Computer program, information processing device, information processing method, and learning model generation method

Abstract

The invention provides a computer program, an information processing device, an information processing method, and a learning model generation method capable of assisting appropriate pain treatment. A computer program causes a computer to execute a process for acquiring pain-related information relating to pain of a patient, inputting the acquired pain-related information to a learning model that outputs an index indicating the effect of pain treatment when the pain-related information is input, and acquiring an index indicating the effect of pain treatment relating to the patient. On the basis of the acquired index, pain treatment assistance information for the patient is generated, and the generated pain treatment assistance information is output.

Description

Technical Field

[0001] The present invention relates to a computer program, an information processing device, an information processing method, and a learning model generating method. Background Art

[0002] Patent document 1 discloses the following diagnosis and treatment information management system, which has multiple treatment department servers that provide diagnosis and treatment information stored in the databases of multiple treatment departments within a medical institution, and a specific disease treatment unified server that obtains specific disease diagnosis and treatment information from the treatment department servers and provides unified specific disease diagnosis and treatment information from a unified database. By being able to refer to the diagnosis and treatment information of specific diseases such as cancer in all therapies that combine surgery, radiotherapy, chemotherapy and palliative care, and registered related information on a wide range of specific diseases, the progress of treatment can be grasped in a highly real-time manner.

[0003] Patent Document 1: Japanese Patent Application Publication No. 2010-3135

[0004] In medical settings, pain caused by diseases such as cancer is evaluated based on indicators such as VAS (visual analog scale) and pain measurement using equipment. These methods are cumbersome for patients, and doctors also have the problem that they can only evaluate when they are in front of the patient. In addition, specialized knowledge is indispensable for effective pain management, and it is difficult for doctors other than specialists to provide accurate pain treatment according to the patient. Summary of the invention

[0005] The present invention has been made in view of such circumstances, and an object of the present invention is to provide a computer program, an information processing device, an information processing method, and a learning model generating method that can assist appropriate pain treatment.

[0006] (1) The computer program of the present invention enables a computer to perform the following processing: obtain pain-related information related to a patient's pain, input the obtained pain-related information into a learning model that outputs an indicator representing the effect of pain treatment when pain-related information is input, so as to obtain an indicator representing the effect of pain treatment related to the patient, generate pain treatment auxiliary information for the patient based on the obtained indicator, and output the generated pain treatment auxiliary information.

[0007] Here, for the embodiments of the present invention,

[0008] (2) In the computer program of (1) above, preferably, the pain-related information includes at least one of an evaluation index for evaluating pain, vital data, medication information, and physical condition.

[0009] (3) The computer program of (1) or (2) above preferably causes the computer to perform the following processing: obtaining medical information related to the diagnosis and treatment of the patient, and then inputting the obtained medical information into the learning model that outputs an indicator representing the effect of pain treatment when the medical information is input, so as to obtain an indicator representing the effect of pain treatment related to the patient.

[0010] (4) In any one of the computer programs described in (1) to (3) above, the medical information preferably includes at least one of diagnosis information, examination information, treatment information, and prescription information.

[0011] (5) Any one of the computer programs (1) to (4) above preferably causes the computer to perform the following processing: obtaining literature information related to pain treatment, and then inputting the obtained literature information into the learning model that outputs an indicator representing the effect of pain treatment when the literature information related to pain treatment is input, so as to obtain an indicator representing the effect of pain treatment related to the patient.

[0012] (6) In any one of the computer programs (1) to (5) above, the pain treatment assistance information preferably includes: an indicator indicating the effectiveness of pain treatment for assisting a physician examining the patient, recommended prescription information, and at least one of recommended treatment information.

[0013] (7) Any one of the computer programs (1) to (6) above preferably causes a computer to execute the following process: outputting the pain treatment support information to a doctor terminal device.

[0014] (8) Any of the computer programs described in (1) to (7) above preferably causes a computer to execute a process of outputting a temporal change in an index indicating an effect of pain treatment to a doctor terminal device.

[0015] (9) Any one of the computer programs (1) to (8) described above preferably causes a computer to execute a process of outputting, to a doctor terminal device, referral information of a doctor who has experience in recommending treatment methods.

