Information processing device, information processing method, and program

Abstract

An information processing device 100 of the present disclosure comprises: a data acquisition unit 121 for acquiring biological data measured from a person subject to assessment of a state relating to agitation, and the result of an assessment of the state on the person by an assessor; and a data selection unit 122 for selecting training data from biological data on the basis of a skill value representing the state assessment ability set with respect to the assessor, and the result of the assessment.

Description

Information processing device, information processing method, and program

[0001] The present disclosure relates to an information processing device, an information processing method, and a program.

[0002] Hospitals monitor the condition of patients, for example, to detect a state of agitation. A state of agitation refers to a state in which a patient is highly alert to their surroundings, restless, and excited. One example of a method for detecting a state of agitation is disclosed in Patent Document 1. Patent Document 1 describes a method for generating a model by learning the relationship between biological information previously measured from a patient and whether the patient is in a state of agitation or not, and detecting a state of agitation based on the model.

[0003] International Publication No. 2020 / 161901

[0004] However, it is difficult to determine whether a patient is in an agitated state. Therefore, it is difficult to distinguish whether the biological information measured from the patient is from a state of agitation, and it is not possible to improve the quality of the biological information to be learned. As a result, there is a problem in that it is not possible to improve the accuracy of detecting a state of agitation using a model generated by learning the biological information.

[0005] Therefore, an object of the present disclosure is to provide an information processing device that can solve the above-mentioned problem of not being able to improve the accuracy of detecting whether a person is in an uneasy state.

[0006] An information processing device according to one embodiment of the present disclosure is configured to include: a data acquisition unit that acquires biometric data measured from a person to be judged for a state related to agitation and a judgment result of the state of the person made by an assessor; and a data selection unit that selects learning data from the biometric data based on a skill value representing the ability to judge the state set for the assessor and the judgment result.

[0007] Furthermore, an information processing method that is one form of the present disclosure is configured to acquire biometric data measured from a person to be judged for a state related to agitation and a judgment result of the state of the person by an assessor, and select learning data from the biometric data based on a skill value representing the ability to judge the state set for the assessor and the judgment result.

[0008] Furthermore, a program that is one form of the present disclosure is configured to cause a computer to execute a process of acquiring biometric data measured from a person to be judged for a state related to agitation and a judgment result of the state of the person by an assessor, and selecting learning data from the biometric data based on a skill value set for the assessor that represents the ability to judge the state and the judgment result.

[0009] With the above-described configuration, the present disclosure can improve the accuracy of detecting whether a person is in an uneasy state.

[0010] FIG. 1 is a block diagram showing the configuration of an information processing device according to a first embodiment of the present disclosure. FIG. 2 is a diagram showing the state of processing by the information processing device disclosed in FIG. 1. FIG. 3 is a diagram showing the state of processing by the information processing device disclosed in FIG. 1. FIG. 4 is a flowchart showing the operation of the information processing device disclosed in FIG. 1. FIG. 5 is a flowchart showing the operation of the information processing device disclosed in FIG. 1. FIG. 6 is a block diagram showing the hardware configuration of an information processing device according to a second embodiment of the present disclosure. FIG. 7 is a block diagram showing the configuration of an information processing device according to a second embodiment of the present disclosure.

[0011] First Embodiment A first embodiment of the present disclosure will be described with reference to Fig. 1 to Fig. 6. Fig. 1 is a diagram for explaining the configuration of an information processing device, and Fig. 2 to Fig. 6 are diagrams for explaining the processing operation of the information processing device.

[0012] [Configuration] The information processing device 10 in this embodiment has a function of generating a model for detecting a state of agitation in a patient in a hospital. In particular, as described below, the information processing device 10 has a function of acquiring biometric data measured from the patient and selecting, from the biometric data, training data to be used when generating a model by machine learning.

