±0%) and the predicted outcome value ym0 (20%) of the baseline measure P0 at the base time (m=3).

[0043] The user can select the improvement effect of the t-th intervention measure Pt by operating the input device 103.

[0044] The selected intervention measure breakdown display area 802 is an area that displays, as predicted information, the breakdown of the intervention measures (selected intervention measures) Pt whose improvement effects have been selected in the improvement effect ranking display area 801. For example, suppose that the user operates the input device 103 to select the second-ranked improvement effect (shown in a bold frame) in the improvement effect ranking display area 801 as the first improvement effect.

[0045] In this case, the decision support device 100 acquires the values ​​of the input variables X1 to XN related to the intervention measure Pt (first improvement measure Pt) of the selected rank and the values ​​of the input variables X1 to XN related to the baseline measure P0 in the base round (m=3) from the test measure DB 211. Then, the decision support device 100 sets the values ​​of the input variables X1 to XN related to the baseline measure P0 as the values ​​before change (start end side of the arrow) and displays them as the values ​​of the input variables X1 to XN related to the intervention measure Pt of the selected rank (end end side of the arrow) in the breakdown display area 802 of the selected intervention measure Pt.

[0046] The decision support device 100 does not need to display input variables that have not changed between the selected intervention measure Pt and the baseline measure P0. In Fig. 8, the value of the input variable X2 has changed from "0" to "2" and the value of the input variable X3 has changed from "5" to "4".

[0047] The prediction result display area 803 is an area that displays, as prediction information, the time-dependent variation information 700 that indicates the prediction result from the auxiliary information calculated in step S405. Assume that the user operates the input device 103 to select, for example, the second-ranked improvement effect (shown in a bold frame) in the improvement effect ranking display area 801 as the first improvement effect.

[0048] In this case, the decision-making support device 100 acquires the base time-course fluctuation data 702 and the intervention time-course fluctuation data 703 of the intervention measure Pt (first improvement measure Pt) in the selected order at the base time (m=3) as time-course fluctuation information 700 and displays it in the prediction result display area 803.

[0049] 9 is an explanatory diagram showing a display example 1 of the prediction result display area 803. In the time-over-time variation information 700, a vertical line 900 indicates the base number (m=3). This allows the user to visually recognize where the base number (m=3) is located in the time-over-time variation information 700 being displayed.

[0050] Returning to FIG. 8, the result display adjustment area 804 is an area that displays, as prediction information, a user interface for adjusting the value of the time-dependent variation score SCt among the attribute information calculated in step S405.

[0051] 10 is an explanatory diagram showing a first display example of the result display adjustment area 804. A user interface 1000 for adjusting the value of the time-dependent variation score SCt, which is the first score, is displayed in the result display adjustment area 804. The user interface 1000 has a numerical axis 1001 and a slider 1002.

[0052] The numerical axis 1001 is a slider bar with 0% at the right end and 100% at the left end. The slider 1002 can be moved on the numerical axis 1001 by the user operating the input device 103. The default position of the slider 1002 is 100%. The 100% position corresponds to the time-varying score SCt of the selected intervention measure Pt. As the slider 1002 moves to the right, the percentage value indicated by the slider 1002 decreases, and therefore the time-varying score SCt also decreases. The range from 0% to the percentage indicating the current position of the slider 1002 is called the score range (indicated by the thick black line on the numerical axis 1001 in Figure 10).

[0053] The improvement effects of intervention measures Pt having time-varying scores SCt within the score range are selected for display in the improvement effect ranking display area 801. In the example of Fig. 10, the slider 1002 is positioned at 75% on the numerical axis 1001, so the range shown in black from 0% to 75% is set as the score range.

[0054] That is, the decision support apparatus 100 sets, via the display control unit 205, the second improvement effect of the second intervention measure Pt having a second score that is a time-varying score (0.75×SCt to 0 in the example of FIG. 10) that is equal to or less than the numerical value of the current position of the slider 1002 (75% in the example of FIG. 10) as a display target in the improvement effect ranking display area 801. Therefore, the more the user moves the slider 1002 to the right, the fewer the number of intervention measures Pt displayed in the improvement effect ranking display area 801. As a result, the displayed intervention measures Pt have a smaller time-varying score SCt than the intervention measures Pt that are not displayed, that is, are more sustainable intervention measures Pt.