[0016] (10) The information processing device of the present invention comprises: a first acquisition unit, which acquires pain-related information related to the patient's pain; a second acquisition unit, which inputs the acquired pain-related information into a learning model that outputs an indicator indicating the effect of pain treatment when the pain-related information is input, so as to acquire an indicator indicating the effect of pain treatment related to the patient; a generation unit, which generates pain treatment auxiliary information for the patient based on the acquired indicator; and an output unit, which outputs the generated pain treatment auxiliary information.

[0017] (11) The information processing method of the present invention obtains pain-related information related to the patient's pain, inputs the obtained pain-related information into a learning model that outputs an indicator representing the effect of pain treatment when pain-related information is input, so as to obtain an indicator representing the effect of pain treatment related to the patient, generates pain treatment auxiliary information for the patient based on the obtained indicator, and outputs the generated pain treatment auxiliary information.

[0018] (12) The learning model generation method of the present invention obtains first training data, which includes: pain-related information related to pain of multiple patients, and an indicator representing the effect of pain treatment related to the multiple patients. Based on the obtained first training data, a learning model is generated in a manner that outputs an indicator representing the effect of pain treatment when pain-related information is input.

[0019] (13) The learning model generation method of (12) above is preferably the learning model generation method of (12) above, wherein second training data is obtained, and the second training data also includes: medical information of multiple patients, literature information related to pain treatment, and indicators representing the effects of pain treatment related to the multiple patients. Based on the obtained second training data, the learning model is generated in a manner that outputs indicators representing the effects of pain treatment when the medical information and literature information related to pain treatment are input.

[0020] According to the present invention, appropriate pain treatment can be assisted. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a diagram showing an example of the configuration of the pain treatment support system according to the present embodiment.

[0022] Figure 2 This is a diagram showing an example of the structure of a pain treatment support device.

[0023] Figure 3 is a diagram showing information collection by means of a pain treatment assisting device.

[0024] Figure 4A This is a diagram showing an example of information in a database.

[0025] Figure 4B This is a diagram showing an example of information in a database.

[0026] Figure 4C This is a diagram showing an example of information in a database.

[0027] Figure 5 1 is a diagram showing a first example of a process for generating pain treatment support information by a pain treatment support device.

[0028] Figure 6 1 is a diagram showing a second example of the process of generating pain treatment support information by the pain treatment support device.

[0029] Figure 7 1 is a diagram showing a third example of the process of generating pain treatment support information by the pain treatment support device.

[0030] Figure 8 This is a diagram showing an example of a method for generating a learning model.

[0031] Fig. 9 This is a diagram showing a first example of pain treatment support information.

[0032] Fig.10 This is a diagram showing a second example of pain treatment support information.

[0033] Fig.11 It is a diagram showing a third example of pain treatment support information.

[0034] Fig.12 This is a diagram showing a fourth example of pain treatment support information.

[0035] Fig.13 It is a diagram showing a fifth example of pain treatment support information.

[0036] Fig.14 This is a diagram showing a sixth example of pain treatment support information.

[0037] Fig.15 This is a diagram showing an example of a processing sequence using a pain treatment support device.

[0038] Fig.16 This is a diagram showing an example of the generation order of the learning model. DETAILED DESCRIPTION

[0039] Hereinafter, embodiments of the present invention will be described. Figure 1 1 is a diagram showing an example of the structure of the pain treatment support system of the present embodiment. The pain treatment support system as an information processing system includes a pain treatment support device 50 as an information processing device. The pain treatment support system may also include a patient terminal device 10, a device 20, and a doctor terminal device 30. The patient terminal device 10 and the doctor terminal device 30 are connected to the pain treatment support device 50 via a communication network 1. The device 20 may be configured to be connected to the patient terminal device 10 wirelessly or by wire, or may be configured to be connected to the pain treatment support device 50 via a communication network 1.

[0040] The patient terminal device 10 is a terminal device carried or possessed by the patient. The patient terminal device 10 can be composed of a smartphone, a tablet computer, a personal computer, etc. having a display panel, an operation panel, a microphone, a speaker, etc. An application (application) for using the pain treatment assistance system is introduced into the patient terminal device 10. For example, it is assumed that the patient suffers from cancer, etc., and regularly goes to a medical institution while recuperating at home, but it is not limited to such patients. In addition, assistants such as family members and caregivers can also operate the patient terminal device 10 instead of the patient. In addition, the application can be a web application or a downloaded application.