[0013] Specifically, the information processing device 10 is composed of one or more information processing devices (computers) each including a calculation device and a storage device. As shown in FIG. 1 , the information processing device 10 includes a data acquisition unit 11, a skill assessment unit 12, a data selection unit 13, and a learning unit 14. The functions of the data acquisition unit 11, the skill assessment unit 12, the data selection unit 13, and the learning unit 14 can be realized by the calculation device executing a program for realizing each function stored in the storage device. The information processing device 10 also includes a patient data storage unit 16, a nurse data storage unit 17, and a learning data storage unit 18. The patient data storage unit 16, the nurse data storage unit 17, and the learning data storage unit 18 are each composed of a storage device. Each component will be described in detail below.

[0014] The data acquisition unit 11 acquires biometric data measured from a patient (person) to be assessed for a state related to agitation, i.e., a state of agitation or serenity. The biometric data is data representing the patient's movements and states measured by a sensor attached to the patient, and in this embodiment, the data is acquired by measuring acceleration. The data acquisition unit 11 then associates the acquired biometric data with the time at which the data was measured and stores the data in the patient data storage unit 16. That is, as shown in FIG. 2 , the data acquisition unit 11 acquires biometric data represented as time-series data on a graph with time (Time) on the horizontal axis and acceleration (Acc) on the vertical axis. However, the biometric data is not limited to the patient's acceleration, and may be other biometric data representing the patient's movements and states, such as heartbeat intervals and skin temperature.

[0015] The data acquisition unit 11 also acquires imaging data of the patient's movements while measuring the biometric data. The time of the imaging data is synchronized with the time of the biometric data. The data acquisition unit 11 then associates the acquired imaging data with the biometric data of the same patient and stores it in the patient data storage unit 16.

[0016] The data acquisition unit 11 also acquires the results of the assessment of the patient's condition by a nurse (evaluator). In this embodiment, the nurse reviews the photographed data stored in the patient data storage unit 16 and assesses the patient's level of agitation in stages, such as "agitated" or "likely agitated." Note that "agitated" is considered to be a higher level of agitation than "likely agitated." The data acquisition unit 11 then associates information identifying the nurse who made the assessment, a label representing the level of agitation, which is the assessment result, and a assessment time, which is the time of the assessment on the photographed data, with the patient's photographed data, and stores them in the patient data storage unit 16. Note that the above-mentioned assessment is not limited to being made by a nurse, and may be made by any person who is the subject of calculating a skill value representing the ability to assess a patient's condition, as described below.

[0017] The data acquisition unit 11 also acquires correct data on the patient's condition. In this embodiment, the correct data is a judgment result by a doctor or a nurse with high judgment ability who can accurately judge the patient's level of agitation, such as "in an agitated state" or "suspected to be agitated," from the patient's photographed data. The data acquisition unit 11 then associates a label indicating the level of agitation determined as correct data with a judgment time, which is the time of the judgment on the photographed data, for the patient's photographed data, and stores the associated data in the patient data storage unit 16. Note that the correct data described above is not limited to a judgment result by a doctor or a nurse with high judgment ability, and may be a judgment result by any person, or may be a judgment result based on an analysis result using a computer.

[0018] In the above, the degree of agitation of a patient is rated in two stages, "agitated" or "likely agitated," in the nurse's assessment and the correct answer data. However, it may be rated in one stage, such as "agitated," or in more stages. For example, the degree of agitation of a patient may be expressed based on a preset sedation scale called the Richmond Agitation-Sedation Scale (RASS). Furthermore, the patient's condition may be expressed as "calm" or "degree of calm" in the nurse's assessment and the correct answer data.

