Pulse wave information processing device, pulse wave information processing method, and pulse wave information processing program

Abstract

An information processing device comprising a prescription candidate output unit that outputs output information containing Chinese herbal medicine candidate information indicating candidates of Chinese herbal medicines to be prescribed to a first subject, on the basis of first waveform information of a pulse waveform of the first subject.

Description

[Technical Field]

[0001] The present disclosure provides: Pulse wave information processing device, Pulse wave Information Processing Method and pulse wave information processing Regarding the program. This application claims priority based on Japanese Patent Application No. 2022-074662, filed on April 28, 2022, the contents of which are incorporated herein by reference. [Background technology]

[0002] Research and development is underway into technologies that assist doctors in prescribing medications to patients.

[0003] In this regard, an information processing device is known that outputs information indicating a prescription for a Chinese herbal medicine associated with the symptoms of a subject whose symptoms are to be diagnosed by a Chinese herbal medicine doctor, based on a database containing information that associates, for each of a plurality of subjects, the symptoms that the subject has with the history of the prescription of the Chinese herbal medicine for the subject (see Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-139268 Summary of the Invention [Problem to be solved by the invention]

[0005] Here, the symptoms of the subject described in Patent Document 1 include headache, dizziness, menopausal disorder, etc., and refer to the physical or mental state of the subject diagnosed by a traditional Chinese medicine doctor. For this reason, with an information processing device such as that described in Patent Document 1, the traditional Chinese medicine doctor must diagnose the symptoms based on diagnostic methods such as interviewing, facial examination, tongue examination, abdominal examination, and pulse examination, and input information indicating the diagnosis results into the information processing device. For this reason, there are cases where the information processing device cannot sufficiently reduce the effort required for the traditional Chinese medicine doctor to prescribe traditional Chinese medicine to the subject.

[0006] The present disclosure has been made in consideration of such circumstances, and can reduce the effort required for a Chinese medicine doctor to prescribe a Chinese medicine to a first subject. Pulse wave information processing device, Pulse wave Information Processing Method oh and pulse wave information processing The objective is to provide a program. [Means for solving the problem]

[0007] One aspect of the present disclosure is a prescription candidate output unit that outputs output information including herbal medicine candidate information indicating candidates for herbal medicine to be prescribed to a first subject based on first waveform information indicating a waveform of a pulse wave of the first subject. and based on the first waveform information, one or more first waveforms that exhibit characteristics of the waveform indicated by the first waveform information. a calculation unit for calculating a shape index; Equipped with The prescription candidate output unit outputs one or more first waves calculated by the calculation unit. Based on the shape index, the first machine learning model, the second machine learning model, and the first waveform information, and identifying a candidate herbal medicine, and the first machine learning model generating a second waveform representing a pulse wave waveform of the second subject. one or more second waveform indices that indicate characteristics of the waveform indicated by the information, and a diagnosis result of the second subject by a Chinese medicine doctor. This is a machine learning model that learns first correspondence information that corresponds to diagnostic result information that shows the effect. The second machine learning model is a second model in which the diagnosis result information and one or more pieces of herbal medicine information are associated. It is a machine learning model that has learned corresponding information, and one or more pieces of herbal medicine information are used by a herbalist to Information indicating each of one or more herbal medicines prescribed to the examiner. Ru, Pulse wave An information processing device (20).

[0008] Furthermore, one aspect of the present disclosure is In a computer, Based on first waveform information indicating a waveform of the pulse wave of the first subject, Calculating one or more first waveform indices that indicate characteristics of the waveform indicated by the first waveform information a calculation step of calculating one or more first waveform indices calculated, a first machine learning model, and a second Based on the machine learning model and the first waveform information, The candidate herbal medicine to be prescribed to the first subject Identify, A prescription candidate output step is provided for outputting output information including herbal medicine candidate information. And the first aircraft The machine learning model is a model that shows the characteristics of the waveform shown by the second waveform information that shows the waveform of the pulse wave of the second subject. The second waveform index and the diagnostic result information indicating the diagnostic result of the second subject by the Chinese medicine doctor are associated with each other. The first machine learning model is trained on the corresponding information received from the first machine learning model, and the second machine learning model is used for diagnosis. The machine learning model trains the second correspondence information in which the result information and one or more pieces of herbal medicine information are associated. and the one or more herbal medicine information is information on one or more herbal medicines prescribed by a herbal medicine doctor to the second subject. Information showing each a Ru, Pulse wave It is an information processing method.

[0009] Furthermore, one aspect of the present disclosure provides a method for controlling a computer to perform a pulse wave analysis based on first waveform information indicating a waveform of a pulse wave of a first subject, A calculation for calculating one or more first waveform indices that indicate characteristics of a waveform indicated by the first waveform information. a step of extracting the waveform data, and calculating one or more first waveform indices, a first machine learning model, and a second machine learning model. Based on the learning model and the first waveform information, The candidate herbal medicine to be prescribed to the first subject Identify 、 A prescription candidate output step for outputting output information including herbal medicine candidate information and Information processing to execute a program, the first machine learning model representing a waveform of a pulse wave of a second subject; one or more second waveform indices indicating characteristics of the waveform indicated by the second waveform information, and A machine learning model that learns first correspondence information that corresponds to diagnostic result information that indicates the diagnostic result of The second machine learning model is a model that associates diagnostic result information with one or more pieces of herbal medicine information. The machine learning model is trained on the second correspondence information, and the first correspondence information is used by a Chinese medicine doctor. 2) Pulse wave, which is information indicating each of one or more herbal medicines prescribed to the subject. It is an information processing program. [Effects of the Invention]

[0010] According to the present disclosure, it is possible to reduce the effort required for a Chinese medicine doctor to prescribe a Chinese medicine to a first subject. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a diagram showing an example of the configuration of a pulse wave information processing system 1 including a pulse wave information processing device 20. [Figure 2] FIG. 2 is a diagram showing an example of the hardware configuration of a pulse wave information processing device 20. [Figure 3] FIG. 2 is a diagram showing an example of the functional configuration of a pulse wave information processing device 20. [Figure 4] FIG. 3 is a diagram showing an example of the flow of processing by the pulse wave information processing device 20 to generate waveform information. [Figure 5] FIG. 10 is a diagram showing an example of the flow of processing in which the pulse wave information processing device 20 causes the first machine learning model to learn the first correspondence information and causes the second machine learning model to learn the second correspondence information. [Figure 6]FIG. 10 is a diagram showing an example of a waveform indicated by learning waveform information associated with the target subject identification information selected in step S220. [Figure 7] A diagram visually showing an example of the mth wave out of M waves included in the waveform indicated by the learning waveform information associated with the target subject identification information selected in step S220, and the first peak value P1m, second peak value P2m, lower limit value P3m, first period t1m, and second period t2m for that wave. [Figure 8] FIG. 10 is a visualized image diagram illustrating an example of the processing in step S270. [Figure 9] FIG. 10 is a visualized image diagram illustrating an example of the processing in step S280. [Figure 10] FIG. 4 is a diagram showing an example of the flow of processing by the pulse wave information processing device 20 to receive diagnostic result information. [Figure 11] FIG. 10 is a diagram showing an example of an information reception image PCT1. [Figure 12] FIG. 10 is a diagram showing an example of how each of six drop-down menus is displayed. [Figure 13] FIG. 10 is a diagram showing an example of the flow of processing by which the pulse wave information processing device 20 outputs candidate herbal medicine information. [Figure 14] FIG. 10 is a diagram showing an example of likelihood for each combination of six vein types that can be selected as a target vein type set. [Figure 15] An image visualizing the process by which the second machine learning model identifies one or more candidate herbal medicines that are estimated to be plausible as the one or more herbal medicines to be prescribed to subject S3. DETAILED DESCRIPTION OF THE INVENTION

[0012] <Embodiment> Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0013] < Pulse wave Overview of information processing equipment> First, according to this embodiment Pulse wave Information processing device (Hereinafter, simply referred to as "information processing device") This section provides an overview of the system.

[0014] Currently, Western medicine is mainstream due to its compatibility with science. However, there are other traditional medical practices around the world, such as Greek medicine, Arabic medicine (Unicine medicine), Indian medicine (Ayurveda), Tibetan medicine, Mongolian medicine, traditional Chinese medicine (Traditional Chinese Medicine), Korean medicine, and Wakan medicine (Kampo medicine). In Japan, the narrow definition of Oriental medicine, which utilizes acupuncture, moxibustion, medicinal foods, and herbal medicine derived from Traditional Chinese Medicine, is called Kampo medicine. This form of Kampo medicine has developed uniquely in Japan. In recent years, Japan has been reevaluating not only Western medicine but also complementary and alternative medicine. As a result, homeopathy, naturopathy, aromatherapy, and other therapies have also been gaining attention. In Japan, the combination of Western medicine and Kampo medicine as part of integrative medicine, including Kampo medicine, is becoming increasingly common, with approximately 90% of doctors prescribing Kampo medicine.

[0015] In Japan, health insurance is approved for several hundred types of herbal medicines, 148 types of medical herbal preparations (preparations approved by the Ministry of Health, Labor and Welfare), and a few decoctions.In addition to these herbal medicines approved for health insurance coverage, Japan also distributes over-the-counter (OTC) medicines (medicines that can be purchased without a prescription at pharmacies and drug stores) that are not covered by health insurance, and herbal medicines available for private treatment at specialized hospitals.