[0055] Furthermore, the third intervention measure Pt corresponding to the time-varying score outside the score range shown in black will no longer be displayed, so the number of options when selecting an intervention measure Pt is reduced, reducing the burden on the user.

[0056] Furthermore, if the number of rankings to be displayed is set in advance, the decision support device 100 may automatically adjust the score according to the number of rankings to be displayed, and output the adjustment result as the score range of the time-varying score SCt.

[0057] Steps S407 and S408: Returning to FIG. 4, if there is a user operation (step S407: Yes), the decision-making support device 100 adjusts the display content of the prediction information display screen 800 (step S408) via the display control unit 205, and displays the adjusted prediction information on the prediction information display screen 800 (step S406).

[0058] Specifically, the user operation in step S407 is, for example, the selection of the improvement effect of the intervention measure Pt ranked t in the improvement effect ranking display area 801 described above, or the movement of the slider 1002 in the result display adjustment area 804.

[0059] The adjustment of the display content of the prediction information display screen 800 in step S408 is to display time-varying information 700 about the selected intervention measure Pt in the prediction result display area 803 if the user operation in step S407 is to select the improvement effect of the intervention measure Pt ranked t in the improvement effect ranking display area 801, or to display the improvement effect (difference dm, outcome predicted value ymt) about the intervention measure Pt having a time-varying score SCt equal to or less than the numerical value of the current position of the slider 1002 in the improvement effect ranking display area 801 if the user operation in step S407 is to move the slider 1002 in the result display adjustment area 804. The adjustment result in step S408 is stored in the prediction result DB and can be called up upon request from the user.

[0060] If there is no user operation (step S407: No), the decision-making process by the decision-making support device 100 ends if an end condition (for example, pressing an end button not shown) is satisfied.

[0061] In this way, when a user sets a range for the time-varying score SCt, which is the ancillary information, the decision support device 100 according to the first embodiment displays the predicted results of intervention measures Pt that meet these conditions. That is, the user can check the sustainability of the intervention measures Pt using two evaluation items, the outcome predicted value ymt and the time-varying score SCt. This makes it possible to select an optimal intervention measure Pt even when there are many changeable input variables and their values.

[0062] In the first embodiment, the input variables X1 to XN can be either qualitative variables with a nominal scale or an ordinal scale, or continuous variables with an interval scale or a proportional scale. The prediction model PMam is a neural network model, but is not limited to this. For example, prediction models such as a logistic regression model, a random forest model, or a Bayesian network model can also be applied.

[0063] In step S402, the value of the number of repetitions m is set to a value ranging from 1 to M in increments of 1, but this is not limitative. For example, it is also possible to set an increment of 2 or more, and in this case, the number of required prediction models PMam can be reduced to a value obtained by dividing M by the increment.

[0064] 9, the time-varying information 700 is expressed as a line graph, but this is not limiting and the information may be in a table format, for example. Furthermore, the decision-making support device 100 displays only the intervention measures Pt that match the set range of the time-varying score SCt, but this is not limiting. For example, the decision-making support device 100 can determine the order of the improvement effect ranking by taking into account both the improvement effect in the improvement effect ranking display area 801 and the degree of match with the set range of the time-varying score SCt. [Example]

[0065] Example 2 is an example in which the time-course variation information 700 in Example 1 is changed to the confidence interval of the outcome prediction value group (y1t, y2t, ..., yKt). In Example 2, differences from Example 1 will be mainly described, and descriptions of the same contents as Example 1 will be omitted.

[0066] The prediction model DB 212 of the second embodiment stores K (K is an integer equal to or greater than 2) prediction models PMb1 to PMbK each having different training data. That is, the learning parameters of the prediction models PMb1 to PMbK are different, and when the same input data is given, different output data is obtained. When the prediction models PMb1 to PMbK are not distinguished from each other, they are referred to as prediction model PMbk (k is an integer satisfying 1≦k≦K). The training data for prediction model PMbk is, for example, a bootstrap sample extracted with replacement from the original training data.

[0067] FIG. 11 is an explanatory diagram illustrating an example of processing in step S402 and step S403 according to the second embodiment.