[0019] The skill assessment unit 12 determines a skill value representing the assessment ability of a nurse who assessed a patient's condition as described above. Specifically, the skill assessment unit 12 first reads, from the patient data storage unit 16, a label representing the degree of agitation, which is the assessment result of the nurse to be assessed for a specific patient, and a label representing the correct data for the same patient. At this time, the skill assessment unit 12 reads the assessment results and the correct data for multiple patients for the nurse to be assessed for skill. The skill assessment unit 12 then compares the label of the nurse's assessment result with the label of the correct data and calculates a score based on the difference between them. For example, the skill assessment unit 12 calculates a higher score value the more frequently the labels of the nurse's assessment result match the labels of the correct data, or the closer the assessment time between the labels of the assessment result and the labels of the correct data match. The skill assessment unit 12 then checks whether the calculated score value is equal to or greater than a predetermined threshold. If the calculated score value is equal to or greater than the threshold, the skill value of the nurse is determined to be high-skilled. If the calculated score value is less than the threshold, the skill value of the nurse is determined to be low-skilled. Here, high skill means a higher skill value than low skill. In other words, the skill assessment unit 12 determines that the smaller the difference between the assessment result by the nurse and the correct data, the higher the skill value.

[0020] In the above example, two values, high skill and low skill, are set as the skill value of a nurse, but more values ​​may be used to set the skill value in stages. For example, five levels of skill values, such as "1, 2, 3, 4, 5," in which the skill value increases as the value increases, may be set as the skill value, and the skill value may be determined to one of the values ​​depending on the score value described above. However, the skill value may be data in any format as long as it represents the judgment ability of the nurse.

[0021] The skill determination unit 12 then associates the skill value determined for each nurse with the nurse's identification information and stores it in the nurse data storage unit 17. Note that the above-mentioned nurse skill values ​​may be determined by another information processing device or by another method, set in advance, and stored in the nurse data storage unit 17. Therefore, the above-mentioned skill determination unit 12 does not need to be equipped in the information processing device 10.

[0022] The data selection unit 13 selects learning data to be used for machine learning to generate a model from the patient's biometric data stored in the patient data storage unit 16. In particular, the data selection unit 13 selects learning data from the biometric data using a selection method set according to the skill value of the nurse who assessed the patient's condition.

[0023] Specifically, the data selector 13 first searches the patient's biometric data for a judgment time at which the patient is judged to be in an "agitated state" or "likely agitated state," i.e., a judgment time at which the label "agitated state" is assigned. Then, for the biometric data labeled as "agitated," a time interval is set based on the judgment time according to the skill value of the nurse who made the judgment, and the biometric data for the set time interval is set as learning data. For example, when a judgment time at which the biometric data is labeled as "agitated state" or "likely agitated" exists and the skill value of the judged nurse is "high skill," the data selector 13 sets a time interval having a predetermined time width centered on the judgment time, and selects the biometric data for the time interval as learning data. Then, the data selector 13 assigns a label "agitated state" to the biometric data selected as learning data and stores it in the learning data storage unit 18.

[0024] Here, an example of a data selection method when the determined skill value of a nurse is "highly skilled" will be described with reference to FIG. 2 . First, symbol G1 in the upper diagram of FIG. 2 indicates a method of selecting learning data from acceleration Acc, which is biometric data labeled as "unstable state." In this case, the data selector 13 sets an acceleration threshold Aa lower than the acceleration Acc at the determination time La labeled as "unstable state," and sets a time interval Ra having a predetermined duration in which acceleration Acc equal to or greater than the acceleration threshold Aa is measured. In this way, to determine that the determined skill value of a nurse is "highly skilled," the data selector 13 selects, as learning data, the acceleration Acc for the time interval Ra having a predetermined duration centered on the determination time La. Furthermore, to determine that the determined skill value of a nurse is "highly skilled," the data selector 13 duplicates the acceleration data for the set time interval Ra and selects the duplicated data as learning data. For example, the data selector 13 duplicates the acceleration data for the time interval Ra having a predetermined duration two or more times and selects the duplicated data as learning data. The acceleration threshold value Aa may be a value calculated from the acceleration during the determination time La, or may be a preset value. For example, the acceleration threshold value Aa may be set to a value lower than the acceleration at which a person may be determined to be in an "uneasy state" based on the results of past analysis and examination of biometric data of many people.