[0016] In addition to interviews, facial examinations, tongue examinations, and abdominal examinations, pulse diagnosis is also known as a diagnostic technique in traditional Chinese medicine. Pulse diagnosis is based on the idea that pulse waves reflect the state of an organ and involve diagnosing the characteristics of the pulse wave in relation to the organ's condition. For ease of explanation, the types of characteristics that appear in pulse waves depending on the organ's condition will be referred to simply as "pulse types." One method of classifying pulse types according to organ condition is the 28 Disease Pulses, which classifies the characteristics of pulse waves into 28 types depending on the organ's condition. Each of these 28 Disease Pulses is further divided into six pulse types: the Floating Net Pulse, the Shen Net Pulse, the Slow Net Pulse, the Su Net Pulse, the Xu Net Pulse, and the Shi Net Pulse. For ease of explanation, these six pulse types will be referred to as the Six Major Pulse Types. The Floating Net Pulse category includes the following six pulse types: the Bu Mai, the Kang Mai (a Chinese character with a grass crown over the hole), the Hong Mai, the Kang Mai, the Wei Mai, and the San Mai. The category of shenwang pulses includes four pulse types: shen, fu, xiang, and lao. The category of slowwang pulses includes five pulse types: slow pulse, brady, shibu, jie, and dai. The category of severalwang pulses includes three pulse types: several, arterial, and accelerating. The category of virtualwang pulses includes four pulse types: virtual, short, thin, and micro. The category of fullwang pulses includes six pulse types: real, long, xuan, tight, slippery, and large.

[0017] For example, a Chinese medicine doctor will diagnose the characteristics of a patient's pulse wave, identify the primary illness by assigning a diagnostic name to the eight primary pulses that frequently appear in the pulse wave out of 28 diseased pulses, and prescribe a type of Chinese medicine corresponding to the primary illness. In other words, a Chinese medicine doctor's pulse diagnosis is directly linked to the prescription of Chinese medicine. The effectiveness of Chinese medicine prescribed by a Chinese medicine doctor is based on statistics.

[0018] Information related to diagnosis by Chinese medicine doctors is explained in detail, for example, on the following reference websites, so further detailed explanation will be omitted in this specification. Reference website 1: Ministry of Health, Labour and Welfare website / Committee on the Future of "Integrated Medicine," https: / / www.mhlw.go.jp / stf / shingi / other-isei_127369.html Reference website 2: Japan Society of Oriental Medicine Homepage / Chinese medicine examination, http: / / www.jsom.or.jp / universally / examination / index.html

[0019] Currently, one of the challenges in promoting integrated medical care combining Western medicine and traditional Chinese medicine is the current state of traditional Chinese medicine prescriptions, which are based on the qualitative diagnostic methods of traditional Chinese medicine and statistics that are difficult to standardize.

[0020] Unlike Western medicine, which is based on scientific classification of diseases and pathological elucidation, the qualitative diagnostic method of Kampo medicine is based on subjective, relative diagnosis, which captures imbalances from each patient's normal state. Therefore, a more appropriate approach is to use statistical methods to accumulate data and mathematically model the diagnostic results of Kampo practitioners, who have a high level of empirical knowledge. However, diagnostic biases inherent in Kampo practitioners' diagnoses, and the statistical methods themselves are currently poorly understood. For this reason, expectations are high for automated diagnosis based on a database constructed using accurate waveform information indicating the pulse wave, noise removal from the acquired waveform information, feature extraction from multiple consecutive waveforms, and related information (e.g., diagnostic results from interviews, facial examinations, tongue examinations, abdominal examinations, etc.).

[0021] On the other hand, the difficulty in standardizing statistics lies in the fact that the nature of the mild, long-term improvement effects of herbal medicines makes them difficult to distinguish from placebo effects. It is thought that an approach involving the accumulation of statistical data on diagnosis / prescription based on the experience of skilled herbalists and modeling would be appropriate, but this is still not fully understood.

[0022] The following three references, Reference 1 to Reference 3, are cited as references for pulse wave diagnosis.

[0023] Reference 1: Japanese Patent Application Laid-Open No. 2009-011585 Reference 2: Japanese Patent Application Laid-Open No. 2004-195204 Reference 3: JP 2020-108819 A

[0024] Reference 1 describes a wristwatch-type 24-hour wearable pulse wave monitoring device that performs health management based on exercise and heart rate.

[0025] Reference 2 describes a device that detects indicators that represent the characteristics of blood pressure waveforms and determines the prescription of Western medicines such as calcium channel blockers and beta blockers based on the systolic blood pressure and AI (Augmentation Index) values.

[0026] Reference 3 describes a device that estimates blood glucose levels by utilizing the correlation between the AI ​​value of a blood pressure waveform and postprandial blood glucose levels.

[0027] As described above, the devices described in References 1 to 3 automatically perform physical condition monitoring, prescribing Western medicines, estimating blood glucose levels, etc. through automatic diagnostic analysis of pulse waves, but they are not yet capable of automatically prescribing herbal medicines based on automatic analysis of pulse waves.

[0028] Meanwhile, prior art for prescribing herbal medicines based on pulse wave diagnostic results includes efforts to have natural language processing AI (Artificial Intelligence) learn disease diagnoses and prescribed medications from electronic medical records that contain prescriptions for herbal medicines, and a turntable for prescribing herbal medicines that provides a quick reference table of diagnosis and prescribed medications based on the Chinese herbal medicine dialectical treatment method.

[0029] In recent years, information processing devices that use estimation models based on statistical learning have emerged to automate the prescription of herbal medicines. Details of this information processing device are described in Patent Document 1, cited as a prior art document. That is, as mentioned above, this information processing device outputs information indicating a prescription for a herbal medicine associated with the symptoms of a target subject whose symptoms are to be diagnosed by a herbal medicine doctor, based on a database containing information that associates, for each of a plurality of subjects, the symptoms of the subject with the history of the herbal medicine prescriptions given to the subject.

[0030] Here, the symptoms of the subject described in Patent Document 1 include headache, dizziness, menopausal disorder, etc., and refer to the physical or mental state of the subject diagnosed by a traditional Chinese medicine doctor. For this reason, with an information processing device such as that described in Patent Document 1, the traditional Chinese medicine doctor must diagnose the symptoms based on diagnostic methods such as interviewing, facial examination, tongue examination, abdominal examination, and pulse examination, and input information indicating the diagnosis results into the information processing device. For this reason, there are cases where the information processing device cannot sufficiently reduce the effort required for the traditional Chinese medicine doctor to prescribe traditional Chinese medicine to the subject.

[0031] Therefore, the information processing device of the embodiment is equipped with a prescription candidate output unit that outputs output information including candidate herbal medicine information indicating candidates for herbal medicine to be prescribed to the first subject based on first waveform information indicating the waveform of the pulse wave of the first subject.

[0032] This allows the information processing device according to the embodiment to automate the process from pulse diagnosis to prescribing a herbal medicine, thereby reducing the time and effort required for a herbal medicine doctor to prescribe a herbal medicine to the first subject.

[0033] The configuration of the information processing device according to the embodiment and the processing performed by the information processing device will be described in detail below.

[0034] <Configuration of information processing device> The configuration of the information processing device according to the embodiment will be described below using the information processing device 20 as an example of the information processing device according to the embodiment. Note that in the embodiment, the symptoms of a subject refer to the physical or mental state of the subject diagnosed by a traditional Chinese medicine doctor. Therefore, in the embodiment, the symptoms of a certain subject do not include the subject's pulse wave and the waveform of that pulse wave.

[0035] FIG. 1 is a diagram illustrating an example of the configuration of an information processing system 1 including an information processing device 20. Here, the three-dimensional coordinate system TC is a three-dimensional Cartesian coordinate system that indicates directions in a diagram in which the three-dimensional coordinate system TC is drawn. In the following, for convenience of explanation, the X-axis in the three-dimensional coordinate system TC will be simply referred to as the X-axis. In the following, for convenience of explanation, the Y-axis in the three-dimensional coordinate system TC will be simply referred to as the Y-axis. In the following, for convenience of explanation, the Z-axis in the three-dimensional coordinate system TC will be simply referred to as the Z-axis. In the following, for convenience of explanation, the positive direction of the Z-axis will be referred to as the up or upward direction, and the negative direction of the Z-axis will be referred to as the down or downward direction.

[0036] The information processing system 1 includes a pulse wave detecting device 10 and an information processing device 20 which is an example of an information processing device according to an embodiment.

[0037] The pulse wave detecting device 10 detects the pulse wave of a subject. In this embodiment, the subject may be any person whose pulse wave is detected by the pulse wave detecting device 10.

[0038] Pulse wave detecting device 10 may have any configuration as long as it is capable of detecting the pulse wave of a subject. In the example shown in Fig. 1, pulse wave detecting device 10 includes a first member 11 on which one arm of the subject can be placed and fixed, a pulse wave sensor 12 that is brought into contact with the arm of the subject fixed by first member 11 to detect the pulse wave of the subject, and a second member 13 that supports pulse wave sensor 12.

[0039] The first member 11 is, for example, a platform on which one arm of the subject can be placed and fixed. The first member 11 may be configured to allow the position of the subject's arm relative to the pulse wave sensor 12 to be moved along a horizontal plane, or may be configured not to allow the position to be moved along a horizontal plane.

[0040] The pulse wave sensor 12 may be any sensor capable of detecting the pulse wave of the subject, such as a MEMS (Micro Electro Mechanical Systems) pressure sensor capable of detecting the pulse wave as a pressure fluctuation. The pulse wave sensor 12 is communicatively connected to the information processing device 20 via a wired or wireless connection. Thus, the pulse wave sensor 12 detects the pressure of the pulse wave and outputs an electrical signal corresponding to the detected pressure to the information processing device 20. As a result, the information processing device 20 can generate waveform information indicating the waveform of the subject's pulse wave during a predetermined measurement period based on the electrical signal acquired from the pulse wave sensor 12 during the measurement period.