[0068] Step S402: The decision support device 100 uses the duplication unit 202 to duplicate all entries of the serialized test measures 301 (P0 to PT) (K-1 copies) to create test measure data 1100-1, 1100-2, 1100-4, ..., 1100-K. Unlike the first embodiment, the repetition number, which is the value of the input variable X1, is not changed for each of the test measure data 1100-1, 1100-2, 1100-4, ..., 1100-K. That is, in the test measure data 1100-1, 1100-2, 1100-4, ..., 1100-K, the value of the input variable X1 is the base number m=3.

[0069] Step S403: The decision-making support device 100, using the prediction unit 203, obtains K prediction models PMb1 to PMbK from the prediction model DB 212, and sequentially inputs the K test policy data 1100-1 to 1100-K into each prediction model PMb1 to PMbK. The decision-making support device 100 then obtains K groups of outcome predicted values ​​Rb1 to RbK sequentially predicted from the K prediction models PMb1 to PMbK using the prediction unit 203. The decision-making support device 100 then groups the K groups of outcome predicted values ​​Rb1 to RbK by policy Pt using the prediction unit 203.

[0070] FIG. 12 is an explanatory diagram showing an example of grouping K outcome prediction value groups Rb1 to RbK. The K outcome prediction value groups Rb1 to RbK are grouped into (T+1) outcome prediction value groups RB0 to RbT, organized by measure Pt. The outcome prediction value group RB0 is a collection of outcome prediction values ​​y10 to yK0 for the baseline measure P0. The outcome prediction value group RBt is a collection of outcome prediction values ​​y1t to yKt for the intervention measure Pt.

[0071] Step S404: The decision-making support device 100 sequentially calculates average values ​​y(0), y(1), ..., y(T) for each of the (T+1) groups of outcome prediction values ​​RB0 to RbT using the prediction unit 203. The decision-making support device 100 then sequentially calculates the difference between each of the average values ​​y(0), y(1), ..., y(T) for the (T+1) groups of outcome prediction values ​​RB0 to RbT and the average value y(0) of the outcome prediction values ​​for the baseline measure P0. When the average values ​​y(0), y(1), ..., y(T) are not distinguished, they are referred to as average values ​​y(t).

[0072] 13 is an explanatory diagram showing an example of differential data in Example 2. The differential data Db has (T+1) differences d(0) to d(T). When the differences d(0) to d(T) are not distinguished, they are expressed as difference d(t). The differential data Db is used for the improvement effect ranking, which will be described later.

[0073] Step S405: The decision support device 100, via the calculation unit 204, calculates auxiliary information for each of the (T+1) groups of outcome prediction values ​​RB0 to RbT. The auxiliary information for the group of outcome prediction values ​​RBt is the confidence interval and confidence interval score for the group of outcome prediction values ​​RBt. The confidence interval for each measure Pt ranges, for example, from the 5th percentile to the 95th percentile of the K outcome prediction values ​​y1t to yKt in the group of outcome prediction values ​​RBt. The confidence interval score for each measure Pt is calculated, for example, by (AB) where A is the upper limit and B is the lower limit of the confidence interval for the measure Pt.

[0074] Step S408: The decision-making support apparatus 100 acquires the range of the confidence interval input by the user via the input device 103 in step S407. The decision-making support apparatus 100 generates and displays prediction information based on the acquired range of the confidence interval through the display control unit 205 (step S406). The decision-making support apparatus 100 also stores the prediction information in the storage device 102.

[0075] Specifically, for example, the decision-making support device 100 displays, via the display control unit 205, a confidence interval based on the group of outcome prediction values ​​RBt (y1t, y2t, ..., yKt) for the intervention measure Pt (first improvement measure Pt) ranked by the user in the improvement effect ranking (hereinafter referred to as the confidence interval of the selected intervention measure Pt) in the prediction result display area 803, together with a confidence interval based on the group of outcome prediction values ​​RB0 (y10, y20, ..., yK0) for the baseline measure P0 (hereinafter referred to as the confidence interval of the baseline measure P0).