[0025] Furthermore, symbol G2 in the lower diagram of FIG. 2 indicates a method for selecting learning data from acceleration Acc, which is biometric data labeled as "probably agitated." In this case, the data selector 13 sets an acceleration threshold Ab lower than the acceleration Acc at the determination time Lb labeled as "probably agitated," and sets a time interval Rb having a predetermined duration during which acceleration Acc equal to or greater than the acceleration threshold Ab is measured. In this way, to determine that the skill value of the determined nurse is "high skill," the data selector 13 selects, as learning data, acceleration Acc for a time interval Rb having a predetermined duration centered on the determination time Lb. In this case, the acceleration threshold Ab is set to a value higher than the acceleration threshold Aa set when the label "probably agitated" is assigned. Therefore, when a determination is made that the degree of agitation is "probably agitated," which is lower than when a determination is made that the patient is "agitated," a narrower time interval Rb centered on the determination time Lb is selected. In other words, the data selector 13 varies and sets the above-mentioned time interval depending on the level of agitation determined by the nurse, and in particular, the higher the level of agitation determined, the longer the time interval set. Furthermore, if the determined nurse's skill value is "high skill," the data selector 13 duplicates the acceleration data for the set time interval Rb and selects it as learning data. For example, the data selector 13 duplicates two or more acceleration data for the time interval Rb having a predetermined time width and selects them as learning data. Note that the acceleration threshold Ab may be a value calculated from the acceleration during the determination time Lb or may be a preset value. For example, the acceleration threshold Ab may be set to a value lower than the acceleration Acc set as a value that can be determined to be "likely agitated" based on the results of past analysis and examination of the biometric data of many people, but higher than the above-mentioned acceleration threshold Aa.

[0026] In the above example, when the determined skill value of a nurse is "high skill," acceleration thresholds Aa and Ab are set, and time intervals Ra and Rb having time widths before and after the determination times La and Lb are set. However, the method for setting the time intervals Ra and Rb is not limited to the above. For example, the data selector 13 may set a time width in advance according to the label "in a state of agitation" or "likely to be in a state of agitation," and set the time intervals Ra and Rb based on the determination times La and Lb according to the time width. In this case, the data selector 13 may set the above-mentioned time width, i.e., the time interval, to vary according to the level of agitation determined by the nurse, and may set a longer time interval as the level of agitation increases.

[0027] Next, an example of a data selection method when the determined skill value of the nurse is "low skill" will be described with reference to FIG. 3. First, symbol G3 in the upper diagram of FIG. 3 indicates a method of selecting learning data from the acceleration Acc, which is biometric data labeled as "unstable state." In this case, the data selector 13 sets only the biometric data for the determination time La labeled as "unstable state" as learning data. In other words, when the nurse's skill value is "low skill" and the label is assigned as "unstable state," acceleration data for only the determination time La is selected as learning data without selecting acceleration data for a long time interval as described above. Furthermore, in this case, the selected acceleration data is used as learning data without being duplicated.

[0028] Furthermore, symbol G4 in the lower diagram of Figure 3 shows a method of selecting learning data from the acceleration Acc, which is biometric data labeled "likely agitated" when the determined nurse's skill value is "low skill." In this case, even if there is biometric data labeled "likely agitated" at the determination time Lb, the data selector 13 does not select such data as learning data. Therefore, if the nurse's skill value is low and the determined level of agitation is low, learning data will not be selected from the biometric data.

[0029] In this way, the higher the skill value of the determined nurse, the longer the time interval of acceleration data selected by the data selector 13, and the data is further duplicated and used as learning data. Therefore, the higher the skill value of the determined nurse, the greater the amount of learning data selected by the data selector 13 from the biometric data. However, even if the determined skill value of the nurse is "low skill," a time interval with a shorter time width than in the case of "high skill" may be set and selected as learning data, or fewer copies than in the case of "high skill" may be selected as learning data.