[0041] Device 20 is a measuring device for measuring the patient's vital data, and can be a wearable terminal or a fixed device. Device 20 can measure vital data such as body temperature, heartbeat, brain wave, EDA (electrodermal activity: galvanic skin reflex). In addition, vital data is not limited to these, and any data that is considered to be related to the patient's pain caused by the disease can also be included. For example, vital data can also include blood pressure, pulse pressure, pulse, blood sugar level, weight, number of steps, activity level, etc.

[0042] The doctor terminal device 30 is a terminal device used by doctors in medical institutions, etc. The doctor terminal device 30 can be composed of a personal computer, a tablet computer, etc. with a display panel, an operation panel, etc. Medical practitioners such as health workers and nurses can also use the doctor terminal device 30 under the guidance of the doctor. An application (such as a Web application, etc.) for using the pain treatment support system is introduced into the doctor terminal device 30.

[0043] Figure 2 : is a diagram showing an example of the structure of the pain treatment auxiliary device 50. The pain treatment auxiliary device 50 includes a control unit 51 for controlling the entire device, a communication unit 52, a memory 53, a pain-related information DB 54, a diagnosis and treatment information DB 55, a pain treatment auxiliary information DB 56, a literature information DB 57, an auxiliary information generation unit (generation unit) 58, and a storage unit 59. The pain treatment auxiliary device 50 may also disperse the processing functions and be composed of a plurality of devices. Databases such as the pain-related information DB 54, the diagnosis and treatment information DB 55, the pain treatment auxiliary information DB 56, and the literature information DB 57 may be configured to be built into the pain treatment auxiliary device 50, or may be installed in an external data server. In addition, the storage unit 59 may also be installed in an external data server.

[0044] The storage unit 59 can be composed of, for example, a hard disk or a semiconductor memory, and stores a computer program 60 (program product), a learning model 61, and necessary information. The computer program 60 can be downloaded from an external device via the communication unit 52 and stored in the storage unit 59. In addition, the computer program 60 recorded in a recording medium (for example, an optically readable disk storage medium such as a CD-ROM) can be read by a recording medium reading unit and stored in the storage unit 59, or the computer program 60 can be read by a recording medium reading unit and expanded in the memory 53.

[0045] The control unit 51 is configured to be equipped with a required number of CPUs (Central Processing Units), MPUs (Micro-Processing Units), GPUs (Graphics Processing Units), etc. The control unit 51 is capable of executing the processing specified by the computer program 60. That is, the processing performed by the control unit 51 is also the processing performed by the computer program 60. The control unit 51 is capable of executing the functions of the auxiliary information generation unit 58 by executing the computer program 60. The auxiliary information generation unit 58 can be configured by hardware, can be configured to be implemented by software, or can be implemented by a combination of hardware and software. The control unit 51 is capable of performing processing using the learning model 61. In addition, the control unit 51 has the functions of a first acquisition unit, a second acquisition unit, a generation unit, and an output unit.

[0046] The communication unit 52 includes a communication module, and can exchange necessary information with the patient terminal device 10 and the doctor terminal device 30 via the communication network 1. The communication unit 52 can also exchange necessary information with the device 20.

[0047] The memory 53 can be formed of a semiconductor memory such as SRAM (Static Random Access Memory), DRAM (Dynamic Random Access Memory), or flash memory. The control unit 51 can expand the computer program 60 in the memory 53 and execute the computer program 60 .

[0048] Figure 3 5 is a diagram showing information collection by the pain treatment supporting device 50. The pain treatment supporting device 50 obtains pain-related information related to the patient's pain caused by the disease from the patient terminal device 10 and the device 20 via the communication unit 52. The pain-related information is appropriately repeatedly sent from the patient terminal device 10 and the device 20. For example, the pain-related information may be sent once during the period from the patient's last visit to the hospital to the next visit to the hospital, or may be sent multiple times depending on the length of the period. The pain-related information obtained is classified for each patient and at each acquisition time and stored in the pain-related information DB 54.

[0049] Furthermore, the pain treatment support device 50 acquires medical information related to the medical treatment of the patient from the doctor terminal device 30 via the communication unit 52. The acquired medical information is classified for each patient and for each medical treatment time and stored in the medical information DB 55.

[0050] Figure 4A , 4B And 4C is a diagram showing an example of information in the database. Figure 4A An example of information in the pain-related information DB 54 is shown. Pain-related information can be classified into pain evaluation index (evaluation index for evaluating pain), vital data, medication information, and physical condition. The pain-related information sent from the patient terminal device 10 and the device 20 includes at least one of the pain evaluation index, vital data, medication information, and physical condition. In addition, the classification is an example and is not limited to Figure 4A Example.

