Inference device, trained model, inference method, trained model generation method, and computer program

Abstract

The present disclosure provides an inference device, a trained model, an inference method, a trained model generation method, and a computer program. The inference device infers blood index information for a subject. The inference device comprises: an inference unit that inputs input information to a trained model and acquires output information from the trained model, said trained model being constructed by training using a training dataset; and a storage unit that stores the output information. The training dataset includes vital sign information for training subjects, behavior information for the training subjects, and blood index information for the training subjects. The input information includes vital sign information for the subject and behavior information for the subject. The output information includes the blood index information for the subject.

Description

Estimation device, learning model, estimation method, learning model generation method, and computer program

[0001] The present disclosure relates to an estimation device, a learning model, an estimation method, a learning model generation method, and a computer program.

[0002] The blood test device described in Patent Document 1 measures blood indexes such as CRP.

[0003] Japanese Patent Application Laid-Open No. 2016-166856

[0004] However, in the blood test device described in Patent Document 1, it is necessary to collect blood from a subject. Therefore, the burden on the subject may increase.

[0005] An object of the present disclosure is to provide an estimation device, a learning model, an estimation method, a learning model generation method, and a computer program that can estimate blood index information without requiring blood collection.

[0006] According to the present disclosure, there is provided an estimation device for estimating blood index information of a subject, the estimation device including: an estimation unit that inputs input information to a learning model constructed by learning using a learning data set and obtains output information from the learning model; and a storage unit that stores the output information, wherein the learning data set includes vital information of a learning subject, action information of the learning subject, and blood index information of the learning subject, the input information includes vital information of the subject and action information of the subject, and the output information includes the blood index information of the subject.

[0007] Further, according to the present disclosure, there is provided a learning model constructed by learning using a learning data set and causing a computer to function so as to estimate blood index information of a subject, wherein the learning data set includes vital information of a learning subject, action information of the learning subject, and blood index information of the learning subject, and causes the computer to function so as to input input information and output output information, the input information includes vital information of the subject and action information of the subject, and the output information includes the blood index information of the subject.

[0008] Furthermore, the present disclosure provides an estimation method for estimating blood index information of a subject, comprising the steps of: inputting input information into a learning model constructed by learning using a learning dataset; and obtaining output information from the learning model into which the input information has been input, wherein the learning dataset includes vital information of the learning subject, behavioral information of the learning subject, and blood index information of the learning subject, the input information includes the vital information of the subject and behavioral information of the subject, and the output information includes the blood index information of the subject.

[0009] Furthermore, this disclosure provides a computer program that causes a computer to execute the above estimation method.

[0010] Furthermore, according to this disclosure, a method for generating a learning model is provided, which includes the steps of: acquiring a learning dataset; and generating a learning model that outputs output information when input information is input by performing learning using the learning dataset, wherein the learning dataset includes vital information of a subject, behavioral information of the subject, and blood index information of the subject, the input information includes vital information of the subject and behavioral information of the subject, and the output information includes the blood index information of the subject.

[0011] Furthermore, this disclosure provides a computer program that causes a computer to execute the above-described learning model generation method.

[0012] According to this disclosure, it is possible to provide an estimation device, a learning model, an estimation method, a learning model generation method, and a computer program that can estimate blood indicator information without requiring blood collection.

[0013] Figure 1 is a block diagram showing an example configuration of an information processing system according to the present disclosure. Figure 2 is a diagram showing an example of a learning dataset according to the present embodiment. Figure 3 is a diagram showing an example of input information and output information according to the present embodiment. Figure 4 is a block diagram showing an example configuration of an estimation system according to the present embodiment. Figure 5 is a flowchart showing an example of an estimation method executed by the estimation system according to the present embodiment. Figure 6 is a block diagram showing an example configuration of an estimation device according to the present embodiment. Figure 7 is a graph showing an example of multiple unit data arranged in time series according to the present embodiment. Figure 8 is a graph for explaining the classification execution process according to the present embodiment. Figure 9 is a graph showing an example of first state data and second state data arranged in time series according to the present embodiment. Figure 10 is a graph showing an example of correction data arranged in time series according to the present embodiment. Figure 11 is a graph showing an example of cumulative data arranged in time series according to the present embodiment. Figure 12 is a diagram schematically showing an example of a deep neural network according to the present embodiment. Figure 13 is a diagram schematically showing another example of a deep neural network according to the present embodiment. Figure 14 is a diagram schematically showing yet another example of a deep neural network according to the present embodiment. Figure 15 is a flowchart showing an example of an estimation process according to the present embodiment. Figure 16 is a block diagram showing an example configuration of a learning device according to this embodiment. Figure 17 is a flowchart showing an example of a learning method using the learning device according to this embodiment. Figure 18 is a block diagram showing an example configuration of a learning data creation device according to this embodiment. Figure 19 is a flowchart showing an example of a learning data creation method using the learning data creation device according to this embodiment. Figure 20 is a graph showing the ROC curve calculated for the learning model according to Embodiment 1 of this disclosure. Figure 21 is a graph showing the SHAP values ​​calculated for the learning model according to Embodiment 1 of this disclosure. Figure 22 is a graph showing the SHAP values ​​calculated for the learning model according to Embodiment 2 of this disclosure.

[0014] Preferred embodiments of this disclosure will be described in detail below with reference to the attached drawings. In this specification and the drawings, components having substantially the same functional configuration are denoted by the same reference numerals, and redundant descriptions will be omitted.

[0015] An information processing system according to an embodiment of this disclosure uses a learning model to estimate blood index information of a subject from at least the subject's biological state information (vital information and / or behavioral information). In this specification, blood index information is information that directly or indirectly indicates blood indexes. A blood index is an index that quantitatively indicates at least one of the following: blood components, the state of blood components, substances in the blood, and the state of substances in the blood. Blood indexes are, for example, biochemical test indices or hematological test indices. Biochemical test indices are, for example, CRP, γ-GTP value, blood glucose level, HDL cholesterol level, LDL cholesterol level, or iron level. Hematological test indices are, for example, ESR, red blood cell count, hemoglobin level, hematocrit, white blood cell count, neutrophil level, lymphocyte level, or monocyte level.

[0016] In this specification, blood indicator information includes, for example, at least one of the following: information on CRP, information on ESR, information on γ-GTP levels, information on blood glucose levels, information on HDL cholesterol levels, information on LDL cholesterol levels, information on iron levels, information on red blood cell count, information on hemoglobin levels, information on hematocrit, information on white blood cell count, information on neutrophil levels, information on lymphocyte levels, and information on monocyte levels. Information on CRP is information that directly or indirectly represents CRP. This also applies to other blood indicator information.

[0017] CRP indicates the mass of C-reactive protein per unit volume of blood. The unit is typically "mg / dL". C-reactive protein appears in the blood when inflammatory reactions and / or tissue destruction are occurring in the body. C-reactive protein is a type of acute-phase reaction protein. Factors that affect CRP (hereinafter referred to as CRP influencing factors) include, for example, various diseases that cause inflammation in the body. Diseases that cause inflammation in the body include, for example, rheumatoid arthritis, bacterial and viral infections, myocardial infarction, polymyositis, acute osteitis, Pehçet's disease, vasculitis syndromes, burns, trauma, postoperative conditions, and cholelithiasis. Other research reports indicate that smoking, late pregnancy, or obesity are contributing factors to CRP influencing factors.

[0018] ESR indicates the rate at which red blood cells settle in a reagent. In other words, ESR indicates the rate at which red blood cells settle. The unit is typically "mm / h". Factors that affect ESR (hereinafter referred to as ESR influencing factors) include, for example, various diseases that cause inflammation in the body. Diseases that cause inflammation in the body include, for example, rheumatoid arthritis, acute and chronic infections, acute myocardial infarction, multiple myeloma, primary macroglobulinemia, ankylosing spondylitis, polymyalgia rheumatica, psoriatic arthritis, systemic sclerosis, giant cell arteritis, and granulomatous disease with polyangiitis.

[0019] Furthermore, CRP-influencing factors and ESR-influencing factors include, for example, gout and pseudogout, cirrhosis, malignant tumors, (acute) leukemia, systemic lupus erythematosus (ESR), and Sjögren's syndrome.

[0020] γ-GTP stands for γ-glutamyltransferase. The unit of γ-GTP value is typically "U / L". γ-GTP is an amino acid metabolic enzyme. Factors that affect γ-GTP (hereinafter referred to as γ-GTP influencing factors) include, for example, alcohol and alcohol-related diseases.

[0021] Blood glucose level is the concentration of glucose in the blood. The unit is typically "mg / dL". Factors that affect blood glucose levels (hereinafter referred to as blood glucose influencing factors) include, for example, endocrine disorders, stress, and pregnancy. Endocrine disorders include, for example, diabetes mellitus and diseases originating from the adrenal cortex.

[0022] HDL cholesterol (HDL-C) is called "good cholesterol." LDL cholesterol (LDL-C) is called "bad cholesterol." The units for HDL cholesterol levels and LDL cholesterol levels are typically "mg / dL." Factors that affect HDL-C and LDL-C (hereinafter referred to as cholesterol influencing factors) include, for example, smoking, obesity, lack of exercise, and dyslipidemia.

[0023] Iron is a component of hemoglobin in red blood cells. The unit of iron level is typically "μg / dL". Factors affected by iron (hereinafter referred to as iron-affecting factors) include, for example, anemia.

[0024] The red blood cell count is the number of red blood cells in the blood. The unit is typically the number of red blood cells per 1 μL of blood. Hemoglobin is the oxygen-carrying protein found in red blood cells. The unit of hemoglobin value is typically "g / dL". Hematocrit (Hct) is the volume ratio of red blood cells to total blood volume. The unit is typically "%". Factors that influence the red blood cell count, hemoglobin, and hematocrit (hereinafter referred to as red blood cell influencing factors) include, for example, the oxygen-carrying function in the blood.

[0025] White blood cell count is the number of white blood cells in the blood. The unit is typically the number of white blood cells per μL of blood. Neutrophils, lymphocytes, and monocytes are types of white blood cells. The units for neutrophil counts, lymphocyte counts, and monocyte counts are typically the number per μL of blood or the percentage of white blood cells. Factors that affect white blood cell counts, neutrophils, lymphocytes, and monocytes (hereinafter referred to as white blood cell influencing factors) include, for example, infections, inflammation, and stress.

[0026] Hereafter, HDL cholesterol levels and LDL cholesterol levels may be collectively referred to as "cholesterol levels." Red blood cell count, hemoglobin levels, and hematocrit levels may be collectively referred to as "red blood cell levels." White blood cell count, neutrophil levels, lymphocyte levels, and monocyte levels may be collectively referred to as "white blood cell levels."

[0027] Next, an information processing system 1 according to an embodiment of the present disclosure will be described with reference to Figures 1 to 19. Figure 1 is a block diagram showing an example configuration of the information processing system 1. As shown in Figure 1, the information processing system 1 comprises a learning data creation device 2, a learning device 3, and an estimation device 4. Each of the learning data creation device 2, the learning device 3, and the estimation device 4 is a computer. The estimation device 4 comprises a pre-processing unit 41, an estimation unit 42, and a learning model TM1. The estimation device 4 may further comprise a post-processing unit 43.

[0028] The learning data creation device 2 acquires raw biological state data A1 and correct answer information Z1 of the learning subject. Raw biological state data A1 is raw data indicating the biological state of the learning subject. A learning subject is a subject from which raw data for learning is acquired. Typically, a learning subject is a human. Raw biological state data A1 includes raw vital data and raw behavioral data of the learning subject. Raw vital data is raw data indicating the vital signs of the organism. Raw behavioral data is raw data indicating the behavior of the organism. Correct answer information Z1 indicates blood indicator information.

[0029] The learning data creation device 2 creates a learning dataset F1 based on the raw biological state data A1 and the ground truth information Z1. The learning dataset F1 includes feature information G1 and the ground truth label B1 of the training subject. Feature information G1 includes at least one of the vital information H1 and behavioral information J1 of the training subject. In this embodiment, feature information G1 includes at least the vital information H1 and behavioral information J1 of the training subject. Feature information G1 is an explanatory variable. Feature information G1 is created based on the raw biological state data A1. The ground truth label B1 is the target variable. The ground truth label B1 is created based on the ground truth information Z1. The ground truth label B1 indicates blood index information M1.

[0030] The learning device 3 performs learning using the learning dataset F1 by executing a machine learning algorithm. The learning device 3 generates a learning model TM1 by repeatedly performing learning using multiple learning datasets F1. The learning device 3 performs supervised learning. The learning model TM1 outputs output information L2 when input information K2 is input. Typically, the learning model TM1 is a pre-trained model. Also, the learning model TM1 is a computer program.

[0031] Meanwhile, the estimation device 4 uses the learning model TM1 to estimate the subject's blood index information M2. The subject is typically a human.

[0032] Specifically, the preprocessing unit 41 performs preprocessing on the subject's biological state raw data A2 and generates input information K2, which is the result of the preprocessing. The biological state raw data A2 is raw data indicating the subject's biological state. The biological state raw data A2 includes the subject's vital raw data and behavioral raw data. The vital raw data is raw data indicating the vital signs of the organism. The behavioral raw data is raw data indicating the behavior of the organism. The input information K2 includes at least one of the vital information H2 and behavioral information J2. In this embodiment, the input information K2 includes at least the vital information H2 and behavioral information J2.

[0033] The estimation unit 42 inputs the input information K2 to the learning model TM1. As a result, the learning model TM1 outputs output information L2. The input information K2 is an explanatory variable. The output information L2 is an objective variable. The output information L2 includes the subject's blood index information M2. The estimation unit 42 obtains the output information L2 from the learning model TM1. The post-processing unit 43 may perform post-processing on the output information L2 and generate output information N2, which is the result of the post-processing. The output information N2 includes the post-processed blood index information M3.

[0034] In the following, unless it is necessary to distinguish between blood indicator information M2 and M3, the blood indicator information M2 and M3 of the subject may be referred to as "blood indicator information MX".

[0035] In this embodiment, the estimation device 4 can output blood indicator information MX with high estimation accuracy by inputting at least vital information H2 and behavioral information J21 to the learning model TM1. In particular, the estimation device 4 does not require a blood test (blood collection and analysis) of the subject by a medical professional. The medical professional is, for example, a doctor or nurse. In addition, the estimation device 4 automatically and continuously acquires biological state raw data A2 from a wearable device and / or a mobile terminal. Therefore, blood indicator information MX can be obtained while reducing the burden on the subject.

[0036] Next, we will explain the correlation between the vital information H1 and behavioral information J1 of the subject and the blood indicator information M1 in the training dataset F1. Figure 2 shows an example of the training dataset F1. As shown in Figure 2, the training dataset F1 includes feature information G1 and ground truth labels B1 of the training subject.

[0037] Feature information G1 includes at least vital information H1 and behavioral information J1 of the training subject. Vital information H1 is information that directly or indirectly indicates the vital signs of the organism. Behavioral information J1 is information that directly or indirectly indicates the behavior of the organism. In this specification, the organism is typically a human. On the other hand, the correct label B1 includes blood index information M1 of the training subject. Blood index information M1 includes at least one of the following: information on CRP, information on ESR, information on γ-GTP value, information on blood glucose level, information on cholesterol level, information on iron level, information on red blood cell count, and information on white blood cell count. For example, in the correct label B1, CRP, ESR, γ-GTP value, blood glucose level, cholesterol level, iron level, red blood cell count, and white blood cell count are measured values.

[0038] Blood indicators in living organisms are affected by the state of the organism. The state of the organism is, for example, a pathological condition. A pathological condition is, for example, a disease, inflammation, symptom, symptom, or sign. On the other hand, vital information H1 and behavioral information J1 are affected by the state of the organism. As a result, according to syllogistic reasoning, it can be inferred that there is a correlation between blood indicator information M1 and vital information H1 and behavioral information J1. Therefore, according to this embodiment, the learning model TM1 constructed by learning using the learning dataset F1 (vital information H1, behavioral information J1, and blood indicator information M1) can output (estimate) the subject's blood indicator information M2 when the subject's vital information H2 and behavioral information J2 are input. In other words, the estimation device 4 can estimate blood indicator information M2 by using the learning model TM1 without requiring blood collection. Below, as an example, syllogism or its extension (multi-stage reasoning) is used to infer the correlation.

[0039] As an example, we will explain CRP and ESR, which are blood indicators. We will also use rheumatoid arthritis as an example as appropriate. The factors influencing CRP and ESR are various diseases in which inflammation occurs in the body. On the other hand, vital information H1 and behavioral information J1 are affected by various inflammations in the body. As a result, it can be inferred that there is a correlation between CRP and ESR and vital information H1 and behavioral information J1. Therefore, the learning model TM1, constructed by learning using the learning dataset F1, can output (estimate) information about the subject's CRP and / or ESR when the subject's vital information H2 and behavioral information J2 are input.

[0040] For example, the DAS28 is an index used to assess disease activity in rheumatoid arthritis. The DAS28 consists of DAS28_CRP and DAS28_ESR. CRP is used to calculate DAS28_CRP. ESR is used to calculate DAS28_ESR. On the other hand, vital information H1 and behavioral information J1 are affected by the inflammatory response in rheumatoid arthritis. Therefore, it can be inferred that there is a correlation between vital information H1 and behavioral information J1 and CRP and ESR.

[0041] Furthermore, for example, rheumatoid arthritis is a chronic disease accompanied by a systemic inflammatory response. Therefore, the physical, mental, and behavioral changes that occur in conjunction with the inflammatory response are reflected in vital information H1 and behavioral information J1. In addition, in the inflammation of rheumatoid arthritis, joint symptoms such as deformation, pain, and stiffness (difficulty moving) of some joints occur as one of the main symptoms. Therefore, the physical, mental, and behavioral changes resulting from joint symptoms are reflected in behavioral information J1. On the other hand, CRP-influencing factors and ESR-influencing factors are various diseases in which inflammation occurs in the body. Therefore, it can be inferred that there is a correlation between vital information H1 and behavioral information J1 and CRP and ESR.

[0042] As a result of the above, according to this embodiment, the learning model TM1 constructed by learning using the learning dataset F1 can output information regarding the subject's CRP and / or ESR when the subject's vital information H2 and behavioral information J2 are input.

[0043] Preferably, vital information H1 includes information about heart rate. In this specification, heart rate is not limited to heart rate based on electrocardiogram waveforms obtained by electrocardiography (ECG), but also includes pulse rate based on pulse wave waveforms. In other words, heart rate based on electrocardiogram waveforms and pulse rate based on pulse wave waveforms are treated as "heart rate" in this specification. The method for obtaining the pulse wave waveform is not particularly limited and may be obtained, for example, by photoplethysmography (PCG).

[0044] Information related to the heartbeat is, for example, the heart rate (HR), the R-R interval, a time-domain index related to the heartbeat, a frequency-domain index related to the heartbeat, or a non-linear index related to the heartbeat. The heart rate is the number of times the heart beats within a certain period of time, and is expressed, for example, as the number of times per minute (bpm: beat per minutes). The certain period of time in the heart rate may be referred to as the first certain period of time. The R-R interval is the time interval from the QRS wave to the next QRS wave in the electrocardiogram waveform. The time-domain index related to the heartbeat, the frequency-domain index related to the heartbeat, and the non-linear index related to the heartbeat are collectively referred to as the heart rate variability index (HRV index). In this specification, for example, the pulse rate (PR) is treated as the heart rate, the pulse interval (PI) is treated as the R-R interval, and the pulse variability is treated as the heart rate variability.

