Information processing device and information processing method

Abstract

To allow for choosing a therapeutic method that matches intent of a patient and his / her family.SOLUTION: An information processing device according to an embodiment comprises an estimation unit and an output control unit. The estimation unit estimates the future living situation of a patient for each therapeutic method that can be applied to the patient according to an attribute of the patient. The output control unit outputs information based on the living situation via an output unit.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The embodiments disclosed in the present specification and drawings relate to an information processing device and an information processing method. [Background technology]

[0002] Traditionally, doctors and other medical professionals have unilaterally decided on a patient's treatment plan. In recent medical settings, it has become common for medical professionals to decide on a treatment plan together with the patient and their family, taking into account their wishes and values. Deciding on a treatment plan after reaching an agreement with the patient and their family is called shared decision-making.

[0003] The following challenges exist when selecting a treatment method that takes into account the wishes of the patient and their family. When patients and their families are unable to use medical information to envision their daily lives after discharge, they may lack the information and knowledge, or they may be unable to translate medical information into information relevant to daily life, creating gaps. Because the choice of treatment method itself comes first and does not take into account daily life thereafter, it can sometimes be a difficult choice for patients and their families to handle. Because patients and their families are unable to clearly define their tolerance levels and priorities, it is difficult to reach an agreement on a treatment method that envisions the future.

[0004] With conventional technology, the life desired by patients and their families after treatment was not input, and there was a lack of mutual communication with the medical side to reach a consensus regarding treatment methods, i.e., life after treatment. Furthermore, when reaching a consensus between patients and their families and the medical side, it was difficult for them to imagine the actual life from medical information, leading to situations where they were unable to make a decision, and the medical side was unable to notice this gap. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2004-258978 Summary of the Invention [Problem to be solved by the invention]

[0006] The problem to be solved by the embodiments disclosed in this specification and the drawings is to enable selection of a treatment method that suits the wishes of the patient and their family. However, the problem to be solved by the embodiments disclosed in this specification and the drawings is not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0007] According to an embodiment, an information processing device includes an estimation unit and an output control unit. The estimation unit estimates a future living situation of a target patient for each treatment method to be applied to the target patient based on attributes of the target patient. The output control unit outputs information based on the living situation via an output unit. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of an information processing system 1 according to a first embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of a terminal device 10 according to the first embodiment. [Figure 3] FIG. 10 is a diagram illustrating an example of an intention input screen. [Figure 4] FIG. 1 is a diagram illustrating an example of the arrangement of an information processing device 100 according to a first embodiment. [Figure 5] 4 is a flowchart showing the flow of a series of processes by the processing circuit 120 according to the first embodiment. [Figure 6] 10 is a flowchart showing the flow of processing for estimating the prognosis of a target patient P1. [Figure 7] FIG. 10 is a diagram illustrating an example of attribute distribution. [Figure 8] FIG. 10 is a diagram for explaining stratification processing. [Figure 9] FIG. 10 is a diagram for explaining a method for comparing attribute distributions. [Figure 10] FIG. 10 is a diagram showing an example of items estimated as future living conditions. [Figure 11] 10 is a flowchart showing the flow of a series of processes by a processing circuit 120 according to the second embodiment. [Figure 12] A diagram showing an example of desired living situation. [Figure 13] FIG. 10 is a diagram showing an example of an estimated living situation. [Figure 14] FIG. 10 is a diagram illustrating an example in which a gap occurs. [Figure 15] FIG. 10 is a diagram showing an example of output of a gap elimination method. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an information processing apparatus and an information processing method according to an embodiment will be described with reference to the drawings.

[0010] (First embodiment) [Configuration of information processing system] 1 is a diagram illustrating an example of the configuration of an information processing system 1 according to the first embodiment. The information processing system 1 includes, for example, a terminal device 10 and an information processing device 100. The terminal device 10 and the information processing device 100 are communicably connected via a communication network NW.

[0011] The communication network NW may refer to any information and communication network that uses telecommunications technology. For example, the communication network NW may include wireless / wired LANs such as hospital backbone LANs (Local Area Networks), the Internet, telephone communication networks, optical fiber communication networks, cable communication networks, satellite communication networks, etc.

[0012] The terminal device 10 is a terminal device such as a personal computer, tablet terminal, or mobile phone used by a medical worker P2. The medical worker P2 is typically a doctor, but may also be a nurse or other person involved in medical care, or a person involved in local nursing care services. The medical worker P2, for example, inputs information about a patient who is the target of treatment (hereinafter referred to as target patient P1) into the terminal device 10. Also, instead of the medical worker P2 inputting the information, the target patient P1 or his / her family may input the information about the target patient P1 into the terminal device 10. Also, like patients, family members are also subjects who input information about themselves.

[0013] The terminal device 10 transmits information input by the medical worker P2 to the information processing device 100 or receives information from the information processing device 100 via the communication network NW.

[0014] The information processing device 100 receives information from the terminal device 10 via the communication network NW, processes the received information, and transmits the processed information to the terminal device 10 via the communication network NW.

[0015] The information processing device 100 may be a single device, or may be a system in which multiple devices connected via a communication network NW operate in cooperation with each other. That is, the information processing device 100 may be realized by multiple computers (processors) included in a distributed computing system or a cloud computing system. Furthermore, the information processing device 100 does not necessarily have to be a separate device from the terminal device 10, and may be a device integrated with the terminal device 10.

[0016] [Terminal device configuration] 2 is a diagram illustrating an example of the configuration of the terminal device 10 according to the first embodiment. The terminal device 10 includes, for example, a communication interface 11, an input interface 12, an output interface 13, a memory 14, and a processing circuit 20.

[0017] The communication interface 11 communicates with the information processing device 100 and the like via the communication network NW. The communication interface 11 includes, for example, a network interface card (NIC) and an antenna for wireless communication.

[0018] The input interface 12 receives various input operations from an operator (e.g., medical worker P2), converts the received input operations into electrical signals, and outputs the electrical signals to the processing circuitry 20. For example, the input interface 12 includes a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch panel, etc. The input interface 12 may be a user interface that receives audio input from a microphone, for example. When the input interface 12 is a touch panel, the input interface 12 may also have the display function of a display 13a, which will be described later.