[0051] The pain evaluation index includes the intensity of pain, changes in pain, the timing of pain, etc. The pain evaluation index can be sent from the patient terminal device 10 to the pain treatment support device 50 by, for example, the patient starting an application of the patient terminal device 10 and answering questions related to the intensity of pain, changes in pain, the timing of pain, etc.

[0052] The vital data include data that changes due to the response to pain, such as body temperature, heart rate, brain wave, EDA, pulse, etc. In addition, it may also include blood pressure, pulse pressure, blood sugar level, weight, number of steps, amount of activity, etc. The vital data can be acquired using the device 20.

[0053] The medication information includes the type of medication, the amount of medication, the medication period, the medication time (timing), whether medication is taken, etc. The medication information may include medications that have been taken in the past, not just medications that are currently being taken.

[0054] Physical conditions include activity level, sleep time, sleepiness, fatigue, delusions, constipation, voice, and expression, etc. It can include items that can evaluate whether there is a disorder due to pain, medicine, disease symptoms, etc.

[0055] The pain-related information includes at least one of a pain evaluation index (an evaluation index for evaluating pain), vital data, medication information, and physical condition.

[0056] By acquiring the pain-related information as described above from the patient terminal device 10 and the equipment 20 , the patient can accurately communicate the pain and symptoms he or she feels at a necessary timing and with a necessary frequency during the period from the previous examination to the next examination.

[0057] Figure 4BAn example of information in the medical information DB 55 is shown. Medical information can be classified into diagnosis information, examination information, prescription information, and treatment information. The medical information sent from the doctor terminal device 30 includes at least one of diagnosis information, examination information, prescription information, and treatment information. In addition, the classification is an example and is not limited to Figure 4B Example.

[0058] Diagnostic information includes the results of the interview, visual inspection, palpation or percussion during the examination, electronic medical records, etc. Examination information includes blood tests, urine tests, imaging tests, etc. Prescription information includes the type and amount of medicine prescribed by the doctor. In addition, it can also include the period of taking the medicine. Treatment information includes the treatment method and the period of treatment. In addition, pain treatment includes not only the literal treatment but also the prescription of medicine.

[0059] Figure 4C An example of information in the document information DB57 is shown. Document information can be classified into guidance and papers. Guidance is guidance related to pain treatment, for example, including guidelines indicating what kind of pain treatment is recommended for the patient's symptoms and pain. Papers are papers related to pain treatment, for example, including objective information indicating what kind of pain treatment is effective for the patient's symptoms and pain. Document information DB57 is updated appropriately to record the latest information.

[0060] Figure 5 1 is a diagram showing a first example of the generation process of pain treatment auxiliary information by means of a pain treatment auxiliary device 50. The control unit 51 reads pain-related information of a specific patient from the pain-related information DB54, and inputs the read pain-related information into a learning model 61. The pain-related information of a specific patient is time series data of one or more moments. For example, it may include not only the current pain-related information of the specific patient, but also pain-related information of past moments traced back from the present. The control unit 51 is capable of performing a vectorization operation on the patient's pain-related information to generate a unique vector to generate a pain-related information vector, and inputs the generated pain-related information vector into the learning model 61. In addition, a pain-related information vector may be generated for each of a plurality of moments. In addition, although not shown in the figure, past indicators of the pain treatment auxiliary information DB56 may also be input into the learning model 61.

[0061] The learning model 61 is generated (learned) to output an indicator representing the effect of the current pain treatment when pain-related information is input. The indicator representing the effect of the pain treatment is an indicator representing the degree of effect of the current prescription and treatment for the patient, and can be represented by multiple levels, such as 10 levels from 1 to 10, or 5 levels from 1 to 5, and the larger the index value, the stronger the patient's pain. In addition, the larger the index value, the more the patient's pain is relieved. In addition, the indicator can also be classified as being appropriately effective, low, or high (there is a concern about side effects).

[0062] The learning model 61 can use, for example, SVM (Support Vector Machine), decision tree, random forest, adaptive boosting, neural network (for example, RNN: Recurrent Neural Network, LSTM: Long Short Term Memory, etc.), etc.

[0063] The control unit 51 reads the index indicating the effect of the past pain treatment of the patient recorded in the pain treatment auxiliary information DB 56. The control unit 51 inputs the read past index (index indicating the effect of the pain treatment) and the index (index indicating the effect of the pain treatment) obtained from the learning model 61 to the auxiliary information generating unit 58.

