Computer program, information processing method, and information processing device

Abstract

Provided are a computer program, an information processing method, and an information processing device.　The present invention acquires information on a peritoneal dialysis patient including at least one of edema, urine output, and ultrafiltration volume, inputs the acquired information to a trained model, which is trained to output information related to a dialysis prescription for the peritoneal dialysis patient when the information on the peritoneal dialysis patient is input, executes computation with the trained model, generates a dialysis prescription on the basis of information output from the trained model, and outputs the generated dialysis prescription.

Description

Computer program, information processing method, and information processing device

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

[0002] Various techniques have been proposed to detect the condition of a patient undergoing dialysis treatment. For example, patent documents disclose a technique in which the period of dialysis treatment is divided into multiple periods and the patient's blood pressure is measured during each period.

[0003] Japanese Patent Application Laid-Open No. 2000-000217

[0004] Although Patent Document 1 makes it possible to manage the transition of blood pressure during the dialysis period, it is not possible to propose a dialysis prescription for a peritoneal dialysis patient.

[0005] In one aspect, an object of the present invention is to provide a computer program, an information processing method, and an information processing device that can propose a dialysis prescription for a peritoneal dialysis patient.

[0006] (1) The computer program disclosed herein is a computer program for causing a computer to execute a process of acquiring information about a peritoneal dialysis patient, including at least one of edema, urine volume, and amount of water removed, inputting the acquired information into a learning model that has been trained to output information related to a dialysis prescription for the peritoneal dialysis patient when the information about the peritoneal dialysis patient is input, performing a calculation using the learning model, generating a dialysis prescription based on the information output from the learning model, and outputting the generated dialysis prescription.

[0007] (2) In the computer program of (1) above, the dialysis prescription preferably includes at least one of the type of peritoneal dialysis solution to be prescribed and the number of peritoneal dialysis sessions per day.

[0008] (3) In the computer program of (1) or (2) above, the dialysis prescription preferably includes replacing a currently prescribed peritoneal dialysis fluid with a different type of peritoneal dialysis fluid.

[0009] (4) In any one of the computer programs (1) to (3) above, it is preferable to have the computer execute a process of generating an image representing the body fluid status of a peritoneal dialysis patient based on the acquired information about the peritoneal dialysis patient and outputting the generated image.

[0010] (5) In the computer program of (4) above, it is preferable to make the computer execute a process of generating, as the image, an image that schematically represents the sodium storage amount and sodium excretion amount in the peritoneal dialysis patient.

[0011] (6) In any one of the computer programs (1) to (5) above, it is preferable that the information further includes information on at least one of hANP (human atrial natriuretic peptide), cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion.

[0012] (7) In any one of the computer programs (1) to (6) above, it is preferable that the information on the peritoneal dialysis patient further includes information on at least one of the body weight, dry mouth, antihypertensive drugs, diuretics, and blood pressure of the peritoneal dialysis patient.

[0013] (8) In any one of the computer programs (1) to (7) above, it is preferable that the information on the peritoneal dialysis patient further includes information on at least one of urinary cortisol, urinary creatinine, skin sodium, and Na-MRI (nuclear magnetic resonance).

[0014] (9) In any one of the computer programs (1) to (8) above, it is preferable that the learning model is a language generation model trained to output sentences related to peritoneal dialysis prescriptions, and that the computer is caused to execute a process of outputting the sentences related to the peritoneal dialysis prescriptions by inputting acquired information into the language generation model.

[0015] (10) The information processing method disclosed herein acquires information about a peritoneal dialysis patient, including at least one of edema, urine volume, and amount of water removed, inputs the acquired information into a learning model that has been trained to output information related to a dialysis prescription for the peritoneal dialysis patient when the information about the peritoneal dialysis patient is input, performs calculations using the learning model, generates a dialysis prescription based on the information output from the learning model, and outputs the generated dialysis prescription, all of which are performed by a computer.