[0045] Time-domain metrics include, for example, SDNN (Standard deviation of NN intervals), RMSSD (Root Mean Square of Successive Differences), CVRR (Coefficent of Variation of RR intervals), SDRR (Standard deviation of RR intervals), SDANN (Standard Deviation of the Average NN intervals for each 5-minute segment of a 24-hour HRV recording), SDNN index, NN50 (the number of pairs of successive NN intervals that differ by more than 50 ms), pNN50 (the proportion of NN50 divided by the total number of NN intervals), HR Max, HR Min, (HR Max - HR Min), HTI (HRV Triangular Index), or TINN (Triangular Interpolation of the NN Interval Histogram). HRV stands for heart rate variability. RMSSD, in terms of heart rate, is the square root of the average of the squared differences between consecutive adjacent R-R intervals.

[0046] Frequency domain indicators include, for example, LF (low-frequency power or peak), HF (high-frequency power or peak), LF / HF, Total Power, LF Norm, HF Norm, ULF (ultra-low frequency power), or VLF (very low frequency power) of heart rate variability.

[0047] Nonlinear indices include, for example, entropy, SD1 (standard deviation in the direction orthogonal to y = x in the Poincaré plot), SD2 (standard deviation in the direction along y = x in the Poincaré plot), SD1 / SD2, ApEn (Approximate entropy), SampEn (Sample entropy), DFA α1 (Detrended Fluctuation Analysis, which describes short-term fluctuations), DFA α2 (Detrended Fluctuation Analysis, which describes long-term fluctuations), (DFA α1) / (DFA α2), CVI (Cardiac Vagal Index), or CSI (Cardiac Sympathetic Index).

[0048] The inflammatory reaction of rheumatoid arthritis is controlled by the autonomic nervous system, resulting in the activation state of the sympathetic nerve. As a physical reaction accompanying the activation of the sympathetic nerve, for example, there is an increase in heart rate. Therefore, it can be speculated that the increase in heart rate may lead to the enhancement of the inflammatory reaction of rheumatoid arthritis and the increase in disease activity. By the same logic, for example, it can be speculated that the change in the heart rate variability index that captures the activity of the autonomic nervous system may lead to the enhancement of the inflammatory reaction and the increase in disease activity. On the other hand, CRP influencing factors and ESR influencing factors are various diseases in which inflammation occurs in the living body. From these results, it can be speculated that there is a correlation between the information related to the heart rate and the blood index information M1.

[0049] Note that the vital information H1 may include, for example, one or more of blood pressure information, respiratory information, body temperature information, and electroencephalogram information. These information may be related to or potentially related to the inflammatory reaction of rheumatoid arthritis. Respiratory information is, for example, the respiratory rate. Body temperature information includes, for example, at least one of skin temperature, body temperature, and deep body temperature. In this case, the body temperature information may be, for example, the skin temperature during sleep, the body temperature during sleep, or the deep body temperature during sleep.

[0050] Preferably, the behavioral information J1 includes at least one of the following: information regarding the number of steps and information regarding exercise intensity. In this embodiment, the behavioral information J1 includes information regarding the number of steps and information regarding exercise intensity. Information regarding the number of steps and information regarding exercise intensity are types of exercise volume information. The number of steps and exercise intensity may be collectively referred to as exercise volume. Information regarding the number of steps indicates, for example, the number of steps (spm: steps per minute) within a certain period of time (e.g., 1 minute). A certain period of time in terms of steps may be referred to as a second certain period of time. Information regarding exercise intensity is indicated, for example, by METs (Metabolic equivalents). METs indicate, for example, METs over a certain period of time (e.g., 1 minute). A certain period of time in terms of METs may be referred to as a third certain period of time. METs is a unit that expresses the intensity of physical activity as how many times greater it is than the resting state. Furthermore, information regarding exercise intensity is not limited to METs; for example, it may be expressed as a relative value to maximum oxygen uptake (%VO2max) or by methods based on maximum heart rate (%HRmax, %MHR).

[0051] The systemic inflammatory response observed in rheumatoid arthritis can cause fatigue or malaise. Fatigue or malaise restricts bodily activity, leading to a decrease in step count and exercise intensity. Furthermore, joint symptoms caused by rheumatoid arthritis also restrict bodily activity, resulting in a decrease in step count and exercise intensity. Therefore, it can be inferred that the decrease in step count and exercise intensity may be contributing to an exacerbation of the inflammatory response and increased disease activity in rheumatoid arthritis. On the other hand, CRP and ESR influencing factors are various diseases in which inflammation occurs in the body. From these results, it can be inferred that there is a correlation between information on step count and exercise intensity and blood index information M1.

[0052] Furthermore, joint stiffness and pain caused by inflammation in rheumatoid arthritis can also occur due to disuse of the joints. In other words, stiffness and pain can also occur due to low levels of exercise, such as low step count and exercise intensity. On the other hand, too much exercise worsens joint stiffness and pain. Thus, stiffness and pain can occur even without joint use, but they can also occur if the joints are subjected to excessive stress. In other words, there is an optimal amount of exercise that maintains joint range of motion with minimal load. Meanwhile, CRP and ESR influencing factors are various diseases in which inflammation occurs in the body. From these results, it can be inferred that there is a correlation between exercise levels, such as step count and exercise intensity, and blood indicator information M1.

[0053] Furthermore, the behavioral information J1 may include, for example, one or more pieces of information from energy consumption and body movement information. Body movement information may be, for example, the amount or frequency of a specific body movement (e.g., turning over or struggling). In addition, the behavioral information J1 may include the time the learning subject wears a biological state detection device (e.g., a wearable device).

[0054] As explained above with reference to Figure 2, according to this embodiment, it can be inferred that information regarding heart rate, step count, and exercise intensity is correlated with blood index information M1. Therefore, according to this embodiment, the learning model TM1 constructed by performing learning using the learning dataset F1 (information regarding heart rate, information regarding step count, information regarding exercise intensity, and blood index information M1) can output the subject's blood index information M2 with high estimation accuracy when information regarding the subject's heart rate, step count, and exercise intensity is input.

[0055] Furthermore, in this embodiment, the behavioral information J1 may preferably include the sleep information of the learning subject. The sleep information is information relating to the sleep of a living organism. The sleep information includes at least one of the following: information relating to sleep duration and information relating to sleep rhythm.

[0056] Joint pain caused by inflammation in rheumatoid arthritis can make it difficult to fall asleep, potentially leading to shorter sleep durations and altered sleep rhythms. On the other hand, CRP and ESR influencing factors are various diseases that cause inflammation in the body. Therefore, it can be inferred that there is a correlation between sleep information and blood index information M1. As a result, according to this embodiment, by adding sleep information to the behavioral information J1 of the learning dataset F1, the learning model TM1 can output the subject's blood index information M2 with even higher estimation accuracy.

[0057] Furthermore, in this embodiment, behavioral information J1 may preferably include the learning subject's drinking information. Drinking information is information about a living organism's drinking. Drinking information is a type of information about a living organism's lifestyle. Therefore, lifestyle information includes information about a living organism's preferences. Drinking information is, for example, information indicating whether or not a person drinks alcohol, information indicating the amount of alcohol consumed, or an indicator for estimating drinking. Details of the indicator for estimating drinking will be described later.

[0058] Studies have shown that moderate alcohol consumption is associated with lower disease activity in rheumatoid arthritis. Furthermore, studies have indicated that alcohol consumption inhibits the synthesis of inflammatory cytokines and lowers CRP levels. Therefore, alcohol consumption influences the inflammatory response and disease activity of rheumatoid arthritis. On the other hand, CRP and ESR influencing factors are related to various diseases in which inflammation occurs in the body. Additionally, studies have shown that habitual alcohol consumption affects the values ​​of blood inflammatory biomarkers (e.g., ESR, CRP). From these results, it can be inferred that there is a correlation between alcohol consumption information and blood index information M1. As a result, according to this embodiment, by adding alcohol consumption information to the behavioral information J1 of the learning dataset F1, the learning model TM1 can output the subject's blood index information M2 with even higher estimation accuracy.

[0059] Furthermore, in this embodiment, the vital information H1 may preferably include at least one of the following: physical and mental recovery capacity information and exercise responsiveness information. Physical and mental recovery capacity information is information indicating the degree of physical and mental recovery capacity from the end of exercise. Typically, physical and mental recovery capacity information is information indicating the degree of heart rate recovery capacity from the end of exercise. In this case, cardiopulmonary function, parasympathetic nervous system function (autonomic nervous system disorder), and exercise deficiency can be evaluated using physical and mental recovery capacity information. Physical and mental recovery capacity information is, for example, an index for estimating physical and mental recovery. Details of the index for estimating physical and mental recovery will be described later. On the other hand, exercise responsiveness information is information indicating the degree of physical and mental response to exercise. Typically, exercise responsiveness information is information indicating the degree of increase in heart rate after the start of exercise. In this case, the state of the sympathetic nervous system (autonomic nervous system disorder) and exercise deficiency can be evaluated using exercise responsiveness information. Note that exercise responsiveness information may also include information indicating the degree of decrease in heart rate at the end of exercise.

[0060] Inflammation in rheumatoid arthritis can cause various accompanying symptoms or secondary diseases in addition to joint symptoms. For example, interstitial pneumonia as a secondary disease can adversely affect cardiopulmonary and respiratory function. For example, chronic lack of exercise due to joint pain and fatigue can lead to a decline in cardiopulmonary function. For example, severe joint symptoms can cause autonomic nervous system dysfunction. As a method to quantify the decline in cardiopulmonary and respiratory function, changes in heart rate at the start of exercise such as walking, and changes in heart rate at the end of exercise such as walking (when transitioning from exercise to rest) can be used. On the other hand, CRP-influencing factors and ESR-influencing factors are various diseases in which inflammation occurs in the body. Therefore, it can be inferred that there is a correlation between information on physical and mental recovery ability and exercise responsiveness, and blood index information M1. As a result, according to this embodiment, by adding at least one piece of information from among the information on physical and mental recovery ability and the information on motor responsiveness to the vital information H1 of the learning dataset F1, the learning model TM1 can output the subject's blood index information M2 with even higher estimation accuracy.

[0061] Furthermore, in this embodiment, preferably, at least one of the vital information H1 and behavioral information J1 may include vital information H11 or behavioral information J11 on which cumulative processing has been performed. Cumulative processing refers to the process of accumulating vital information H1 or behavioral information J1 while avoiding value divergence. Details of cumulative processing will be described later. As an example, cumulative processing is performed on each of vital information H1 and behavioral information J1. Vital information H11 includes information on heart rate on which cumulative processing has been performed. Vital information H11 may include at least one of the information on physical and mental recovery ability and exercise responsiveness on which cumulative processing has been performed. Behavioral information J11 includes at least one of the information on step count and exercise intensity on which cumulative processing has been performed. Behavioral information J11 may include at least one of the information on sleep and alcohol consumption on which cumulative processing has been performed.

[0062] Through the cumulative processing of vital information H1, the accumulated physical and mental state over time is expressed quantitatively and chronologically from the perspective of the organism's vital signs. Through the cumulative processing of behavioral information J1, the accumulated physical and mental state over time is expressed quantitatively and chronologically from the perspective of the organism's behavior. As a result, the vital information H11 and behavioral information J11 of the learning dataset F1 can reflect the homeostasis of the organism. Homeostasis refers to the organism's property of trying to maintain a constant physical and mental state in response to changes in the organism's internal environment and external environment. In other words, the state of the organism is not only affected by the state at a given point in time, but also by the accumulated state from the past. Therefore, through cumulative processing, the vital information H11 and behavioral information J11 can be given characteristics that approximate the mechanism of the organism that ensures homeostasis. As a result, according to this embodiment, the learning model TM1 can output blood index information M2 of the subject that appropriately reflects the mechanism of the organism and has even higher estimation accuracy.

[0063] In particular, by performing cumulative processing, it is possible to reflect vicious cycle trends and virtuous cycle trends related to various diseases in which inflammation occurs in the body (e.g., rheumatoid arthritis) in the training dataset F1. For example, each of the vicious cycles and virtuous cycles can occur over periods ranging from several days to several weeks or more.

[0064] In various diseases where inflammation occurs in the body, a vicious cycle of symptom exacerbation is expected. First, let's illustrate a vicious cycle from the perspective of vital information H1. A vicious cycle occurs when inflammation (e.g., inflammation in rheumatoid arthritis) and disease activity gradually increase day by day. The tendency of a vicious cycle can be captured by performing cumulative processing on heart rate-related information such as heart rate and heart rate variability. For example, by performing cumulative processing on nocturnal heart rate or nocturnal heart rate variability, a persistent trend of inflammation can be captured, and consequently, the gradual worsening of pain and joint deformation can also be captured. From this, by performing cumulative processing on heart rate-related information in vital information H1, the vicious cycle caused by various diseases where inflammation occurs in the body (e.g., inflammation in rheumatoid arthritis) can be reflected in the training dataset F1. This is also true when performing cumulative processing on psychosomatic function recovery information and exercise responsiveness information related to heart rate.

[0065] Next, we will illustrate a vicious cycle from the perspective of behavioral information J1. For example, joint pain caused by inflammation in the body (e.g., inflammation in rheumatoid arthritis) reduces the amount of physical activity such as walking, which induces a depressive state. As a result, the perceived pain intensifies, further reducing activity, and cyclically worsening joint symptoms. For example, joint pain caused by inflammation in the body reduces the amount of physical activity such as walking, which narrows the range of motion of the joints. As a result, joint pain becomes more likely to occur, further reducing the amount of physical activity such as walking, and cyclically worsening joint symptoms. From these examples, by performing cumulative processing on the number of steps and exercise intensity of behavioral information J1, it is possible to reflect, for example, the trend of decreasing exercise volume over two or more days in the learning dataset F1, and consequently, the vicious cycle caused by various diseases in which inflammation occurs in the body (e.g., rheumatoid arthritis). Also, for example, if sleep is not possible due to pain caused by various diseases in which inflammation occurs in the body, sleep duration is shortened, which induces a depressive state or reduces activity. As a result, the amount of physical activity decreases, making it even more difficult to fall asleep, and cyclically worsening sleep quality. Therefore, by performing cumulative processing on the sleep information in behavioral information J1, the learning dataset F1 can reflect the tendency for sleep to worsen, which is a vicious cycle caused by various diseases in which inflammation occurs in the body (e.g., rheumatoid arthritis). Furthermore, for example, continuous or habitual alcohol consumption affects the inflammatory response and disease activity of rheumatoid arthritis. Accordingly, by performing cumulative processing on the alcohol consumption information in behavioral information J1, the learning dataset F1 can reflect the trend of symptom changes in rheumatoid arthritis caused by continuous or habitual alcohol consumption.

[0066] Furthermore, virtuous cycle trends in various diseases involving inflammation in the body (e.g., rheumatoid arthritis) can also be reflected in the training dataset F1 by performing cumulative processing on vital information H1 and / or behavioral information J1, similar to the case of vicious cycle trends. In other words, improvement trends in various diseases involving inflammation in the body (e.g., rheumatoid arthritis) can be reflected in the training dataset F1.

[0067] Furthermore, as mentioned above, with regard to inflammation in the body (for example, inflammation in rheumatoid arthritis), stiffness and pain can occur even without using the joints, but excessive strain on the joints can also cause stiffness and pain. Such exercise-induced stiffness and pain may be related not only to the amount of exercise in a single day, but also to the cumulative amount of exercise over two or more days. Therefore, by performing cumulative processing on the number of steps and exercise intensity in the behavioral information J1, it is possible to reflect the trend of exercise-induced stiffness and pain over two or more days in the training dataset F1.

[0068] Furthermore, in this embodiment, preferably, at least one of the vital information H1 and behavioral information J1 may include vital information or behavioral information during a period indicated by specific conditions. The specific conditions indicate any of the following: conditions related to sleep, conditions related to wakefulness, and conditions related to the time of day caused by the sun. The period indicated by the conditions related to sleep is the period during sleep, a predetermined period before falling asleep (e.g., 30 minutes), or a predetermined period after falling asleep (e.g., 30 minutes). The period indicated by the conditions related to wakefulness is the period during wakefulness, a predetermined period before getting up (e.g., 30 minutes), or a predetermined period after getting up (e.g., 30 minutes). The period indicated by the conditions related to the time of day caused by the sun is the daytime period (e.g., from 5:00 to 20:00), or the nighttime period (from 0:00 to 4:00, and from 21:00 to 23:00).

[0069] For example, systemic inflammation occurs in rheumatoid arthritis. Inflammation exhibits diurnal variation. In particular, in rheumatoid arthritis, the response is heightened at night and during sleep. For example, inflammation can cause an increase in heart rate at night and during sleep. For example, inflammation can maintain a high heart rate immediately after falling asleep. That is, under normal circumstances, the heart rate decreases immediately after falling asleep, but when inflammation occurs, the heart rate does not decrease. For example, inflammation causes the nighttime heart rate to be higher than the daytime heart rate (e.g., resting heart rate). Furthermore, if the inflammation in rheumatoid arthritis gradually progresses day by day, the sustained trend of inflammation can be captured by performing cumulative processing on the nighttime heart rate or nighttime heart rate variability index.

[0070] On the other hand, in other inflammations besides rheumatoid arthritis, for example, the inflammatory response may be heightened during the daytime activity phase and reduced during the night and sleep. In this case, a higher heart rate may be maintained during the body's activity. As a result, the nighttime heart rate may be lower than the daytime heart rate.

[0071] Furthermore, inflammation in rheumatoid arthritis (especially systemic inflammation) can cause fatigue or malaise. As a result, daytime and nighttime physical activity may decrease. In addition, inflammatory rheumatoid arthritis can cause joint deformation, stiffness, and pain. Joint stiffness and pain are more likely to occur upon waking (for example, in the morning) because the viscosity of synovial fluid in the joint capsule increases. Other possible underlying factors include reduced joint flexion and extension during sleep, or reduced metabolism and circulation during sleep. Moreover, stiffness and pain can lead to decreased daytime physical activity. Furthermore, stiffness and pain can increase movements such as turning over and struggling during sleep, or increase the frequency of awakenings during the night. This can lead to increased exercise intensity during sleep or increased steps taken due to temporary awakenings. In particular, movements such as turning over and struggling increase movement during sleep before waking, while stiffness and pain are thought to reduce physical activity immediately after waking. Furthermore, if the inflammation of rheumatoid arthritis gradually progresses day by day, the tendency for persistent inflammation can be captured by performing cumulative processing on the intensity of exercise during sleep or the number of steps taken at night, as movements such as turning over and struggling during sleep, or the number of steps taken due to temporary waking, continue every day.

[0072] As a result of the above, according to this embodiment, by including at least one of the vital information H1 and behavioral information J1 in the learning dataset F1, changes in heart rate, step count, and / or exercise intensity that are prominently displayed during a specific period due to an inflammatory disease in the body (e.g., rheumatoid arthritis) can be reflected in the learning dataset F1. As a result, the estimation accuracy of blood indicator information M2 by the learning model TM1 can be further improved.