[0064] The auxiliary information generating unit 58 generates pain treatment auxiliary information based on the input index (index indicating the effect of pain treatment), and outputs the generated pain treatment auxiliary information. The auxiliary information generating unit 58 stores the generated pain treatment auxiliary information in the pain treatment auxiliary information DB 56. The pain treatment auxiliary information includes, for example, the temporal change of the index indicating the effect of pain treatment for the patient, the recommended prescription information or the recommended treatment information for the patient. The pain treatment auxiliary information only needs to include at least one of the index indicating the effect of pain treatment, the recommended prescription information and the recommended treatment information for assisting the doctor who examines the patient.

[0065] The temporal change of an indicator indicating the effect of pain treatment includes, for example, information indicating the transition of the indicator from the past to the present for the patient. For example, any information indicating the change of the indicator value at multiple times from the time of the initial diagnosis to the present will suffice. Recommended prescription information is information used to assist doctors who examine patients, including information used to determine the recommended prescription among the prescriptions of medicines for the patient. Recommended treatment information is information used to assist doctors, including information used to determine the recommended treatment methods among the treatment methods other than the prescriptions of medicines for the patient.

[0066] The auxiliary information generating unit 58 may generate the pain treatment auxiliary information in the rule base using, for example, a table that indicates the correspondence between the index (time series information) indicating the effect of pain treatment and the pain treatment auxiliary information. The table may also indicate the correspondence between the index (time series information) indicating the effect of pain treatment and the pain treatment auxiliary information.

[0067] The auxiliary information generating unit 58 can be configured by a model using an algorithm obtained by performing machine learning, and for example, SVM (Support Vector Machine), decision tree, random forest, adaptive boosting, neural network, etc. can be used.

[0068] As described above, the control unit 51 is able to obtain pain-related information related to the patient's pain, input the obtained pain-related information into a learning model 61 that outputs an indicator indicating the effect of pain treatment when pain-related information is input, thereby obtaining an indicator indicating the effect of pain treatment related to the patient, generate pain treatment auxiliary information for the patient based on the obtained indicator, and output the generated pain treatment auxiliary information.

[0069] Figure 6 2 is a diagram showing a second example of the process of generating pain treatment support information by the pain treatment support device 50. Figure 5 The difference of the first example illustrated in is that in addition to the pain-related information, medical information and literature information are also input into the learning model 61. The control unit 51 reads the medical information of a specific patient from the medical information DB55, and inputs the read medical information into the learning model 61. The medical information of a specific patient is time series data of one or more moments. For example, it may include not only the current medical information of a specific patient, but also the medical information of past moments traced back from the present. The control unit 51 can generate a medical information vector by performing a vectorization operation on the medical information to generate a unique vector, and input the generated medical information vector into the learning model 61. In addition, a medical information vector can also be generated for each of multiple moments. In addition, although not shown in the figure, past indicators of the pain treatment auxiliary information DB56 can also be input into the learning model 61.

[0070] In addition, the control unit 51 reads the document information from the document information DB 57, and inputs the read document information into the learning model 61. The control unit 51 can perform a vectorization operation to generate a unique vector on the document information to generate a document information vector, and input the generated document information vector into the learning model 61. By increasing the types of information input into the learning model 61, it can be expected that the accuracy of the index output by the learning model 61 will be improved.

[0071] As described above, the control unit 51 may obtain medical information related to the patient's medical treatment and input the obtained medical information into the learning model 61 which outputs an index indicating the effect of pain treatment when the medical information is input, thereby obtaining an index indicating the effect of pain treatment related to the patient.

[0072] Alternatively, the control unit 51 may obtain literature information related to pain treatment and input the obtained literature information into the learning model 61 which outputs an index indicating the effect of pain treatment when the literature information related to pain treatment is input, thereby obtaining an index indicating the effect of pain treatment related to the patient.

[0073] Figure 7 2 is a diagram showing a third example of the generation process of pain treatment support information by the pain treatment support device 50. Figure 6 The second example shown in FIG. 5 is different in that the patient's pain-related information, diagnosis and treatment information, and literature information are input to the auxiliary information generating unit 58 in addition to the index indicating the effect of the pain treatment. In addition, although not shown, the past index of the pain treatment auxiliary information DB 56 may be input to the learning model 61.