[0019] In this specification, the input interface 12 is not limited to an interface having physical operation parts such as a mouse, keyboard, etc. For example, an example of the input interface 12 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs this electrical signal to a control circuit.

[0020] The output interface 13 outputs information under the control of the processing circuit 20. For example, the output interface 13 includes a display 13a, a speaker 13b, and the like.

[0021] The display 13a displays various types of information. For example, the display 13a displays images generated by the processing circuitry 20, a GUI (Graphical User Interface) for receiving various input operations from the medical staff P2, etc. For example, the display 13a is an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube) display, an organic EL (Electro Luminescence) display, etc.

[0022] The speaker 13b converts various types of information into sound and outputs the sound. For example, the speaker 13b outputs information input from the processing circuit 20 as sound.

[0023] The memory 14 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, or an optical disk. These non-transitory storage media may also be realized by other storage devices connected via a communication network NW, such as a NAS (Network Attached Storage) or an external storage server device. The memory 14 may also include other non-transitory storage media such as a ROM (Read Only Memory) or a register.

[0024] The processing circuitry 20 includes, for example, an acquisition function 21, an output control function 22, and a communication control function 23. The processing circuitry 20 realizes these functions by, for example, a hardware processor (computer) executing a program stored in a memory 14 (storage circuit).

[0025] The hardware processor in the processing circuit 20 refers to a circuit such as a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD) or a complex programmable logic device (CPLD), or a field programmable gate array (FPGA)). Instead of storing the program in the memory 14, the program may be directly embedded in the circuit of the hardware processor. In this case, the hardware processor realizes its function by reading and executing the program embedded in the circuit. The program may be stored in the memory 14 in advance, or may be stored in a non-transitory storage medium such as a DVD or CD-ROM, and installed in the memory 14 from the non-transitory storage medium when the non-transitory storage medium is inserted into a drive (not shown) of the terminal device 10. The hardware processor is not limited to being configured as a single circuit, but may be configured as a single hardware processor by combining multiple independent circuits to realize each function, or multiple components may be integrated into a single hardware processor to realize each function.

[0026] The acquisition function 21 acquires input information via the input interface 12 and acquires information from the information processing device 100 via the communication interface 11 .

[0027] The output control function 22 causes the information acquired by the acquisition function 21 to be displayed as an image on the display 13a or output as sound from the speaker 13b.

[0028] For example, the output control function 22 causes the display 13a to display a screen (hereinafter referred to as an intention input screen) on which the intention of the target patient P1 or his / her family regarding treatment can be input. The intention input screen will be described later.

[0029] The communication control function 23 transmits information input to the input interface 12 to the information processing device 100 via the communication interface 11 .

[0030] FIG. 3 is a diagram showing an example of an intention input screen. The intention input screen allows the user to input items that may be of concern to the target patient P1 or his / her family regarding the patient's living situation (daily life) after treatment. For example, the intention input screen displays questions in a questionnaire format or free-form regarding the economic and social environment surrounding the target patient P1 and his / her family, the burden of medical expenses, and other such items. Specifically, the questions include: (1) future illness status, (2) treatment costs, (3) number of days available to work, (4) physical or recognized functions for life after treatment, (5) family cooperation required after treatment, (6) degree of care required, and (7) community support. For each question, the user can input a score indicating the degree of interest, acceptability, or cooperation. For example, the option "very concerned" represents the highest level of interest / lowest level of tolerance, the option "concerned" represents a lower level of interest / higher level of tolerance than the option "very concerned," and the option "not very concerned" represents a lower level of interest / higher level of tolerance than the option "concerned." Scores such as interest level, tolerance level, and cooperation level are determined depending on which option the target patient P1 or his / her family chooses from these three options. Note that the number of questions is not limited to seven, and the number of options for each question is not limited to three.

[0031] [Configuration of information processing device] 4 is a diagram illustrating an example of the configuration of the information processing device 100 according to the first embodiment. The information processing device 100 includes, for example, a communication interface 111, an input interface 112, an output interface 113, a memory 114, and a processing circuit 120.

[0032] The communication interface 111 communicates with the terminal device 10 and the like via the communication network NW. The communication interface 111 includes, for example, a NIC. The communication interface 111 is an example of an "output unit."

[0033] The input interface 112 accepts various input operations from an operator, converts the accepted input operations into electrical signals, and outputs the electrical signals to the processing circuit 120. For example, the input interface 112 includes a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch panel, etc. The input interface 112 may be, for example, a user interface that accepts audio input from a microphone, etc. If the input interface 112 is a touch panel, the input interface 112 may also have the display function of the display 113a described below.

[0034] In this specification, the input interface 112 is not limited to an interface having physical operation parts such as a mouse, keyboard, etc. For example, an example of the input interface 112 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs this electrical signal to a control circuit.

[0035] The output interface 113 outputs information under the control of the processing circuit 120. For example, the output interface 113 includes a display 113a, a speaker 113b, etc. The output interface 113 is an example of an "output unit."

[0036] The display 113a displays various types of information. For example, the display 13a displays images generated by the processing circuit 120, a GUI for receiving various input operations from an operator, etc. For example, the display 113a is an LCD, a CRT display, an organic EL display, etc.

[0037] The speaker 113b converts various types of information into sound and outputs the sound. For example, the speaker 113b outputs information input from the processing circuit 120 as sound.

[0038] The memory 114 is realized by, for example, a semiconductor memory element such as RAM or flash memory, a hard disk, or an optical disk. These non-transitory storage media may also be realized by other storage devices connected via a communication network NW, such as a NAS or an external storage server device. The memory 114 may also include other non-transitory storage media such as ROM or registers.

[0039] The processing circuit 120 includes, for example, an acquisition function 121, an estimation function 122, a determination function 123, an output control function 124, and a communication control function 125. The estimation function 122 is an example of an "estimation unit," and the determination function 123 is an example of a "determination unit." If the output interface 113 is an example of an "output unit," the output control function 124 is an example of an "output control unit." Furthermore, if the communication interface 111 is another example of an "output unit," the communication control function 125 is another example of an "output control unit."

[0040] The processing circuitry 120 realizes these functions by, for example, a hardware processor (computer) executing a program stored in the memory 114 (storage circuitry).