[0073] Furthermore, in this embodiment, feature information G1 may preferably further include basic physical information Q1 of the training subject. Basic physical information Q1 is basic information about the body. Basic physical information Q1 includes at least one of BMI (Body Mass Index) information and body type information. BMI information can be used as an indicator of the degree of obesity of a living organism. Body type information indicates the body type of the living organism, and is, for example, weight information and / or height information. Basic physical information Q1 may also include age and / or sex information.

[0074] For example, disease activity and inflammatory response in rheumatoid arthritis can be higher the greater the degree of obesity in the individual. For instance, there are research reports showing that CRP levels are higher in obese individuals. These reports state that obesity can be interpreted as a low degree of systemic inflammation. Furthermore, CRP and ESR influencing factors are various diseases that cause inflammation in the body. Therefore, it can be inferred that there is a correlation between basic physical information Q1 and blood indicator information M1. As a result, according to this embodiment, by adding basic physical information Q1 to the feature information G1 of the learning dataset F1, the learning model TM1 can output the subject's blood indicator information M2 with even higher estimation accuracy.

[0075] Furthermore, in this embodiment, the feature information G1 may preferably further include environmental information R1 of the training subject. Environmental information R1 is information indicating the environment in which the organism lives. Environmental information R1 includes, for example, seasonal information. Seasonal information may be indicated by a season name such as winter, or by a month such as January.

[0076] For example, symptoms caused by inflammation in rheumatoid arthritis, such as joint pain and stiffness, are presumed to worsen when living temperatures are low (e.g., in winter) because the viscosity of synovial fluid in the joint capsule increases. Therefore, the likelihood of joint pain and stiffness occurring varies depending on the season. Living temperature is indicated, for example, by air temperature or room temperature. Also, when living temperatures fluctuate drastically (e.g., during the change of seasons), various stresses on the body can disrupt autonomic nervous system function, potentially affecting the level of inflammatory response. On the other hand, CRP influencing factors and ESR influencing factors are various diseases in which inflammation occurs in the body. Therefore, it can be inferred that there is a correlation between environmental information R1 and blood index information M1. As a result, according to this embodiment, by adding environmental information R1 to the feature information G1 of the learning dataset F1, the learning model TM1 can output the subject's blood index information M2 with even higher estimation accuracy.

[0077] Next, we will explain the information regarding γ-GTP values, which is an example of blood indicator information M1. Factors influencing γ-GTP include, for example, alcohol and alcohol-related diseases. On the other hand, vital information H1 and behavioral information J1 are affected by the presence or absence and amount of alcohol consumption. For example, alcohol consumption increases heart rate when not walking. As a result, it can be inferred that there is a correlation between γ-GTP and vital information H1 and behavioral information J1. Therefore, the learning model TM1, constructed by learning using the learning dataset F1, can output (estimate) information regarding the subject's γ-GTP when the subject's vital information H2 and behavioral information J2 are input.

[0078] Next, we will explain information regarding blood glucose levels, which is an example of blood indicator information M1. Factors that affect blood glucose levels include, for example, endocrine disorders, stress, and pregnancy. Endocrine disorders include, for example, diabetes and diseases originating from the adrenal cortex. For example, the hormones released from the adrenal cortex affect the autonomic nervous system activity of the sympathetic and parasympathetic nerves. Exposure to stress and pregnancy also affect autonomic nervous system activity. On the other hand, vital information H1 and behavioral information J1 are affected by autonomic nervous system activity. For example, information regarding heart rate (e.g., heart rate variability index) can capture stress and endocrine abnormalities. As a result, it can be inferred that there is a correlation between blood glucose levels and vital information H1 and behavioral information J1. Therefore, the learning model TM1, constructed by learning using the learning dataset F1, can output (estimate) information regarding the subject's blood glucose level when the subject's vital information H2 and behavioral information J2 are input.

[0079] Next, we will explain information regarding cholesterol levels, which is an example of blood indicator information M1. Factors that influence cholesterol include, for example, smoking, obesity, lack of exercise, and dyslipidemia. For example, smoking can lead to a decrease in oxygen exchange efficiency in the lung lobes. Obesity and lack of exercise can increase the physical load on exercise. Also, for example, dyslipidemia is associated with dietary habits. On the other hand, vital information H1 and behavioral information J1 are affected by oxygen exchange efficiency, physical load, and dietary habits. For example, decreased oxygen exchange efficiency and increased physical load can contribute to a rapid increase in heart rate at the start and during exercise. Also, for example, decreased oxygen exchange efficiency and increased physical load can contribute to a longer time for heart rate to recover after exercise. Furthermore, for example, alcohol consumption is an example of dietary habits. For example, alcohol consumption can increase heart rate when not walking. As a result of these factors, it can be inferred that there is a correlation between cholesterol levels and vital information H1 and behavioral information J1. Therefore, the learning model TM1, constructed by learning using the learning dataset F1, can output (estimate) information about the subject's cholesterol levels when the subject's vital information H2 and behavioral information J2 are input. For example, by including the number of steps and exercise time at different exercise intensities as input information K2, cholesterol levels affected by lack of exercise can be estimated. Also, for example, by including alcohol consumption frequency or exercise responsiveness information as input information K2, cholesterol levels affected by smoking, obesity, lack of exercise, and dyslipidemia can be estimated.

[0080] Next, we will explain information regarding iron levels, which is an example of blood indicator information M1. Factors that affect iron include, for example, anemia. For example, in the case of anemia, phenomena such as "it takes time to start moving after waking up," "a long period of rest is required after relatively strenuous exercise," or "it takes time for heart rate to recover" may occur. Therefore, it can be inferred that there is a correlation between vital information H1 and behavioral information J1 and iron levels. Thus, the learning model TM1, constructed by learning using the learning dataset F1, can output (estimate) information regarding the subject's iron levels when the subject's vital information H2 and behavioral information J2 are input. For example, by including information such as the time from waking up to starting to walk, the resting time after strenuous exercise, or information on the body's recovery ability in the input information K2, it is possible to estimate information regarding the iron level in the blood, which is the main cause of anemia.

[0081] Next, we will explain information regarding red blood cell counts, which is an example of blood indicator information M1. Factors influencing red blood cells include, for example, the oxygen transport function in the blood. For example, a low red blood cell count indicates a decrease in oxygen transport efficiency. In other words, it is thought that a high heart rate and the need to maintain a high heart rate arise in order to supply oxygen to the oxygen consumption of cells during exercise. Therefore, it can be inferred that there is a correlation between vital information H1 and behavioral information J1 and red blood cell counts. Thus, the learning model TM1, constructed by learning using the learning dataset F1, can output (estimate) information regarding the subject's red blood cell count when the subject's vital information H2 and behavioral information J2 are input. For example, information regarding red blood cell count, hemoglobin level, and hematocrit, which affect the oxygen transport function in the blood, can be estimated based on the height of the basal heart rate or exercise responsiveness information.

[0082] Next, we will explain information regarding white blood cell counts, which is an example of blood indicator information M1. Factors that affect white blood cells include, for example, infection, inflammation, and stress. For example, infection and inflammation increase daytime and nighttime heart rates through immune responses. In addition, infection and inflammation cause, for example, fatigue or fever, and reduce activity. Furthermore, for example, stress is thought to correlate with heart rate variability indices of the autonomic nervous system. Therefore, it can be inferred that there is a correlation between vital information H1 and behavioral information J1 and white blood cell counts. Thus, the learning model TM1, constructed by learning using the learning dataset F1, can output information regarding the subject's white blood cell count when the subject's vital information H2 and behavioral information J2 are input.

[0083] Next, with reference to Figure 3, the input information K2 input to the learning model TM1 and the output information L2 output from the learning model TM1 will be explained. Figure 3 is a diagram showing an example of input information K2 and output information L2. As shown in Figure 3, the input information K2 includes at least the subject's vital information H2 and behavioral information J2. The vital information H2 and behavioral information J2 are the same as the vital information H1 and behavioral information J1 in Figure 2, respectively.

[0084] Vital information H2 includes at least information related to heart rate. Vital information H2 may also include at least one of the following: information on physical and mental recovery ability and information on exercise responsiveness. Behavioral information J2 includes at least one of the following: information related to step count and information related to exercise intensity. In this embodiment, behavioral information J2 includes information related to step count and information related to exercise intensity. Behavioral information J2 may also include at least one of the following: information on sleep and information on alcohol consumption. Behavioral information J2 may also include the time the subject wore the biological state detection device 103 (e.g., a wearable device) (Figure 4).

[0085] The information regarding heart rate, physical and mental recovery ability, exercise responsiveness, step count, exercise intensity, sleep, and alcohol consumption in Figure 3 is the same as the information regarding heart rate, physical and mental recovery ability, exercise responsiveness, step count, exercise intensity, sleep, and alcohol consumption in Figure 2, respectively.

[0086] At least one of the vital information H2 and behavioral information J2 may include vital information H21 or behavioral information J21 on which cumulative processing has been performed. Cumulative processing refers to the process of accumulating vital information H2 or behavioral information J2 while avoiding value divergence. As an example, cumulative processing is performed on each of vital information H2 and behavioral information J2. Vital information H21 includes information on heart rate on which cumulative processing has been performed. Vital information H21 may include at least one of the information on physical and mental recovery ability and exercise responsiveness on which cumulative processing has been performed. Behavioral information J21 includes at least one of the information on step count and exercise intensity on which cumulative processing has been performed. Behavioral information J21 may include at least one of the information on sleep and alcohol consumption on which cumulative processing has been performed.

[0087] The vital information H21 and behavioral information J21 in Figure 3 are the same as the vital information H11 and behavioral information J11 in Figure 2, respectively. The cumulative processing performed on the heart rate information, physical and mental recovery capacity information, exercise responsiveness information, step count information, exercise intensity information, sleep information, and alcohol consumption information in Figure 3 is the same as the cumulative processing performed on the heart rate information, physical and mental recovery capacity information, exercise responsiveness information, step count information, exercise intensity information, sleep information, and alcohol consumption information in Figure 2, respectively.

[0088] At least one of the vital information H2 and behavioral information J2 may include vital information or behavioral information during a period indicated by specific conditions. The specific conditions refer to any of the following: conditions related to sleep, conditions related to wakefulness, and conditions related to the time of day caused by the sun. The specific conditions for vital information H2 and behavioral information J2 are the same as the specific conditions for vital information H1 and behavioral information J1 in Figure 2.

[0089] Input information K2 may include at least one of the following: basic physical information Q2 and environmental information R2. Basic physical information Q2 and environmental information R2 in Figure 3 are the same as basic physical information Q1 and environmental information R1 in Figure 2, respectively. Basic physical information Q2 includes at least one of the following: BMI information and body shape information. Environmental information R2 includes seasonal information. BMI information, body shape information, and seasonal information in Figure 3 are the same as the BMI information, body shape information, and seasonal information in Figure 2, respectively.

[0090] On the other hand, the output information L2 includes the subject's blood index information M2. The blood index information M2 includes at least one of the following: information on CRP, information on ESR, information on γ-GTP values, information on blood glucose levels, information on cholesterol levels, information on iron levels, information on red blood cell counts, and information on white blood cell counts.

[0091] In the explanation of Figure 2, for example, the terms "training subject," "feature information G1," "vital information H1, H11," "behavioral information J1, J11," "basic physical information Q1," "environmental information R1," "correct label B1," and "blood index information M1" can be replaced with "subject," "input information K2," "vital information H2, H21," "behavioral information J2, J21," "basic physical information Q2," "environmental information R2," "output information L2," and "blood index information M2," respectively, thereby substituting for the explanation of input information K2 and output information L2.

[0092] Typically, vital information H2 and behavioral information J2 are information created based on raw biological state data A2 acquired from a wearable device (e.g., the biological state detection device 103 described later) and / or a mobile terminal (e.g., the first terminal 102 described later).

[0093] Next, the stages of using the learning model TM1 will be described with reference to Figures 4 to 15. Figure 4 is a block diagram showing an example configuration of the estimation system 40 according to this embodiment. As shown in Figure 4, the estimation system 40 is connected to a network NW. The network NW includes, for example, the Internet, a private network, a public telephone network, a LAN (Local Area Network), and a short-range wireless network. The estimation system 40 is part of the information processing system 1 in Figure 1.

[0094] At least one client system 100 is connected to the network NW. The client system 100 comprises a cloud server 101, a plurality of first terminals 102, and a plurality of biological state detection devices 103. The cloud server 101, the first terminals 102, and the biological state detection devices 103 are connected to the network NW. In addition, a plurality of second terminals 200 are connected to the network NW.

[0095] The biological state detection device 103 detects the biological state of the subject. The biological state detection device 103 has, for example, a sensor that detects the biological state by contact or non-contact. The biological state detection device 103 outputs biological state raw data A2, which is raw data indicating the biological state of the subject. The biological state raw data A2 includes the subject's vital raw data and behavioral raw data. The vital raw data is raw data indicating the vital signs of the organism. The behavioral raw data is raw data indicating the behavior of the organism. The sensor that detects the vital raw data includes, for example, an optical sensor (light-emitting element and light-receiving element) that performs PCG. In this case, for example, the subject's pulse wave waveform is detected. Therefore, the biological state detection device 103 calculates the pulse rate (bpm) based on the pulse wave waveform. The sensor that detects the vital raw data may include, for example, a sensor that detects the subject's body temperature. The sensor that detects the behavioral raw data includes, for example, an acceleration sensor and / or a gyroscope sensor. In this case, for example, the subject's step count and exercise intensity (METs) are detected. In other words, the biological state detection device 103 calculates the number of steps and exercise intensity (METs) based on the output of the acceleration sensor and / or gyroscope sensor. The sensors for detecting behavioral data may include, for example, an acceleration sensor and / or gyroscope sensor, as well as a microphone. In this case, for example, the subject's sleep state is detected.

[0096] The biological state detection device 103 is, for example, a wearable device. The wearable device is attached to the subject. The wearable device is, for example, a wristwatch, a ring, or a sticker. The biological state detection device 103 is synchronized with the first terminal 102 and transmits the raw biological state data A2 to the first terminal 102. The first terminal 102 is, for example, a mobile terminal such as a smartphone. The mobile terminal is carried by the subject. The biological state detection device 103 may also transmit the raw biological state data A2 to the cloud server 101 or the estimation system 40 via the network NW.

[0097] The biological state detection device 103 may be wearable, portable, or stationary, or it may be a dedicated detector (measuring instrument) for detecting the biological state. Furthermore, the first terminal 102 may have some or all of the functions of the biological state detection device 103. Also, for example, the first terminal 102 may be a personal computer (PC).

[0098] The first terminal 102 transmits the subject's raw biological state data A2, the subject's basic physical information Q2, and the subject's environmental information R2 to the cloud server 101 via the network NW. The cloud server 101 transmits the biological state data A2, basic physical information Q2, and environmental information R2 to the estimation system 40 via the network NW. The estimation system 40 processes the biological state data A2, basic physical information Q2, and environmental information R2.

[0099] Alternatively, without providing a cloud server 101, the first terminal 102 may transmit the biological state raw data A2, basic physical information Q2, and environmental information R2 to the estimation system 40 via the network NW. Furthermore, the basic physical information Q2 and environmental information R2 may be transmitted from the biological state detection device 103 to the first terminal 102, the cloud server 101, or the estimation system 40.

[0100] More specifically, the estimation system 40 comprises a relay server 44, a first database 45, an estimation device 4, a second database 46, and an information providing server 47. The relay server 44, the first database 45, the estimation device 4, the second database 46, and the information providing server 47 are connected to a network NW. The estimation device 4 is, for example, a server.

[0101] Each of the relay server 44 and the information providing server 47 may include a processing unit, a communication unit, and a storage unit, and may also include an input unit and an output unit. The hardware configuration of the processing unit, communication unit, storage unit, input unit, and output unit is the same as the hardware configuration of the processing unit 400, communication unit 401, storage unit 402, input unit 403, and output unit 404 of the estimation device 4 shown in Figure 6, which will be described later. In addition, the first database 45 and the second database 46 include at least a storage device such as a hard disk drive. The first database 45 and the second database 46 may have the same hardware configuration as the relay server 44 or the information providing server 47. The estimation system 40 may not include all or part of the relay server 44, the first database 45, the second database 46, and the information providing server 47.

[0102] Figure 5 is a flowchart showing an example of an estimation method performed by the estimation system 40. The estimation method estimates the blood index information MX of the subject. The estimation method includes steps S1 to S5.

[0103] As shown in Figures 4 and 5, first, in step S1, the relay server 44 receives the subject's biological state raw data A2, the subject's basic physical information Q2, and the subject's environmental information R2 from the cloud server 101. The relay server 44 implements, for example, an API (Application Programming Interface).

[0104] Next, in step S2, the first database 45 stores the biological state raw data A2, basic body information Q2, and environmental information R2 received by the relay server 44.

[0105] Next, in step S3, the estimation device 4 uses the biological state raw data A2, basic physical information Q2, and environmental information R2 stored in the first database 45, along with the learning model TM1, to estimate the subject's blood index information MX. Details of the estimation process will be described later.

[0106] Next, in step S4, the second database 46 stores the subject's blood index information MX estimated by the estimation device 4.

[0107] Next, in step S5, the information provision server 47 transmits the subject's blood index information MX stored in the second database 46 to the second terminal 200 and / or the first terminal 102 via the network NW. Specifically, the information provision server 47 displays the subject's blood index information MX on the second terminal 200 and / or the first terminal 102. The second terminal 200 is, for example, a terminal of a medical institution, a health management institution, or a research institution. The first terminal 102 is, for example, the subject's terminal. Once step S5 is completed, the estimation method is finished.

[0108] In the example shown in Figure 4, the blood index information MX is transmitted to the first terminal 102 via the cloud server 101, but it may also be transmitted directly to the first terminal 102.

[0109] Figure 6 is a block diagram showing an example configuration of the estimation device 4 shown in Figure 4. As shown in Figure 6, the estimation device 4 comprises a processing unit 400, a communication unit 401, and a storage unit 402. The estimation device 4 may further comprise an input unit 403 and an output unit 404.

[0110] The input unit 403 is an input device for inputting various types of information to the processing unit 400. For example, the processing unit 400 may be a keyboard and pointing device, or a touch panel.

[0111] The output unit 404 outputs various types of information. The output unit 404 includes, for example, a display unit that displays various types of information. The display unit is, for example, a liquid crystal display or an organic electroluminescent display.

[0112] The communication unit 401 is connected to a network NW. The communication unit 401 communicates with external devices connected to the network NW. The communication unit 401 is a communication device that performs communication according to a predetermined communication protocol, and includes, for example, a network interface controller. The predetermined communication protocol is, for example, a protocol compliant with Ethernet® and an Internet Protocol Suite. The external devices are, for example, a first terminal 102, a second terminal 200, and a biological state detection device 103.

[0113] The storage unit 402 includes a storage device and stores data and computer programs. The storage unit 402 includes a main storage device such as a semiconductor memory and an auxiliary storage device such as a semiconductor memory and a hard disk drive. The storage unit 402 may also include a removable medium such as an optical disc. The storage unit 402 may be, for example, a non-temporary computer-readable storage medium.