[0041] Second member 13 may have any configuration as long as it is capable of supporting pulse wave sensor 12. Second member 13 may be configured to allow adjustment of the vertical position of pulse wave sensor 12 (i.e., the height of pulse wave sensor 12), or may be configured to prevent adjustment of the position.

[0042] In response to an operation received from the user, the information processing device 20 acquires an electrical signal from the pulse wave sensor 12 during a measurement period designated by the user. The information processing device 20 generates waveform information indicating the waveform of the subject's pulse wave based on the electrical signal acquired during the measurement period from the pulse wave sensor 12. The information processing device 20 stores the generated waveform information.

[0043] The information processing device 20 identifies candidate herbal medicines to be prescribed to the subject based on the stored waveform information. After identifying candidate herbal medicines, the information processing device 20 generates output information including candidate herbal medicine information indicating the identified candidate herbal medicines. After generating the output information, the information processing device 20 outputs the generated output information. This allows the information processing device 20 to reduce the effort required for a herbal medicine doctor to prescribe a herbal medicine to the subject.

[0044] Here, the information processing device 20 identifies candidate herbal medicines to be prescribed for the subject based on, for example, a first machine learning model that has been trained with first correspondence information, a second machine learning model that has been trained with second correspondence information, and pre-stored waveform information. The first correspondence information is information in which one or more waveform indices indicating waveform characteristics indicated by waveform information showing the waveform of the subject's pulse wave are associated with diagnostic result information indicating a diagnosis result of the subject by a Chinese medicine doctor. The first machine learning model is a machine learning model that has been trained with the first correspondence information. When one or more waveform indices indicating waveform characteristics of a certain subject's pulse wave are input, the first machine learning model outputs diagnostic result information indicating a diagnosis result that is estimated to be a plausible diagnosis result of the subject by the Chinese medicine doctor. Meanwhile, the second correspondence information is information in which, for each subject, diagnostic result information indicating a diagnosis result of the subject by the Chinese medicine doctor is associated with Chinese medicine information indicating one or more herbal medicines prescribed for the subject by the Chinese medicine doctor. The second machine learning model is a machine learning model that has been trained with the second correspondence information. When the second machine learning model receives the diagnostic result information output by the first machine learning model, it outputs herbal medicine candidate information indicating one or more herbal medicine candidates estimated to be likely to be prescribed to the subject corresponding to the diagnostic result information. In this way, the information processing device 20 uses the first machine learning model and the second machine learning model to identify herbal medicine candidates to be prescribed to the subject. This allows the information processing device 20 to output herbal medicine candidate information indicating one or more herbal medicine candidates to be prescribed to the subject, while eliminating at least some of the subjective influence of the herbal medicine doctor. As a result, the information processing device 20 can reduce the effort required for the herbal medicine doctor to prescribe a herbal medicine to the subject and can accurately prescribe the herbal medicine to the subject without being influenced by the herbal medicine doctor's experience. Details of the one or more waveform indicators and the diagnostic result information will be described later.

[0045] The information processing device 20 is, for example, a notebook PC (Personal Computer), a desktop PC, a workstation, a tablet PC, a multi-function mobile phone terminal (smartphone), a mobile phone terminal, a PDA (Personal Digital Assistant), etc., but is not limited to these.

[0046] <Hardware configuration of information processing device> The hardware configuration of the information processing device 20 will be described below with reference to Fig. 2. Fig. 2 is a diagram showing an example of the hardware configuration of the information processing device 20.

[0047] The information processing device 20 includes, for example, a processor 21, a storage unit 22, an input receiving unit 23, a communication unit 24, and a display unit 25. The information processing device 20 also communicates with the pulse wave detection device 10 via the communication unit 24. These components are connected to each other via a bus so that they can communicate with each other.

[0048] The processor 21 is, for example, a CPU (Central Processing Unit). Instead of a CPU, the processor 21 may be another processor such as an FPGA (Field Programmable Gate Array). The processor 21 executes various programs stored in the storage unit 22. The processor 21 may be configured by a CPU included in one information processing device (in this example, the information processing device 20), or may be configured by CPUs included in multiple information processing devices.

[0049] The storage unit 22 includes, for example, a hard disk drive (HDD), a solid state drive (SSD), an electrically erasable programmable read-only memory (EEPROM), a read-only memory (ROM), and a random access memory (RAM). The storage unit 22 may be an external storage device connected via a digital input / output port such as a universal serial bus (USB) instead of being built into the information processing device 20. The storage unit 22 stores various information, various programs, and the like processed by the information processing device 20. For example, the storage unit 22 stores the waveform information, the first machine learning model, the second machine learning model, and the like. The storage unit 22 may be configured with one storage device or multiple storage devices. The multiple storage devices may include a storage device provided in an information processing device separate from the information processing device 20.

[0050] The input receiving unit 23 is an input device such as a keyboard, a mouse, a touchpad, etc. The input receiving unit 23 may be a touch panel that is integrated with the display unit 25.

[0051] The communication unit 24 includes, for example, a digital input / output port such as a USB, an Ethernet (registered trademark) port, an antenna for communication, and the like.

[0052] The display unit 25 is, for example, a display panel such as a liquid crystal display panel or an organic EL (ElectroLuminescence) display panel.

[0053] <Functional configuration of information processing device> The functional configuration of the information processing device 20 will be described below with reference to Fig. 3. Fig. 3 is a diagram showing an example of the functional configuration of the information processing device 20.

[0054] The information processing device 20 includes a storage unit 22, an input receiving unit 23, a communication unit 24, a display unit 25, and a control unit 26.

[0055] The control unit 26 controls the entire information processing device 20. The control unit 26 includes an acquisition unit 261, a reception unit 262, a calculation unit 263, a prescription candidate output unit 264, a first learning unit 265, a second learning unit 266, a display control unit 267, and a generation unit 268. These functional units included in the control unit 26 are realized, for example, by the processor 21 executing various programs stored in the storage unit 22. Furthermore, some or all of the functional units may be hardware functional units such as LSI (Large Scale Integration) and ASIC (Application Specific Integrated Circuit).

[0056] The acquisition unit 261 acquires the electrical signal output from the pulse wave sensor 12 .

[0057] The reception unit 262 receives various types of information from the user.

[0058] The calculation section 263 calculates various values ​​calculated by the information processing device 20. For example, based on certain waveform information, the calculation section 263 calculates one or more waveform indices that indicate the characteristics of the waveform indicated by the waveform information.

[0059] The prescription candidate output unit 264 generates the above-mentioned output information and outputs the generated output information. For example, the prescription candidate output unit 264 outputs the generated output information to the storage unit 22 and causes the storage unit 22 to store the output information.

[0060] The first learning unit 265 causes the first machine learning model to learn the first correspondence information.

[0061] The second learning unit 266 causes the second machine learning model to learn the second correspondence information.

[0062] The display control unit 267 generates various images and causes the display unit 25 to display the generated images.

[0063] The generating unit 268 generates waveform information indicating the waveform of the subject's pulse wave based on the electrical signal acquired by the acquiring unit 261.

[0064] <Processing by which information processing device generates waveform information> The process of generating waveform information by the information processing device 20 will be described below with reference to FIG. 4. FIG. 4 is a diagram showing an example of the process flow of generating waveform information by the information processing device 20. Hereinafter, a case will be described in which the information processing device 20 receives a first operation to cause the information processing device 20 to start the process of generating waveform information at a timing before the process of step S110 shown in FIG. 4 is performed. Hereinafter, a case will be described in which the information processing device 20 receives an operation to specify, as a measurement period, a period from the timing of receiving the first operation until a first time period specified by the user has elapsed. The first time period is, for example, one minute, but may alternatively be shorter or longer than one minute. Hereinafter, a case will be described in which the pulse wave detection device 10 starts detecting the pulse wave of the subject S1 at the timing. Here, when the pulse wave detection device 10 starts detecting the pulse wave of the subject S1, the pulse wave sensor 12 starts outputting the aforementioned electrical signal to the information processing device 20. In the following, as an example, a case will be described in which the information processing device 20 receives subject identification information for identifying the subject S1 at this timing.

[0065] After receiving the first operation, the acquisition unit 261 acquires electrical signals from the pulse wave sensor 12 at a predetermined sampling period during the measurement period until the first time has elapsed (step S110). In the embodiment, the process of converting the electrical signals from analog signals to digital signals is known, and therefore a description thereof will be omitted. Here, the sampling period is, for example, 0.002 seconds. Alternatively, the sampling period may be shorter or longer than 0.002 seconds. The acquisition unit 261 stores information indicating the electrical signals acquired during the measurement period in the storage unit 22.

[0066] Next, the generating unit 268 generates waveform information indicating the waveform of the pulse wave of the subject S1 within the measurement period based on the information indicating the electrical signal stored in the storage unit 22 in step S110 (step S120). Here, the method for generating the waveform information based on the information may be a known method or a method to be developed in the future. Furthermore, the information processing device 20 may be configured to perform the process of step S120 in parallel with the process of step S110.

[0067] Next, the generation unit 268 stores the waveform information generated in step S120 in the storage unit 22 (step S130). At this time, the generation unit 268 associates the waveform information with previously accepted subject identification information (i.e., subject identification information that identifies the subject S1) and stores the waveform information in the storage unit 22. After the process of step S130 is performed, the generation unit 268 ends the process of the flowchart shown in FIG. 4.

[0068] In this way, the information processing device 20 can store waveform information for each subject in the storage unit 22.