[0041] The hardware processor in the processing circuit 120 refers to a circuit such as a CPU, a GPU, an application specific integrated circuit, a programmable logic device (e.g., a simple programmable logic device or a complex programmable logic device), or a field programmable gate array. Instead of storing the program in the memory 114, the program may be directly embedded in the circuit of the hardware processor. In this case, the hardware processor realizes its functions by reading and executing the program embedded in the circuit. The program may be stored in the memory 114 in advance, or may be stored in a non-transitory storage medium such as a DVD or CD-ROM, and may be installed into the memory 114 from the non-transitory storage medium when the non-transitory storage medium is inserted into a drive device (not shown) of the information processing device 100. The hardware processor is not limited to being configured as a single circuit, and may be configured as a single hardware processor by combining multiple independent circuits to realize each function. Furthermore, multiple components may be integrated into a single hardware processor to realize each function.

[0042] [Processing flow of information processing device] The following describes the processing of each function by the processing circuitry 120 of the information processing device 100, with reference to the flowcharts. Fig. 5 is a flowchart showing the flow of a series of processes by the processing circuitry 120 according to the first embodiment.

[0043] First, the acquisition function 121 acquires the attributes of the target patient P1 from the terminal device 10 via the communication interface 111 (step S100).

[0044] The attributes of the target patient P1 are composed of multiple properties and characteristics, such as the current condition of the target patient P1, the progress of the condition since the target patient P1 visited the hospital, the progress of the condition of the target patient P1 while being followed up, medical history, and genetic information. Some of these multiple properties and characteristics may be omitted or replaced with other properties or characteristics not exemplified. For example, the attributes of the target patient P1 may include treatment methods, treatment history, etc.

[0045] The current condition of the target patient P1 includes, for example, age, sex, weight, blood type, vital signs, concurrent diseases, expected complications, etc. The transition of the condition of the target patient P1 since the patient P1 visited the hospital includes, for example, weight, cardiac function status, respiratory status, metabolic status, image parameters indicating disease characteristics, non-image parameters indicating disease characteristics, etc. The transition of the condition of the target patient P1 while being followed up includes, for example, weight, cardiac function status, respiratory status, metabolic status, image parameters indicating disease characteristics, non-image parameters indicating disease characteristics, etc.

[0046] For example, suppose that a medical professional P2 such as a doctor interviews the target patient P1 and his / her family about the target patient P1's current condition, progress of condition, medical history, genetic information, etc., and inputs the interview results to the terminal device 10. In this case, the terminal device 10 transmits the input information as attributes of the target patient P1 to the information processing device 100. When the communication interface 111 receives the attributes of the target patient P1 from the terminal device 10, the acquisition function 121 of the information processing device 100 acquires the attributes from the communication interface 111.

[0047] Next, the estimation function 122 determines one or more treatment methods applicable to the target patient P1 (step S102). For example, the estimation function 122 may determine the treatment method based on an input from the medical worker P2 to the terminal device 10. The estimation function 122 may also determine the treatment method based on the attributes of the target patient P1 (particularly the current condition of the target patient P1).

[0048] For example, the output control function 22 of the terminal device 10 causes the display 13a to display a plurality of treatment methods applicable to the target patient P1. A medical professional P2 (particularly a doctor) selects one or more treatment methods from the plurality of treatment methods displayed on the display 13a and inputs the selection results to the input interface 12. The communication control function 23 transmits the selection results of the treatment methods input by the medical professional P2 to the input interface 12 to the information processing device 100 via the communication interface 11. In response to this, the acquisition function 121 of the information processing device 100 acquires the selection results of the treatment methods selected by the medical professional P2 from the terminal device 10 via the communication interface 111. Then, the estimation function 122 determines the selection results of the treatment methods acquired by the acquisition function 121, i.e., the treatment methods selected by the medical professional P2, as treatment methods applicable to the target patient P1.

[0049] Next, the estimation function 122 selects any one of the determined treatment methods (i) from among the one or more treatment methods (step S104). i is a temporary internal parameter in the processing circuit 120, and is a so-called temporary parameter.

[0050] Next, the estimation function 122 estimates the prognosis of the target patient P1 when the treatment method (i) is applied (step S106).

[0051] [Subprocess] The processing of S106 will be explained in detail below. Fig. 6 is a flowchart showing the flow of processing for estimating the prognosis of the target patient P1. This flowchart corresponds to the processing (sub-process) of S106.

[0052] First, the estimation function 122 calculates (step S200) a distribution (hereinafter referred to as an attribute distribution) that quantitatively represents the attributes of the target patient P1 acquired by the acquisition function 121. The attribute distribution of the target patient P1 is an example of a "second distribution."

[0053] For example, the estimation function 122 converts each of a plurality of attributes of the target patient P1 into a quantitative value in accordance with guidelines established by government agencies or diagnostic criteria established by each medical institution, and calculates the quantified attributes as a distribution. The estimation function 122 may quantify the attributes of the target patient P1 using a predetermined database, or may quantify the attributes of the target patient P1 using statistics or machine learning (deep learning, etc.).

[0054] FIG. 7 is a diagram showing an example of an attribute distribution. As shown in the figure, the attribute distribution may be represented as a radar chart in which the degree of each attribute is graded on a five-point scale from 0 to 5. That is, the value of each attribute may be normalized so that the minimum value is 0 and the maximum value is 5, and then expressed as a distribution like a radar chart. In the attribute distribution, area (a) plots, for example, quantitative values ​​of age, sex, and blood type. Area (b) plots, for example, quantitative values ​​of vital signs at the current time. Area (c) plots, for example, quantitative values ​​of disease parameters (image parameters and non-image parameters) at the current time. Area (d) plots, for example, quantitative values ​​of transition information during a hospital visit. Area (e) plots, for example, quantitative values ​​of transition information during follow-up. Area (f) plots, for example, quantitative values ​​of medical history and genetic information.

[0055] Attributes (b) and (c) (e.g., weight, vital signs, concurrent diseases, and parameters representing disease characteristics) are factors that can be controlled (i.e., control factors) before, after, or during the course of treatment by the subject patient P1. Attributes (a), (d), (e), and (f) (e.g., age, blood type, medical history, and family history) are factors that cannot be controlled (i.e., uncontrollable factors) before, after, or during the course of treatment by the subject patient P1. A control factor is an example of a "first factor," and an uncontrollable factor is an example of a "second factor."