[0074] The auxiliary information generating unit 58 may generate the pain treatment auxiliary information in the rule base, for example, by using a table that indicates the corresponding relationship between the index (time series information) indicating the effect of pain treatment, the pain-related information, the diagnosis and treatment information, and the literature information, and the pain treatment auxiliary information. The table may also indicate the corresponding relationship between the index (time series information) indicating the effect of pain treatment, the pain-related information, the diagnosis and treatment information, and the literature information, and the pain treatment auxiliary information.

[0075] In addition, similar to the first and second examples, the auxiliary information generating unit 58 can be configured by a model using an algorithm obtained by performing machine learning, for example, SVM (support vector machine), decision tree, random forest, adaptive boosting, neural network, etc. can be used.

[0076] Figure 8is a diagram showing an example of a method for generating a learning model 61. In the following, it is assumed that the pain treatment supporting device 50 generates the learning model 61, but it may also be assumed that a learning processing device other than the pain treatment supporting device 50 generates the learning model 61, and the pain treatment supporting device 50 obtains the generated learning model 61. The control unit 51 obtains training data (second training data) including pain-related information related to pain of a plurality of patients, diagnosis and treatment information, literature information related to pain treatment, and an index indicating the effect of pain treatment related to the plurality of patients. The control unit 51 can generate the learning model 61 based on the obtained training data so that when the pain-related information, diagnosis and treatment information, and literature information related to pain treatment are input, an index indicating the effect of pain treatment is output. When generating the learning model 61, the parameters of the learning model 61 may be adjusted in such a way that the index indicating the effect of pain treatment output by the learning model 61 is close to the teacher data included in the training data, that is, the index indicating the effect of pain treatment.

[0077] In addition, Figure 8 In the example, the input data for learning includes pain-related information, diagnosis and treatment information, and literature information, but for example, the input data for learning may not include at least one of the diagnosis and treatment information and the literature information. For example, the control unit 51 can obtain training data (first training data) including pain-related information related to pain of multiple patients and indicators representing the effects of pain treatment related to the multiple patients, and generate a learning model 61 based on the obtained training data so that when pain-related information is input, an indicator representing the effect of pain treatment is output. However, if the input data for learning includes at least one of the diagnosis and treatment information and the literature information, it is more likely that the inference accuracy of the learning model 61 will be improved.

[0078] Next, a specific example of pain treatment auxiliary information is described. In the following example, the place where the doctor's input is required can also be set as voice input. The control unit 51 outputs the pain treatment auxiliary information to the doctor terminal device 30. The pain treatment auxiliary information is displayed on the display panel of the doctor terminal device 30, and the doctor can perform input operations and selection operations according to the displayed content.

[0079] Fig. 9 300 is a diagram showing a first example of pain treatment support information 300. The pain treatment support information 300 displays patient information (patient ID, patient name, etc.), medication status (type of medication, medication amount, medication period, etc.), treatment status (treatment method, treatment period, etc.), and indicators showing the effectiveness of pain treatment. Fig. 9 In the example, the index representing the effect of pain treatment is displayed in a line graph, and the patient's index value changes from the past to the present can be seen. Fig. 9In the example of , the larger the index value, the stronger the patient's pain. The doctor can read from the patient's pain treatment so far that the pain has been temporarily relieved but is gradually getting stronger.

[0080] In addition, as advice (supplementary information) to the doctor, “Pain persists, so medication needs to be added or changed. How about changing the prescription?” If the doctor operates the “recommended prescription information” icon 301, recommended prescription information described later can be displayed.

[0081] As described above, the control unit 51 outputs the temporal change of the index indicating the effect of the pain treatment to the doctor terminal device 30 .

[0082] Fig.10 3 is a diagram showing a second example of pain treatment support information 310. The pain treatment support information 310 includes recommended prescription information. The recommended prescription information displays patient information and recommended content. Fig.10 In the example, as recommendation 1, a change in the type of medicine and a change in the amount of medicine are displayed. As recommendation 2, it is recommended to stop taking a specific medicine. In addition to the recommended content, guidance to doctors such as "Please refer to the above recommended prescription information and study it in combination with the diagnosis results" can also be displayed.