[0016] (11) The information processing device of the present disclosure includes at least one processor, which acquires information about a peritoneal dialysis patient including at least one of edema, urine volume, and amount of water removed, inputs the acquired information into a learning model that has been trained to output information related to a dialysis prescription for the peritoneal dialysis patient when the information about the peritoneal dialysis patient is input, performs calculations using the learning model, generates a dialysis prescription based on the information output from the learning model, and outputs the generated dialysis prescription.

[0017] In one aspect, a dialysis prescription can be suggested for a peritoneal dialysis patient.

[0018] FIG. 1 is a schematic diagram showing an example of the configuration of a peritoneal management support system according to an embodiment. FIG. 1 is a block diagram illustrating the internal configuration of a server device. FIG. 2 is a block diagram illustrating the internal configuration of a patient terminal. FIG. 3 is a block diagram illustrating the internal configuration of a doctor terminal. FIG. 4 is a schematic diagram illustrating an example of a patient information input screen on a patient terminal. FIG. 5 is a schematic diagram illustrating an example of a patient information input screen on a doctor terminal. FIG. 6 is a schematic diagram illustrating an example of the configuration of a learning model. FIG. 7 is a schematic diagram illustrating an example of a dialysis prescription generated by the server device. FIG. 8 is a flowchart illustrating the procedure of processing executed by the server device. FIG. 9 is a schematic diagram illustrating an example of the configuration of a learning model. FIG. 10 is a schematic diagram illustrating an example of image generation. FIG. 11 is a schematic diagram illustrating an example of image generation. FIG. 12 is a schematic diagram illustrating an example of image generation. FIG. 13 is a schematic diagram illustrating an example of output in embodiment 2.

[0019] The present invention will be described in detail below with reference to the drawings illustrating embodiments. (Embodiment 1) Fig. 1 is a schematic diagram illustrating an example of the configuration of a peritoneal management support system according to an embodiment. The peritoneal management support system according to the embodiment includes a server device 10, a patient terminal 20, and a doctor terminal 30. The server device 10 is a server computer installed, for example, by a hospital. The patient terminal 20 is a client computer used by a peritoneal dialysis patient (hereinafter simply referred to as a patient), and the doctor terminal 30 is a client computer used by a medical professional such as a doctor. The server device 10, the patient terminal 20, and the doctor terminal 30 are connected to each other so as to be able to communicate with each other via a communication network NW.

[0020] The server device 10 acquires information about peritoneal dialysis patients (hereinafter referred to as patient information) from the patient terminal 20 and the doctor terminal 30. The patient information acquired by the server device 10 includes information about at least one of edema, urine volume, and water removal volume. The information about edema, urine volume, and water removal volume can be received through the patient terminal 20. The patient information acquired by the server device 10 may further include information such as dry mouth and weight input from the patient terminal 20.

[0021] The patient information acquired by the server device 10 may further include information on at least one of hANP, cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion. This information is managed for each patient and may be input from the doctor's terminal 30 and pre-registered in the server device 10, for example. The patient information registered in the server device 10 may further include information on antihypertensive drugs, diuretics, blood pressure, etc., and may further include information on urinary cortisol, urinary creatinine, skin sodium, Na-MRI, etc.

[0022] The server device 10 generates a dialysis prescription for a peritoneal dialysis patient in response to input patient information and outputs the generated dialysis prescription. For example, the server device 10 displays the generated dialysis prescription on its own display screen. Alternatively, the server device 10 may transmit the generated dialysis prescription to a patient terminal 20 or a doctor terminal 30 via the communication network NW.

[0023] The internal configuration of each device that constitutes the peritoneal management support system will be described below.

[0024] 2 is a block diagram illustrating the internal configuration of the server device 10. The server device 10 is a dedicated or general-purpose computer, and includes a control unit 11, a storage unit 12, a communication unit 13, an operation unit 14, and a display unit 15.

[0025] The control unit 11 includes, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory). The ROM included in the control unit 11 stores a control program that controls the operation of each hardware unit included in the server device 10. The CPU in the control unit 11 reads and executes the control program stored in the ROM and a computer program (described below) stored in the storage unit 12, and controls the operation of each hardware unit, thereby causing the entire device to function as the server device 10 (information processing device) of the present disclosure. The RAM included in the control unit 11 temporarily stores data used during calculation and control.