[0114] The memory unit 402 stores the learning model TM1. The learning model TM1 is a trained model. The learning model TM1 is a computer program. The learning model TM1 causes the computer to function in order to estimate the subject's blood index information M2. Specifically, the learning model TM1 takes input information K2 as input and causes the computer to output output information L2.

[0115] The processing unit 400 performs various calculations. The processing unit 400 includes processors such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit).

[0116] Specifically, the processing unit 400 includes a pre-processing unit 41 and an estimation unit 42. The processing unit 400 may further include a post-processing unit 43. For example, the processing unit 400 functions as the pre-processing unit 41, estimation unit 42, and post-processing unit 43 by executing a computer program stored in the memory unit 402. The pre-processing unit 41 preferably includes a statistical processing unit 410, a condition processing unit 411, a sleep processing unit 412, and an accumulation processing unit 416. The pre-processing unit 41 may further include at least one of an alcohol consumption estimation unit 413, a mental and physical recovery ability estimation unit 414, and an exercise responsiveness estimation unit 415.

[0117] The preprocessing unit 41 acquires biological state raw data A2, basic physical information Q2, and environmental information R2 from the first database 45. The storage unit 402 stores the biological state raw data A2, basic physical information Q2, and environmental information R2. The vital raw data of biological state raw data A2 includes, for example, heart rate data. The behavioral raw data of biological state raw data A2 includes, for example, step count data, exercise intensity data, and sleep data.

[0118] Heart rate data includes information on heart rate (bpm). Heart rate is measured or aggregated, for example, at a sampling rate of at least once per minute. Heart rate data is accompanied by the corresponding measurement date, time, and subject identification information. Heart rate data includes heart rate information arranged in a time series.

[0119] Step count data includes information on the number of steps (spm). Step counts are measured or aggregated, for example, at a sampling rate of at least once per minute. Step count data is accompanied by the corresponding measurement date, time, and subject identification information. Step count data includes step count information arranged in chronological order.

[0120] Exercise intensity data includes METs information per unit of time (1 minute). In other words, exercise intensity is typically expressed in METs. Exercise intensity data is measured or aggregated, for example, at a sampling rate of at least once per minute. Exercise intensity data is accompanied by the corresponding measurement date, time, and subject identification information. Exercise intensity data includes METs information arranged in a time series.

[0121] The sleep data includes information on the start time (day, hour, minute) and end time (day, hour, minute, second) for each sleep period, as detected by the algorithm of the biological state detection device 103. The sleep data is accompanied by the date on which the sleep start time and end time were measured, and the subject's identification information.

[0122] The preprocessing unit 41 performs preprocessing on the biological state raw data A2 and generates input information K2, which is the result of the preprocessing. The storage unit 402 stores the input information K2.

[0123] As an example, the statistical processing unit 410 of the preprocessing unit 41 performs statistical processing on each of the heart rate data, step count data, and exercise intensity data for each unit period. As a result, the statistical processing unit 410 outputs multiple statistical indicators, which are the results of the statistical processing, for each of the heart rate data, step count data, and exercise intensity data. The statistical processing by the statistical processing unit 410 represents mathematical processing to quantitatively express the characteristics and trends of the data being processed. The unit period is, for example, one day. The heart rate data, step count data, and exercise intensity data are, in detail, extracted heart rate data, extracted step count data, and extracted exercise intensity data, which will be described later. In addition, the statistical processing unit 410 may perform difference processing and percentage processing for each unit period. Details of this will be described later.

[0124] Statistical indicators include, for example, the mean, sum, standard deviation, xth percentile, median, minimum, maximum, RMSSD, coefficient of variation CV (= standard deviation / mean), reciprocal of the coefficient of variation CV, mean change, percentage of changes greater than or equal to a specified value, and statistically processed values ​​of the moving median over the time window. Furthermore, RMSSD is calculated not only for heart rate (bpm) but also for steps per minute (SPM) and exercise intensity (METs / min). In this case, RMSSD is the square root of the mean of the squared differences between consecutive adjacent values. Consecutive adjacent values ​​are, for example, the reciprocal of heart rate (bpm), consecutive adjacent steps per minute (SPM), or consecutive adjacent exercise intensity (METs / min).

[0125] The statistical processing unit 410 may generate all or some of the multiple types of statistical indicators.

[0126] As an example, the condition processing unit 411 of the preprocessing unit 41 performs processing (condition processing) according to specific conditions for each of the vital raw data and behavioral raw data of the biological state raw data A2 for each unit period.

[0127] Specifically, the condition processing unit 411 performs the process of extracting vital data from the subject's raw vital data for a period indicated by specific conditions, and / or the process of extracting behavioral data from the subject's raw behavioral data for a period indicated by specific conditions. This will be explained in detail below.

[0128] First, let's explain the processing of heart rate data from the vital data. The condition processing unit 411 extracts heart rate data for a period indicated by a specific condition from the heart rate data for a unit period. The specific condition includes at least one of the following conditions: conditions related to sleep, conditions related to wakefulness, and conditions related to the time of day caused by the sun.

[0129] For example, the condition processing unit 411 extracts heart rate data during wakefulness, during sleep, during the day, at night, at a predetermined time before falling asleep, at a predetermined time after falling asleep, at a predetermined time before waking up, and at a predetermined time after waking up from the heart rate data for a unit period. Hereinafter, the heart rate data extracted according to specific conditions may be referred to as "extracted heart rate data".

[0130] The condition processing unit 411 passes multiple types of extracted heart rate data for a unit period to the statistical processing unit 410. The statistical processing unit 410 performs different statistical processing for each of the extracted heart rate data and outputs multiple different statistical indicators.

[0131] Furthermore, the statistical processing unit 410 calculates the difference (hereinafter referred to as the day-night difference) between the statistical indicator obtained by statistically processing the "daytime heart rate data" and the statistical indicator obtained by statistically processing the "nighttime heart rate data" for each different statistical indicator (difference processing).

[0132] As described above, multiple types of statistical indicators and multiple types of diurnal differences are calculated for each of the multiple types of extracted heart rate data for each unit period, and these constitute a part of the input information K2. In this case, the statistical indicators and diurnal differences are sometimes collectively referred to as heart rate statistical indicators. The storage unit 402 stores the multiple heart rate statistical indicators as part of the input information K2.

[0133] The heart rate statistical index is an example of vital information H2 from the input information K2. The extracted heart rate data is an example of "extracted raw vital data." Therefore, the statistical processing unit 410 generates vital information H2 by performing statistical processing on the extracted raw vital data.

[0134] The statistical processing unit 410 and the condition processing unit 411 may generate all or some of the multiple types of heart rate statistical indices.

[0135] Next, we will explain the processing of step count data from the raw behavioral data. The condition processing unit 411 extracts step count data for a period indicated by a specific condition from the step count data for a unit period. The specific condition in this case is the same as the specific condition for heart rate data.

[0136] For example, the condition processing unit 411 extracts step count data during wakefulness, step count data during sleep, step count data during the day, step count data at night, step count data for a predetermined time before falling asleep, step count data for a predetermined time after falling asleep, step count data for a predetermined time before waking up, and step count data for a predetermined time after waking up from the step count data for a unit period. Hereinafter, the step count data extracted according to specific conditions may be referred to as "extracted step count data".

[0137] The condition processing unit 411 passes multiple types of extracted step count data for a unit period to the statistical processing unit 410. The statistical processing unit 410 performs different statistical processing for each of the extracted step count data and outputs multiple statistical indicators.

[0138] Furthermore, the statistical processing unit 410 calculates the difference (hereinafter referred to as the day-night difference) between the statistical indicator obtained by statistically processing the "daytime step count data" and the statistical indicator obtained by statistically processing the "nighttime step count data" for each different statistical indicator (difference processing).

[0139] Furthermore, the statistical processing unit 410 calculates the ratio of the period in which the number of steps is zero to the period indicated by the specific conditions (hereinafter referred to as the occurrence ratio) (ratio processing).

[0140] For example, the statistical processing unit 410 calculates the incidence rate during wakefulness, during sleep, during the day, at night, a predetermined time before falling asleep, a predetermined time after falling asleep, a predetermined time before waking up, and a predetermined time after waking up.

[0141] As described above, multiple types of statistical indicators, multiple types of day-night differences, and multiple types of occurrence rates are calculated for each of the multiple types of extracted step count data for each unit period, and constitute a part of the input information K2. In this case, the statistical indicators, day-night differences, and occurrence rates are sometimes collectively referred to as step count statistical indicators. The storage unit 402 stores the multiple step count statistical indicators as part of the input information K2.

[0142] The step count statistics index is an example of behavioral information J2 from input information K2. The extracted step count data is also an example of "extracted raw behavioral data." Therefore, the statistical processing unit 410 generates behavioral information J2 by ​​performing statistical processing on the extracted raw behavioral data.

[0143] The statistical processing unit 410 and the condition processing unit 411 may generate all or some of the multiple types of step count statistical indicators.

[0144] Next, we will explain the processing of exercise intensity data from the behavioral data. The condition processing unit 411 extracts exercise intensity data for a period indicated by a specific condition from the exercise intensity data for a unit period. The specific condition in this case is the same as the specific condition for heart rate data.

[0145] For example, the condition processing unit 411 extracts exercise intensity data from exercise intensity data for a unit period, including exercise intensity data during wakefulness, exercise intensity data during sleep, daytime exercise intensity data, nighttime exercise intensity data, exercise intensity data for a predetermined time before falling asleep, exercise intensity data for a predetermined time after falling asleep, exercise intensity data for a predetermined time before waking up, and exercise intensity data for a predetermined time after waking up. Hereinafter, the exercise intensity data extracted according to specific conditions may be referred to as "extracted exercise intensity data".

[0146] The condition processing unit 411 passes multiple types of extracted exercise intensity data for a unit period to the statistical processing unit 410. The statistical processing unit 410 performs different statistical processing for each of the extracted exercise intensity data and outputs multiple statistical indicators.

[0147] Furthermore, the statistical processing unit 410 calculates the difference (hereinafter referred to as the day-night difference) between the statistical indicator obtained by statistically processing the "daytime exercise intensity data" and the statistical indicator obtained by statistically processing the "nighttime exercise intensity data" for each different statistical indicator (difference processing).

[0148] Furthermore, the statistical processing unit 410 calculates the ratio of the period in which METs is less than or equal to a first specified value (hereinafter referred to as the first occurrence ratio) to the period indicated by the specific conditions (ratio processing). The condition processing unit 411 calculates the ratio of the period in which METs is greater than or equal to a second specified value (hereinafter referred to as the second occurrence ratio) to the period indicated by the specific conditions (ratio processing).

[0149] For example, the statistical processing unit 410 calculates the first incidence rate and the second incidence rate for each of the following periods: while awake, while asleep, during the day, at night, a predetermined time before falling asleep, a predetermined time after falling asleep, a predetermined time before waking up, and a predetermined time after waking up.

[0150] As described above, multiple types of statistical indicators, multiple types of diurnal differences, and multiple types of occurrence rates are calculated for each of the multiple types of extracted exercise intensity data for each unit period, and constitute a part of the input information K2. In this case, the statistical indicators, diurnal differences, and occurrence rates are sometimes collectively referred to as exercise intensity statistical indicators. The storage unit 402 stores the multiple exercise intensity statistical indicators as part of the input information K2.

[0151] The exercise intensity statistical index is an example of behavioral information J2 from the input information K2. The extracted exercise intensity data is also an example of "extracted raw behavioral data." Therefore, the statistical processing unit 410 generates behavioral information J2 by ​​performing statistical processing on the extracted raw behavioral data.

[0152] The statistical processing unit 410 and the condition processing unit 411 may generate all or some of the multiple types of exercise intensity statistical indices.

[0153] As described above, when calculating step count statistics and exercise intensity statistics, statistical processing is performed by the statistical processing unit 410 after processing by the condition processing unit 411. Therefore, according to this embodiment, statistical processing can be performed during a period that accurately reflects the pathological condition (for example, disease activity or symptoms of rheumatoid arthritis). As a result, the estimation accuracy of blood index information M2 by the learning model TM1 can be further improved.

[0154] As described above, the statistical processing unit 410 generates vital information H2 by performing statistical processing on the raw vital data extracted by the condition processing unit 411, and / or generates behavioral information J2 by ​​performing statistical processing on the raw behavioral data extracted by the condition processing unit 411.

[0155] Next, we will explain the processing of sleep data from behavioral data. As an example, the sleep processing unit 412 of the preprocessing unit 41 calculates several types of sleep indices based on sleep data over a unit period. Sleep indices are indicators related to the sleep of living organisms.

[0156] For example, the sleep processing unit 412 calculates sleep indicators from the first to the fifth sleep indicator. The first sleep indicator is the sleep duration during the target unit period. The second sleep indicator is the time of falling asleep during the target unit period. The third sleep indicator is the time of waking up during the target unit period. The fourth sleep indicator is the midpoint time during the target unit period. The midpoint time is the time midway between the time of falling asleep and the time of waking up. The fifth sleep indicator is information on the number of sleep cycles during the target unit period.

[0157] As described above, multiple types of sleep indicators are calculated for each unit period and constitute a part of the input information K2. The memory unit 402 stores the multiple sleep indicators as part of the input information K2. The sleep indicators are an example of the behavioral information J2 of the input information K2.

[0158] The sleep processing unit 412 may generate all or some of the multiple types of sleep indicators.

[0159] As an example, the alcohol consumption estimation unit 413 of the preprocessing unit 41 generates alcohol consumption information for each unit period based on the heart rate data and step count data of the vital raw data. For example, the alcohol consumption estimation unit 413 calculates an alcohol consumption estimation index. The alcohol consumption estimation index is an index for estimating alcohol consumption, calculated based on heart rate excluding heart rate during walking.

[0160] Specifically, the alcohol consumption estimation unit 413 calculates a unit alcohol consumption index. The unit alcohol consumption index is defined using the total heart rate change, a first function, and a second function.

[0161] The total heart rate change is calculated by summing up the heart rate changes within a third time period (e.g., within 80 minutes). The heart rate change is the difference between the subject's heart rate within a first time period (e.g., within 1 minute) and the subject's heart rate within a second time period following the first time period (e.g., within 1 minute), calculated based on the subject's heart rate (bpm) measured at first fixed time intervals (e.g., every 1 minute). The heart rate change is calculated at predetermined time intervals (e.g., every 1 minute).

[0162] The total change in heart rate is calculated by shifting the starting point of the third time period by a predetermined amount of time (for example, one minute at a time). Therefore, the total change in heart rate is calculated at predetermined time intervals. Since heart rate increases with alcohol consumption, the total change in heart rate is a useful indicator for estimating alcohol consumption.

[0163] The first function is defined based on the number of steps (spm) measured at two fixed time intervals (for example, every minute) and indicates whether the subject is walking or not. For example, at predetermined time intervals (for example, every predetermined time interval), if the number of steps is 0, the first function is set to "1", and if the number of steps is 1 or more, the second function is set to "0". In this example, the value of the first function is obtained at predetermined time intervals.

[0164] The second function indicates whether the representative heart rate exceeds a baseline heart rate (e.g., 80) that is estimated to indicate possible intoxication. The representative heart rate is a representative heart rate selected within a fourth time period (e.g., within 40 minutes) based on the heart rate (bpm). The representative heart rate can be, for example, the minimum, mode, median, or mean. The representative heart rate is calculated by shifting the starting point of the fourth time period by a predetermined amount of time (e.g., 1 minute at a time). Therefore, the representative heart rate is calculated at predetermined time intervals. Thus, for example, at predetermined time intervals, if the representative heart rate exceeds the baseline heart rate, the second function is set to "1", and if the representative heart rate is less than or equal to the baseline heart rate, the second function is set to "0". In this example, the value of the second function is obtained at predetermined time intervals.

[0165] The "third time" and the "fourth time" are longer than the "first fixed time," the "second fixed time," the "first time," and the "second time."

[0166] Let the unit alcohol consumption index be "f(t)", the total change in heart rate be "SumΔHR(t)", the first function be "L_ST(t)", and the second function be "L_DR(t)". ​​t represents a unit of time, where one unit is a predetermined time (for example, 1 minute). In this case, the unit alcohol consumption index is given by equation (1).

[0167] f(t)=SumΔHR(t)×L_ST(t)×L_DR(t)…(1)

[0168] In equation (1), L_ST(t) is 0 during walking. Therefore, in the unit alcohol consumption index, it is possible to consider the potential immediate effect that the subject's walking (number of steps) may have on the subject's heart rate. Also, in equation (1), if SumΔHR(t) is a negative value, f(t) is set to 0.

[0169] In this embodiment, the alcohol consumption estimation unit 413 performs statistical processing on the unit alcohol consumption index for one or more target time periods within a unit period and calculates an alcohol consumption estimation index as a result of the statistical processing. In this case, the statistical processing is, for example, a process to calculate the sum of the unit alcohol consumption index, or a process to calculate the average value of the unit alcohol consumption index. In this case, the value of the alcohol consumption parameter may be changed for each target time period to calculate the alcohol consumption estimation index. The alcohol consumption parameter is, for example, one or more parameters selected from the third time, the fourth time, and the reference heart rate.

[0170] In this embodiment, as an example, the alcohol consumption estimation unit 413 calculates four types of alcohol consumption estimation indicators for each of two different target time periods, using two different values ​​of the same type of alcohol consumption parameter. The four types of alcohol consumption estimation indicators are calculated for each unit period and constitute a part of the input information K2. The storage unit 402 stores the multiple alcohol consumption estimation indicators as part of the input information K2. The alcohol consumption estimation indicators are an example of the behavioral information J2 of the input information K2.

[0171] The alcohol consumption estimation unit 413 may generate all or some of the multiple types of alcohol consumption estimation indicators.

[0172] As an example, the mental and physical recovery capacity estimation unit 414 of the preprocessing unit 41 generates mental and physical recovery capacity information for each unit period based on vital raw data (heart rate data) and behavioral raw data (e.g., exercise intensity data). For example, the mental and physical recovery capacity estimation unit 414 generates a mental and physical recovery estimation index. The mental and physical recovery estimation index indicates the degree of the subject's heart rate recovery capacity from the end of exercise.

[0173] The index for estimating physical and mental recovery includes a first parameter value and a second parameter value that realize a model function that approximates the relationship between the change in heart rate and the corresponding heart rate. The change in heart rate is the difference between the subject's heart rate (bpm) at first constant time intervals (e.g., every minute) during a fifth period of time (e.g., within one minute) in which the subject is exercising and the heart rate during a sixth unit time period (e.g., within one minute) following the fifth period in which the subject is at rest. The corresponding heart rate indicates the heart rate during the fifth period (e.g., within one minute). For example, if the subject's exercise intensity is 1 MET, the subject is judged to be in a resting state, and if the subject's exercise intensity exceeds 1 MET, the subject is judged to be in an exercise state.

[0174] The model function includes a regression line with a slope and an intercept. The magnitude of the slope represents the heart rate's recovery capacity. The intercept represents the magnitude of the heart rate requiring recovery. The first parameter value indicates the slope of the regression line. The second parameter value indicates the intercept of the regression line.