[0083] Fig.11 3 is a diagram showing a third example of pain treatment support information 320. The pain treatment support information 310 includes recommended prescription information. Fig.11 In the example of , it is displayed as "Pump is filled, administer ○○ medicine", and the "administer" icon 321 is displayed. The pump is, for example, a syringe pump. In addition, the recommended prescription information displays a reminder to the doctor such as "Please pay attention to the condition after administration". If the doctor operates the "administer" icon 321, the medicine connected to the pump can be administered to the patient through the operation on the doctor's side. In addition, instructions can be sent to medical practitioners, nursing staff, etc. to remotely start the pump at home. Therefore, the pump does not need to be operated remotely.

[0084] Fig.12 3 is a diagram showing a fourth example of pain treatment support information 330. Fig. 9 Compared with the first example, in the fourth example, the indicator value remains at a higher value and moves from the past to the present. Fig.12 In the example of , the larger the index value, the stronger the patient's pain. The doctor can read that the pain has not been relieved in the patient's pain treatment so far.

[0085] In addition, as advice (supplementary information) to the doctor, “Pain seems to occur frequently. How about studying new treatment methods?” If the doctor operates the “recommended prescription information” icon 331 , recommended prescription information described later can be displayed.

[0086] Fig.13 3 is a diagram showing a fifth example of pain treatment support information 340. Pain treatment support information 340 includes recommended treatment methods. The recommended treatment methods display patient information and recommended contents. Fig.13 In the example, as recommendation 1, treatment method AA is displayed, and as recommendation 2, treatment method BB is displayed. In addition to the recommended content, "Introducing experienced doctors when there is no actual record of implementing the recommended treatment method" is displayed. If the doctor operates the "display doctor list" icon 341, introduction information of specialists who have experience in recommending treatment methods is displayed in the form of a list.

[0087] Fig.14 3 is a diagram showing a sixth example of pain treatment support information 350. A list of doctors is displayed in the pain treatment support information 350. The list of doctors includes, for example, the name of a medical institution, the name of a doctor, a selection box 351, and a "Contact" icon 352. As a guide for doctors, for example, "Please select a doctor you wish to receive guidance from and operate the "Contact" icon" is displayed. If the doctor selects the desired medical institution and doctor's name and operates the "Contact" icon 352, he or she can get in touch with the specialist he or she wishes to receive guidance from.

[0088] As described above, the control unit 51 outputs the introduction information of the doctor who has experience in recommending treatment methods to the doctor terminal device 30 .

[0089] As described above, according to this embodiment, it is possible to assist (support) a doctor's decision on a pain treatment policy, and even a doctor with little experience in pain treatment can be provided with the information necessary for pain treatment, so that even doctors other than specialists can perform appropriate pain treatment.

[0090] Fig.15 5 is a diagram showing an example of a processing sequence by means of the pain treatment auxiliary device 50. In the following, for convenience, the main body of the processing is described as the control unit 51. The control unit 51 obtains the patient's pain-related information (S11), and stores the obtained pain-related information in the pain-related information DB54 (database) (S12). The pain-related information can be repeatedly obtained for each patient at a necessary time. For example, it can be obtained once or multiple times during the period from the last visit to the hospital to the next visit to the hospital.

[0091] The control unit 51 acquires the patient's medical information (S13), and stores the acquired medical information in the medical information DB55 (database) (S14). The medical information can be acquired when the doctor determines that it is necessary, not only during the examination.

[0092] The control unit 51 inputs the patient's pain-related information, medical treatment information, and literature information related to pain treatment into the learning model 61, and obtains an index indicating the effect of the patient's pain treatment output by the learning model 61 (S15). The control unit 51 reads an index indicating the effect of the patient's past pain treatment from the pain treatment auxiliary information DB 56 (database) (S16).

[0093] The control unit 51 generates pain treatment auxiliary information (S17) including the time-dependent change of the index indicating the effect of the pain treatment for the patient based on the index indicating the effect of the pain treatment for the patient read from the pain treatment auxiliary information DB 56 and the index indicating the effect of the pain treatment for the patient obtained from the learning model 61. In this case, the pain treatment auxiliary information may be generated based on the patient's pain-related information, diagnosis and treatment information, and literature information in addition to the index indicating the effect of the pain treatment for the patient.

[0094] The control unit 51 stores the generated pain treatment auxiliary information in the pain treatment auxiliary information DB 56 (database) ( S18 ), outputs the generated pain treatment auxiliary information ( S19 ), and ends the processing.