[0076] 14 is an explanatory diagram showing a second display example of the prediction result display area 803. In the prediction result display area 803, confidence interval information 1400 includes a confidence interval 1401 of the baseline measure P0, a confidence interval 1402 of the selected intervention measure Pt, a confidence interval score 1411 of the baseline measure P0, and a confidence interval score 1412 of the selected intervention measure Pt, which is the first score. In FIG. 14, the confidence interval 1402 and the confidence interval score 1412, which is the first score, of the selected intervention measure Pt, which is ranked second by the user in the improvement effect ranking, are displayed.

[0077] 15 is an explanatory diagram showing a second display example of the result display adjustment area 804. A user interface 1500 for adjusting the confidence interval score 1412 of the selected intervention measure Pt, which is the first score, is displayed in the result display adjustment area 804. The user interface 1500 has a numerical axis 1501 and a slider 1502.

[0078] The numeric axis 1501 is a slider bar with 20% at the right end and 100% at the left end. The slider 1502 can be moved on the numeric axis 1501 by the user operating the input device 103. The default position of the slider 1502 is 100%. The 100% position corresponds to the confidence interval score 1412 of the selected intervention measure Pt. As the slider 1502 moves further to the right, the percentage value indicated by the slider 1502 decreases, and therefore the confidence interval score 1412 also decreases. The range from 20% to the percentage indicating the current position of the slider 1502 is the score range (indicated by the thick black line on the numeric axis 1501 in FIG. 15 ).

[0079] The improvement effects of intervention measures Pt having confidence interval scores 1412 within the score range are selected for display in the improvement effect ranking display area 801. In the example of Fig. 15, the slider 1502 is positioned at 70% on the numerical axis 1501, so the range shown in black from 20% to 70% is set as the score range.

[0080] That is, the decision-making support apparatus 100, via the display control unit 205, sets the improvement effects (second improvement effects) of intervention measures Pt (second intervention measures Pt) having a confidence interval score 1102 equal to or less than the numerical value of the current position of the slider 1302 as display targets in the improvement effect ranking display area 801. Therefore, the more the user moves the slider 1302 to the right, the fewer the number of intervention measures Pt displayed in the improvement effect ranking display area 801. As a result, the displayed intervention measures Pt have a smaller confidence interval score 1202 than the intervention measures Pt that are not displayed, i.e., are more certain intervention measures Pt.

[0081] Furthermore, the third intervention measure Pt corresponding to the confidence interval score 1102 outside the score range shown in black will no longer be displayed, so the number of options when selecting an intervention measure Pt is reduced, reducing the burden on the user.

[0082] Furthermore, if the number of rankings to be displayed is set in advance, the decision support device 100 may automatically adjust the scores according to the number of rankings to be displayed, and output the adjustment results as the score range of the confidence interval score 1102.

[0083] In this way, when the user sets the range of the confidence interval score 1202, which is the ancillary information, the decision support device 100 according to the second embodiment displays the predicted results of the intervention plan Pt that meet these conditions. That is, the user can confirm the reliability of the intervention plan Pt using two evaluation items, namely, the group of outcome predicted values ​​(y1t, y2t, ..., yKt) and the confidence interval score 1202. This makes it possible to select the optimal intervention plan Pt even when there are many changeable input variables and their values.

[0084] Note that Example 2 can also be applied when the intervention plan Pt and the input variables X1 to XN of the prediction model PMbk do not include a time-related variable. As in Example 1, the prediction model PMbk is not limited to a neural network model. However, it is desirable that the model be one that can calculate the confidence interval of the outcome prediction value using analytical methods such as matrix operations or statistical methods such as bootstrap. [Example]

[0085] Example 3 is an example in which the time-dependent variation information 700 in Example 1 is changed to a latent variable of a group of outcome predicted values. In Example 3, differences from Example 1 will be mainly described, and descriptions of the same contents as in Example 1 will be omitted.

[0086] The prediction model DB 212 of the third embodiment stores, for example, a partial least squares regression model as a prediction model. The partial least squares regression model is a model that predicts a predicted outcome value using mutually uncorrelated latent variables that aggregate input variables X1 to XN. The number of latent variables is, for example, six for each measure Pt, and the meaning of each latent variable is assumed to have been interpreted in advance.

[0087] Step S402: Unlike the first embodiment, the decision support device 100 does not copy the test action data 300-3, and proceeds to step S403.

[0088] FIG. 16 is an explanatory diagram illustrating an example of processing in steps S403 and S404 according to the third embodiment.