[0056] 7, the attribute distribution is described as a radar chart, but is not limited to this. For example, the attribute distribution may be represented by other statistical charts such as a histogram, a stacked graph, or a heat map. The number of attribute levels is also not limited to five, and may be four or less, or six or more.

[0057] Furthermore, instead of or in addition to calculating the attributes of the target patient P1 as a distribution, the estimation function 122 may integrate all of the attributes of the target patient P1 and calculate them as a single index value (scalar value). For example, when the number of attributes of the target patient P1 is n, the estimation function 122 may calculate the sum T(i) = Σαi × τ(i) taking into account the influence αi of each attribute (τ(i); i = 1 to n) on the disease QI (Quality Index) as an index value representing all of the attributes of the target patient P1. The disease QI will be described later.

[0058] Returning to the explanation of the subprocess flowchart in Figure 6, the estimation function 122 then filters the patients in the population using the treatment method (i) selected in the processing of S104 (step S202). That is, the estimation function 122 extracts, from the population, patients to whom the treatment method (i) selected in the processing of S104 has been applied as a sample.

[0059] A population is composed of multiple patients who have previously received treatment from a medical professional P2 who is going to treat a target patient P1, or from a medical institution where the medical professional P2 works. The medical institution may be, for example, a hospital, a clinic, or other facility where medical care is provided. The population may also be a patient population in medical statistics. The population can be stratified (also called classified, grouped, or clustered) into multiple groups based on the hospital performance index (PI) and / or disease QI.

[0060] The hospital PI is an index value relating to the time or economic costs spent by each patient in the population, such as the number of days of hospitalization or treatment costs. From another perspective, the hospital PI is an index value relating to the time or economic costs spent by a medical institution, such as the number of days of hospitalization or medical fees. The hospital PI is an example of a "first index value."

[0061] A disease QI is an index value used to measure the effectiveness of treatment, i.e., the extent to which a patient's disease has been treated when each patient in a population is treated according to a certain treatment method. For example, if a patient's disease is cancer, the disease QI may be the 5-year survival rate, the number of days in hospital after surgery, the recurrence rate, the cancer survival rate, the rate of breast-conserving surgery, etc. If a patient's disease is acute myocardial infarction, the disease QI may be the average number of days in hospital, etc. If a patient's disease is diabetes, the disease QI may be the HbA1c (Hemoglobin A1c) improvement rate, the number of patient referrals, the number of patient reverse referrals, etc. If a patient's disease is pneumonia, the disease QI may be the average number of days in hospital, the initial treatment success rate, etc. The disease QI is an example of a "second index value."

[0062] For example, if the treatment method (i) selected by the process of S104 is a method called "AAA," the estimation function 122 extracts, as samples, a plurality of patients who have previously received the treatment method called "AAA" from the population.

[0063] Next, the estimation function 122 stratifies the plurality of patients (i.e., samples) extracted from the population into a plurality of groups based on the treatment method (i), and calculates the attribute distribution of each stratified group (step S204).

[0064] For example, before calculating the attribute distribution of each group of samples, the estimation function 122 selects one or more index values ​​as hospital PI and / or disease QI from among multiple index values ​​for measuring treatment effects.

[0065] When the estimation function 122 selects the hospital PI and / or disease QI, it stratifies the samples to which the treatment method (i) was applied into multiple groups based on the selected hospital PI and / or disease QI. The estimation function 122 may perform such stratification processing at a timing different from that described in the flowchart. Examples of such timing include days when the medical institution is closed or at night when there are relatively few outpatients. In other words, when the processing described in the flowchart is started, the sample or the population from which it was extracted may already be stratified into multiple groups.

[0066] 8 is a diagram for explaining the stratification process. As shown in the figure, for example, the estimation function 122 calculates a probability density distribution F(X) of the sample when the hospital PI and the disease QI are random variables X. Then, the estimation function 122 stratifies the sample into a plurality of groups on the probability density distribution F(X) according to a certain criterion.

[0067] For example, the estimation function 122 may classify a group in which the random variable X is less than the second threshold TH2 into group A, a group in which the random variable X is equal to or greater than the second threshold TH2 and less than the first threshold TH1 into group B, and a group in which the random variable X is equal to or greater than the first threshold TH1 into group C. The first threshold TH1 and the second threshold TH2 may be fixed values ​​determined based on, for example, medical statistical results or guidelines, or may be reference values ​​to which a certain margin is added or subtracted from the national average or the average within each medical institution. The first threshold TH1 and the second threshold TH2 may be standard deviations such as ±1σ, ±2σ, or ±3σ. The number of thresholds is not limited to two, and may be one, or three or more. In other words, the number of groups may be two, four, or more.

[0068] For example, when the cancer recurrence rate is selected as the disease QI, the estimation function 122 extracts cancer patients or patients to whom a cancer treatment method has been applied as samples from the population by filtering, and calculates a probability density distribution F(X) in which the cancer recurrence rate of the extracted samples is set as a random variable X. Then, the estimation function 122 stratifies the samples on the probability density distribution F(X) for the cancer recurrence rate into three groups, for example, A, B, and C. In this case, group A is a group with a low cancer recurrence rate, group B is a group with a higher cancer recurrence rate than group A, and group C is a group with a higher cancer recurrence rate than group B.

[0069] When the sample is stratified into multiple groups, the estimation function 122 calculates the attribute distribution of each group. For example, the estimation function 122 averages the attribute distributions of the multiple patients included in each group and sets the averaged attribute distribution as the attribute distribution of each group. Specifically, if group A includes 100 patients, the estimation function 122 averages the attribute distributions of each of the 100 patients and sets the averaged attribute distribution of the 100 patients as the attribute distribution of group A. The estimation function 122 may similarly calculate the attribute distribution of each group for other groups such as group B and group C by averaging the attribute distributions of multiple patients. The attribute distribution of each group is an example of a "first distribution."

[0070] Returning to the explanation of the sub-process flowchart in Fig. 6, the estimation function 122 then compares the attribute distribution of the target patient P1 with the attribute distribution of each of the multiple groups stratified from the sample (step S206).

[0071] 9 is a diagram for explaining a method for comparing attribute distributions. For example, assume that there are three groups of attribute distributions: group A, group B, and group C. In this case, the estimation function 122 compares the attribute distribution of the target patient P1 with the attribute distributions of each of group A, group B, and group C, and calculates the similarity between the attribute distributions.