[0069] <Processing by an information processing device in which a first machine learning model learns first correspondence information and a second machine learning model learns second correspondence information> Hereinafter, with reference to FIG. 5, a process in which the information processing device 20 causes the first machine learning model to learn the first correspondence information and causes the second machine learning model to learn the second correspondence information will be described. FIG. 5 is a diagram showing an example of a process flow in which the information processing device 20 causes the first machine learning model to learn the first correspondence information and causes the second machine learning model to learn the second correspondence information. Hereinafter, as an example, a case will be described in which N pieces of waveform information generated by the processing of the flowchart shown in FIG. 4 are stored in the storage unit 22 at a timing before the processing of step S210 shown in FIG. 5 is performed. N may be any integer equal to or greater than 1. These N pieces of waveform information are waveform information for each of N subjects. Therefore, hereinafter, for convenience of explanation, each of these N subjects will be referred to as a training subject. Furthermore, hereinafter, for convenience of explanation, the waveform information for each of the N training subjects will be referred to as training waveform information. That is, hereinafter, as an example, a case will be described in which, at that timing, learning waveform information indicating the pulse wave waveforms of each of N learning subjects is stored in the storage unit 22. Also, hereinafter, as an example, a case will be described in which, at that timing, N pieces of diagnostic result information are stored in the storage unit 22. These N pieces of diagnostic result information are diagnostic result information for each of the N learning subjects. The diagnostic result information for a learning subject is information indicating the diagnosis result of the learning subject by a Chinese medicine doctor. In this case, the diagnostic result information for a learning subject is associated with subject identification information that identifies the learning subject. Also, hereinafter, as an example, a case will be described in which, at that timing, N pieces of herbal medicine information are stored in the storage unit 22. These N pieces of herbal medicine information are Chinese medicine information for each of the N learning subjects. The Chinese medicine information for a learning subject is information indicating one or more Chinese medicines prescribed by a Chinese medicine doctor for the learning subject. In this case, the herbal medicine information for a certain study subject is associated with subject identification information that identifies the study subject.

[0070] The control unit 26 reads out from the storage unit 22 each of the N pieces of learning-time waveform information stored in advance in the storage unit 22 (step S210). Note that in Fig. 5, the process of step S210 is indicated by "read waveform information".

[0071] Next, the control unit 26 selects, one by one, the subject identification information associated with each of the N pieces of learning-time waveform information read out in step S210 as target subject identification information, and repeats the processes of steps S230 to S270 for each selected piece of target subject identification information (step S220). Note that in Fig. 5, the process of step S220 is indicated by "for each subject identification information."

[0072] After the target subject identification information is selected in step S220, the first learning unit 265 refers to the N pieces of diagnostic result information stored in the memory unit 22 and reads out from the memory unit 22 the diagnostic result information associated with the target subject identification information selected in step S220 (step S230).

[0073] Here, we will explain the diagnosis result information. As mentioned above, the diagnosis result information for a certain study subject is information indicating the diagnosis result of that study subject by a Chinese medicine doctor. According to the concept of the 28 pathological pulses, the study subject's pulse wave is believed to exhibit at least six pulse types: one pulse type included in the floating network pulse category, one pulse type included in the sunken network pulse category, one pulse type included in the slow network pulse category, one pulse type included in the few network pulse category, one pulse type included in the virtual network pulse category, and one pulse type included in the solid network pulse category. Therefore, the information processing device 20 accepts, as diagnosis result information, information indicating the combination of the six pulse types included in the study subject's pulse wave, which represents the results of a prior diagnosis of the study subject by a Chinese medicine doctor. Hereinafter, for convenience of explanation, the combination of the six pulse types will be referred to as the target pulse type set. That is, the diagnostic result information is information indicating the target pulse type set. Note that other information may be associated with this diagnostic result information. In the following, as an example, a case will be described in which information about the subject at the time of learning, information indicating the detection position of the pulse wave of the subject at the time of learning, and information indicating the medical history of the subject at the time of learning are associated with the diagnostic result information. Here, the information about the subject at the time of learning includes, for example, information indicating the gender of the subject at the time of learning, information indicating the age of the subject at the time of learning, information indicating the height of the subject at the time of learning, and information indicating the weight of the subject at the time of learning.

[0074] Next, the first learning unit 265 refers to the N pieces of herbal medicine information stored in the storage unit 22, and reads out from the storage unit 22 the herbal medicine information associated with the target subject identification information selected in step S220 (step S240).

[0075] Next, calculation unit 263 selects the learning waveform information associated with the target subject identification information selected in step S220 from the learning waveform information read out in step S210, and calculates one or more waveform indices that indicate characteristics of the waveform indicated by the selected learning waveform information (step S250). For ease of explanation, each of the one or more waveform indices calculated based on the learning waveform information will be referred to as a learning waveform index below.

[0076] Here, the one or more waveform indices will be described. FIG. 6 is a diagram showing an example of a waveform indicated by the learning waveform information associated with the target subject identification information selected in step S220. The vertical axis of the graph shown in FIG. 6 indicates the signal amplitude of the pulse wave. The horizontal axis of the graph indicates elapsed time. A curve plotted on the graph indicates the waveform. The waveform includes multiple waves. As an example, the following describes a case where the number of waves included in the waveform is M. M may be any integer equal to or greater than 2. For example, based on the learning waveform information, the calculation unit 263 calculates six values ​​for each of the M waves included in the waveform, including a first peak value, a second peak value, a lower limit value, a fluctuation range, a first period, and a second period. The first peak value of a certain wave among the M waves is the largest displacement of that wave. The second peak value of a certain wave among the M waves is the second largest displacement of that wave. The lower limit value of a certain wave among the M waves is the smallest displacement of that wave. The fluctuation range of a certain wave among the M waves is the value obtained by subtracting the lower limit value of that wave from the first peak value of that wave. The first period of a certain wave among the M waves is the period of that wave. The second period of a certain wave among the M waves is the time from the first peak of that wave to the second peak of that wave. The first peak of that wave is the timing when the displacement of that wave reaches the first peak value. The second peak of that wave is the timing when the displacement of that wave reaches the second peak value. Here, FIG. 7 is a diagram visually illustrating an example of the m-th wave among the M waves included in the waveform indicated by the learning-time waveform information associated with the target subject identification information selected in step S220, and the first peak value P1m, second peak value P2m, lower limit value P3m, first period t1m, and second period t2m for that wave. Based on these six values ​​calculated for each of the M waves, the calculation unit 263 calculates the first average value P1, the second average value P2, the third average value P3, the fourth average value P4, the fifth average value P5, the sixth average value P6, the seventh average value P7, the first standard deviation Sd1, and the second standard deviation Sd2 as one or more waveform indices based on the learning waveform information.The first average value P1 for the M waves is the average value of the first peak values ​​of each of the M waves. The second average value P2 for the M waves is the average value of the second peak values ​​of each of the M waves. The third average value P3 for the M waves is the average value of the lower limit values ​​of each of the M waves. The fourth average value P4 for the M waves is the average value of the fluctuation range of each of the M waves. The fifth average value P5 for the M waves is the average value of the first period of each of the M waves. The sixth average value P6 for the M waves is the average value of the second period of each of the M waves. The seventh average value P7 for the M waves is the difference between the second average value P2 and the third average value P3 divided by the difference between the first average value P1 and the third average value P3. The first standard deviation Sd1 for the M waves is the standard deviation of the fluctuation range. The second standard deviation Sd2 for the M waves is the standard deviation of the first period. Calculating the first standard deviation Sd1 and the fifth average value P5 can be associated with diagnosing cardiac tachycardia or bradycardia. Calculating the seventh average value P7 can be associated with diagnosing cardiac aortic ejection (diagnosis of cardiac output, left ventricular contraction, and left atrial performance). The calculation unit 263 may be configured to calculate one or more waveform indices based on the learning waveform information from some of the first average value P1, the second average value P2, the third average value P3, the fourth average value P4, the fifth average value P5, the sixth average value P6, the seventh average value P7, the first standard deviation Sd1, and the second standard deviation Sd2. FIG. 6 illustrates an example of a waveform that is relatively frequently detected as a subject's pulse wave, i.e., a waveform with two peak values. However, even if the subject's pulse wave waveform has three or more peak values, one or more waveform indices can be calculated using the same process as described in FIG. 6.