[0175] In this embodiment, the mental and physical recovery ability estimation unit 414 calculates mental and physical recovery estimation indices (first parameter value and second parameter value) for each unit period, specifically for the nighttime period and the daytime period (periods other than the nighttime period). In addition, the mental and physical recovery ability estimation unit 414 calculates the difference between the first parameter value during the nighttime period and the first parameter value during the daytime period (hereinafter referred to as the day-night difference), and the difference between the second parameter value during the nighttime period and the second parameter value during the daytime period (hereinafter referred to as the day-night difference).

[0176] As described above, two types of parameter values ​​(four types in total) for two different time periods, and two types of diurnal differences are calculated for each unit period and constitute a part of the input information K2. In this case, the parameter values ​​and diurnal differences are sometimes collectively referred to as the mental and physical recovery index. The memory unit 402 stores multiple mental and physical recovery indices as part of the input information K2. The mental and physical recovery index is an example of the vital information H2 of the input information K2.

[0177] The mental and physical recovery ability estimation unit 414 may generate all or some of the multiple types of mental and physical recovery indicators.

[0178] As an example, the exercise response estimation unit 415 of the preprocessing unit 41 generates exercise response information for each unit period based on vital raw data (heart rate data) and behavioral raw data (e.g., exercise intensity data). For example, the exercise response estimation unit 415 generates a first exercise response estimation index and a second exercise response estimation index. The first exercise response estimation index is an index that shows the degree of increase in heart rate after the start of exercise. The second exercise response estimation index is an index that shows the degree of decrease in heart rate at the end of exercise.

[0179] In this embodiment, the motor responsiveness estimation unit 415 calculates a first motor responsiveness estimation index and a second motor responsiveness estimation index for each unit period, for the nighttime period and the daytime period (periods other than the nighttime period). In addition, the motor responsiveness estimation unit 415 calculates the difference between the first motor responsiveness estimation index between the nighttime period and the daytime period (hereinafter referred to as the day-night difference), and the difference between the second motor responsiveness estimation index between the nighttime period and the daytime period (hereinafter referred to as the day-night difference).

[0180] As described above, two types of exercise responsiveness estimation indices (a total of four types) for two different time periods, and two types of diurnal differences, are calculated for each unit period and constitute part of the input information K2. In this case, the exercise responsiveness estimation indices and diurnal differences are sometimes collectively referred to as exercise responsiveness indices. The memory unit 402 stores multiple exercise responsiveness indices as part of the input information K2. The exercise responsiveness indices are an example of vital information H2 in the input information K2.

[0181] The motor responsiveness estimation unit 415 may generate all or some of the multiple types of motor responsiveness indices.

[0182] The accumulation processing unit 416 of the preprocessing unit 41 executes the accumulation process.

[0183] Specifically, the cumulative processing unit 416 performs cumulative processing on the heart rate statistical indicators for each different type of heart rate statistical indicator during the observation period, and outputs cumulative data for each heart rate statistical indicator.

[0184] The cumulative processing unit 416 performs cumulative processing on the step count statistics for each different type of step count statistics during the observation period and outputs cumulative data for each step count statistics.

[0185] The cumulative processing unit 416 performs cumulative processing on the exercise intensity statistical indicators for each different type of exercise intensity statistical indicator during the observation period, and outputs cumulative data for each exercise intensity statistical indicator.

[0186] The cumulative processing unit 416 performs cumulative processing on the sleep indicators for each different type of sleep indicator during the observation period and outputs cumulative data for each sleep indicator.

[0187] The cumulative processing unit 416 performs cumulative processing on each different type of alcohol consumption estimation indicator for the observation period and outputs cumulative data for each alcohol consumption estimation indicator.

[0188] The cumulative processing unit 416 performs cumulative processing on the mental and physical recovery indicators for each different type of mental and physical recovery indicator during the observation period, and outputs cumulative data for each mental and physical recovery indicator.

[0189] The cumulative processing unit 416 performs cumulative processing on the motor responsiveness index for each different type of motor responsiveness index during the observation period and outputs cumulative data for each motor responsiveness index.

[0190] The observation period subject to cumulative processing may be a fixed value or may be changed dynamically.

[0191] As described above, the multiple types of cumulative data calculated for multiple types of indicators constitute a part of the input information K2. The memory unit 402 stores the multiple types of cumulative data as part of the input information K2. The cumulative data for heart rate statistics, physical and mental recovery indicators, and exercise responsiveness indicators are examples of "vital information H21 on which cumulative processing has been performed" in the input information K2. The cumulative data for step count statistics, exercise intensity statistics, sleep indicators, and alcohol consumption estimation indicators are examples of "behavioral information J21 on which cumulative processing has been performed" in the input information K2. Hereinafter, cumulative data may be referred to as cumulative indicators.

[0192] The cumulative processing unit 416 may perform cumulative processing on all of the multiple types of indicators, or it may perform cumulative processing on some of them.

[0193] The details of the cumulative processing unit 416 will now be explained. In this case, the indicator for a unit period (for example, one day) is referred to as unit data. That is, the unit data is the heart rate statistical indicator, step count statistical indicator, exercise intensity statistical indicator, sleep indicator, alcohol consumption estimation indicator, physical and mental recovery indicator, or exercise responsiveness indicator for the unit period. The heart rate statistical indicator, physical and mental recovery indicator, and exercise responsiveness indicator are each vital data. The step count statistical indicator, exercise intensity statistical indicator, sleep indicator, and alcohol consumption indicator are each behavioral data. Therefore, the unit data is either vital data or behavioral data.

[0194] The cumulative processing includes classification processing, correction processing, and cumulative processing. Furthermore, multiple unit data points are arranged in a time series during the observation period.

[0195] The cumulative processing unit 416 performs classification execution processing. The classification execution processing involves classifying multiple unit data arranged in a time series into at least a first state group representing a first state of mind and body and a second state group representing a second state of mind and body, thereby obtaining multiple first state data belonging to the first state group and multiple second state data belonging to the second state group.

[0196] Next, the cumulative processing unit 416 performs a correction execution process. The correction execution process involves calculating multiple time-series correction data by adjusting the balance of magnitudes between multiple first state data and multiple second state data.

[0197] Next, the cumulative processing unit 416 performs cumulative execution processing. Cumulative execution processing involves calculating multiple time-series cumulative data by accumulating correction data along the time axis.

[0198] As described above, according to this embodiment, the correction execution process is a process that adjusts the balance of magnitude between the first state data and the second state data, which represent different states. Therefore, when the accumulation processing unit 416 accumulates the correction data, it is possible to suppress the divergence of the accumulated data. By performing a correction that suppresses divergence, that is, a correction that adjusts the balance, the accumulated data can be given characteristics that approximate the mechanisms of living organisms that ensure homeostasis. In other words, it is possible to derive accumulated data that reflects the homeostasis of living organisms.

[0199] Furthermore, in this embodiment, cumulative data is calculated by accumulating multiple correction data representing the first and second states of the mind and body on a time axis. Therefore, the cumulative data reflects the accumulated states of mind and body (first and second states) over time. As a result, the cumulative data quantitatively and temporally represents the accumulated states of mind and body (first and second states) over time.

[0200] For example, living organisms maintain homeostasis by having a biological mechanism that repeats between a first state, such as deterioration, and a second state, such as improvement, thereby attempting to keep the state of mind and body constant. Therefore, if the accumulated biological data diverges to one of the first or second states, the correlation with the biological mechanism is lost. In this embodiment, therefore, the divergence of the accumulated data is suppressed by a correction execution process, and the correlation between the accumulated data and the biological mechanism is ensured. Furthermore, the accumulated data quantitatively and temporally represents the first state, such as deterioration, and the second state, such as improvement, that have accumulated over time.

[0201] Referring to Figures 7 to 11, we will now explain the cumulative processing unit 416 in more detail. First, we will explain the classification execution process. Figure 7 is a graph showing an example of multiple unit data d1 arranged in a time series. Figure 8 is a graph for explaining the classification execution process. In Figures 7 and 8, the vertical axis shows the value of the unit data d1 (arbitrary unit). For ease of understanding, a scale is shown on the vertical axis. q1 to q4 represent positive real numbers. The horizontal axis shows time t (for example, days). The unit of time t is a unit period (for example, 1 day). This is the same in Figures 8 to 11.

[0202] As shown in Figures 7 and 8, the cumulative processing unit 416 classifies multiple unit data d1 arranged in time series during the observation period T1 into a first state group g1 representing the first state of mind and body and a second state group g2 representing the second state of mind and body. In Figures 8 to 10, black circles represent the first state, and white diamonds represent the second state.

[0203] The first state is different from the second state. Specifically, the first state represents a mental and physical state that is incompatible with the second state. For example, the first state may represent a mental and physical state opposite to that of the second state.

[0204] Specifically, the first state data b1 of the first state group g1 has one of two signs: positive or negative. The second state data b2 of the second state group g2 has the other of two signs: positive or negative. Figure 9 is a graph showing an example of the first state data b1 and second state data b2 arranged in a time series. The horizontal axis represents time t (for example, days), and the vertical axis represents the relative value of the unit data d1 (= d1 - c1). For ease of understanding, a scale is shown on the vertical axis. p represents a positive real number. In the example in Figure 9, the first state data b1 is a positive value with a positive sign. The second state data b2 is a negative value with a negative sign.

[0205] More specifically, as shown in Figure 7, the cumulative processing unit 416 calculates first classification criterion information c1 based on multiple unit data d1 during the observation period T1. The first classification criterion information c1 is, for example, the mean, median, mode, or percentile value of the multiple unit data d1 during the observation period T1. In the example in Figure 7, the first classification criterion information c1 shows the median.

[0206] As shown in Figure 8, the cumulative processing unit 416 compares the first classification criterion information c1 with the unit data d1 and classifies the unit data d1 based on the comparison result. As an example, the cumulative processing unit 416 subtracts the first classification criterion information c1 from each unit data d1. Then, as shown in Figure 9, the cumulative processing unit 416 classifies each unit data d1 into either the first state group g1 or the second state group g2 depending on whether each subtraction result (= d1 - c1) is positive or negative. In Figure 9, the cumulative processing unit 416 sets the subtraction result showing a positive value as the first state data b1 and the subtraction result showing a negative value as the second state data b2.

[0207] The cumulative processing unit 416 may also subtract each unit data d1 from the first classification criterion information c1.

[0208] Next, the correction execution process will be explained. As shown in Figure 9, the cumulative processing unit 416 calculates multiple time-series correction data d2 by performing a correction execution process on multiple first state data b1 and multiple second state data b2 during the observation period T1. Figure 10 is a graph showing an example of correction data d2 arranged in a time series. The horizontal axis represents time t (for example, days), and the vertical axis represents the corrected relative value of the unit data. For ease of understanding, a scale is shown on the vertical axis as an example. p represents a positive real number. This is the same as in Figure 9.

[0209] Returning to Figure 9, the correction execution process is a process that adjusts the balance of magnitude between the multiple first state data b1 and the multiple second state data b2. In other words, the correction execution process is a process that adjusts the ratio R (=FS2 / FS1) of the sum of the absolute values ​​of the multiple second state data b2 to the sum of the absolute values ​​of the multiple first state data b1 FS1 during the observation period T1. Preferably, the correction execution process represents a process that makes the ratio R (=FS2 / FS1) during the observation period T1 substantially "1".

[0210] In this preferred example, the correction execution process includes a process for calculating a ratio R and a process for correcting the first state data b1 or the second state data b2 based on the ratio R. That is, the cumulative processing unit 416 calculates the ratio R during the observation period T1. Then, for example, if the total value FS1 is greater than the total value FS2, the cumulative processing unit 416 corrects each second state data b2 during the observation period T1 by multiplying it by the reciprocal of the ratio R (1 / R). Thus, the cumulative processing unit 416 obtains a plurality of corrected second state data b2# as a result of the multiplication. As a result, after the correction execution process, the ratio R becomes "1". As shown in Figure 10, the plurality of first state data b1 and the plurality of corrected second state data b2# constitute a plurality of corrected data d2.

[0211] For example, if the total value FS1 is greater than the total value FS2, the cumulative processing unit 416 may multiply each first state data b1 in the observation period T1 by a ratio R to obtain a plurality of corrected first state data b1# (not shown) as a result of multiplication. In this case, the plurality of corrected first state data b1# and the plurality of second state data b2 constitute a plurality of corrected data d2.

[0212] Next, the cumulative execution process will be explained. As shown in Figure 10, the cumulative processing unit 416 calculates multiple time-series cumulative data d3 by accumulating time-series correction data d2 along the time axis. Figure 11 is a graph showing an example of cumulative data d3 arranged in a time series. The horizontal axis represents time t (e.g., days), and the vertical axis represents the cumulative value of the corrected relative value of the unit data d1. For ease of understanding, a scale is shown on the vertical axis. m represents a positive real number. The correction data d2 to be accumulated is the correction data d2 during the observation period T1. Therefore, the cumulative processing unit 416 calculates cumulative data d3 at each time t (e.g., each day) during the observation period T1. In other words, the cumulative data d3 at each time t (e.g., each day) represents the cumulative value of the correction data d2 from the start of accumulation (e.g., the start date of accumulation) to that time t (e.g., that day).

[0213] As an example, the cumulative processing unit 416 calculates the cumulative sum d3j as cumulative data d3 using equation (2). d3j represents the cumulative sum at time t=j during the observation period T1. d2i represents the corrected data d2 at time t=i. ts represents the start of the cumulative process (for example, the start date of the cumulative process). The cumulative processing unit 416 calculates multiple cumulative sums d3j of time series during the observation period T1 while updating j.

[0214]

[0215] The cumulative processing unit 416 may calculate multiple cumulative data d4 after statistical processing by performing statistical processing on multiple cumulative data d3 during the observation period T1. Statistical processing may include, for example, centering, standardization, or normalization.

[0216] As explained above with reference to Figures 7 to 11, according to this embodiment, the correction execution process represents a process that substantially sets the ratio R to "1". Therefore, the divergence of the cumulative data d3 can be suppressed more effectively, and cumulative data d3 that better reflects the homeostasis of the living organism can be derived.

[0217] The fact that the ratio R is substantially "1" indicates that it is not only the case that the ratio R is "1", but that the ratio R can reflect constancy in the cumulative data d3. For example, the ratio R may include "0.8 or more and 1.2 or less", or it may include "0.9 or more and 1.1 or less". The closer the ratio R is to "1", the better, but it is most preferable when it is "1".

[0218] Here, the cumulative processing unit 416 may, for example, use the second classification criterion information c2 instead of the first classification criterion information c1 when the unit data d1 is a step count statistical index or an exercise intensity statistical index. The second classification criterion information c2 is set to a value of the unit data d1 that substantially indicates a cessation of physical and mental activity. The cessation of physical and mental activity may include a state of rest for the living body. For example, the second classification criterion information c2 is set to a step count value (zero) that substantially indicates a cessation of physical and mental activity. Alternatively, for example, the second classification criterion information c2 is set to an exercise intensity value (METs = 1) that substantially indicates a cessation of physical and mental activity. When using the second classification criterion information c2, the unit data d1 is classified into active states and rest states, and then corrected and accumulated. Therefore, the cumulative data d3 reflects the comparison results and balance between active states and rest states. As a result, the cumulative data d3 is more suitable for evaluating the mechanism of the living body that repeats activity and rest.

[0219] Specifically, the accumulation processing unit 416 calculates the first state data b1 belonging to the first state group g1 by classifying unit data d1 that are greater than the second classification criterion information c2 from among a plurality of unit data d1. Furthermore, the accumulation processing unit 416 calculates the second state data b2 belonging to the second state group g2 by classifying unit data d1 that are equal to the second classification criterion information c2 from among a plurality of unit data d1. In this case, the accumulation processing unit 416 sets the second state data b2 to a predetermined value. For example, the predetermined value may be a negative value. The predetermined value is determined experimentally and / or empirically.

[0220] Furthermore, the accumulation processing unit 416 may, for example, compare the accumulated data d3 with a threshold when the unit data d1 is a sleep index, and based on the comparison result, replace the accumulated data d3 with the threshold and set it as the new accumulated data d3. For example, in cases where sleep duration cannot be accumulated and sleep debt arises due to sleep deprivation, it may be possible to more appropriately approximate the biological mechanism by setting a limit on the accumulated data d3 based on the premise that sleep duration cannot be accumulated, rather than uniformly accumulating the correction data d2. For example, by setting a threshold as a limit, the characteristics of sleep duration that cannot be accumulated can be represented by the accumulated data d3. In other words, sleep debt is represented by the accumulated data d3.

[0221] As an example, the accumulation processing unit 416 calculates the cumulative sum d3j as cumulative data d3 using equations (3) to (5). d3#j represents the cumulative sum at time t=j. d2i represents the corrected data d2 at time t=i. ts represents the start of accumulation (for example, the start date of accumulation). The accumulation processing unit 416 calculates multiple cumulative sums d3#j of the time series while updating j. This is the same as in equation (2).

[0222]

[0223] The cumulative processing unit 416 determines whether the cumulative sum d3#j exceeds the threshold Th. If the cumulative processing unit 416 determines that the cumulative sum d3#j does not exceed the threshold Th, it calculates the cumulative sum d3j using equation (4). On the other hand, if the cumulative processing unit 416 determines that the cumulative sum d3#j exceeds the threshold Th, it calculates the cumulative sum d3j using equation (5).

[0224]

[0225]

[0226] As explained above with reference to Figures 6 to 11, the heart rate statistical index, step count statistical index, exercise intensity statistical index, sleep index, alcohol consumption estimation index, physical and mental recovery index, exercise responsiveness index, and cumulative data constitute the input information K2. Hereinafter, the heart rate statistical index, step count statistical index, exercise intensity statistical index, sleep index, alcohol consumption estimation index, physical and mental recovery index, exercise responsiveness index, cumulative data, basic physical information Q2, and environmental information R2 may be collectively referred to as "features." The number of features is not particularly limited. Note that "features" may also be referred to as "feature FT."

[0227] Here, we will illustrate why the above-mentioned features can contribute to the estimation of blood indicator information M2 from the perspective of inflammatory responses. Below, we will explain using rheumatoid arthritis as an example of a disease in which inflammation occurs in the body. We will also explain CRP and ESR as examples of blood indicators.

[0228] Inflammation in rheumatoid arthritis is a factor influencing CRP and ESR. Since the inflammatory response caused by rheumatoid arthritis is accompanied by sympathetic nerve activation, changes in heart rate (e.g., xth percentile value) and heart rate variability indices that capture autonomic nervous system activity (e.g., RMSSD, CVRR, or LF / HF) can contribute to the estimation of blood indicator information M2.

[0229] Inflammation in rheumatoid arthritis is a factor influencing CRP and ESR. It is believed that inflammatory responses have a circadian rhythm, and in particular, in rheumatoid arthritis, the inflammatory response is enhanced at night and during sleep. Therefore, for example, changes in heart rate and heart rate variability during the night (e.g., from 8 PM to 4 AM the following morning), during sleep, and before and after falling asleep can contribute to the estimation of blood indicator information M2. On the other hand, as mentioned above, in inflammation other than rheumatoid arthritis, for example, the response may be enhanced during the day and decreased at night. In this case, changes in daytime heart rate, resting heart rate while awake, and resting heart rate variability while awake can contribute to the estimation of blood indicator information M2.