[0030] In addition, if a nurse has determined that a patient is in a "sedentary state," the data selector 13 may select learning data based on the determination time labeled "sedentary state" from the biometric data, assign a "sedentary state" label to the learning data, and store the data in the learning data storage unit 18. Here, an example of selecting learning data from biometric data in which the determined nurse's skill value is "highly skilled" and the nurse has been determined to be in a "sedentary state" will be described with reference to FIG. 4 . The data selector 13 sets an acceleration threshold Ac higher than the acceleration Acc at the determination time Lc labeled "sedentary state," and sets a time interval Rc having a predetermined duration in which the acceleration Acc is measured at or below the acceleration threshold Ac. In this way, to determine that the determined nurse's skill value is "highly skilled," the data selector 13 selects the acceleration Acc for the time interval Rc having a predetermined duration centered on the determination time Lc as learning data labeled with the sedentary state. Note that, in order to determine that the skill value of the determined nurse is "high skill," the data selection unit 13 may duplicate several acceleration data for the set time interval Rc and select them as learning data. In addition, in order to determine that the skill value of the determined nurse is "low skill," the data selection unit 13 may select only the acceleration data for the determination time Lc, that is, without setting a time interval with a time width, as learning data, or may not select the acceleration data for the determination time Lc as learning data.

[0031] The learning unit 14 reads out the learning data selected by the data selection unit 13 as described above from the learning data storage unit 18, and learns the acceleration data that is the learning data to generate a model. Specifically, the learning unit 14 generates a model that detects an agitated state from acceleration data newly measured from a patient by learning acceleration data that is labeled as an "agitated state" that is the learning data. The learning unit 14 then stores the generated model in the learning data storage unit 18. Note that, if the learning data includes acceleration data labeled as a "calm state," the learning unit 14 may learn this data to generate a model that detects an agitated state and a calm state from acceleration data newly measured from a patient.

[0032] [Operation] Next, the operation of the information processing device 10 will be described mainly with reference to the flowcharts of Figures 5 and 6. First, the operation of determining the skill value of a nurse will be described with reference to the flowchart of Figure 5.

[0033] First, the information processing device 10 stores biometric data measured from a patient and imaging data capturing the patient's movements. The information processing device 10 then records the results of a nurse's assessment of the patient's condition as seen in the imaging data (step S1). In this embodiment, the information processing device 10 records the degree of agitation, such as "in an agitated state" or "appears to be agitated state," as well as the assessment time, as the assessment results.

[0034] Next, the information processing device 10 calculates a score based on the difference between the label representing the degree of agitation, which is the nurse's assessment of the patient, and the label representing the preset correct data for the same patient (step S2).The information processing device 10 then checks whether the calculated score is equal to or greater than a preset threshold (step S3).If the calculated score is equal to or greater than the threshold (Yes in step S3), the nurse is determined to have a high skill level (step S4).If the calculated score is less than the threshold, the nurse is determined to have a low skill level (step S5).The information processing device 10 then stores the skill level determined for each nurse in association with the nurse's identification information.

[0035] Next, the operation of selecting learning data from the patient's biological data will be described with reference to the flowchart of FIG.

[0036] The information processing device 10 first searches the patient's biometric data for judgment times at which the patient is judged to be in an "agitated state" or "likely in an agitated state," i.e., judgment times labeled as agitated (step S11). If a judgment time labeled as agitated is found (Yes in step S12), the information processing device 10 selects learning data from the biometric data based on the judgment time labeled as agitated. At this time, the information processing device 10 first checks the skill value of the nurse who made the judgment (step S13). If the nurse's skill value is "high skill" (Yes in step S13), the information processing device 10 sets time intervals Ra and Rb with predetermined time widths centered on judgment times La and Lb, as shown in FIG. 2, and copies the biometric data for these time intervals Ra and Rb and selects them as learning data (step S14). The information processing device 10 then labels the biometric data selected as learning data as "agitated state" and stores it. At this time, the information processing device 10 may select the biological data by changing the length of the time intervals Ra and Rb depending on the degree of the nurse's assessment of the state of agitation, as in the difference between symbols G1 and G2 in Figure 2.