[0077] After the process of step S250 is performed, the first learning unit 265 generates first correspondence information (step S260). More specifically, the first learning unit 265 generates information that associates one or more waveform indices calculated in step S250 with the diagnosis result information read out in step S230 as the first correspondence information. At this time, the first learning unit 265 generates a vector X and a vector Y. Here, the vector X is a vector having nine components x1 to x9. x1 is a variable into which the first average value P1 is substituted. x2 is a variable into which the second average value P2 is substituted. x3 is a variable into which the third average value P3 is substituted. x4 is a variable into which the fourth average value P4 is substituted. x5 is a variable into which the fifth average value P5 is substituted. x6 is a variable into which the sixth average value P6 is substituted. x7 is a variable into which the seventh average value P7 is substituted. x8 is a variable into which the first standard deviation Sd1 is substituted. x9 is a variable into which the second standard deviation Sd2 is substituted. That is, the first learning unit 265 generates vector X as a vector having each of the one or more waveform indexes as a component. On the other hand, vector Y is a vector having 28 components y1 to y28. y1 is a variable corresponding to a floating pulse. y2 is a variable corresponding to a rising pulse. y3 is a variable corresponding to a rising pulse. y4 is a variable corresponding to a rising pulse. y5 is a variable corresponding to a falling pulse. y6 is a variable corresponding to a scattering pulse. y7 is a variable corresponding to a sinking pulse. y8 is a variable corresponding to a falling pulse. y9 is a variable corresponding to a weak pulse. y10 is a variable corresponding to a long pulse. y11 is a variable corresponding to a slow pulse. y12 is a variable corresponding to a slow pulse. y13 is a variable corresponding to a slow pulse. y14 is a variable corresponding to a running pulse. y15 is a variable corresponding to a vicarious pulse. y16 is a variable corresponding to a rapid pulse. y17 is a variable corresponding to an artery. y18 is a variable corresponding to a rapid pulse. y19 is a variable corresponding to a weak pulse. y20 is a variable corresponding to a short pulse. y21 is a variable corresponding to a thin pulse. y22 is a variable corresponding to a weak pulse. y23 is a variable corresponding to a strong pulse. y24 is a variable corresponding to a long pulse. y25 is a variable corresponding to a soft pulse. y26 is a variable corresponding to a tight pulse. y27 is a variable corresponding to a smooth pulse.y28 is a variable corresponding to a major pulse. A flag indicating whether the pulse type is selected by a Chinese medicine doctor is assigned to these 28 variables. For example, if the target pulse type set indicated by the diagnosis result information associated with the target subject identification information selected in step S220 is a combination of six pulse types, namely, floating pulse, sunken pulse, slow pulse, rapid pulse, weak pulse, and strong pulse, 1 is assigned to y1 corresponding to floating pulse, y7 corresponding to sunken pulse, y11 corresponding to slow pulse, y16 corresponding to rapid pulse, y19 corresponding to weak pulse, and y23 corresponding to strong pulse. In this case, 0 is assigned to y2 to y6, y8 to y10, y12 to y15, y17, y18, y21, y22, and y24 to y28. The first learning unit 265 generates a vector Y having these 28 variables as its components. The first learning unit 265 generates information associating the generated vector X with the generated vector Y as first correspondence information. That is, the vector X indicates one or more waveform indices calculated in step S250. Furthermore, the vector Y indicates the diagnosis result information read out in step S230. The first learning unit 265 stores the generated first correspondence information in a first database stored in advance in the storage unit 22. That is, the first database is a database that stores N pieces of first correspondence information generated in the repeated processing of steps S220 to S270 shown in FIG. 5.

[0078] Next, the second learning unit 266 generates second correspondence information (step S270). More specifically, the second learning unit 266 generates second correspondence information by associating the diagnosis result information read in step S230 with each of the one or more herbal medicines indicated in the herbal medicine information read in step S240. The second learning unit 266 stores the generated second correspondence information in a second database pre-stored in the storage unit 22. That is, the second database is a database that stores N pieces of second correspondence information generated in the repeated processing of steps S220 to S270 shown in FIG. 5. The second database is a database with a two-dimensional table structure of n1 × m1. That is, the second database represents a two-dimensional table of n1 × m1. n1 is the number of combinations of herbal medicines that can be selected as target herbal medicine combinations. The number of pairs of n1 is 8,640, because the number of vein types in the floating vein category is 6, the number of vein types in the sunken vein category is 4, the number of vein types in the slow vein category is 5, the number of vein types in the few vein category is 3, the number of vein types in the virtual vein category is 4, and the number of vein types in the real vein category is 6. Furthermore, m1 is the number of types of herbal medicines that the information processing device 20 can handle. The second learning unit 266 stores the generated second correspondence information in the second database by adding 1 to the value assigned to the field where the target vein type pair and each of the one or more herbal medicines intersect in the two-dimensional table. Note that each field in the two-dimensional table is assigned a default value of 0. Figure 8 is a visual representation of an example of the processing in step S270.

[0079] After the process of step S270 is performed, the control unit 26 proceeds to step S220 and selects the next target subject identification information. Note that, if there is no unselected subject identification information in step S220, the control unit 26 ends the repeated process of steps S220 to S270.

[0080] After the repeated processing of steps S220 to S270 is completed, the first learning unit 265 causes the first machine learning model to learn the N pieces of first correspondence information stored in the first database during the repeated processing (step S280). More specifically, the first learning unit 265 causes the first machine learning model to learn, using each of the N pieces of first correspondence information, a vector X as an input and a vector Y as an output. As a result, the first learning unit 265 can cause the first machine learning model to calculate a coefficient matrix A acting on the vector X and a bias vector b as a linear regression model that associates the vector X with the vector Y. At this time, the first learning unit 265 reduces the coupling term of the linear combination by causing the first machine learning model to perform a principal factor analysis. This allows the information processing device 20 to reduce the computational cost of the processing of step S280. Note that the first learning unit 265 may be configured not to cause the first machine learning model to perform the principal factor analysis if there is no need to reduce such computational cost. The method of principal factor analysis may be a known method or a method to be developed in the future. The machine learning model used as the first machine learning model may be any type of machine learning model as long as it is capable of calculating the coefficient matrix A and bias vector b. FIG. 9 is a conceptual diagram visualizing an example of the processing of step S280.

[0081] Next, the second learning unit 266 trains the second machine learning model on the N pieces of second correspondence information generated in the repeated processing of steps S220 to S270 (step S290). That is, when a target pulse type set for a certain subject is input, the second learning unit 266 trains the second machine learning model on the second correspondence information so that the second machine learning model outputs herbal medicine candidate information indicating one or more herbal medicines that are likely to be prescribed for the subject as candidates for the herbal medicine to be prescribed for the subject. The machine learning model used as the second machine learning model may be any type of machine learning model as long as it can realize such input-output relationships. After the processing of step S290, the control unit 26 ends the processing of the flowchart shown in FIG. 5.

[0082] As described above, the information processing device 20 can cause the first machine learning model to learn the first correspondence information, and can cause the second machine learning model to learn the second correspondence information.

[0083] <Processing by which the information processing device receives diagnostic result information> Hereinafter, a process in which the information processing device 20 receives diagnostic result information will be described with reference to FIG. 10. FIG. 10 is a diagram showing an example of the flow of a process in which the information processing device 20 receives diagnostic result information. Hereinafter, as an example, a case in which the information processing device 20 receives diagnostic result information about subject S2, who is one of the N learning subjects described above, will be described. Also, below, as an example, a case in which a diagnosis of subject S2 is made by a Chinese medicine doctor at a timing before the processing of step S310 shown in FIG. 10 is performed will be described. Also, below, as an example, a case in which the information processing device 20 receives a second operation that causes the information processing device 20 to start receiving diagnostic result information at that timing will be described.

[0084] After the information processing device 20 accepts the second operation, the display control unit 267 generates an information acceptance image PCT1 (step S310).

[0085] Here, the information reception image PCT1 is an image with which the information processing device 20 receives diagnostic result information. Fig. 11 is a diagram showing an example of the information reception image PCT1. The information reception image PCT1 includes, for example, eight images, namely, a first reception image G1 to an eighth reception image G8. Note that the information reception image PCT1 may be configured to include other images in addition to these eight images.

[0086] The first reception image G1 is a GUI for receiving subject identification information, and includes, for example, an input field for inputting the subject identification information.

[0087] The second reception image G2 is a GUI that receives information indicating the gender of the subject. The second reception image G2 includes, for example, two radio buttons: one for receiving information indicating that the gender of the subject is male, and the other for receiving information indicating that the gender of the subject is female.

[0088] The third reception image G3 is a GUI for receiving information indicating the age of the subject. The third reception image G3 includes, for example, an input field for inputting the information indicating the age of the subject.

[0089] The fourth reception image G4 is a GUI for receiving information indicating the height of the subject. The fourth reception image G4 includes, for example, an input field for inputting information indicating the height of the subject.

[0090] The fifth reception image G5 is a GUI for receiving information indicating the weight of the subject. The fifth reception image G5 includes, for example, an input field for inputting the information indicating the weight of the subject.

[0091] The sixth reception image G6 is a GUI that receives information indicating the position where the pulse wave of the subject is detected. The sixth reception image G6 includes, for example, a radio button for receiving information indicating that the arm where the pulse wave is detected is the left arm, a radio button for receiving information indicating that the arm where the pulse wave is detected is the right arm, a radio button for receiving information indicating that the position where the pulse wave of the subject is detected is 1 / 3, a radio button for receiving information indicating that the position where the pulse wave of the subject is detected is 2 / 3, and a radio button for receiving information indicating that the position where the pulse wave of the subject is detected is 3 / 4.

[0092] The seventh reception image G7 is a GUI for receiving information indicating the subject's past medical conditions. The seventh reception image G7 includes, for example, an input field for inputting the information indicating the subject's past medical conditions.

[0093] The eighth reception image G8 is a GUI for receiving diagnostic result information. The eighth reception image G8 includes, for example, six images, reception image G81 to reception image G86.

[0094] The reception image G81 is a GUI that accepts one of six vein types included in the category of floating net vein. When an operation to select the reception image G81 is performed, a drop-down menu L81 listing information indicating each of the six vein types is displayed. When the drop-down menu L81 is displayed, the user of the information processing device 20 can select one of the pieces of information indicating each of the six vein types listed in the drop-down menu L81. In the information reception image PCT1, when an operation to select one of the pieces of information indicating each of the six vein types listed in the drop-down menu L81 is performed, the display of the drop-down menu L81 disappears, and the information selected in the drop-down menu L81 is displayed in the display field of the reception image G81.

[0095] The reception image G82 is a GUI that accepts one of four vein types included in the category of sunken vein. When an operation to select the reception image G82 is performed, a drop-down menu L82 listing information indicating each of the four vein types is displayed. When the drop-down menu L82 is displayed, the user of the information processing device 20 can select one of the pieces of information indicating each of the four vein types listed in the drop-down menu L82. In the information reception image PCT1, when an operation to select one of the pieces of information indicating each of the four vein types listed in the drop-down menu L82 is performed, the display of the drop-down menu L82 disappears, and the information selected in the drop-down menu L82 is displayed in the display field of the reception image G82.