[0230] Inflammation in rheumatoid arthritis is a factor influencing CRP and ESR. The systemic inflammatory response observed in rheumatoid arthritis causes fatigue, malaise, and pain. Fatigue, malaise, and pain lead to a decrease in physical activity. Therefore, a decrease in physical activity can contribute to the estimation of blood indicator information M2. A decrease in physical activity is indicated, for example, by a low number of steps taken while awake (e.g., xth percentile value) and a low exercise intensity (e.g., xth percentile value).

[0231] Inflammation in rheumatoid arthritis is a factor influencing CRP and ESR. Even a relatively mild inflammatory response in rheumatoid arthritis, if it persists, can lead to exacerbation of various symptoms and disease activity. Therefore, for example, changes in heart rate and heart rate variability can contribute to the estimation of blood indicator information M2. Furthermore, for example, changes in heart rate and heart rate variability during the night, during sleep, and before and after falling asleep can contribute to the estimation of blood indicator information M2. In addition, for example, the number of steps taken and exercise intensity while awake can contribute to the estimation of blood indicator information M2. On the other hand, as mentioned above, in inflammation other than rheumatoid arthritis, for example, the response may be heightened during the day and decreased at night. In this case, changes in daytime heart rate, resting heart rate while awake, and resting heart rate variability while awake can contribute to the estimation of blood indicator information M2. In particular, features obtained by performing cumulative processing on these features can also contribute to the estimation of blood index information M2.

[0232] Next, we will illustrate why the above-mentioned features can contribute to the estimation of blood indicator information M2 from the perspective of joint deformation, pain, and stiffness symptoms.

[0233] Inflammation from rheumatoid arthritis is a factor influencing CRP and ESR. Joint symptoms such as pain caused by inflammation from rheumatoid arthritis can cause tossing and turning or "struggle" during sleep. Therefore, for example, features indicating movement during sleep, features indicating the number of temporary awakenings, and features indicating the number of steps taken during temporary awakenings can contribute to the estimation of blood index information M2. Features indicating movement include, for example, the number of steps or exercise intensity. For example, if the number of steps is greater than "0", or if the exercise intensity is greater than "1" in METs, it can be determined that the subject has moved. In particular, features indicating movement due to tossing and turning and "struggle" before waking, when approaching a state of wakefulness, can contribute to the estimation of blood index information M2.

[0234] Inflammation in rheumatoid arthritis is a factor influencing CRP and ESR. When inflammation occurs in rheumatoid arthritis, joint symptoms such as pain and stiffness may worsen particularly around the time of waking, when the viscosity of synovial fluid within the joint capsule is highest. Therefore, for example, features indicating a decrease in the number of steps taken immediately after waking and features indicating a decrease in exercise intensity can contribute to the estimation of blood indicator information M2.

[0235] Inflammation in rheumatoid arthritis is a factor influencing CRP and ESR. Excessive exercise on the day of and the previous day is a cause of joint symptoms such as pain based on inflammation in rheumatoid arthritis. Therefore, for example, extremely high step counts (e.g., xth percentile value) and extremely high exercise intensity (e.g., xth percentile value), as well as low variability in these, can contribute to the estimation of blood indicator information M2. On the other hand, insufficient exercise (limited joint movement) on the day of and the previous day can also cause joint symptoms such as pain. Therefore, for example, low step counts and low exercise intensity, as well as low variability in these, can contribute to the estimation of blood indicator information M2. Furthermore, the level of exercise load on the joints is affected not only by the day of and the previous day, but also by the accumulation of load over several days or more. Therefore, for example, features obtained by performing cumulative processing on features related to high step counts, high exercise intensity, and high variability in these can contribute to the estimation of blood indicator information M2.

[0236] Next, we will illustrate why the above-mentioned features can contribute to the estimation of blood indicator information M2 from the perspective of vicious and virtuous cycles caused by rheumatoid arthritis.

[0237] Inflammation in rheumatoid arthritis is a factor influencing CRP and ESR. When inflammation in rheumatoid arthritis occurs, a vicious cycle of symptom exacerbation is expected. For example, joint pain reduces physical activity such as walking, inducing a depressive state. Consequently, the perceived pain intensifies, further reducing activity. As a result, joint symptoms cyclically worsen. For example, joint pain reduces physical activity such as walking, narrowing the range of motion in the joints. Consequently, joint pain becomes more likely, further reducing physical activity such as walking. As a result, joint symptoms cyclically worsen. For example, joint pain makes it difficult to fall asleep, shortening sleep duration. Consequently, daytime physical activity decreases and a depressive state occurs, reducing activity. Consequently, pain symptoms worsen further, and falling asleep becomes even more difficult. Quantifying these cyclical features can contribute to the estimation of blood indicator information M2. Features that quantify the cycle include, for example, features related to low step count and low exercise intensity, as well as features related to the low variability of these, obtained by performing cumulative processing. Furthermore, features that quantify the cycle include, for example, features related to sleep duration or sleep rhythm, obtained by performing cumulative processing. Features related to sleep rhythm are, for example, the deviation from the mean of the mid-sleep time.

[0238] Next, we will illustrate why the above-mentioned features can contribute to the estimation of blood index information M2 from the perspective of individual characteristics and lifestyle (including preferences).

[0239] Inflammation in rheumatoid arthritis is a factor influencing both CRP and ESR. It is known that obesity is associated with elevated levels of inflammatory biomarkers (e.g., CRP). Furthermore, some studies suggest that moderate alcohol consumption may contribute to suppressing the progression of inflammation in rheumatoid arthritis. Therefore, for example, the level of height, weight, BMI (body mass index), alcohol consumption, and the amount of alcohol consumed may contribute to the estimation of blood indicator information M2.

[0240] Next, from an environmental perspective, we will illustrate why the above features can contribute to the estimation of blood index information M2.

[0241] Inflammation from rheumatoid arthritis is a factor influencing CRP and ESR. Symptoms such as joint pain and stiffness due to inflammation from rheumatoid arthritis are expected to worsen when ambient temperature (air temperature, room temperature) is low, as this increases the viscosity of synovial fluid within the joint capsule. Furthermore, drastic changes in ambient temperature can place various stresses on the body, potentially disrupting autonomic nervous system function and influencing the level of inflammatory response. Therefore, seasonal features, for example, can contribute to the estimation of blood indicator information M2. These seasonal features include winter months or months at the transition between seasons.

[0242] Next, we will illustrate why the above-mentioned features can contribute to the estimation of blood indicator information M2 from the perspective of secondary symptoms.

[0243] Inflammation in rheumatoid arthritis is a factor influencing CRP and ESR. When inflammation occurs in rheumatoid arthritis, it causes various accompanying symptoms in addition to joint symptoms. For example, if interstitial pneumonia occurs, it also adversely affects cardiopulmonary and respiratory function. One method for quantifying the decline in cardiopulmonary and respiratory function uses the change in heart rate at the start of exercise such as walking, and the change in heart rate at the end of exercise such as walking (when transitioning to rest). Therefore, given that pneumonia occurs as a secondary disease, chronic lack of exercise due to joint pain and fatigue leads to a decline in cardiopulmonary function, and autonomic nervous system dysfunction occurs due to severe symptoms, features that can express the first specific state or the second specific state can contribute to the estimation of blood indicator information M2. The first specific state is characterized by a high or low increase in heart rate at the start of exercise. Therefore, features that can express the first specific state are exercise responsiveness information. The second specific state is characterized by a slow recovery rate of heart rate at the end of exercise. Therefore, the features that can represent the second specific state are information about the body's ability to recover.

[0244] The above examples illustrate, from various perspectives, why the above-mentioned features can contribute to the estimation of blood index information M2. The specific statistical processing that expresses the "height and lowness" and "high and low variability" shown above is implemented by the statistical processing unit 410.

[0245] Returning to Figure 6, the estimation unit 42 uses the learning model TM1 to estimate the subject's blood index information M2. Specifically, the estimation unit 42 inputs input information K2 to the learning model TM1 and obtains output information L2 from the learning model TM1. The storage unit 402 stores the output information L2.

[0246] The learning model TM1 takes input information K2 as input and outputs output information L2 according to a machine learning algorithm. The machine learning algorithm is not particularly limited, but examples include linear regression, neural networks, deep neural networks (hereinafter referred to as DNN), decision trees, random forests, gradient boosting, or regularized regression. Regularized regression is, for example, L1 regularized regression or L2 regularized regression. Below, as an example, we will explain the case where the machine learning algorithm of the learning model TM1 is a DNN.

[0247] Figure 12 is a schematic diagram showing an example of a DNN 50. As shown in Figure 12, the DNN 50 includes an input layer 51, a plurality of hidden layers 52, and an output layer 53. Figure 12 shows a fully connected example. The input layer 51 includes a plurality of nodes 511. Each of the hidden layers 52 includes a plurality of nodes 521. The output layer 53 includes at least one node 531. In the example of Figure 7, the output layer 53 includes a plurality of nodes 531.

[0248] Multiple nodes 511 of the input layer 51 are each input to multiple feature quantities FT that constitute the input information K2. Each node 521 of the hidden layer 52 uses the learned weights and biases and the activation function to convert the output of the previous layer into input to the next layer. Each node 531 of the output layer 53 uses the learned weights and biases and the activation function according to the final output format to output the final result (estimated result) based on the output of the previous layer. In other words, the output layer 53 outputs output information L2. Output information L2 includes blood index information M21, M22, M23, and M24.

[0249] In the example in Figure 12, blood indicator information M21, M22, M23, and M24 represent CSR, ESR, γ-GTP value, and red blood cell count, respectively. In this example, blood indicator information M21, M22, M23, and M24 directly show CSR, ESR, γ-GTP value, and red blood cell count as numerical values.

[0250] In the example shown in Figure 12, the storage unit 402 stores the blood indicator information M21, M22, M23, and M24 as output information L2. The communication unit 401 transmits the output information L2 to the information provision server 47 (Figure 4). The number of nodes 531 is equal to the number of blood indicator information to be output, and may be one or two or more.

[0251] On the other hand, blood indicator information M21, M22, M23, and M24 may indirectly represent CSR, ESR, γ-GTP value, and red blood cell value, respectively. In this case, the post-processing unit 43 in Figure 6 performs post-processing on each of the blood indicator information M21, M22, M23, and M24 to output blood indicator information M31, M32, M33, and M34. These points will be explained with reference to Figures 13 and 14.

[0252] Figure 13 schematically illustrates another example of the DNN 50. Figure 13 shows an example where binary classification is performed. In binary classification, each of the blood index information M21, M22, M23, and M24 output by the output layer 53 is, for example, a real number between 0 and 1.

[0253] For blood indicator information M21 and M22, for example, "0" indicates a negative result and "1" indicates a positive result. A value close to "0" for blood indicator information M21 and M22 indicates a high probability of determining a negative result. A value close to "1" for blood indicator information M21 and M22 indicates a high probability of determining a positive result. For blood indicator information M23 and M24, for example, "0" indicates a normal result and "1" indicates an abnormal result. A value close to "0" for blood indicator information M23 and M24 indicates a high probability of determining a normal result. A value close to "1" for blood indicator information M23 and M24 indicates a high probability of determining an abnormal result.

[0254] In binary classification, the post-processing unit 43 performs threshold processing on each of the blood indicator information M21, M22, M23, and M24. In other words, the post-processing unit 43 sets "0" or "1" for the blood indicator information M31, M32, M33, and M34 depending on whether the blood indicator information M21, M22, M23, and M24 are below the threshold or above the threshold. In short, binary classification is the process of classifying the output information L2 into either the "0" class or the "1" class.

[0255] For example, if the blood index information M21 is less than a threshold (e.g., 0.5), the post-processing unit 43 sets the blood index information M31 to "0" to indicate a negative result from the perspective of CRP. On the other hand, if the blood index information M21 is equal to or greater than a threshold (e.g., 0.5), the post-processing unit 43 sets the blood index information M31 to "1" to indicate a positive result from the perspective of CRP.

[0256] The threshold is not limited to 0.5 and can be set to any value. The threshold is determined, for example, based on experimental, empirical, and / or medical standards. Furthermore, the thresholds for blood indicator information M21, M22, M23, and M24 may be the same or different. Although "0" and "1" were used as binary values, any value can be used. In addition, the number of nodes 531 is the same as the number of blood indicator information to be output, and may be one or two or more.

[0257] Figure 14 schematically illustrates yet another example of DNN50. Figure 14 shows an example where multi-class classification is performed. As an example, the degree of each of the following values ​​is classified into four classes: "Level 1", "Level 2", "Level 3", and "Level 4". The values ​​increase from "Level 1" to "Level 4".

[0258] The number of classifications (classes) is not particularly limited and can be set arbitrarily. Furthermore, the numerical range for each class is determined, for example, based on experimental, empirical, and / or medical standards.

[0259] As shown in Figure 14, the DNN 50 includes an input layer 51, a plurality of intermediate layers 52, and at least one output layer 53A. In the example in Figure 14, the DNN 50 includes a plurality of output layers 53A.

[0260] Multiple output layers 53A are provided, each corresponding to CSR, ESR, γ-GTP value, and red blood cell value. Each output layer 53A outputs output information L2. Specifically, each output layer 53A contains the same number of nodes 531A as the number of classifications (number of classes). Note that in Figure 14, for the sake of simplifying the diagram, only the nodes 531A of the output layers 53A corresponding to CSR and ESR are shown, and the nodes 531A of the output layers 53A corresponding to γ-GTP value and red blood cell value are omitted.

[0261] Let's focus on one output layer 53A. The four nodes 531A each correspond to one of four classes. That is, the output layer 53A includes a node 531A corresponding to "Level 1", a node 531A corresponding to "Level 2", a node 531A corresponding to "Level 3", and a node 531A corresponding to "Level 4".

[0262] Each node 531A outputs an output value VL. The output value VL is, for example, a real number between 0 and 1 (inclusive). The sum of the output values ​​VL of the four nodes 531A is "1". Therefore, the output value VL indicates the probability of being classified into the corresponding class. In other words, the class corresponding to the node 531A that outputs the largest output value VL has the highest probability of being correct.

[0263] Therefore, the post-processing unit 43 acquires the maximum output value from among the multiple output values ​​VL from the multiple nodes 531A for each output layer 53A. The multiple maximum output values ​​output from each of the multiple output layers 53A correspond to the blood index information M21, M22, M23, and M24. In this way, the multiple output values ​​VL (output information L2) from each output layer 53A substantially include blood index information.

[0264] Then, the post-processing unit 43 sets the class corresponding to the node 531A that output the maximum output value for each output layer 53A in the blood indicator information M31, M32, M33, and M34. For example, in the output layer 53A related to CRP, if the output value VL of node 531A corresponding to "Level 1" is "0.9", the output value VL of node 531A corresponding to "Level 2" is "0.05", the output value VL of node 531A corresponding to "Level 3" is "0.03", and the output value VL of node 531A corresponding to "Level 4" is "0.02", the post-processing unit 43 sets "Level 1" in the blood indicator information M31, corresponding to node 531A that shows the maximum output value.

[0265] The number of output layers 53A is equal to the number of blood index information to be output, and may be one or two or more. In addition, the number of nodes 531A in one output layer 53A is equal to the number of classifications (classes).

[0266] In the examples shown in Figures 13 and 14, the storage unit 402 stores the blood indicator information M31, M32, M33, and M34 obtained by the post-processing unit 43 as output information N2. The communication unit 401 transmits the output information N2 to the information provision server 47 (Figure 4).

[0267] Furthermore, as can be seen from Figures 13 and 14, the post-processing is a process of obtaining new output information N2 (blood index information M31, M32, M33, M34) by classifying the output information L2 (blood index information M21, M22, M23, M24) indicated by the output information L2 of the learning model TM1 into one of several classes.

[0268] As described above with reference to Figures 1 to 14, according to this embodiment, the estimation device 4 can output (estimate) blood indicator information MX with high estimation accuracy by inputting at least vital information H2 and behavioral information J21 to the learning model TM1.

[0269] In particular, in this embodiment, blood tests of the subject by medical professionals such as doctors are not required. Therefore, during the utilization stage of the learning model TM1, the estimation device 4 can output (estimate) blood indicator information MX with high estimation accuracy without requiring blood tests. In other words, blood indicator information MX can be obtained while reducing the burden on the subject. In this way, blood indicator information MX can be obtained without lowering the subject's QOL (Quality of Life).

[0270] In particular, the blood index information MX includes at least one of the following: information on CRP, information on ESR, information on γ-GTP levels, information on blood glucose levels, information on cholesterol levels, information on iron levels, information on red blood cell counts, and information on white blood cell counts. Therefore, it can contribute to understanding and diagnosing various pathological conditions while reducing the burden on the subject.

[0271] In addition, the estimation device 4 can continuously acquire raw biological state data A2 from a wearable device (biological state detection device 103) and / or a mobile terminal (first terminal 102). Therefore, it can continuously calculate and monitor blood index information MX. In particular, the raw biological state data A2 is automatically transmitted from the wearable device (biological state detection device 103) and / or the mobile terminal (first terminal 102) to the estimation system 40 (estimation device 4). As a result, the burden on the subject is further reduced. In other words, blood index information MX can be obtained while reducing the burden on the subject.

[0272] As an example, this embodiment offers the following advantages. For instance, even for blood indicators not included in standard health checkup blood tests, blood indicators can be estimated with a certain degree of accuracy without increasing the number of additional tests (while suppressing cost increases). For example, CRP and ESR are generally not included in standard health checkup blood tests. Furthermore, blood indicators not typically obtained in standard health checkups can also be estimated. Therefore, for example, it can contribute to the early detection of inflammatory diseases, the early detection of infectious diseases, and the prevention of hospital-acquired infections and their spread. Moreover, since blood tests are unnecessary, estimated blood indicator information MX can be used as an alternative to blood indicators based on blood tests performed by injection for subjects who have allergies or phobias of injections during blood collection. In other words, it can reduce the physical and psychological burden on the subject.

[0273] Next, with reference to Figures 6 and 15, the estimation process for blood index information MX performed in step S3 of Figure 5 will be described. Figure 15 is a flowchart of an example of the estimation process. The estimation process is performed by the estimation device 4 in Figure 6. As shown in Figure 15, the estimation process includes steps S31 to S42.

[0274] First, in step S31, the condition processing unit 411 obtains the subject's biological state raw data A2 from the first database 45 (Figure 4). The biological state raw data A2 is stored in the storage unit 402.

[0275] Next, in step S32, the condition processing unit 411 performs processing (condition processing) according to specific conditions on the heart rate data, step count data, and exercise intensity data of the subject's biological state raw data A2. As a result, the condition processing unit 411 outputs extracted heart rate data, extracted step count data, and extracted exercise intensity data. The specific conditions are conditions related to sleep, conditions related to wakefulness, and conditions related to the time of day caused by the sun.

[0276] Next, in step S33, the statistical processing unit 410 performs statistical processing and differential processing on each of the extracted heart rate data, extracted step count data, and extracted exercise intensity data. In addition, the statistical processing unit 410 performs ratio processing on each of the extracted step count data and extracted exercise intensity data. As a result, the statistical processing unit 410 outputs a heart rate statistical index based on the extracted heart rate data, a step count statistical index based on the extracted step count data, and an exercise intensity statistical index based on the extracted exercise intensity data. These indices are stored in the storage unit 402 as input information K2. Note that differential processing and ratio processing can be considered as statistical processing in a broad sense.