[0037] Furthermore, when the nurse's skill value is "low skill" (No in step S13), the information processing device 10 sets only the biometric data at the determination time La as learning data (step S15), as shown in the upper diagram of Fig. 3. However, when the biometric data is labeled as "unsettled" as shown in the lower diagram of Fig. 3, the information processing device 10 does not need to select the biometric data at the determination time Lb as learning data.

[0038] In this way, the higher the skill value of the determined nurse, the longer the time interval of acceleration data selected by the information processing device 10, and furthermore, the information processing device 10 duplicates the acceleration data to use as learning data. Therefore, the higher the skill value of the determined nurse, the larger the amount of learning data selected by the information processing device 10 from the biometric data.

[0039] The information processing device 10 then learns the acceleration data, which is the training data selected as described above, to generate a model for detecting a state of agitation. Furthermore, the information processing device 10 uses the generated model to detect a state of agitation from newly measured biological data of the patient.

[0040] As described above, in this embodiment, the higher the skill value, which is the nurse's ability to determine a state of agitation, the more biometric data of patients determined by the nurse to be in a state of agitation is selected as training data. This allows for improved quality of the biometric data to be trained. Furthermore, by learning high-quality biometric data and generating a model for detecting a state of agitation, the accuracy of detecting a state of agitation using the model can be improved.

[0041] <Embodiment 2> Next, a second embodiment of the present disclosure will be described with reference to Fig. 7 and Fig. 8. Fig. 7 and Fig. 8 are block diagrams showing the configuration of an information processing device in embodiment 2. Note that this embodiment shows an outline of the configuration of the information processing device described in the above embodiment.

[0042] First, the hardware configuration of the information processing device 100 in this embodiment will be described with reference to Fig. 7. The information processing device 100 is configured as a general information processing device (computer), and is equipped with the following hardware configuration, for example: CPU (Central Processing Unit) 101 (arithmetic unit); ROM (Read Only Memory) 102 (storage device); RAM (Random Access Memory) 103 (storage device); programs 104 loaded into RAM 103; a storage device 105 that stores the programs 104; a drive device 106 that reads and writes data from and to a storage medium 110 external to the information processing device; a communication interface 107 that connects to a communication network 111 external to the information processing device; an input / output interface 108 that inputs and outputs data; and a bus 109 that connects the various components.

[0043] 7 shows an example of the hardware configuration of the information processing device 100, and the hardware configuration of the information processing device is not limited to the above-described case. For example, the information processing device may be configured with only a part of the above-described configuration, such as excluding the drive device 106. Furthermore, the information processing device may use a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an MPU (Micro Processing Unit), an FPU (Floating Point Number Processing Unit), a PPU (Physics Processing Unit), a TPU (Tensor Processing Unit), a quantum processor, a microcontroller, or a combination thereof, instead of the above-described CPU.

[0044] The information processing device 100 can be equipped with a data acquisition unit 121 and a data selection unit 122 shown in FIG. 8 by having the CPU 101 acquire and execute the program group 104. The program group 104 is stored in advance in the storage device 105 or the ROM 102, for example, and is loaded into the RAM 103 and executed by the CPU 101 as needed. The program group 104 may be supplied to the CPU 101 via the communication network 111, or may be stored in advance in the storage medium 110, with the drive device 106 reading out the program and supplying it to the CPU 101. However, the data acquisition unit 121 and the data selection unit 122 described above may be constructed using dedicated electronic circuits for realizing such means.

[0045] The data acquisition unit 121 acquires biometric data measured from a person to be judged for a state related to agitation and a result of the state judgment made by an assessor for the person. For example, the data acquisition unit 121 acquires the judgment result of the state related to agitation of the person.