[0072] For example, if the attribute distribution is a diagram in which characteristics appear in shape, such as a radar chart, the estimation function 122 calculates the similarity between the graphical shape of the attribute distribution of the target patient P1 and the graphical shape of the attribute distribution of group A as similarity a. Similarly, the estimation function 122 calculates the similarity between the graphical shape of the attribute distribution of the target patient P1 and the graphical shape of the attribute distribution of group B as similarity b, and calculates the similarity between the graphical shape of the attribute distribution of the target patient P1 and the graphical shape of the attribute distribution of group C as similarity c. The closer the shapes of the two attribute distributions being compared are to similar shapes, the greater the similarity. Furthermore, if the attribute distribution is a diagram in which characteristics appear in color or shading, such as a heat map, the estimation function 122 may calculate the distance between the colors or shading (so-called color difference) of the two attribute distributions being compared as similarity. Specifically, the estimation function 122 may calculate a color histogram of each attribute distribution, and calculate the similarity between two attribute distributions from the Euclidean distance or cosine similarity of the color histograms.

[0073] Returning to the explanation of the subprocess flowchart in Fig. 6, the estimation function 122 next estimates at least one index value of the hospital PI and the disease QI, or both index values, for the target patient P1 as the prognosis of the target patient P1, based on the comparison result between the attribute distribution of the target patient P1 and the attribute distribution of each group (step S208).

[0074] For example, the estimation function 122 estimates the hospital PI used when stratifying the group with the greatest similarity in attribute distribution among multiple groups compared with the attribute distribution of the target patient P1 as the hospital PI for the target patient P1, and estimates the disease QI used when stratifying the group with the greatest similarity in attribute distribution as the disease QI for the target patient P1.

[0075] For example, in FIG. 9, assume that similarity b is the largest. In this case, the estimation function 122 estimates the cancer recurrence rate used when stratifying the specimen into group B as the disease QI for the target patient P1. Group B is a group whose cancer recurrence rate is equal to or greater than the second threshold TH2 and less than the first threshold TH1. Therefore, the value estimated as the cancer recurrence rate for the target patient P1 falls within the range from the second threshold TH2 to the first threshold TH1.

[0076] Furthermore, when estimating the disease QI of the target patient P1, the estimation function 122 may take into account the influence βi of each attribute (τ(i); i = 1 to n) on the disease QI. Specifically, the estimation function 122 may add the product of an arbitrary conversion coefficient γ and the sum Σ{βi × (f(i) - τ(i))} to the estimated disease QI within a certain reference range. The influence βi is a coefficient for weighting an attribute that is desired to receive more attention among the attributes of the target patient P1. In addition, the estimation function 122 may estimate the disease QI based on a similarity probability obtained by machine learning or deep learning.

[0077] Furthermore, the estimation function 122 may calculate statistical values ​​such as the average, maximum, and minimum of all hospital PIs and disease QIs of multiple groups compared with the attribute distribution of the target patient P1. For example, if the multiple groups compared with the attribute distribution of the target patient P1 are three groups, A, B, and C, the estimation function 122 may calculate the average hospital PIs and average disease QIs of groups A, B, and C. This completes the subprocess flowchart in Figure 6.

[0078] Returning to the explanation of the main flowchart in Fig. 5, when the estimation function 122 estimates the hospital PI and / or disease QI as the prognosis of the target patient P1, it estimates the future living situation (daily life) of the target patient P1 to which the treatment method (i) has been applied, based on the prognosis and attributes of the target patient P1 (step S108).

[0079] 10 is a diagram showing an example of items estimated as future living conditions. As shown in the figure, the estimation function 122 may estimate each item displayed on the intention input screen described above as the future living conditions of the target patient P1 ((1) future illness condition, (2) treatment costs, (3) number of working days, (4) physical or certified functions for life after treatment, (5) family cooperation required after treatment, (6) degree of need for nursing care, and (7) community support). These items are estimated using databases, statistics, and machine learning (including deep learning).

[0080] For example, the estimation function 122 may use the survival rate, recurrence rate, etc. estimated as disease QI as the future pathological condition of the target patient P1, and (1) estimate the future state of the disease (prognosis and side effects) based on the future pathological condition of the target patient P1 and the current attributes of the target patient P1 (medical history, etc.).

[0081] The estimation function 122 may also estimate (2) treatment costs, such as drug costs, surgery costs, hospitalization costs, and outpatient costs, depending on the treatment method (i), and may estimate various expenses required for follow-up depending on the number of follow-up visits. Regarding drugs, several variations in drug costs may be estimated depending on whether generic drugs are used or the devices used (for example, the type of stoma or the type of implant).

[0082] Furthermore, the estimation function 122 may estimate (3) the number of days that the target patient P1 can work, such as the number of days that the target patient P1 will be in good health after treatment and the number of days that the target patient P1 will need to visit the hospital, based on the target patient P1's disease QI, such as survival rate and recurrence rate, and the target patient P1's attributes, such as medical history. Furthermore, the estimation function 122 may estimate the number of days that the target patient P1 can work, taking into account the rehabilitation environment and lifestyle information after discharge. The number of days that the target patient P1 will need to visit the hospital may be estimated based on the number of follow-up visits, the number of days of treatment, etc., depending on the treatment method (i).

[0083] Furthermore, the estimation function 122 may estimate (4) physical or certified functions for life after treatment, such as physical strength, walking, excretion, cognitive ability, and mental health. Physical strength may be estimated from muscle mass, physical condition during hospitalization, and rehabilitation status. Walking (whether or not the patient will become bedridden) is estimated according to the level of living required for nursing care certification using a database, statistics, and machine learning (including deep learning) based on the state of the above-mentioned physical strength. Excretion is estimated according to the level of living required for nursing care certification based on the treatment method, including whether or not an artificial anus can be installed, and the physical condition of the patient (target patient P1). Excretion may be estimated taking into account not only the patient's physical condition but also the patient's willingness to manage their condition.

[0084] In addition, the estimation function 122 may estimate (5) the family cooperation required after treatment, (6) the degree of care required, and (7) community support by comprehensively considering the estimation results of (1) to (4).