[0095] Fig.16 is a diagram showing an example of the order of generating the learning model 61. The control unit 51 obtains training data including pain-related information, diagnosis and treatment information, an index indicating the effect of pain treatment, and literature information related to pain treatment for a plurality of patients (S31). Based on the obtained training data, the control unit 51 generates the learning model 61 so that when the pain-related information, diagnosis and treatment information, and literature information related to pain treatment for each patient are input, an index indicating the effect of pain treatment for each patient is output (S32). The control unit 51 stores the generated learning model 61 in the storage unit 59 (S33), and ends the processing.

[0096] Description of Reference Numerals

[0097] 1…communication network; 10…patient terminal device; 20…equipment; 30…doctor terminal device; 50…pain treatment auxiliary device; 51…control unit; 52…communication unit; 53…storage; 54…pain-related information DB; 55…medical information DB; 56…pain treatment auxiliary information DB; 57…literature information DB; 58…auxiliary information generation unit; 59…storage unit; 60…computer program; 61…learning model.

Claims

1. A computer program, characterized in that Causes the computer to perform the following processing: Obtain pain-related information related to the patient's pain, The acquired pain-related information is input into a learning model that outputs an index indicating the effect of pain treatment when pain-related information is input, thereby acquiring an index indicating the effect of pain treatment on the patient. Based on the obtained indicators, pain treatment auxiliary information for the patient is generated, The generated pain treatment auxiliary information is output.

2. The computer program according to claim 1, characterized in that The pain-related information includes at least one of an evaluation index for evaluating pain, vital data, medication information, and physical condition.

3. The computer program according to claim 1, characterized in that Causes the computer to perform the following processing: obtaining medical information related to the medical treatment of the patient, Furthermore, the acquired medical information is input to the learning model that outputs an index indicating the effect of pain treatment when medical information is input, thereby acquiring an index indicating the effect of pain treatment for the patient.

4. The computer program according to claim 3, characterized in that The diagnosis and treatment information includes at least one of: diagnosis information, examination information, treatment information and prescription information.

5. The computer program according to claim 1, characterized in that Causes the computer to perform the following processing: Obtain literature information related to pain treatment, Furthermore, the acquired literature information is input to the learning model that outputs an index indicating the effect of pain treatment when literature information related to pain treatment is input, thereby acquiring an index indicating the effect of pain treatment related to the patient.

6. The computer program according to any one of claims 1 to 5, characterized in that The pain treatment assistance information includes at least one of an index indicating the effect of pain treatment for assisting a doctor examining the patient, recommended prescription information, and recommended treatment information.

7. The computer program according to any one of claims 1 to 5, characterized in that Causes the computer to perform the following processing: The pain treatment auxiliary information is output to a doctor terminal device.

8. The computer program according to any one of claims 1 to 5, characterized in that Causes the computer to perform the following processing: The temporal change of an index indicating the effect of pain treatment is output to the doctor terminal device.

9. The computer program according to any one of claims 1 to 5, characterized in that Causes the computer to perform the following processing: The introduction information of the doctor who has experience in recommending treatment methods is output to the doctor terminal device.

10. An information processing device, characterized in that: have: a first acquisition unit that acquires pain-related information related to the patient's pain; a second acquisition unit that inputs the acquired pain-related information into a learning model that outputs an index indicating the effect of pain treatment when pain-related information is input, thereby acquiring an index indicating the effect of pain treatment related to the patient; a generating unit that generates pain treatment auxiliary information for the patient based on the acquired index; as well as An output unit outputs the generated pain treatment auxiliary information.

11. An information processing method, characterized in that: Obtain pain-related information related to the patient's pain, The acquired pain-related information is input into a learning model that outputs an index indicating the effect of pain treatment when pain-related information is input, thereby acquiring an index indicating the effect of pain treatment on the patient. Based on the obtained indicators, pain treatment auxiliary information for the patient is generated, The generated pain treatment auxiliary information is output.

12. A learning model generation method, characterized in that: obtaining first training data, the first training data comprising: pain-related information related to pain of a plurality of patients, and an index indicating the effect of pain treatment related to the plurality of patients, Based on the acquired first training data, a learning model is generated so as to output an index indicating the effect of pain treatment when pain-related information is input.

13. The learning model generation method according to claim 12, characterized in that: obtaining second training data, the second training data further comprising: diagnosis and treatment information of a plurality of patients, literature information related to pain treatment, and an index indicating the effect of pain treatment related to the plurality of patients, The learning model is generated based on the acquired second training data so as to output an index indicating the effect of pain treatment when medical treatment information and literature information related to pain treatment are input.

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