[0046] The data selection unit 122 selects learning data from the biometric data based on a skill value representing the state determination ability set for the assessor and the determination result. For example, the higher the skill value, the larger the amount of learning data selected by the data selection unit 122 from the biometric data determined to be in an uneasy state.

[0047] With the above-described configuration, the present disclosure selects more biometric data of individuals judged by an assessor as training data, as the skill value of the assessor increases. This improves the quality of the biometric data used for training. Furthermore, by generating a model for detecting a condition by learning high-quality biometric data, the accuracy of detecting the condition using the model can be improved.

[0048] The above-described program can be stored and supplied to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program may also be supplied to a computer by various types of transitory computer-readable media. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer-readable media can be supplied to a computer via wired communication paths such as electric wires and optical fibers, or via wireless communication paths.

[0049] Although the present disclosure has been described above with reference to the above-described embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that are understandable to those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, at least one or more of the functions of the data acquisition unit 121 and the data selection unit 122 described above may be executed by an information processing device installed and connected anywhere on a network, i.e., may be executed by so-called cloud computing.

[0050] <Supplementary Notes> Some or all of the above embodiments may be described as in the following supplementary notes. The following provides an overview of the configurations of an information processing device, an information processing method, and a program according to the present disclosure. However, the present disclosure is not limited to the following configurations. (Supplementary Note 1) An information processing device comprising: a data acquisition unit that acquires biometric data measured from a person to be judged for a state related to agitation and a result of a state judgment made by an assessor on the person; and a data selection unit that selects learning data from the biometric data based on a skill value set for the assessor that represents an ability to judge the state and the judgment result. (Supplementary Note 2) The information processing device according to Supplementary Note 1, wherein the data selection unit selects learning data from the biometric data using a selection method that is preset according to the skill value of the assessor. (Supplementary Note 3) The information processing device according to Supplementary Note 1 or 2, wherein the data selection unit selects a larger amount of learning data from the biometric data as the skill value of the assessor increases. (Supplementary Note 4) The information processing device according to any one of Supplements 1 to 3, wherein the data selection unit replicates the biometric data to select learning data as the skill value of the judge is higher. (Supplementary Note 5) The information processing device according to any one of Supplements 1 to 4, wherein the data acquisition unit acquires the biometric data which is time-series data measured from the person and the judgment result including the judgment time of the judge for the person, and the data selection unit sets a time interval of the biometric data based on the judgment time according to the skill value of the judge, and selects the biometric data of the set time interval as learning data. (Supplementary Note 6) The information processing device according to Supplementary Note 5, wherein the data selection unit sets the time interval longer as the skill value of the judge is higher.(Supplementary Note 7) The information processing device according to Supplementary Note 5 or 6, wherein the data acquisition unit acquires the judgment result including the degree of the state judged by the judge, and the data selection unit sets a time interval of the biometric data based on the judgment time according to the degree of the state, and selects the biometric data for the set time interval as learning data. (Supplementary Note 8) The information processing device according to any of Supplements 1 to 7, comprising a skill judgment unit that judges the skill value of the judge based on the judgment result by the judge for the person and correct answer data for the state of the person. (Supplementary Note 9) An information processing method that acquires biometric data measured from a person to be judged for a state related to agitation and the judgment result of the judge for the person by the judge, and selects learning data from the biometric data based on the skill value set for the judge and the judgment result. (Supplementary Note 9.1) The information processing method according to Supplementary Note 9, wherein learning data is selected from the biometric data using a selection method that is preset according to the skill value of the judge. (Supplementary Note 9.2) The information processing method according to Supplementary Note 9 or 9.1, wherein the higher the skill value of the judge, the greater the amount of learning data selected from the biometric data. (Supplementary Note 9.3) The information processing method according to any of Supplements 9 to 9.2, wherein the higher the skill value of the judge, the more the biometric data is replicated to select learning data. (Supplementary Note 9.4) The information processing method according to any of Supplements 9 to 9.3, wherein the biometric data is time-series data measured from the person and the judgment result including the judgment time of the state of the person by the judge, and the information processing method according to the skill value of the judge, wherein a time interval of the biometric data based on the judgment time is set according to the skill value of the judge, and the biometric data for the set time interval is selected as learning data. (Supplementary Note 9.5) The information processing method according to Supplementary Note 9.4, wherein the higher the skill value of the assessor, the longer the time interval is set.(Supplementary Note 9.6) The information processing method according to Supplementary Note 9.4 or 9.5, comprising: acquiring the judgment result including the degree of the state judged by the judge; setting a time interval of the biometric data based on the judgment time according to the degree of the state; and selecting the biometric data for the set time interval as learning data. (Supplementary Note 9.7) The information processing method according to any of Supplements 9 to 9.6, comprising: determining the skill value of the judge based on the judgment result by the judge for the person and correct answer data for the state of the person. (Supplementary Note 10) A program for causing a computer to execute a process of acquiring biometric data measured from a person to be judged for a state related to agitation and the judgment result of the state of the person by the judge; and selecting learning data from the biometric data based on the skill value set for the judge and the judgment result, which represents the ability to judge the state.