[0085] Returning to the explanation of the main flowchart in Fig. 5, the output control function 124 then outputs the future living situation of the target patient P1 estimated by the estimation function 122 (hereinafter referred to as the estimated living situation) via the output interface 113 (step S110). Instead of or in addition to the output control function 124 outputting the estimated living situation, the communication control function 125 may transmit the estimated living situation to the terminal device 10 via the communication interface 111. In this case, the estimated living situation is displayed on the display 13a of the terminal device 10.

[0086] Next, the estimation function 122 determines whether or not all treatment methods applicable to the target patient P1 have been selected (step S112). If all treatment methods have been selected, the estimation function 122 ends the processing of this flowchart.

[0087] On the other hand, if all treatment methods have not yet been selected, the estimation function 122 increments the temporary parameter i (step S114) and returns to S104. That is, the estimation function 122 reselects a treatment method that has not yet been selected as a new treatment method (i). This outputs the estimated living situation of the target patient P1 for each treatment method. As a result, the target patient P1 and his / her family can compare the intentions input into the terminal device 10 with the estimated living situation, and can select a treatment method that suits the intentions of the target patient P1 and his / her family.

[0088] According to the first embodiment described above, the information processing device 100 calculates the attribute distribution of the target patient P1, extracts patients who have received the same treatment method (i) as the target patient P1 from a certain population as a sample, stratifies the sample into multiple groups, and calculates the attribute distribution of each stratified group. The information processing device 100 compares the attribute distribution of each group (an example of a "first distribution") with the attribute distribution of the target patient P1 (an example of a "second distribution"), and based on the comparison result, estimates at least one or both of the hospital PI (an example of a "first index value") and the disease QI (an example of a "second index value") for the target patient P1 as the prognosis of the target patient P1. The information processing device 100 estimates the future living conditions of the target patient P1 for each treatment method based on at least the prognosis of the target patient P1. The information processing device 100 then outputs the estimated living conditions via its output interface 113 or transmits the estimated living conditions to the terminal device 10. This allows the target patient P1 and his / her family to compare the intentions input into the terminal device 10 with the future living situation (estimated living situation) estimated by the information processing device 100, and easily imagine what daily life will be like after treatment. As a result, the target patient P1 and his / her family can select a treatment method that will allow them to live the daily life they desire. In other words, knowing what kind of life they will have in the future after treatment allows not only the patient but also their family to receive treatment with peace of mind.

[0089] (Second embodiment) The second embodiment will be described below. The second embodiment differs from the first embodiment in that the gap between the future living situation desired by the target patient P1 and his / her family and the future living situation (estimated living situation) estimated by the information processing device 100 is estimated, and different information is output depending on the gap. The following description will focus on the differences from the first embodiment, and will omit a description of the points in common with the first embodiment. In the description of the second embodiment, the same parts as in the first embodiment will be described with the same reference numerals.

[0090] FIG. 11 is a flowchart showing the flow of a series of processes performed by the processing circuit 120 according to the second embodiment.

[0091] First, the acquisition function 121 acquires the intention information of the target patient P1 and his / her family from the terminal device 10 via the communication interface 111 (step S300). For example, the acquisition function 121 may acquire, as the intention information, information input to the intention input screen of FIG.

[0092] When exploring the wishes of the target patient P1 and his / her family, decision-making process notes and tools (selection of artificial hydration and nutrition (AHN), selection of dialysis, conditions for attaching and detaching a ventilator) and advance care planning tools such as those advocated by the Clinical Ethics Network Japan may be used. The economic and social environment surrounding the target patient P1 and his / her family may be obtained as background data for the wishes information.

[0093] Also, suppose that a medical worker P2 interviews the target patient P1 and his / her family about their intentions regarding treatment and inputs the interview results into the terminal device 10. In this case, the acquisition function 121 may acquire the interview results input into the terminal device 10 by the medical worker P2 as intention information of the target patient P1 and his / her family.

[0094] Next, the estimation function 122 converts the intention information of the target patient P1 and his / her family into future living conditions desired by the target patient P1 and his / her family (hereinafter referred to as desired living conditions) (step S302). For example, the estimation function 122 converts qualitative information, namely intention information, into quantitative information as the desired living conditions.

[0095] For example, on the intention input screen, options such as "very concerned," "concerned," and "not so concerned" are provided, and when one of these options is selected, the estimation function 122 may score each of the items (1) to (7) according to the selected option.

[0096] Furthermore, for example, when a hearing result is acquired as intention information, the estimation function 122 may use natural language processing to convert a sentence representing the hearing result into a relative or absolute score based on keywords. This score conversion may be performed using a database (dictionary) corresponding to keywords, or may be performed using machine learning such as deep learning.

[0097] Each question on the intention input screen may be further subdivided, as shown in Figure 10. For example, (1) a question about the future state of the illness may further include items about prognosis and side effects. (2) a question about medical expenses may further include items about medication costs, surgery costs, hospitalization costs, and outpatient costs. (3) a question about the number of days available to work may further include items about the number of days the patient will be in good health after treatment and the number of days required for outpatient visits. (4) a question about physical or certified functions for life after treatment may further include items about physical strength, walking, excretion, cognitive ability, and mental health. The same applies to (5) a question about the family cooperation required after treatment, (6) a question about the level of care required, and (7) a question about community support.

[0098] If each question on the intention input screen is subdivided and specific numerical values ​​such as an acceptable cost or desired number of days can be entered, the estimation function 122 may use the specific numerical values ​​entered on the intention input screen as the score.

[0099] In this way, the estimation function 122 converts the qualitative information, namely, intention information, into quantitative information, which is used as a desired living situation. The desired living situation is used as a comparison target for the estimated living situation in the processing described below. The desired living situation is an example of a "first living situation," and the estimated living situation is an example of a "second living situation."

[0100] FIG. 12 is a diagram showing an example of desired living conditions. As shown in the figure, the desired living conditions may be information or data in which intentions for each of the items (1) to (7) and their subdivided items are scored. In the example shown, the target patient P1 and his / her family ideally (hopefully) receive treatment that results in a score of level 2 for (2) treatment costs, and the acceptable lower limit for that score is set to level 2, while the acceptable upper limit is set to level 5. These upper and lower limits may be set by the target patient P1 and his / her family. This means that a treatment method in which the score for each item falls within the range of the upper and lower limits is desirable for the target patient P1 and his / her family.