[0089] Step S403: The decision support device 100 acquires the partial least squares regression model PLSM as a prediction model from the prediction model DB 212, sequentially inputs the test exercise data 300-3 to the partial least squares regression model PLSM, and then acquires the set of outcome predicted values ​​Rc and the latent variable scores LV sequentially predicted from the partial least squares regression model PLSM.

[0090] The outcome prediction set Rc includes outcome prediction values ​​y0, y1, ..., yT for the baseline measure P0, intervention measures P1, P2, ..., PT. The latent variable score LV includes latent variable scores LV0 to LVT for the baseline measure P0, intervention measures P1, P2, ..., PT. When the latent variable scores LV0 to LVT are not distinguished, they are referred to as the latent variable score LVt. The latent variable score LVt includes six latent variables lvt1 to lvt6. When the latent variables lvt1 to lvt6 are not distinguished, they are referred to as the latent variable lvt. The latent variable lvt is assumed to be scaled between 0% and 100% based on the training data.

[0091] Step S404: The decision support device 100 sequentially calculates the differences d0 to dT between the outcome predicted values ​​y0, y1, ..., yT of the baseline measure P0 and intervention measures P1, P2, ..., PT and the outcome predicted value y0 of the baseline measure P0, and sets the results as difference data Dc.

[0092] Step S405: Unlike the first embodiment, the decision support apparatus 100 uses the calculation unit 204 to acquire the latent variable score LV as auxiliary information.

[0093] Step S406: The decision-making support device 100 acquires the range of the latent variable lvt input by the user via the input device 103. The decision-making support device 100 generates and displays prediction information based on the acquired range of the latent variable lvt. The decision-making support device 100 also stores the output prediction information in the storage device 102.

[0094] The decision support device 100 causes the display control unit 205 to display, in the prediction result display area 803 as the first score, the latent variable score LV of the rank in the improvement effect ranking where the first improvement effect is selected by the user.

[0095] FIG. 17 is an explanatory diagram showing a display example 3 of the prediction result display area 803. In the prediction result display area 803, a radar chart 1700 is a graph expressed with multiple axes (six axes in this example) in the circumferential direction, and visualizes the values ​​of six latent variables lvt1 to lvt6. In the radar chart 1700, black circles indicate the latent variables lvt1 to lvt6 of the intervention measure Pt (first intervention measure Pt) for which the first improvement effect was selected, and black triangles indicate the latent variables lv01 to lv06 of the baseline measure P0 used as a comparison reference. Here, the latent variables lvt1 to lvt6 are expressed by the radar chart 1700, but this is not limiting and may be in a table format, for example.

[0096] 18 is an explanatory diagram showing a display example 3 of the result display adjustment area 804. A user interface 1800 for adjusting the latent variable score LV is displayed in the result display adjustment area 804. The user interface 1800 has a numerical axis 1801 and sliders 1802 indicated by black circles for each of the latent variables lvt1 to lvt6.

[0097] The slider 1802 is an example of an adjustment means displayed in the result display adjustment area 804, and in the third embodiment, it sets the range of the latent variable score LV. FIG. 18 shows an example in which the range of all latent variables lvt1 to lvt6 is set to a score range between 10% and 90% (indicated by the thick black line on the numerical axis 1801 in FIG. 18). In this case, it is assumed that all measures Pt satisfy the conditions and are targets for display in the improvement effect ranking. The 100% position corresponds to the latent variables lvt1 to lvt6 of the selected intervention measure Pt.

[0098] The white triangles represent the latent variables lv01 to lv06 of the baseline measure P0. When the user moves the two sliders 1802 of the latent variable lvt left and right, the decision support apparatus 100 selects, as the display target in the improvement effect ranking display area 801, the improvement effect (second improvement effect) of the intervention measure Pt (second intervention measure Pt) having, as the second score, the latent variable lvt within the score range specified at that position.

[0099] Therefore, the more the user moves the range of the two sliders 1802 for the latent variable lvt to the right, the fewer the number of second intervention measures Pt displayed in the improvement effect ranking display area 801, and these second improvement effects will be the improvement effects of only the intervention measures Pt with high latent variables lvt, i.e., high improvement effects, compared to the improvement effects of the third intervention measures Pt corresponding to the latent variables lvt1 to lvt6 outside the score range.