[0096] The reception image G83 is a GUI that accepts one of five vein types included in the category of slow-nettle vein. When an operation to select the reception image G83 is performed, a drop-down menu L83 listing information indicating each of the five vein types is displayed. When the drop-down menu L83 is displayed, the user of the information processing device 20 can select one of the pieces of information indicating each of the five vein types listed in the drop-down menu L83. In the information reception image PCT1, when an operation to select one of the pieces of information indicating each of the five vein types listed in the drop-down menu L83 is performed, the display of the drop-down menu L83 disappears, and the information selected in the drop-down menu L83 is displayed in the display field of the reception image G83.

[0097] The reception image G84 is a GUI that accepts one of three vein types included in the category of several vein types. When an operation to select the reception image G84 is performed, a drop-down menu L84 listing information indicating each of the three vein types is displayed. When the drop-down menu L84 is displayed, the user of the information processing device 20 can select one of the pieces of information indicating each of the three vein types listed in the drop-down menu L84. In the information reception image PCT1, when an operation to select one of the pieces of information indicating each of the three vein types listed in the drop-down menu L84 is performed, the display of the drop-down menu L84 disappears, and the information selected in the drop-down menu L84 is displayed in the display field of the reception image G84.

[0098] The reception image G85 is a GUI that accepts one of the four vein types included in the category of imaginary network veins. When an operation to select the reception image G85 is performed, a drop-down menu L85 listing information indicating each of the four vein types is displayed. When the drop-down menu L85 is displayed, the user of the information processing device 20 can select one of the pieces of information indicating each of the four vein types listed in the drop-down menu L85. In the information reception image PCT1, when an operation to select one of the pieces of information indicating each of the four vein types listed in the drop-down menu L85 is performed, the display of the drop-down menu L85 disappears, and the information selected in the drop-down menu L85 is displayed in the display field of the reception image G85.

[0099] The reception image G86 is a GUI that accepts one of six vein types included in the category of actual network veins. When an operation to select the reception image G86 is performed, a drop-down menu L86 listing information indicating each of the six vein types is displayed. When the drop-down menu L86 is displayed, the user of the information processing device 20 can select one of the pieces of information indicating each of the six vein types listed in the drop-down menu L86. In the information reception image PCT1, when an operation to select one of the pieces of information indicating each of the six vein types listed in the drop-down menu L86 is performed, the display of the drop-down menu L86 disappears, and the information selected in the drop-down menu L86 is displayed in the display field of the reception image G86.

[0100] 12 is a diagram showing an example of how each of the six drop-down menus described above is displayed. Here, the information reception image PCT1 may not include some or all of the second reception image G2 to the seventh reception image G7.

[0101] After the process of step S310 is performed, the display control unit 267 causes the display unit 25 to display the information acceptance image PCT1 generated in step S310 (step S320).

[0102] Next, the accepting unit 262 waits until the information processing device 20 accepts an operation via the information acceptance image PCT1 displayed on the display unit 25 in step S320 (step S330).

[0103] If the information processing device 20 receives an operation via the information reception image PCT1 displayed on the display unit 25 in step S320 (step S330-YES), the reception unit 262 determines whether an operation to terminate the reception of diagnostic result information via the information reception image PCT1 has been received in step S330 (step S340).

[0104] If the receiving unit 262 determines that an operation to terminate the reception of diagnostic result information via the information reception image PCT1 has not been received in step S330 (NO in step S340), the receiving unit 262 performs processing in accordance with the operation received in step S330 (step S370). Here, the processing is, for example, processing in which the information processing device 20 receives diagnostic result information via the eighth reception image G8. That is, through the processing in step S370, the information processing device 20 receives various information included in the diagnostic result information for the subject S2. Note that the processing in accordance with the operation received in step S330 may be any processing that can be performed in accordance with an operation received via the information reception image PCT1. After the processing in step S370 is performed, the receiving unit 262 transitions to step S330 and waits again until the information processing device 20 receives an operation via the information reception image PCT1 displayed on the display unit 25 in step S320.

[0105] On the other hand, if the receiving unit 262 determines that an operation to terminate the reception of diagnostic result information via the information reception image PCT1 has been received in step S330 (step S340-YES), the receiving unit 262 identifies a target vein type set based on the information received via each of the reception images G81 to G86 of the information reception image PCT1. Then, the receiving unit 262 generates diagnostic result information indicating the identified target vein type set (step S350). At this time, the receiving unit 262 associates the generated diagnostic result information with the information received via each of the first reception image G1 to seventh reception image G7 of the information reception image PCT1.

[0106] Next, the reception unit 262 stores the diagnostic result information generated in step S350 in the storage unit 22 (step S360), and the process of the flowchart shown in FIG. 10 ends.

[0107] As described above, the information processing device 20 can receive the diagnosis result information.

[0108] <Processing by which an information processing device outputs herbal medicine candidate information> The process of the information processing device 20 outputting herbal medicine candidate information will be described below with reference to FIG. 13. FIG. 13 is a diagram showing an example of the flow of the process of the information processing device 20 outputting herbal medicine candidate information. The following describes, as an example, a case where first waveform information generated by the process of the flowchart shown in FIG. 4 is stored in the storage unit 22 at a timing before the process of step S410 shown in FIG. 13 is performed. The first waveform information is waveform information showing the waveform of the pulse wave of the subject S3. The following also describes, as an example, a case where, at that timing, the information processing device 20 receives subject identification information identifying the subject S3 as subject identification information identifying the subject to whom the herbal medicine candidate information is to be provided, along with a third operation that causes the information processing device 20 to start the process of outputting the herbal medicine candidate information.

[0109] After the information processing device 20 accepts the third operation and the subject identification information, the calculation unit 263 reads out the first waveform information from the storage unit 22 based on the accepted subject identification information (step S410). In Fig. 13, the process of step S410 is indicated by "read waveform information."

[0110] Next, the calculation unit 263 calculates one or more waveform indices indicating characteristics of the waveform of the pulse wave of the subject S3 based on the first waveform information read out in step S410 (step S420).

[0111] Next, the prescription candidate output unit 264 identifies a candidate herbal medicine to be prescribed to the subject S3 based on one or more waveform indexes calculated by the calculation unit 263 in step S420 (step S430). In Fig. 13, the process of step S430 is indicated by "identify candidate herbal medicine." Here, the process of step S430 will be described.

[0112] The prescription candidate output unit 264 inputs the one or more waveform indices calculated by the calculation unit 263 in step S420 as input to the first machine learning model. As described above, when the first machine learning model receives one or more waveform indices indicating waveform characteristics of a subject's pulse wave, it outputs diagnostic result information indicating a diagnostic result that is estimated to be a plausible result of the diagnosis of the subject by a traditional Chinese medicine doctor. Therefore, when the first machine learning model receives one or more waveform indices calculated by the calculation unit 263 in step S420, it outputs diagnostic result information indicating a diagnostic result that is estimated to be a plausible result of the diagnosis of subject S3 by a traditional Chinese medicine doctor. Specifically, when the first machine learning model receives the one or more waveform indices, it outputs, as diagnostic result information for subject S3, a vector Y indicating a target pulse type set that is estimated to be a plausible target pulse type set for subject S3 based on the one or more input waveform indices and a linear regression model calculated by prior learning. In this case, the first machine learning model calculates a likelihood indicating the likelihood of each of the six combinations of pulse types selectable as the target pulse type set for the subject S3, and estimates the combination with the highest calculated likelihood as the target pulse type set. Fig. 14 is a diagram showing an example of the likelihood for each of the six combinations of pulse types selectable as the target pulse type set.

[0113] After the first machine learning model outputs the vector Y, the prescription candidate output unit 264 inputs the vector Y output from the first machine learning model as an input to the second machine learning model. As described above, when the second machine learning model receives the diagnostic result information output by the first machine learning model, it outputs herbal medicine candidate information indicating one or more herbal medicine candidates that are estimated to be likely to be prescribed to the subject S3 corresponding to the diagnostic result information. Therefore, when the second machine learning model receives the vector Y output by the first machine learning model, it outputs herbal medicine candidate information indicating one or more herbal medicine candidates that are estimated to be likely to be prescribed to the subject S3. Specifically, when the second machine learning model receives the vector Y, it identifies, based on the input vector Y and the previously learned second correspondence information, one or more fields associated with the target herbal medicine type pair indicated by the vector Y in the two-dimensional table indicated by the second correspondence information that are assigned a value equal to or greater than a predetermined first threshold. The second machine learning model then identifies the herbal medicines associated with each of the identified fields as one or more likely candidates for the herbal medicine to be prescribed to the subject S3. Here, the first threshold may be any value greater than 0. The first threshold is determined, for example, based on prior experimental results, so as to increase the accuracy of identifying one or more likely candidates for the herbal medicine to be prescribed to the subject S3. After identifying one or more likely candidates for the herbal medicine to be prescribed to the subject S3, the second machine learning model outputs herbal medicine candidate information indicating each of the identified one or more herbal medicine candidates. Here, FIG. 15 is a diagram visualizing the process by which the second machine learning model identifies one or more likely candidates for the herbal medicine to be prescribed to the subject S3.

[0114] After the second machine learning model outputs one or more pieces of herbal medicine candidate information, the prescription candidate output unit 264 identifies the herbal medicine candidates indicated by each of the one or more pieces of herbal medicine candidate information output by the second machine learning model as one or more herbal medicine candidates that are estimated to be plausible as the one or more herbal medicines to be prescribed to the subject S3. As described above, the prescription candidate output unit 264 identifies the one or more herbal medicine candidates in step S430.