[0101] Returning to the description of the flowchart in Fig. 11, the acquisition function 121 then acquires the attributes of the target patient P1 from the terminal device 10 via the communication interface 111 (step S304).

[0102] Next, the estimation function 122 determines one or more treatment methods applicable to the target patient P1 (step S306).

[0103] Next, the estimation function 122 selects any one treatment method (i) from the one or more determined treatment methods (step S308).

[0104] Next, the estimation function 122 estimates the prognosis of the target patient P1 when the treatment method (i) is applied (step S310). The method of estimating the prognosis is the same as that in the sub-process flowchart of Figure 6 described above, so a description thereof will be omitted here.

[0105] Next, the estimation function 122 estimates the future living conditions of the target patient P1 to which the treatment method (i) has been applied, based on the prognosis and attributes of the target patient P1 (step S312).

[0106] FIG. 13 is a diagram showing an example of estimated living conditions. Like desired living conditions, estimated living conditions may be information or data in which each of the items (1) to (7) or their sub-items is scored. In the example shown, when a certain treatment method A is applied to target patient P1, the score for (2) treatment costs is level 3, and when a certain treatment method B is applied to target patient P1, the score for (2) treatment costs is level 2. In FIG. 13, the lower limit of the score is set to level 2 for (1) future disease state, and to level 3 for all other items including (2) treatment costs.

[0107] Returning to the explanation of the flowchart in Fig. 11, the determination function 123 then calculates the gap between the desired living situation converted from the intention information in the process of S302 and the future living situation of the target patient P1 estimated in the process of S312 (i.e., estimated living situation) (step S314).

[0108] FIG. 14 is a diagram showing an example of a gap. In the example of FIG. 14, the estimated living conditions when treatment method A in FIG. 13 is applied to target patient P1 are compared with the lower limit of the score quantified as the desired living conditions. For example, for (3) number of days available to work, the lower limit of the score is set to level 3, but when treatment method A is applied to target patient P1, it is level 2. Therefore, the determination function 123 calculates a gap of one level for the score related to (3) number of days available to work. Similarly, the determination function 123 calculates gaps for the scores related to other items.

[0109] Returning to the explanation of the flowchart in Figure 11, the determination function 123 next determines whether the calculated gap is within the allowable range (step S316). The allowable range is the upper and lower limits set by the target patient P1 and his / her family. In the example of Figure 14, when treatment method A is applied to target patient P1, at least (3) the score related to the number of days available to work is below the lower limit. Therefore, the determination function 123 determines that the gap is outside the allowable range.

[0110] If the gap is within the allowable range, the output control function 124 outputs the future living situation of the target patient P1 estimated by the estimation function 122, i.e., the estimated living situation, via the output interface 113 (step S318). Instead of or in addition to the output control function 124 outputting the estimated living situation, the communication control function 125 may transmit the estimated living situation to the terminal device 10 via the communication interface 111. In this case, the estimated living situation is displayed on the display 13a of the terminal device 10.

[0111] On the other hand, if the gap is outside the allowable range, the determination function 123 further determines whether there is a way to eliminate the gap (step S320).

[0112] As mentioned above, there are both control and non-control factors among the attributes of the target patient P1, and adjusting these control factors may narrow the gap even with the same treatment method. For example, if the target patient P1 loses weight, the gap may narrow compared to if the same treatment method were not used and the target patient P1 did not lose weight. Similarly, if the target patient P1 quits smoking, the gap may narrow compared to if the target patient P1 continued smoking, even with the same treatment method. In this way, depending on the target patient P1's efforts, the same treatment method may be adapted to the desired lifestyle.

[0113] Therefore, when the determination function 123 determines that the gap is outside the allowable range, the estimation function 122 virtually adjusts (changes) control factors, such as weight, included in the attributes of the target patient P1 acquired in the processing of S304. The estimation function 122 estimates the prognosis of the target patient P1 based on the attributes of the target patient P1 with the control factors adjusted. In other words, the estimation function 122 simulates the prognosis of the target patient P1 based on the attributes of the target patient P1 adjusted to control factors different from the actual ones. Then, the estimation function 122 estimates the future living situation of the target patient P1 to which the treatment method (i) has been applied, based on the simulated prognosis and attributes of the target patient P1. The simulated future living situation of the target patient P1 is an example of a "third living situation."

[0114] The determination function 123 calculates the gap between the desired living situation converted from the intention information in the processing of S302 and the simulated future living situation of the subject patient P1, and determines whether the gap is within an acceptable range. If the gap is within an acceptable range, the determination function 123 determines that changing the control factors under the conditions before the simulation is a way to eliminate the gap.

[0115] If the gap is outside the allowable range but there is a way to resolve the gap, the output control function 124 outputs the estimated living situation and the way to resolve the gap via the output interface 113 (step S322).

[0116] FIG. 15 is a diagram showing an example of a gap elimination method. For example, as indicated by arrows V1 and V2, suppose that, after adjusting attribute (b) and attribute (d) in the attribute distribution of the target patient P1 through simulation, the gap falls within an acceptable range. Both of these attributes are control factors. Therefore, the output control function 124 may output information via the output interface 113 encouraging the target patient P1 to improve attributes such as vital signs and progress information during hospital visits, without changing the treatment method. For example, if the target patient P1 loses weight and the gap is reduced, the output control function 124 may output information encouraging the target patient P1 to lose weight. Furthermore, if the target patient P1 suffers from a complication and the gap is reduced by reducing the complication, the output control function 124 may output information encouraging the target patient P1 to reduce the complication. Furthermore, the communication control function 125 may transmit a gap elimination method to the terminal device 10 via the communication interface 111. In this case, the gap elimination method is displayed on the display 13a of the terminal device 10.

[0117] If the gap is outside the allowable range and there is no way to eliminate the gap, the output control function 124 outputs a message (hereinafter referred to as a non-adoption notice) that the treatment method (i) selected in the processing of S308 cannot be selected via the output interface 113 (step S324). The communication control function 125 may transmit the non-adoption notice to the terminal device 10 via the communication interface 111. In this case, the non-adoption notice of the treatment method (i) is displayed on the display 13a of the terminal device 10.

[0118] Next, the estimation function 122 determines whether or not all treatment methods applicable to the target patient P1 have been selected (step S326). If all treatment methods have been selected, the estimation function 122 ends the processing of this flowchart.