[0100] The range of the latent variable lvt can be set independently for each of the six latent variables lvt1 to lvt6. In other words, it is possible to make adjustments such as preferentially displaying specific intervention measures Pt with a high latent variable lvt, or preferentially hiding specific intervention measures Pt with a low latent variable lvt.

[0101] Furthermore, the third intervention measure Pt corresponding to the latent variables lvt1 to lvt6 outside the score range shown in black will no longer be displayed, so the number of options when selecting an intervention measure Pt is reduced, reducing the burden on the user.

[0102] Furthermore, if the number of rankings to be displayed is set in advance, the decision support device 100 may automatically adjust the scores in accordance with the number of rankings to be displayed, and output the adjustment results as the score range of the latent variable score LV.

[0103] In this way, when the user sets the range of the latent variable lvt, which is the ancillary information, the decision support device 100 according to the third embodiment displays only the improvement effects of the intervention measures Pt that meet these conditions. In other words, the interpretability of the intervention measures Pt can be confirmed using two evaluation items, the outcome predicted value yt and the latent variable lvt. This enables the user to select the optimal intervention measure Pt even when there are many changeable input variables X1 to XN and their values. Note that, like the first embodiment, the third embodiment can also be applied when there is no time-related variable in the input variables of the test measures or the prediction model.

[0104] In addition, in Example 3, the prediction model is a partial least squares regression model (PLSM), but this is not limited to this. For example, it is possible to apply a prediction model that can interpret latent variables, such as a principal component regression model or a structural equation model. Furthermore, the number of latent variables used is six, but this is not limited to this. Generally, as the number of latent variables used increases, the prediction accuracy of the prediction model improves, and the improvement converges above a certain number. Therefore, it is recommended that the number of latent variables used be set near the convergence point of prediction accuracy, for example. [Example]

[0105] Example 4 is an example that includes all of the functions shown in Examples 1 to 3. The decision support device 100 stores, in a prediction model DB 212, M prediction models PMa1 to PMaM shown in Example 1, K prediction models PMb1 to PMbK shown in Example 2, and a prediction model with latent variables (for example, a partial least squares regression model PLSM) shown in Example 3.

[0106] The decision support device 100 performs outcome prediction using each of the prediction models PMam, PMbk, and PLSM. There are three types of outcome prediction results for each of the prediction models PMam, PMbk, and PLSM. However, the decision support device 100 employs a method for calculating the average value y(t) of outcome prediction values ​​based on the prediction model PMbk in the second embodiment, for example.

[0107] The decision-making support device 100 further acquires auxiliary information for each measure Pt from each of the prediction models PMam, PMbk, and PLSM. The decision-making support device 100 displays this auxiliary information on the prediction information display screen 800 (step S406), and changes the display content of the improvement effect ranking based on each range of the auxiliary information set by a user operation (step S407: Yes) (step S408). There are three types of auxiliary information and three types of auxiliary information range adjustment means, one for each of the prediction models PMam, PMbk, and PLSM. Therefore, the decision-making support device 100 displays this prediction information by, for example, appropriately switching the prediction information display screen 800.

[0108] 19 is an explanatory diagram illustrating an example of prediction result data by the decision support device 100 according to Example 4. The prediction result data 1900 is a table having, for each measure Pt, an outcome prediction value 1911, a repetition number 1912, a temporal variation score 1913, a confidence interval 1914, a confidence interval score 1915, and a latent variable score 1916.

[0109] In Figure 19, intervention measure P3 is the intervention measure selected as the second most effective in the improvement ranking, and the numerical values ​​shown in Figures 8, 9, 10, 14, 15, 17, and 18 are stored. Note that the column data 1923 for intervention measure P3 and the column data values ​​for intervention measures P1, P2, P4 to PT other than column data 1920 for the baseline measure are left blank for convenience of description, but actually each measure has its own value.

[0110] Furthermore, in Example 4, the decision-making support device 100 capable of executing processing related to the functions of Examples 1 to 3 has been described as an example, but the decision-making support device 100 may also be capable of executing processing related to the functions of two of Examples 1 to 3.