[0115] After the processing of step S430 is performed, the prescription candidate output unit 264 generates output information including herbal medicine candidate information indicating each of the one or more herbal medicine candidates identified in step S430. The output information may include any information in addition to the herbal medicine candidate information indicating each of the one or more herbal medicine candidates. The prescription candidate output unit 264 outputs the generated output information to the display control unit 267. As a result, the display control unit 267 generates, for example, an image including the output information output from the prescription candidate output unit 264. The display control unit 267 then displays the generated image on the display unit 25 (step S440), and the processing of the flowchart shown in FIG. 13 ends. Note that in FIG. 13, the processing of step S440 is indicated by "display herbal medicine candidate information."

[0116] As described above, the information processing device 20 outputs output information including candidate herbal medicine information indicating candidate herbal medicines to be prescribed to the subject S3 based on the first waveform information indicating the waveform of the pulse wave of the subject S3. This allows the information processing device 20 to identify candidate herbal medicines to be prescribed to the subject S3 without having a herbal medicine doctor take a pulse diagnosis on the subject S3. As a result, the information processing device 20 can reduce the effort required for the herbal medicine doctor to prescribe the herbal medicine to the subject S3. This also allows the information processing device 20 to efficiently suppress variations in treatment results due to the proficiency of the herbal medicine doctor.

[0117] <Modifications of the embodiment> A modified example of the embodiment will be described below. In this modified example, the diagnostic result information for a certain subject includes information indicating at least one of the diagnostic results of the subject's interview, facial examination, tongue examination, and abdominal examination by a traditional Chinese medicine doctor. The following describes, as an example, a case in which the diagnostic result information for a certain subject includes information indicating the diagnostic results of the subject's interview by a traditional Chinese medicine doctor. In this case, the diagnostic result information for a certain subject includes, for example, information about the subject, information indicating the detection position of the subject's pulse wave, and information indicating the subject's medical history. That is, the information about the subject, the information indicating the detection position of the subject's pulse wave, and the information indicating the subject's medical history are each examples of information indicating the diagnostic results of the subject's interview by a traditional Chinese medicine doctor. In this case, the aforementioned vector Y includes 10 components y29 to y38 in addition to the 28 components y1 to y28. Here, y29 is a variable that is assigned 1 if the subject is male and 0 if the subject is female. y30 is a variable that is assigned 1 if the subject is female and 0 if the subject is male. y31 is a variable that is assigned the subject's age. y32 is a variable that is assigned the subject's height. y33 is a variable that is assigned the subject's weight. y34 is a variable that is assigned 1 if the arm in which the pulse wave was detected is the left arm and 0 if the arm in which the pulse wave was detected is the right arm. y34 is a variable that is assigned 0 if the arm in which the pulse wave was detected is the right arm. y35 is a variable that is assigned 1 if the position in which the subject's pulse wave was detected is in inches and 0 if the position in which the subject's pulse wave was detected is in inches or feet. y36 is a variable to which 1 is assigned if the position at which the subject's pulse wave is detected is a kana, and 0 is assigned if the position at which the subject's pulse wave is detected is sun or shaku. y37 is a variable to which 1 is assigned if the position at which the subject's pulse wave is detected is shaku, and 0 is assigned if the position at which the subject's pulse wave is detected is sun or kana. y38 is a variable to which 1 is assigned if the subject has a pre-existing condition, and 0 is assigned if the subject does not have a pre-existing condition.The vector Y according to the modified example of the embodiment may include some of y29 to y38.

[0118] 10, the receiving unit 262 identifies a target pulse type set based on information received via each of the reception images G81 to G86 of the information reception image PCT1. Then, the receiving unit 262 generates, as diagnostic result information, information including diagnosis result information indicating the identified target pulse type set and information received via each of the second to seventh reception images G2 to G7. At this time, the receiving unit 262 associates the generated diagnostic result information with the subject identification information received via the first reception image G1 of the information reception image PCT1.

[0119] In this case, the second database is a database with a two-dimensional table structure of (n1 × n2) × m1. Here, n2 is the number of combinations of 10 values ​​assigned to each of y29 to y38. This allows the information processing device 20 to output output information including candidate herbal medicine information indicating candidate herbal medicines to be prescribed for a given subject based on a combination of information indicating the target pulse type set for the subject, information about the subject, information indicating the detected position of the subject's pulse wave, and information indicating the subject's medical history. This allows the information processing device 20 to increase the accuracy of identifying candidate herbal medicines to be prescribed for the subject. As a result, the information processing device 20 can more reliably reduce the effort required by a herbal medicine doctor to prescribe a herbal medicine to subject S3.

[0120] The above-described items may be combined in any manner. Furthermore, the method of classifying pulse types according to the state of the organs described above may be another classification method such as 38 pathological pulses instead of 28 pathological pulses. In this case, the target pulse type set may be a combination of seven or more pulse types. However, even in this case, the overall flow of the processing performed by the information processing device 20 remains the same as the flow described above.

[0121] <Additional Notes> [1] An information processing device (in the example described above, information processing device 20) that includes a prescription candidate output unit (in the example described above, prescription candidate output unit 264) that outputs output information including herbal medicine candidate information indicating candidates for herbal medicines to be prescribed to a first subject (in the example described above, subject S3) based on first waveform information indicating the waveform of the pulse wave of the first subject.

[0122] [2] The information processing device described in [1] further includes a calculation unit (in the example described above, calculation unit 263) that calculates one or more first waveform indices that indicate the characteristics of the waveform indicated by the first waveform information based on the first waveform information, and the prescription candidate output unit identifies the candidate herbal medicine based on the one or more first waveform indices calculated by the calculation unit.

[0123] [3] The prescription candidate output unit identifies the candidate herbal medicine based on a first machine learning model, a second machine learning model, and the first waveform information, and the first machine learning model is a machine learning model that has been trained with first correspondence information that corresponds one or more second waveform indicators that indicate waveform characteristics indicated by second waveform information that shows the waveform of the pulse wave of the second subject with diagnosis result information that indicates the diagnosis result of the second subject by a Chinese medicine doctor, and the second machine learning model is a machine learning model that has been trained with second correspondence information that corresponds the diagnosis result information with one or more pieces of Chinese medicine information, and the one or more pieces of Chinese medicine information are information that indicate each of one or more Chinese medicines prescribed by the Chinese medicine doctor to the second subject.

[0124] [4] The calculation unit calculates the one or more second waveform indices based on the second waveform information, and the information processing device further comprises: a reception unit (reception unit 262 in the example described above) that receives the diagnostic result information of the second subject (vector Y in the example described above) and the one or more pieces of herbal medicine information; a first learning unit (first learning unit 265 in the example described above) that generates information that corresponds the one or more second waveform indices calculated by the calculation unit with the diagnostic result information of the second subject received by the reception unit as the first correspondence information and trains the generated first correspondence information in the first machine learning model; and a second learning unit (second learning unit 266 in the example described above) that generates information that corresponds the diagnostic result information of the second subject received by the reception unit with the one or more pieces of herbal medicine information received by the reception unit as the second correspondence information and trains the generated second correspondence information in the second machine learning model.

[0125] [5] The information processing device described in [3] or [4], wherein the diagnostic result information includes at least the first diagnostic result information out of the first diagnostic result information indicating the result of pulse diagnosis of the second subject and the second diagnostic result information indicating the result of one or more types of diagnosis other than pulse diagnosis made by the Chinese medicine doctor on the second subject (in the example described above, information about the subject, information indicating the detection position of the subject's pulse wave, and information indicating the subject's medical history).

[0126] [6] The information processing device described in [5], wherein the second diagnostic result information includes information indicating at least one of the diagnostic results of the Chinese medicine doctor's interview, facial examination, tongue examination, and abdominal examination of the second subject.

[0127] [7] Each of the one or more first waveform indexes and the one or more second waveform indexes includes at least one of a first average value (in the example described above, the first average value P1), a second average value (in the example described above, the second average value P2), a third average value (in the example described above, the third average value P3), a fourth average value (in the example described above, the fourth average value P4), a fifth average value (in the example described above, the fifth average value P5), a sixth average value (in the example described above, the sixth average value P6), a seventh average value (in the example described above, the seventh average value P7), a first standard deviation (in the example described above, the first standard deviation Sd1), and a second standard deviation (in the example described above, the second standard deviation Sd2), The information processing device according to any one of [3] to [6], wherein the first average value is an average value of first peak values ​​of a wave indicating a pulse, the second average value is an average value of second peak values ​​of a wave indicating a pulse, the third average value is an average value of lower limits of a wave indicating a pulse, the fourth average value is a difference between the first average value and the third average value, the fifth average value is an average value of periods of the wave indicating a pulse, the sixth average value is an average value of a time from a first peak to a second peak in the wave indicating a pulse, the seventh average value is a value obtained by dividing the difference between the second average value and the third average value by the difference between the first average value and the third average value, the first standard deviation is a standard deviation of a fluctuation range of a wave indicating a pulse, and the second standard deviation is a standard deviation of a period of a wave indicating a pulse.

[0128] [8] The information processing device described in [4], or the information processing device described in any one of [5] to [7] subordinate to [4], wherein the reception unit receives the diagnostic result information of the second subject via an information reception image including one or more GUIs (Graphical User Interfaces) that receive the diagnostic result information of the second subject.

[0129] [9] The one or more GUIs include a GUI for receiving floating vein type information indicating vein types included in the floating vein category, a GUI for receiving sinking vein type information indicating vein types included in the sinking vein category, a GUI for receiving slow vein type information indicating vein types included in the slow vein category, a GUI for receiving multiple vein type information indicating vein types included in the multiple vein category, a GUI for receiving imaginary vein type information indicating vein types included in the imaginary vein category, and a GUI for receiving real vein type information indicating vein types included in the real vein category.

[0130]

[10] An information processing device described in any one of [3] to [9], wherein when the one or more first waveform indices are input, the first machine learning model outputs the diagnostic result information indicating a diagnostic result that is estimated to be plausible as the diagnostic result of the first subject by the Chinese medicine doctor.