[0119] On the other hand, if all the treatment methods have not yet been selected, the estimation function 122 increments the temporary parameter i (step S328) and returns the process to S308.

[0120] According to the second embodiment described above, the information processing device 100 determines the gap between the desired living situation and the estimated living situation and outputs different information depending on the gap. For example, if the gap is within an acceptable range, or if the gap is outside the acceptable range but there is a way to resolve it, a medical professional P2, such as a doctor, can explain the results output by the information processing device 100 to the target patient P1 and his / her family, thereby smoothly reaching an agreement on a treatment method with the target patient P1 and his / her family. On the other hand, if the gap is outside the acceptable range and there is no way to resolve it, the medical professional P2 can explain that there is no treatment method that the target patient P1 and his / her family can agree on (that is, there is no treatment method that cannot be agreed upon or that meets the patient's desire). This allows the target patient P1 and his / her family to reconsider their intentions regarding a treatment method. If the intentions are reconsidered, the processing of the above-described flowchart can be repeated based on the reconsidered intentions to facilitate consensus building between the target patient P1 and his / her family.

[0121] (Other embodiments) Other embodiments will be described below. In the above-described embodiment, the terminal device 10 and the information processing device 100 are described as being different devices, but this is not limited to this. For example, the terminal device 10 and the information processing device 100 may be integrated into a single device. For example, the processing circuit 20 of the terminal device 10 may further include the estimation function 122, the estimation function 122, and the estimation function 122 provided in the processing circuit 120 of the information processing device 100, in addition to the acquisition function 21, the output control function 22, and the communication control function 23. In this case, the terminal device 10 can perform the processes of the various flowcharts described above in a standalone (offline) manner.

[0122] 11 has been described as being executed only by the information processing device 100, but this is not limiting. For example, part of the processing in the flowchart of FIG. 11 may be executed by the terminal device 10. Specifically, the processing from S316 to S324 may be executed by the terminal device 10, and the other processing from S300 to S314, S326, and S328 may be executed by the information processing device 100. In this case, the terminal device 10 may be used by the care manager or the attending physician of the target patient P1, and the information processing device 100 may be used by a doctor who has jurisdiction over the care manager or the medical department to which the attending physician belongs.

[0123] Although several embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0124] 1...information processing system, 10...terminal device, 11...communication interface, 12...input interface, 13...output interface, 14...memory, 20...processing circuit, 21...acquisition function, 22...output control function, 23...communication control function, 100...information processing device, 111...communication interface, 112...input interface, 113...output interface, 114...memory, 120...processing circuit, 121...acquisition function, 122...estimation function, 123...determination function, 124...output control function, 125...communication control function

Claims

1. an estimation unit that estimates a future living situation of the target patient for each treatment method to be applied to the target patient based on the attributes of the target patient; an output control unit that outputs information based on the living situation via an output unit, The estimation unit stratifying the plurality of patients into a plurality of groups based on a first index value related to time or economic cost for each of the plurality of patients and a second index value related to the therapeutic effect of each treatment method applied to each of the plurality of patients; calculating a first distribution quantitatively representing an attribute of each of the plurality of groups stratified from the plurality of patients; Calculating a second distribution quantitatively representing the attributes of the target patients; predicting a prognosis for the subject patient based on a comparison between the first distribution and the second distribution; predicting the future living conditions of the subject patient for each of the treatment methods based on the prognosis; Information processing device.

2. the estimation unit estimates, based on a comparison between the first distribution and the second distribution, at least one of a third index value related to the time or economic cost of the target patient and a fourth index value related to the therapeutic effect of each treatment method applied to the target patient as the prognosis of the target patient. The information processing device according to claim 1 .

3. The estimation unit calculating a similarity between the second distribution and the first distribution of each of the plurality of groups, and estimating the first index value of the first distribution having the greatest similarity to the second distribution as the third index value for the target patient, or estimating the second index value of the first distribution having the greatest similarity to the second distribution as the fourth index value for the target patient; The information processing device according to claim 2 .

4. a determination unit that determines whether a first gap has occurred between a first living situation, which is a future living situation of the target patient desired by the target patient or a family of the target patient, and a second living situation, which is a future living situation of the target patient estimated by the estimation unit; the output control unit causes the output unit to output different information depending on whether the first gap occurs or not. The information processing device according to claim 1 .

5. The first gap is the difference between the score quantified as the first living situation and the score quantified as the second living situation. The information processing device according to claim 4 .

6. When the first gap occurs, the determination unit further determines whether or not there is a method for eliminating the first gap; the output control unit, when there is a method for resolving the first gap, outputs the method for resolving the first gap via the output unit; The attributes of the target patient include a first factor that is controllable by the target patient and a second factor that is not controllable by the target patient; The determination unit adjusting the first factor through simulation; If the first gap can be narrowed by adjusting the first factor even with the same treatment method, it is determined that there is a method for eliminating the first gap. The information processing device according to claim 4 .

7. When the first gap occurs, the estimation unit further estimates the future living situation of the target patient based on the attributes of the target patient after adjusting the first factor, the determination unit determines whether a second gap has occurred between the first living situation and a third living situation, which is the future living situation of the subject patient estimated by the estimation unit when the first factor is adjusted, and determines that there is a method for eliminating the first gap when the second gap has not occurred; the output control unit outputs, via the output unit, information that the method for eliminating the first gap includes adjusting the first factor. The information processing device according to claim 6 .

8. The second gap is the difference between the score quantified as the first living situation and the score quantified as the third living situation. The information processing device according to claim 7 .

9. The computer Based on the attributes of the target patient, estimate the future living conditions of the target patient for each treatment method to be applied to the target patient; outputting information based on the living situation via an output unit; stratifying the plurality of patients into a plurality of groups based on a first index value related to time or economic cost for each of the plurality of patients and a second index value related to the therapeutic effect of each treatment method applied to each of the plurality of patients; calculating a first distribution quantitatively representing an attribute of each of the plurality of groups stratified from the plurality of patients; Calculating a second distribution quantitatively representing the attributes of the target patients; predicting a prognosis for the subject patient based on a comparison between the first distribution and the second distribution; predicting the future living conditions of the subject patient for each of the treatment methods based on the prognosis; Information processing methods.

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