[0111] In the first to third embodiments, the decision support device 100 displays the predicted information display screen 800 and the user sets the range of values ​​of various types of attached information, but this is not limited to this method. For example, the decision support device 100 can display a range of attached information effective for an intervention measure Pt as a recommendation based on a set of an intervention measure Pt and its attached information stored in advance in the storage device 102, and the user can set the range based on this information.

[0112] For example, when the number of intervention measures Pt is equal to or greater than a threshold value, or when the difference in the improvement effect for each intervention measure Pt is within a predetermined range, the decision-making support device 100 may recommend settings that narrow the range of values ​​of the attached information. The recommendation presentation operation can be realized, for example, by the decision-making support device 100 executing a program according to the conditions. Alternatively, the decision-making support device 100 can learn the user's past setting information and present setting information that is close to the current conditions.

[0113] As described above, according to the decision support device 100 of Examples 1 to 4 described above, even when there are a large number of changeable input variables and their values, and a large number of prediction results are displayed, the user can select an intervention measure based on the predicted improvement effect of the intervention measure at his / her own discretion.

[0114] Furthermore, in the decision-making support device 100 according to the above-described first to fourth embodiments, the prediction unit 203 obtains the outcome prediction value using a prediction model, but the decision-making support device 100 may obtain the outcome prediction value (including the latent variable in the case of the third embodiment) from an external device capable of communicating with the decision-making support device 100.

[0115] The present invention is not limited to the above-described embodiments, and includes various modifications and equivalent configurations within the spirit and scope of the appended claims. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to configurations including all of the described configurations. Furthermore, part of the configuration of one embodiment may be replaced with the configuration of another embodiment. Furthermore, the configuration of another embodiment may be added to the configuration of one embodiment. Furthermore, part of the configuration of each embodiment may be added to, deleted from, or replaced with other configurations.

[0116] Furthermore, the aforementioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole in hardware, for example by designing them as integrated circuits, or may be realized in software by having a processor interpret and execute a program that realizes each function.

[0117] Information such as programs, tables, files, etc. that realize each function can be stored in storage devices such as memory, hard disks, SSDs (Solid State Drives), or recording media such as IC (Integrated Circuit) cards, SD cards, and DVDs (Digital Versatile Discs).

[0118] In addition, the control lines and information lines shown are those that are considered necessary for explanation, and do not necessarily represent all the control lines and information lines that are necessary for implementation. In reality, it can be assumed that almost all components are interconnected. [Explanation of symbols]

[0119] 100 Decision support equipment 101 processors 102 Storage Devices 201 Acquisition Department 202 Reproduction Department 203 Prediction Department 204 Calculation Unit 205 Display control unit 211 Test Measures DB 212 Prediction Model DB

Claims

1. A decision support device having a processor that executes a program and a storage device that stores the program, The storage device stores a score serving as a relative index between a plurality of intervention measures for each intervention measure, The processor, A selection process for receiving a selection of a first improvement effect from among a plurality of improvement effects related to the plurality of intervention measures; a first output process for outputting adjustable information of the first score within an adjustment range based on a first score of the first intervention program selected by the selection process; an input process for receiving a score range set by the first score after adjustment within the adjustment range; a second output process for outputting a second improvement effect of a second intervention measure having a second score included in the score range received by the input process, among the plurality of improvement effects; A decision support device that executes the above steps.

2. 2. The decision support device according to claim 1, In the second output process, the processor does not output a third improvement effect of a third intervention measure having a third score that is not included in the score range among the multiple improvement effects. A decision support device comprising:

3. 2. The decision support device according to claim 1, The upper limit of the adjustment range corresponds to the first score before adjustment. A decision support device comprising:

4. 2. The decision support device according to claim 1, The storage device stores a time-series set of prediction results for each intervention measure, The processor, Executing a calculation process to calculate the score regarding the time-series fluctuation of the time-series prediction result group for each intervention measure based on the time-series prediction result group for each intervention measure; In the first output process, the processor outputs information on the first score being adjustable within an adjustment range based on the first score regarding the time-series fluctuation of the first prediction result group of the first intervention measure, In the second output process, the processor outputs a second improvement effect of the second intervention measure that is included in the score range and has the second score regarding the time-series fluctuation of a second prediction result group of the second intervention measure, among the multiple improvement effects. A decision support device comprising:

5. 5. The decision support device according to claim 4, The storage device stores a time-series prediction result group for each intervention measure and a time-series prediction result group for a baseline measure that serves as a reference for the plurality of intervention measures; The processor, a third output process for outputting a first time-series prediction result group of the first intervention measure and a time-series prediction result group of the baseline measure when the first improvement effect is selected by the selection process; A decision support device that executes the above steps.