[0131]

[11] The information processing device described in

[10] , wherein the first machine learning model generates a regression model based on the first correspondence information, and when the one or more first waveform indices are input, outputs the diagnostic result information indicating a diagnostic result that is estimated to be plausible as the diagnostic result of the first subject by the Chinese medicine doctor based on the one or more first waveform indices input and the generated regression model.

[0132]

[12] The information processing device according to any one of [3] to

[11] , wherein the second correspondence information is information with a two-dimensional table structure.

[0133]

[13] An information processing method comprising a prescription candidate output step of outputting output information including candidate herbal medicine information indicating candidates for herbal medicine to be prescribed to a first subject based on first waveform information indicating the waveform of the pulse wave of the first subject.

[0134]

[14] A program for causing a computer to execute a prescription candidate output step of outputting output information including candidate herbal medicine information indicating candidates for herbal medicine to be prescribed to a first subject based on first waveform information indicating the waveform of the pulse wave of the first subject.

[0135] The embodiments of this disclosure have been described in detail above with reference to the drawings, but the specific configuration is not limited to this embodiment, and may be changed, substituted, deleted, etc. as long as it does not deviate from the gist of this disclosure.

[0136] Furthermore, a program for implementing the functions of any of the components of the above-described device may be recorded on a computer-readable recording medium and loaded into a computer system for execution. Here, the device in question may be, for example, the pulse wave detection device 10 or the information processing device 20. The term "computer system" as used herein includes hardware such as an operating system (OS) and peripheral devices. The term "computer-readable recording medium" refers to portable media such as a flexible disk, a magneto-optical disk, a ROM, or a compact disk (CD-ROM), as well as storage devices such as a hard disk built into a computer system. The term "computer-readable recording medium" also includes devices that retain a program for a certain period of time, such as volatile memory within a computer system that acts as a server or client when the program is transmitted via a network such as the Internet or a communication line such as a telephone line.

[0137] The above program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by transmission waves in the transmission medium. Here, the "transmission medium" that transmits the program refers to a medium that has the function of transmitting information, such as a network such as the Internet or a communication line such as a telephone line. The program may also be a program for implementing some of the functions described above, or may be a so-called differential file or differential program that can implement the functions described above in combination with a program already recorded in the computer system. [Explanation of symbols]

[0138] 1… Pulse wave Information Processing Systems (Information Processing System) , 10... pulse wave detection device, 11... first member, 12... pulse wave sensor, 13... second member, 20... Pulse wave Information processing device (Information processing device) , 21...processor, 22...storage unit, 23...input reception unit, 24...communication unit, 25...display unit, 26...control unit, 261...acquisition unit, 262...reception unit, 263...calculation unit, 264...prescription candidate output unit, 265...first learning unit, 266...second learning unit, 267...display control unit, 268...generation unit, TC...three-dimensional coordinate system

Claims

1. A prescription candidate output unit that outputs output information including herbal medicine candidate information indicating candidates for herbal medicine to be prescribed to a first subject based on first waveform information indicating a waveform of the pulse wave of the first subject; a calculation unit that calculates, based on the first waveform information, one or more first waveform indices that indicate characteristics of the waveform indicated by the first waveform information; The prescription candidate output unit identifies the herbal medicine candidate based on one or more first waveform indexes calculated by the calculation unit, a first machine learning model, a second machine learning model, and the first waveform information; the first machine learning model is a machine learning model that has been trained to learn first correspondence information in which one or more second waveform indices indicating characteristics of a waveform indicated by second waveform information indicating a waveform of a pulse wave of a second subject are associated with diagnosis result information indicating a diagnosis result of the second subject by a Chinese medicine doctor; The second machine learning model is a machine learning model that has learned second correspondence information in which the diagnosis result information is associated with one or more pieces of herbal medicine information, The one or more pieces of herbal medicine information are information indicating one or more herbal medicines prescribed by the herbal medicine doctor to the second subject, respectively. Pulse wave information processing device.

2. the calculation unit calculates the one or more second waveform indexes based on the second waveform information; The information processing device includes: a receiving unit that receives the diagnostic result information of the second subject and the one or more pieces of herbal medicine information; a first learning unit that generates information that associates the one or more second waveform indices calculated by the calculation unit with the diagnostic result information of the second subject received by the reception unit as the first correspondence information, and causes the first machine learning model to learn the generated first correspondence information; a second learning unit that generates second correspondence information by associating the diagnosis result information of the second subject received by the receiving unit with the one or more pieces of herbal medicine information received by the receiving unit, and causes the second machine learning model to learn the generated second correspondence information; Further provided with The pulse wave information processing device according to claim 1 .

3. The diagnostic result information includes at least the first diagnostic result information out of first diagnostic result information indicating the result of pulse diagnosis of the second subject and second diagnostic result information indicating the result of one or more types of diagnosis other than pulse diagnosis among the diagnoses of the second subject made by the Chinese medicine doctor. The pulse wave information processing device according to claim 1 or 2.

4. The second diagnosis result information includes information indicating at least one of the diagnosis results of the Chinese medicine doctor on the second subject, including a medical interview, a facial examination, a tongue examination, and an abdominal examination. The pulse wave information processing device according to claim 3 .

5. each of the one or more first waveform indexes and the one or more second waveform indexes includes at least one of a first average value, a second average value, a third average value, a fourth average value, a fifth average value, a sixth average value, a seventh average value, a first standard deviation, and a second standard deviation; the first average value is an average value of first peak values ​​of a wave indicating a pulse rate, the second average value is an average value of second peak values ​​of a wave indicating a pulse rate, the third average value is an average value of the lower limit of the wave indicating the pulse rate, the fourth average value is the difference between the first average value and the third average value, the fifth average value is an average value of the period of a wave indicating a pulse rate, the sixth average value is an average value of the time from the first peak to the second peak in the wave indicating the pulse; the seventh average value is a value obtained by dividing the difference between the second average value and the third average value by the difference between the first average value and the third average value, the first standard deviation is a standard deviation of a fluctuation range of a wave indicating a pulse rate, The second standard deviation is the standard deviation of the period of a wave indicating a pulse. The pulse wave information processing device according to claim 1 or 2.

6. the receiving unit receives the diagnostic result information of the second subject via an information receiving image including one or more GUIs (Graphical User Interfaces) for receiving the diagnostic result information of the second subject. The pulse wave information processing device according to claim 2 .

7. The one or more GUIs include a GUI for receiving floating vein type information indicating vein types included in the floating vein category, a GUI for receiving sinking vein type information indicating vein types included in the sinking vein category, a GUI for receiving slow vein type information indicating vein types included in the slow vein category, a GUI for receiving multiple vein type information indicating vein types included in the multiple vein category, a GUI for receiving imaginary vein type information indicating vein types included in the imaginary vein category, and a GUI for receiving real vein type information indicating vein types included in the real vein category. The pulse wave information processing device according to claim 6 .

8. When the one or more first waveform indices are input, the first machine learning model outputs the diagnostic result information indicating a diagnostic result that is estimated to be plausible as a diagnostic result of the first subject by the Chinese medicine doctor. The pulse wave information processing device according to claim 1 or 2.

9. the first machine learning model generates a regression model based on the first correspondence information, and when the one or more first waveform indices are input, outputs the diagnostic result information indicating a diagnostic result that is estimated to be a plausible result of the diagnosis of the first subject by the Chinese medicine doctor based on the one or more first waveform indices that have been input and the generated regression model. The pulse wave information processing device according to claim 8 .

10. the second correspondence information is information having a two-dimensional table structure; The pulse wave information processing device according to claim 1 or 2.

11. In a computer, a calculation step of calculating, based on first waveform information indicating a waveform of the pulse wave of the first subject, one or more first waveform indices indicating characteristics of the waveform indicated by the first waveform information; a prescription candidate output step of identifying a candidate herbal medicine to be prescribed to the first subject based on the calculated one or more first waveform indices, a first machine learning model, a second machine learning model, and the first waveform information, and outputting output information including the candidate herbal medicine information; the first machine learning model is a machine learning model that has learned first correspondence information in which one or more second waveform indices indicating characteristics of a waveform indicated by second waveform information indicating a waveform of a pulse wave of a second subject are associated with diagnosis result information indicating a diagnosis result of the second subject by a Chinese medicine doctor; the second machine learning model is a machine learning model that has been trained with second correspondence information in which the diagnosis result information is associated with one or more pieces of herbal medicine information; The one or more pieces of information on Chinese herbal medicines are information indicating each of one or more Chinese herbal medicines prescribed by the Chinese herbal medicine doctor to the second subject. Pulse wave information processing method.

12. On the computer, a calculation step of calculating, based on first waveform information indicating a waveform of the pulse wave of the first subject, one or more first waveform indices indicating characteristics of the waveform indicated by the first waveform information; a prescription candidate output step of identifying a candidate herbal medicine to be prescribed to the first subject based on the calculated one or more first waveform indices, a first machine learning model, a second machine learning model, and the first waveform information, and outputting output information including the candidate herbal medicine information; An information processing program for executing the first machine learning model is a machine learning model that has learned first correspondence information in which one or more second waveform indices indicating characteristics of a waveform indicated by second waveform information indicating a waveform of a pulse wave of a second subject are associated with diagnosis result information indicating a diagnosis result of the second subject by a Chinese medicine doctor; the second machine learning model is a machine learning model that has been trained with second correspondence information in which the diagnosis result information is associated with one or more pieces of herbal medicine information; The one or more pieces of information on Chinese herbal medicines are information indicating each of one or more Chinese herbal medicines prescribed by the Chinese herbal medicine doctor to the second subject. Pulse wave information processing program.

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