[0277] Next, in step S34, the sleep processing unit 412 calculates a sleep index based on the sleep data of the subject's biological state raw data A2 (an example of sleep processing). The sleep index is stored in the storage unit 402 as input information K2.

[0278] Next, in step S35, the alcohol consumption estimation unit 413 calculates an alcohol consumption estimation index based on the heart rate data and step count data of the subject's biological state raw data A2 (an example of alcohol consumption estimation processing). The alcohol consumption estimation index is stored in the storage unit 402 as input information K2.

[0279] Next, in step S36, the mental and physical recovery ability estimation unit 414 calculates a mental and physical recovery index based on the subject's biological state raw data A2, which includes heart rate data and behavioral raw data (e.g., exercise intensity data) (an example of mental and physical recovery ability estimation processing). The mental and physical recovery index is stored in the storage unit 402 as input information K2.

[0280] Next, in step S37, the exercise responsiveness estimation unit 415 calculates an exercise responsiveness index based on the subject's biological state raw data A2, which includes heart rate data and behavioral raw data (e.g., exercise intensity data) (an example of exercise responsiveness estimation processing). The exercise responsiveness index is stored in the storage unit 402 as input information K2.

[0281] Next, in step S38, the accumulation processing unit 416 performs accumulation processing on the heart rate statistical index, step count statistical index, exercise intensity statistical index, sleep index, physical and mental recovery index, and exercise responsiveness index, respectively. As a result, the accumulation processing unit 416 outputs accumulated data (cumulative index) for the heart rate statistical index, step count statistical index, exercise intensity statistical index, sleep index, physical and mental recovery index, and exercise responsiveness index, respectively. The accumulated data is stored in the storage unit 402 as input information K2. The accumulation processing includes classification execution processing, correction execution processing, and accumulation execution processing.

[0282] Next, in step S39, the estimation unit 42 inputs input information K2, which includes the feature quantities generated in steps S31 to S37, to the learning model TM1. As a result, the learning model TM1 outputs output information L2. In this case, it is preferable that the input information K2 includes basic physical information Q2 and environmental information R2.

[0283] Next, in step S40, the estimation unit 42 acquires output information L2 from the learning model TM1. The output information L2 includes the subject's blood index information M2. The output information L2 is stored in the storage unit 402.

[0284] Next, in step S41, the estimation unit 42 determines whether or not post-processing is required for the blood index information M2.

[0285] If it is determined in step S41 that no post-processing is required (NO), the estimation process is completed and the system returns to the main routine shown in Figure 5. If no post-processing is required, it is because the blood index information M2 directly indicates the blood index numerically (for example, Figure 12).

[0286] On the other hand, if it is determined in step S41 that post-processing is necessary (YES), the process proceeds to step S42. Post-processing is necessary when the blood indicator information M2 indirectly indicates a blood indicator (for example, Figures 13 and 14).

[0287] Next, in step S42, the post-processing unit 43 performs post-processing on the blood indicator information M2 and outputs the blood indicator information M3. The blood indicator information M3 is stored in the storage unit 402 as output information N2. Then the estimation process is completed and the system returns to the main routine shown in Figure 5.

[0288] As described above with reference to Figure 15, according to this embodiment, the estimation device 4 can obtain output information L2 (blood index information M2) with high estimation accuracy from the learning model TM1 by inputting input information K2 to the learning model TM1. Furthermore, the estimation device 4 can obtain output information N2 (blood index information M3) by performing post-processing on the output information L2.

[0289] In this case, a blood test of the subject by a medical professional is not required. Therefore, according to this embodiment, by using the learning model TM1, blood index information MX can be estimated with high estimation accuracy while improving the quality of life of the subject.

[0290] Next, the generation stage of the learning model TM1 will be described with reference to Figures 1, 16, and 17. Figure 16 is a block diagram showing an example configuration of the learning device 3 according to this embodiment. As shown in Figure 16, the learning device 3 comprises a processing unit 31, a communication unit 34, and a storage unit 35. The learning device 3 may also include an input unit 32 and an output unit 33. The hardware configuration of the processing unit 31, communication unit 34, storage unit 35, input unit 32, and output unit 33 is the same as the hardware configuration of the processing unit 400, communication unit 401, storage unit 402, input unit 403, and output unit 404 of the estimation device 4 in Figure 6.

[0291] The storage unit 35 stores data and computer programs. The processing unit 31 includes a learning data acquisition unit 310 and a learning unit 311. For example, the processing unit 31 functions as the learning data acquisition unit 310 and the learning unit 311 by executing the computer programs stored in the storage unit 35.

[0292] Figure 17 is a flowchart showing an example of a learning method using the learning device 3. The learning method is an example of the "learning model generation method" of this disclosure. As shown in Figure 17, the learning method includes steps S101 to S108.

[0293] First, in step S101, the learning data acquisition unit 310 acquires multiple learning datasets F1 from the learning data creation device 2 (Figure 1). The storage unit 35 stores the multiple learning datasets F1. A portion of the multiple learning datasets F1 is training data, another portion is evaluation data, and yet another portion is test data.

[0294] Next, in step S102, the learning unit 311 prepares the pre-training learning model TM1. In the pre-training learning model TM1, various parameters are set to their initial values.

[0295] Next, in step S103, the learning unit 311 obtains one learning dataset F1 from the multiple learning datasets F1 stored in the storage unit 35. In this case, the learning dataset F1 is the training data.

[0296] Next, in step S104, the learning unit 311 inputs the feature information G1 contained in the learning dataset F1 to the learning model TM1 before (or during) learning. As a result, output information is output from the learning model TM1 as an estimation result according to the machine learning algorithm.

[0297] Next, in step S105, the learning unit 311 compares the correct labels B1 included in the learning dataset F1 with the output information output as the estimation result in step S104, and performs machine learning by adjusting various parameters according to the machine learning algorithm. In this way, the learning unit 311 trains the learning model TM1 on the correlation between the feature information G1 and the correct labels B1.

[0298] Machine learning algorithms are not particularly limited, but examples include linear regression, neural networks, deep neural networks (DNNs), decision trees, random forests, gradient boosting, or regularized regression.

[0299] Next, in step S106, the learning unit 311 determines whether the learning termination condition has been met. The learning termination condition is, for example, that the evaluation value of the loss function based on the correct label B1 and the output information output as an estimation result has reached the target value. Alternatively, the learning termination condition is, for example, that the number of learning iterations (epochs) has reached the target number.

[0300] If it is determined in step S106 that the learning termination condition is not met (NO), the process proceeds to step S103. Steps S103 to S105 are repeated until the learning termination condition is met.

[0301] On the other hand, if it is determined in step S106 that the learning termination condition has been met (YES), the process proceeds to step S107.

[0302] Next, in step S107, the learning unit 311 adjusts the hyperparameters of the learning model TM1 based on the learning dataset F1 as evaluation data and the input values ​​input from the input unit 32 by the machine learning engineer.

[0303] Next, in step S108, the learning unit 311 evaluates the estimation accuracy of the learning model TM1 using the learning dataset F1 as test data. Then the learning method is completed.

[0304] As described above with reference to Figure 17, according to this embodiment, the learning device 3 generates a learning model TM1 that outputs output information L2 when input information K2 is input by performing learning using the learning dataset F1 (step S105). That is, the learning device 3 generates a trained learning model TM1 having various trained parameters by repeating learning using a plurality of learning datasets F1. The storage unit 35 stores the trained learning model TM1.

[0305] The input information K2 includes the subject's vital information H2 and the subject's behavioral information J2. The output information L2 includes the subject's blood index information M2. Furthermore, a blood test of the subject by a medical professional is not required. Thus, according to this embodiment, a learning model TM1 can be generated that can estimate blood index information M2 while improving the subject's quality of life (QOL).

[0306] Next, the generation stage of the learning dataset F1 will be described with reference to Figures 1, 18, and 19. Figure 18 is a block diagram showing an example configuration of the learning data creation device 2 according to this embodiment. As shown in Figure 18, the learning data creation device 2 comprises a processing unit 21, a communication unit 24, and a storage unit 25. The learning data creation device 2 may also include an input unit 22 and an output unit 23. The hardware configuration of the processing unit 21, communication unit 24, storage unit 25, input unit 22, and output unit 23 is the same as the hardware configuration of the processing unit 400, communication unit 401, storage unit 402, input unit 403, and output unit 404 of the estimation device 4 in Figure 6.

[0307] The memory unit 25 stores data and computer programs. The processing unit 21 preferably includes a statistical processing unit 210, a conditional processing unit 211, a sleep processing unit 212, an accumulation processing unit 216, and a correct label creation unit 217. The processing unit 21 may further include at least one of the following: an alcohol consumption estimation unit 213, a mental and physical recovery ability estimation unit 214, and an exercise responsiveness estimation unit 215. For example, the processing unit 400 functions as the statistical processing unit 210, the conditional processing unit 211, the sleep processing unit 212, the accumulation processing unit 216, the correct label creation unit 217, the alcohol consumption estimation unit 213, the mental and physical recovery ability estimation unit 214, and the exercise responsiveness estimation unit 215 by executing a computer program stored in the memory unit 402.

[0308] The processing of the statistical processing unit 210, the condition processing unit 211, the sleep processing unit 212, the accumulation processing unit 216, the alcohol consumption estimation unit 213, the mental and physical recovery ability estimation unit 214, and the exercise responsiveness estimation unit 215 are the same as the processing of the statistical processing unit 410, the condition processing unit 411, the sleep processing unit 412, the accumulation processing unit 416, the alcohol consumption estimation unit 413, the mental and physical recovery ability estimation unit 414, and the exercise responsiveness estimation unit 415 of the estimation device 4 (Figure 6).

[0309] For example, in the descriptions of the statistical processing unit 410, condition processing unit 411, sleep processing unit 412, accumulation processing unit 416, alcohol consumption estimation unit 413, mental and physical recovery ability estimation unit 414, and exercise responsiveness estimation unit 415 of the estimation device 4, the subject can be read as the learning subject, the biological state raw data A2 as the biological state raw data A1, the input information K2 as the feature information G1, the output information L2 as the correct label B1, the basic physical information Q2 as the basic physical information Q1, the environmental information R2 as the environmental information R1, and the blood index information M2 as the blood index information M1, thereby substituting the descriptions of the statistical processing unit 210, condition processing unit 211, sleep processing unit 212, accumulation processing unit 216, alcohol consumption estimation unit 213, mental and physical recovery ability estimation unit 214, and exercise responsiveness estimation unit 215 of the learning data creation device 2.

[0310] Figure 19 is a flowchart showing an example of a method for creating learning data using the learning data creation device 2. As shown in Figure 19, the learning data creation method includes steps S201 to S209.

[0311] First, in step S201, the processing unit 21 acquires the raw biological state data A1 and the correct answer information Z1 of the training subject. The raw biological state data A1 and the correct answer information Z1 are stored in the storage unit 25. Preferably, the processing unit 21 acquires basic physical information Q1 and environmental information R1, and stores the basic physical information Q1 and environmental information R1 as feature information G1 in the storage unit 25.

[0312] Next, in step S202, the condition processing unit 211 performs processing (condition processing) according to specific conditions on the heart rate data, step count data, and exercise intensity data of the learning subject's biological state raw data A1. As a result, the condition processing unit 211 outputs extracted heart rate data, extracted step count data, and extracted exercise intensity data. The specific conditions are conditions related to sleep, conditions related to wakefulness, and conditions related to the time of day caused by the sun.

[0313] Next, in step S203, the statistical processing unit 210 performs statistical processing and differential processing on each of the extracted heart rate data, extracted step count data, and extracted exercise intensity data. In addition, the statistical processing unit 210 performs ratio processing on each of the extracted step count data and extracted exercise intensity data. As a result, the statistical processing unit 210 outputs a heart rate statistical index based on the extracted heart rate data, a step count statistical index based on the extracted step count data, and an exercise intensity statistical index based on the extracted exercise intensity data. These indices are stored in the storage unit 25 as feature information G1. Note that differential processing and ratio processing can be considered as statistical processing in a broad sense.

[0314] Next, in step S204, the sleep processing unit 212 calculates a sleep index based on the sleep data of the learning subject's biological state raw data A1 (an example of sleep processing). The sleep index is stored in the storage unit 25 as feature information G1.

[0315] Next, in step S205, the alcohol consumption estimation unit 213 calculates an alcohol consumption estimation index based on the heart rate data and step count data of the learning subject's biological state raw data A1 (an example of alcohol consumption estimation processing). The alcohol consumption estimation index is stored in the storage unit 25 as feature information G1.

[0316] Next, in step S206, the mental and physical recovery ability estimation unit 214 calculates a mental and physical recovery index based on the heart rate data and behavioral raw data (e.g., exercise intensity data) of the learning subject's biological state raw data A1 (an example of mental and physical recovery ability estimation processing). The mental and physical recovery index is stored in the storage unit 25 as feature information G1.

[0317] Next, in step S207, the exercise responsiveness estimation unit 215 calculates an exercise responsiveness index based on the heart rate data and behavioral raw data (e.g., exercise intensity data) of the learning subject's biological state raw data A1 (an example of exercise responsiveness estimation processing). The exercise responsiveness index is stored in the storage unit 25 as feature information G1.

[0318] Next, in step S208, the accumulation processing unit 216 performs accumulation processing on the heart rate statistical index, step count statistical index, exercise intensity statistical index, sleep index, physical and mental recovery index, and exercise responsiveness index, respectively. As a result, the accumulation processing unit 216 outputs accumulated data (cumulative index) for the heart rate statistical index, step count statistical index, exercise intensity statistical index, sleep index, physical and mental recovery index, and exercise responsiveness index, respectively. The accumulated data is stored in the storage unit 25 as feature information G1. The accumulation processing includes classification execution processing, correction execution processing, and accumulation execution processing.

[0319] Next, in step S209, the correct label creation unit 217 creates a correct label B1 based on the correct information Z1 and associates it with the feature information G1 (correct label creation process). The correct label B1 is stored in the storage unit 25. As an example, the correct information Z1 is a numerical value that directly indicates blood indicators. For example, the correct information Z1 is a numerical value that directly indicates CRP, ESR, γ-GTP value, blood glucose level, cholesterol level, iron level, red blood cell count, and / or white blood cell count.

[0320] As a first example, when the estimation device 4 estimates a numerical value that directly indicates a blood index (for example, in the case of Figure 12), the correct label creation unit 217 associates the correct information Z1 with the correct label B1 and the feature information G1.

[0321] As a second example, when the estimation device 4 performs binary classification (for example, in the case of Figure 13), the correct label creation unit 217 sets the correct label B1 to, for example, "0" or "1" depending on whether the correct information Z1 (a numerical value that directly indicates a blood index) is below a threshold or above a threshold. The threshold is determined, for example, based on experimental, empirical, and / or medical standards. For example, if the correct information Z1 is CRP or ESR, then in the correct label B1, "0" indicates a negative result and "1" indicates a positive result. For example, if the correct information Z1 is γ-GTP value or red blood cell value, then in the correct label B1, "0" indicates a normal result and "1" indicates an abnormal result.

[0322] As a third example, when the estimation device 4 performs multi-class classification (for example, in the case of Figure 14), the correct label creation unit 217 sets an evaluation corresponding to a class for the correct label B1, depending on whether the correct information Z1 (a numerical value directly indicating a blood index) falls within the numerical range of one of the multiple classes. The correct label creation unit 217 sets, for example, "Level 1," "Level 2," "Level 3," or "Level 4" for the correct label B1, depending on the class to which the correct information Z1 indicating CRP, etc., belongs. The numerical range for each class is determined, for example, based on experimental, empirical, and / or medical standards.

[0323] In the second and third examples, the correct answer information Z1 after binary classification or multi-class classification may be input via the input unit 22. In this case, the correct label creation unit 217 associates the correct answer information Z1 with the feature information G1 as the correct label B1, similar to the first example.

[0324] Once step S209 is completed, the training data creation method is finished. Steps S201 to S209 are repeatedly executed to create multiple training datasets F1.

[0325] As described above with reference to Figure 19, according to this embodiment, the learning data creation device 2 can create a learning dataset F1 suitable for a learning model TM1 that can estimate blood index information M2 while improving the quality of life of the subject.

[0326] In the embodiments of this disclosure, the estimation accuracy (classification ability) of the learning model and each feature were evaluated. In particular, the contribution of each feature to the estimation result of the learning model was evaluated by the average absolute value M of the SHAP (SHapley Additive exPlanation) value. The average absolute value M of the SHAP value has the characteristic that the value itself becomes low when multiple features contribute to the prediction, that is, when the contribution of a feature is distributed among many features. Therefore, the magnitude of the average M did not indicate the absolute contribution to the estimation result. For this reason, even features with a small average M only indicate a relatively low contribution, and it was inferred that they still contribute to the estimation result and improve the estimation accuracy.

[0327] Furthermore, in Figures 21 and 22, which show the SHAP values, the feature quantities on the horizontal axis of the graph are abbreviated using the alphabets defined in Tables 1 to 6.

[0328] The learning models according to Embodiments 1 and 2 of this disclosure were the learning models TM1 according to the embodiments described above with reference to Figures 1 to 11. The learning model TM1 was a model for performing binary classification. The machine learning algorithm was gradient boosting. The feature information G1 of the learning dataset F1 used to train the learning model TM1 consisted of 1022 types of data (various statistical indicators, various cumulative indicators) from the multiple types of data (vital information H1, behavioral information J1) and multiple types of cumulative data for the multiple types of data shown in the above embodiment, as well as basic physical information Q1 and environmental information R1.

[0329] The correct labels B1 for the training dataset F1 were "0" for negative and "1" for positive. In creating the correct labels B1 for Example 1, a CRP of 0.14 mg / dL or less was assigned "0" to indicate a negative result, and a CRP greater than 0.14 mg / dL was assigned "1" to indicate a positive result. In creating the correct labels B1 for Example 2, for males, an ESR of 10 mm / hr or less was assigned "0" to indicate a negative result, and an ESR greater than 10 mm / hr was assigned "1" to indicate a positive result. For females, an ESR of 15 mm / hr or less was assigned "0" to indicate a negative result, and an ESR greater than 15 mm / hr was assigned "1" to indicate a positive result.

[0330] The input information K2 to the learning model TM1 in Examples 1 and 2 consisted of 1022 types of data (various statistical indicators, various cumulative indicators) from the multiple types of data (vital information H2, behavioral information J2) and multiple types of cumulative data for the said multiple types of data, as well as basic physical information Q2 and environmental information R2.

[0331] The output information L2 of the learning model TM1 in Example 1 was blood index information M2 related to CRP. The output information L2 of the learning model TM1 in Example 2 was blood index information M2 related to ESR.

[0332] First, let's describe Example 1. Figure 20 is a graph showing the ROC (Receiver Operating Characteristic) curve 500 calculated for the learning model TM1 according to Example 1. The horizontal axis represents "1 - specificity," and the vertical axis represents sensitivity. The AUC (Area Under the Curve) was 0.802. AUC represents the area under the ROC curve 500. The closer the AUC is to 1, the higher the estimation accuracy (classification ability) of the learning model TM1. Therefore, it was confirmed that the estimation accuracy of the learning model TM1 according to Example 1 is high.