6. 2. The decision support device according to claim 1, Executing a calculation process to calculate the score related to a confidence interval of the time-series prediction result group for each intervention measure based on the time-series prediction result group for each intervention measure; In the first output process, the processor outputs adjustable information of the first score within an adjustment range based on the first score regarding a confidence interval of a time-series first prediction result group of the first intervention measure, In the second output process, the processor outputs a second improvement effect of the second intervention measure that is included in the score range and has the second score related to a confidence interval of a time-series second prediction result group of the second intervention measure, among the multiple improvement effects. A decision support device comprising:

7. 7. The decision support device according to claim 6, The storage device stores a time-series prediction result group for each intervention measure and a time-series prediction result group for a baseline measure that serves as a reference for the plurality of intervention measures; The processor, a third output process for outputting, when the first improvement effect is selected by the selection process, a confidence interval of a first group of time-series prediction results of the first intervention measure and a confidence interval of a group of time-series prediction results of the baseline measure; A decision support device that executes the above steps.

8. 7. The decision support device according to claim 6, The storage device stores a time-series prediction result group for each intervention measure and a time-series prediction result group for a baseline measure that serves as a reference for the plurality of intervention measures; The processor, a third output process for outputting, when the first improvement effect is selected by the selection process, the first score related to a confidence interval of a first group of time-series prediction results of the first intervention measure and the score related to a confidence interval of a group of time-series prediction results of the baseline measure; A decision support device that executes the above steps.

9. 2. The decision support device according to claim 1, the storage device stores a time-series group of prediction results for each intervention measure, and stores a latent variable of a prediction model that outputs the group of prediction results as the score; In the first output process, the processor outputs adjustable information of the first latent variable within an adjustment range based on a first latent variable of the first intervention policy; In the second output process, the processor outputs a second improvement effect of the second intervention measure that is included in the score range and has a second latent variable of a second time-series prediction result group of the second intervention measure, among the multiple improvement effects. A decision support device comprising:

10. 10. The decision support device according to claim 9, The predictive model is any one of a partial least squares regression model, a principal component regression model, and a structural equation model. A decision support device comprising:

11. 10. The decision support device according to claim 9, The storage device stores a latent variable for each of the intervention measures and a latent variable of a baseline measure that serves as a reference for the multiple intervention measures, The processor, a third output process for outputting a first latent variable of the first intervention measure and a latent variable of the baseline measure when the first improvement effect is selected by the selection process; A decision support device that executes the above steps.

12. 12. The decision support device according to claim 11, In the third output process, the processor outputs a first latent variable of the first intervention measure and a latent variable of the baseline measure in a graph represented by a plurality of axes in a circumferential direction. A decision support device comprising:

13. A decision support method using a decision support apparatus having a processor that executes a program and a storage device that stores the program, comprising: The storage device stores a score serving as a relative index between a plurality of intervention measures for each intervention measure, The processor, A selection process for receiving a selection of a first improvement effect from among a plurality of improvement effects related to the plurality of intervention measures; a first output process for outputting adjustable information of the first score within an adjustment range based on a first score of the first intervention program selected by the selection process; an input process for receiving a score range set by the first score after adjustment within the adjustment range; a second output process for outputting a second improvement effect of a second intervention measure having a second score included in the score range received by the input process, among the plurality of improvement effects; A decision support method comprising the steps of:

14. A processor having access to a storage device that stores a score for each of a plurality of intervention measures that is a relative index between the intervention measures, A selection process for receiving a selection of a first improvement effect from among a plurality of improvement effects related to the plurality of intervention measures; a first output process for outputting adjustable information of the first score within an adjustment range based on a first score of the first intervention program selected by the selection process; an input process for receiving a score range set by the first score after adjustment within the adjustment range; a second output process for outputting a second improvement effect of a second intervention measure having a second score included in the score range received by the input process, among the plurality of improvement effects; A decision support program characterized by executing the above.

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