[0051] REFERENCE SIGNS LIST 10 Information processing device 11 Data acquisition unit 12 Skill determination unit 13 Data selection unit 14 Learning unit 16 Patient data storage unit 17 Nurse data storage unit 18 Learning data storage unit 100 Information processing device 101 CPU 102 ROM 103 RAM 104 Program group 105 Storage device 106 Drive device 107 Communication interface 108 Input / output interface 109 Bus 110 Storage medium 111 Communication network 121 Data acquisition unit 122 Data selection unit

Claims

1. a data acquisition unit that acquires biometric data measured from a person who is to be judged for a state related to agitation and a result of the state judgment made by an evaluator for the person; a data selection unit that selects learning data from the biometric data based on a skill value that is set for the assessor and indicates the ability to determine the condition, and the assessment result; An information processing device comprising:

2. 2. The information processing device according to claim 1, the data selection unit selects learning data from the biometric data using a selection method that is preset according to the skill value of the assessor. Information processing device.

3. 2. The information processing device according to claim 1, the data selection unit selects a larger amount of learning data from the biometric data as the skill value of the evaluator is higher; Information processing device.

4. 2. The information processing device according to claim 1, the data selection unit is more likely to replicate the biometric data and select learning data as the skill value of the evaluator is higher; Information processing device.

5. 2. The information processing device according to claim 1, the data acquisition unit acquires the biometric data, which is time-series data measured from the person, and the determination result, which includes a determination time of the state of the person by the determiner; the data selection unit sets a time interval of the biometric data based on the judgment time in accordance with the skill value of the evaluator, and selects the biometric data in the set time interval as learning data. Information processing device.

6. 6. The information processing device according to claim 5, the data selection unit sets the time interval to be longer as the skill value of the judge is higher; Information processing device.

7. 6. The information processing device according to claim 5, the data acquisition unit acquires the judgment result including the degree of the state judged by the judge, the data selection unit sets a time interval of the biometric data based on the determination time in accordance with the degree of the state, and selects the biometric data of the set time interval as learning data. Information processing device.

8. 2. The information processing device according to claim 1, a skill determination unit that determines the skill value of the determiner based on the determination result by the determiner for the person and correct answer data of the state of the person; Information processing device.

9. Acquire biometric data measured from a person to be judged for a state of agitation and a result of the judgement made by an assessor on the state of the person; selecting learning data from the biometric data based on a skill value set for the assessor that indicates the ability to determine the condition and the assessment result; Information processing methods.

10. Acquire biometric data measured from a person to be judged for a state of agitation and a judgment result of the state of the person by an evaluator; selecting learning data from the biometric data based on a skill value set for the assessor that indicates the ability to determine the condition and the assessment result; A program that causes a computer to execute a process.