[0333] Figure 21 is a graph showing the SHAP values ​​calculated for the learning model TM1 according to Example 1. Figure 21 shows the top 30 features. The horizontal axis represents each feature, and the vertical axis represents the average of the absolute values ​​of the SHAP values ​​for each feature, M(|SHAP|).

[0334] As shown in Figure 21, vital information H2 such as the feature "Sleep_Med_HR", and behavioral information J2 such as the features "Sleep_iCV_STEP" and "Sleep_iCV_METs" were found to have a relatively high contribution. Furthermore, cumulative data such as the features "AdjCumOrg_lAwake_tLiftR_METs", "AdjCumOrg_Diff_tLiftR_STEP", and "AdjCumOrg_lSleep_Sum_HR" were found to have a relatively high contribution. In particular, when the feature was cumulative data ("AdjCumOrg"), the relative contribution was generally higher. In addition, information with added conditions related to sleep, such as the feature "lSleep_dtMin_HR", was found to have a relatively high contribution. It was confirmed that information with conditions related to arousal, such as the features "Awake_dtMin_STEP" and "Awake_Slope_METs," had a relatively high contribution. It was confirmed that information with conditions related to the time of day caused by sunlight, such as the feature "Night_Slope_HR," had a relatively high contribution. It was confirmed that information related to alcohol consumption, such as the feature "score2," had a relatively high contribution. It was confirmed that the feature "BMI" had a relatively high contribution.

[0335] Next, we will describe Example 2. The AUC obtained from the ROC curve calculated for the learning model TM1 in Example 2 was 0.698. From the AUC of Example 2, as well as Example 1, we were able to confirm that the estimation accuracy of the learning model TM1 in Example 2 is good.

[0336] Figure 22 is a graph showing the SHAP values ​​calculated for the learning model TM1 according to Example 2. Figure 22 shows the top 20 features. The horizontal axis represents each feature, and the vertical axis represents the average of the absolute values ​​of the SHAP values ​​for each feature, M(|SHAP|).

[0337] As shown in Figure 22, vital information H2 such as the feature "Night_tSD_HR", and behavioral information J2 such as the features "Sleep_iCV_STEP" and "CENTERED_lSleep_Med_METs" were found to have a relatively high contribution. Furthermore, cumulative data such as the features "AdjCumOrg_lAwake_Low_HR", "AdjCumOrg_Night_1Rate_METs", and "AdjCumOrg_Awake_Low_STEP" were found to have a relatively high contribution. In particular, when the feature was cumulative data ("AdjCumOrg"), the relative contribution was generally higher. In addition, information with conditions related to sleep, such as the feature "Sleep_iCV_STEP", was found to have a relatively high contribution. Information with conditions related to wakefulness, such as the feature "AdjCumOrg Awake_dtSD_HR", was found to have a relatively high contribution. We confirmed that information with conditions related to the time of day caused by sunlight, such as the feature "Night_tSD_HR," had a relatively high contribution. We also confirmed that information related to alcohol consumption, such as the feature "score2," had a relatively high contribution. Furthermore, we confirmed that the feature "BMI" had a relatively high contribution.

[0338] Although Examples 1 and 2 demonstrated binary classification, it was hypothesized that even when directly estimating blood indicators numerically, or in the case of multi-class classification, using the same training dataset F1 would generate a similarly high or good training model TM1.

[0339] (Explanation of abbreviations on the horizontal axis of SHAP values) Table 1 shows abbreviations related to Basic Physical Information Q2.

[0340]

[0341] Table 2 shows the abbreviations for cumulative processing and centering processing applied to each indicator. Centering processing is the process of subtracting the average value of an individual's indicators over the observation period from the individual's indicators over each unit period.

[0342]

[0343] Table 3 shows abbreviations regarding sleep information, wakefulness information, and conditions related to the time of day caused by the sun.

[0344]

[0345] Table 4 shows the processing applied to heart rate, steps, and exercise intensity.

[0346]

[0347] Table 5 shows abbreviations for heart rate, steps, and exercise intensity.

[0348]

[0349] Table 6 shows other abbreviations.

[0350]

[0351] Preferred embodiments and modifications of the present disclosure have been described in detail above with reference to the attached drawings, but the technical scope of the present disclosure is not limited to such examples. It is clear to any person with ordinary skill in the art of the present disclosure that various modifications or alterations can be conceived within the scope of the technical idea set forth in the claims, and these too are understood to fall within the technical scope of the present disclosure.

[0352] The apparatus or system described herein may be implemented as a single apparatus, or it may be implemented by a plurality of apparatuses (e.g., a cloud server) that are partially or entirely connected by a network. For example, some or all of the statistical processing unit 410, condition processing unit 411, sleep processing unit 412, alcohol consumption estimation unit 413, mental and physical recovery ability estimation unit 414, motor responsiveness estimation unit 415, accumulation processing unit 416, estimation unit 42, and post-processing unit 43 in Figure 6 may be implemented by the same computer or server. For example, the statistical processing unit 410, condition processing unit 411, sleep processing unit 412, alcohol consumption estimation unit 413, mental and physical recovery ability estimation unit 414, motor responsiveness estimation unit 415, accumulation processing unit 416, estimation unit 42, and post-processing unit 43 in Figure 6 may each be implemented by separate computers or servers. These points also apply to the statistical processing unit 210, condition processing unit 211, sleep processing unit 212, alcohol consumption estimation unit 213, mental and physical recovery ability estimation unit 214, motor responsiveness estimation unit 215, cumulative processing unit 216, and correct label creation unit 217 in Figure 18. For example, the relay server 44 or estimation device 4 in Figure 4 may also have the functions of an information provision server 47. For example, the first database 45 and the second database 46 may be implemented on a single computer or server. For example, the estimation unit 42 may have the functions of a relay server 44.

[0353] The series of processes performed by the apparatus described herein may be implemented using software, hardware, or a combination of software and hardware. Computer programs for implementing each function of the processing units 21, 31, and 400 can be created and implemented on a PC or the like. Furthermore, a computer-readable storage medium containing such a computer program can also be provided. Examples of storage media include magnetic disks, optical disks, magneto-optical disks, and flash memory. Alternatively, the above-mentioned computer program may be distributed without using a storage medium, for example, via a network.

[0354] Furthermore, the processes described using flowcharts in this specification do not necessarily have to be executed in the order shown. Some processing steps may be executed in parallel. Additional processing steps may be adopted, and some processing steps may be omitted.

[0355] In Figures 15, 17, and 19, the processing units 400, 31, and 21 execute the computer programs stored in the memory units 402, 35, and 25, thereby executing each step included in the estimation process (estimation method), learning method, and learning data creation method. In other words, the computer program causes the processing units 400, 31, and 21 to execute each step included in the estimation process (estimation method), learning method, and learning data creation method. The processing units 400, 31, and 21 correspond to an example of the "computer" in this disclosure. To put it another way, the computer program product realizes each step included in the estimation process (estimation method), learning method, and learning data creation method when the computer program is executed by the processing units 400, 31, and 21.

[0356] Furthermore, the estimation device 4 in Figure 6 may not include all or part of, for example, the sleep processing unit 412, the alcohol consumption estimation unit 413, the mental and physical recovery ability estimation unit 414, the motor responsiveness estimation unit 415, and the accumulation processing unit 416. The estimation process in Figure 15 may not include all or part of, for example, steps S34 to S38. Also, the estimation process in Figure 15 may not include, for example, steps S41 and S42. The learning data creation device 2 in Figure 18 may not include all or part of, for example, the sleep processing unit 212, the alcohol consumption estimation unit 213, the mental and physical recovery ability estimation unit 214, the motor responsiveness estimation unit 215, and the accumulation processing unit 216. The learning data creation method in Figure 19 may not include all or part of, for example, steps S304 to S308.

[0357] Furthermore, the learning device 3 may generate a new learning model (distilled model) by performing learning using the input information K2 input to the learning model TM1 and the output information L2 or output information N2 output by the learning model TM1 as a learning dataset. The input information K2 and output information L2 and N2 are information about the subject for the learning model TM1 at the utilization stage. However, in the generation of the distilled model, the input information K2 and output information L2 and N2 that constitute the learning dataset correspond to information about the learning subject for the learning device 3.

[0358] Furthermore, in the above embodiment, the learning dataset F1 may include at least one of the vital information H1 and behavioral information J1 of the learning subject, and the blood index information of the learning subject. The input information K2 may include at least one of the vital information H2 and behavioral information J2 of the subject.

[0359] Furthermore, the effects described herein are merely descriptive or illustrative and not limiting. In other words, the technology relating to this disclosure may produce other effects that are obvious to those skilled in the art from the description herein, in addition to or in lieu of the effects described herein.

[0360] Furthermore, the following configurations also fall within the technical scope of this disclosure.

[0361] (Item 1) Estimation device for estimating blood index information of a subject, comprising: an estimation unit that inputs input information to a learning model constructed by learning using a learning dataset and obtains output information from the learning model; and a storage unit that stores the output information, wherein the learning dataset includes vital information of a learning subject, behavioral information of the learning subject, and blood index information of the learning subject; the input information includes vital information of the subject and behavioral information of the subject; and the output information includes the blood index information of the subject.

[0362] (Item 2) The estimation device according to Item 1, wherein the blood indicator information includes at least one of the following: information on CRP, information on ESR, information on γ-GTP values, information on blood glucose levels, information on cholesterol levels, information on iron levels, information on red blood cell levels, and information on white blood cell levels.

[0363] (Item 3) The estimation device according to Item 1 or Item 2, wherein the vital information includes information relating to heart rate, and the behavioral information includes at least one of the following: information relating to the number of steps and information relating to exercise intensity.

[0364] (Item 4) The estimation device according to any one of Items 1 to 3, wherein the vital information further includes at least one of the following: information on physical and mental recovery ability and information on exercise responsiveness, the information on physical and mental recovery ability includes information indicating the degree of physical and mental recovery ability from the end of exercise, and the information on exercise responsiveness is information indicating the degree of physical and mental response to exercise.

[0365] (Item 5) The estimation device described in any of Items 1 to 4, wherein the behavioral information further includes information relating to sleep.

[0366] (Item 6) The estimation device described in any of Items 1 to 5, wherein the behavioral information further includes information regarding alcohol consumption.

[0367] (Item 7) The estimation device according to any one of Items 1 to 6, wherein at least one of the vital information and the behavioral information includes vital information or behavioral information during a period indicated by specific conditions, and the specific conditions include at least one of the conditions relating to sleep, conditions relating to wakefulness, and conditions relating to the time of day caused by the sun.

[0368] (Item 8) The estimation device according to Item 7, further comprising: a condition processing unit that performs a process for extracting vital raw data from the subject's vital raw data for a period indicated by the specific conditions, and / or a process for extracting behavioral raw data from the subject's behavioral raw data for a period indicated by the specific conditions; and a statistical processing unit that generates vital information by performing statistical processing on the extracted vital raw data, and / or generates behavioral information by performing statistical processing on the extracted behavioral raw data.

[0369] (Item 9) The estimation device according to any one of Items 1 to 8, wherein at least one of the vital information and the behavioral information includes vital information or behavioral information on which cumulative processing has been performed, the cumulative processing indicates a process of accumulating the vital information or behavioral information while avoiding the value diverging, and the device further comprises a cumulative processing unit that performs the cumulative processing.

[0370] (Item 10) The estimation device according to Item 9, wherein the cumulative processing includes a classification execution process, a correction execution process, and a cumulative execution process, wherein the classification execution process classifies a plurality of vital data or behavioral data arranged in a time series into at least a first state group indicating a first state of mind and body and a second state group indicating a second state of mind and body, thereby obtaining a plurality of first state data belonging to the first state group and a plurality of second state data belonging to the second state group, the correction execution process calculates a plurality of correction data in a time series by adjusting the balance of magnitude between the plurality of first state data and the plurality of second state data, and the cumulative execution process calculates a plurality of cumulative data in a time series by accumulating the correction data along the time axis, and the vital information or behavioral information on which the cumulative processing has been performed is composed of the plurality of cumulative data.

[0371] (Item 11) The estimation device according to any one of Items 1 to 10, wherein the learning dataset further includes basic information about the body of the learning subject, the input information further includes basic information about the body of the subject, and the basic information includes at least one of BMI information and body shape information.

[0372] (Item 12) The estimation device according to any one of Items 1 to 11, wherein the training dataset further includes information about the environment of the training subject, the input information further includes information about the environment of the subject, and the environment includes seasonal information.

[0373] (Item 13) A learning model constructed by learning using a learning dataset, which causes a computer to function in order to estimate blood index information of a subject, wherein the learning dataset includes vital information of the learning subject, behavioral information of the learning subject, and blood index information of the learning subject, and the computer is made to function to take input information as input and output information as output, wherein the input information includes the vital information of the subject and behavioral information of the subject, and the output information includes the blood index information of the subject.

[0374] (Item 14) An estimation method for estimating blood index information of a subject, comprising the steps of: inputting input information into a learning model constructed by learning using a learning dataset; and obtaining output information from the learning model into which the input information has been input, wherein the learning dataset includes vital information of a learning subject, behavioral information of the learning subject, and blood index information of the learning subject; the input information includes vital information of the subject and behavioral information of the subject; and the output information includes the blood index information of the subject.

[0375] (Item 15) A computer program that causes a computer to execute the estimation method described in Item 14.

[0376] (Item 16) A method for generating a learning model, comprising the steps of: acquiring a learning dataset; and generating a learning model that outputs output information when input information is input by performing learning using the learning dataset, wherein the learning dataset includes vital information of a subject, behavioral information of the subject, and blood index information of the subject; the input information includes vital information of the subject and behavioral information of the subject; and the output information includes the blood index information of the subject.

[0377] (Item 17) A computer program that causes a computer to execute the learning model generation method described in Item 16.

[0378] This disclosure provides an estimation device, a learning model, an estimation method, a learning model generation method, and a computer program, and has industrial applicability.

[0379] 2 Learning data creation device, 3 Learning device, 4 Estimation device, 42 Estimation unit, 402 Memory unit, 410 Statistical processing unit, 411 Condition processing unit, 412 Sleep processing unit, 413 Alcohol consumption estimation unit, 414 Mental and physical recovery ability estimation unit, 415 Motor responsiveness estimation unit, 416 Cumulative processing unit

Claims

1. An estimation device for estimating blood index information of a subject, comprising: an estimation unit that inputs input information to a learning model constructed by learning using a learning dataset and obtains output information from the learning model; and a storage unit that stores the output information, wherein the learning dataset includes vital information of the learning subject, behavioral information of the learning subject, and blood index information of the learning subject; the input information includes vital information of the subject and behavioral information of the subject; and the output information includes the blood index information of the subject.

2. The estimation device according to claim 1, wherein the blood indicator information includes at least one of the following: information on CRP, information on ESR, information on γ-GTP values, information on blood glucose levels, information on cholesterol levels, information on iron levels, information on red blood cell levels, and information on white blood cell levels.

3. The estimation device according to claim 1 or 2, wherein the vital information includes information relating to heart rate, and the behavioral information includes at least one of the following: information relating to the number of steps and information relating to exercise intensity.

4. The estimation device according to claim 1 or 2, wherein the vital information further includes at least one of the following: information on physical and mental recovery ability and information on exercise responsiveness, the information on physical and mental recovery ability includes information indicating the degree of physical and mental recovery ability from the end of exercise, and the information on exercise responsiveness is information indicating the degree of physical and mental response to exercise.

5. The estimation device according to claim 1 or 2, wherein the behavioral information further includes information relating to sleep.

6. The estimation device according to claim 1 or 2, wherein the behavioral information further includes information relating to alcohol consumption.

7. The estimation device according to claim 1 or 2, wherein at least one of the vital information and the behavioral information includes vital information or behavioral information during a period indicated by specific conditions, and the specific conditions include at least one of conditions relating to sleep, conditions relating to wakefulness, and conditions relating to the time of day caused by the sun.

8. The estimation device according to claim 7, further comprising: a condition processing unit that performs a process for extracting vital raw data from the subject's vital raw data for a period indicated by the specific conditions, and / or a process for extracting behavioral raw data from the subject's behavioral raw data for a period indicated by the specific conditions; and a statistical processing unit that generates vital information by performing statistical processing on the extracted vital raw data, and / or generates behavioral information by performing statistical processing on the extracted behavioral raw data.

9. The estimation device according to claim 1 or 2, wherein at least one of the vital information and the behavioral information includes vital information or behavioral information on which cumulative processing has been performed, the cumulative processing represents a process of accumulating the vital information or behavioral information while avoiding the value diverging, and the device further comprises a cumulative processing unit that performs the cumulative processing.

10. The estimation device according to claim 9, wherein the cumulative processing includes a classification execution process, a correction execution process, and a cumulative execution process, the classification execution process involves classifying a plurality of vital data or behavioral data arranged in a time series into at least a first state group indicating a first state of mind and body and a second state group indicating a second state of mind and body, thereby obtaining a plurality of first state data belonging to the first state group and a plurality of second state data belonging to the second state group, the correction execution process involves adjusting the balance of magnitude between the plurality of first state data and the plurality of second state data to calculate a plurality of correction data in a time series, the cumulative execution process involves accumulating the correction data along a time axis to calculate a plurality of cumulative data in a time series, and the vital information or behavioral information on which the cumulative processing has been performed is composed of the plurality of cumulative data.

11. The estimation device according to claim 1 or 2, wherein the learning dataset further includes basic information relating to the body of the learning subject, the input information further includes basic information relating to the body of the subject, and the basic information includes at least one of BMI information and body shape information.

12. The estimation apparatus according to claim 1 or 2, wherein the training dataset further includes information about the environment of the training subject, the input information further includes information about the environment of the subject, and the environment includes seasonal information.

13. A learning model constructed by learning using a training dataset, which causes a computer to function in order to estimate blood index information of a subject, wherein the training dataset includes vital information of the training subject, behavioral information of the training subject, and blood index information of the training subject, and the computer is configured to take input information as input and output information as output, wherein the input information includes vital information of the subject and behavioral information of the subject, and the output information includes the blood index information of the subject.

14. An estimation method for estimating blood index information of a subject, comprising the steps of: inputting input information into a learning model constructed by learning using a learning dataset; and obtaining output information from the learning model into which the input information has been input, wherein the learning dataset includes vital information of a learning subject, behavioral information of the learning subject, and blood index information of the learning subject; the input information includes vital information of the subject and behavioral information of the subject; and the output information includes the blood index information of the subject.

15. A computer program that causes a computer to execute the estimation method described in claim 14.

16. A method for generating a learning model, comprising the steps of: obtaining a learning dataset; and generating a learning model that outputs output information when input information is input by performing learning using the learning dataset, wherein the learning dataset includes vital information of a learning subject, behavioral information of the learning subject, and blood index information of the learning subject; the input information includes vital information of a subject and behavioral information of the subject; and the output information includes the blood index information of the subject.

17. A computer program that causes a computer to execute the learning model generation method described in claim 16.

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