[0026] In the embodiment, the control unit 11 is configured to include a CPU, a ROM, and a RAM, but the configuration of the control unit 11 is not limited to the above. The control unit 11 may be, for example, one or more control circuits or arithmetic circuits including a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor), a quantum processor, volatile or non-volatile memory, etc. The control unit 11 may also have functions such as a clock that outputs date and time information, a timer that measures the elapsed time from when a measurement start instruction is given until when a measurement end instruction is given, and a counter that counts numbers.

[0027] The storage unit 12 includes a storage device such as a hard disk drive (HDD), a solid state drive (SSD), etc. The storage unit 12 stores various computer programs executed by the control unit 11 and various data used by the control unit 11.

[0028] The computer program (program product) stored in the storage unit 12 includes a prescription generation program PG1 for causing a computer to execute a process of generating a dialysis prescription to be prescribed to a patient using a learning model LM1 described below and outputting the generated dialysis prescription. The prescription generation program PG1 may be a single computer program or a program group consisting of multiple computer programs. The prescription generation program PG1 may also partially use an existing library. The prescription generation program PG1 may be executed by a single computer or may be executed by multiple computers working together.

[0029] A computer program including the prescription generating program PG1 is provided by a non-transitory recording medium RM on which the computer program is readably recorded. The recording medium RM is a portable memory such as a CD-ROM, a USB memory, or an SD (Secure Digital) card. The control unit 11 reads various computer programs from the recording medium RM using a reading device (not shown) and stores the read various computer programs in the memory unit 12. The computer program including the prescription generating program PG1 may be provided via communication. The control unit 11 acquires the computer program including the prescription generating program PG1 by communication via the communication unit 13 and stores the acquired computer program in the memory unit 12.

[0030] The memory unit 12 also stores a learning model LM1 that has been trained to output information related to a dialysis prescription for a peritoneal dialysis patient (hereinafter also referred to as prescription information) when information related to at least one of edema, urine volume, and water removal volume is input. Specifically, the memory unit 12 stores information describing the learned learning model LM1, such as the layer structure of the model, the number and connection relationships of nodes constituting each layer, and weight coefficients and biases between nodes, as well as learned parameters, for the trained learning model LM1.

[0031] The communication unit 13 includes a communication interface for transmitting and receiving various data to and from an external device. The communication interface of the communication unit 13 may be a communication interface conforming to a communication standard such as Wi-Fi (registered trademark), LAN (Local Area Network), Bluetooth (registered trademark), ZigBee (registered trademark), 3G, 4G, 5G, or LTE (Long Term Evolution). When data to be transmitted is input from the control unit 11, the communication unit 13 transmits the data to the destination external device. When data transmitted from the external device is received, the communication unit 13 outputs the received data to the control unit 11. In this embodiment, examples of external devices are the patient terminal 20 and the doctor terminal 30.

[0032] The operation unit 14 includes operation devices such as a touch panel, a keyboard, and switches, and receives various operations and settings from a user, etc. The control unit 11 performs appropriate control based on various pieces of operation information provided by the operation unit 14, and stores setting information in the storage unit 12 as necessary.

[0033] The display unit 15 includes a display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display, and displays information to be presented in response to an instruction from the control unit 11 .

[0034] In this embodiment, the server device 10 may be a single computer, or may be a computer system configured with multiple computers and peripheral devices, etc. Furthermore, the server device 10 may be a virtual machine whose entity is virtualized, or may be a cloud.

[0035] 3 is a block diagram illustrating the internal configuration of the patient terminal 20. The patient terminal 20 is a computer such as a smartphone, a tablet terminal, or a personal computer, and includes a control unit 21, a storage unit 22, a communication unit 23, an operation unit 24, and a display unit 25.

[0036] The control unit 21 includes, for example, a CPU, a ROM, a RAM, etc. The CPU of the control unit 21 controls the operation of the various hardware components described above by loading various programs stored in advance in the ROM or the storage unit 22 into the RAM and executing them, thereby causing the entire device to function as the patient terminal 20 of the present disclosure.

[0037] The control unit 21 is not limited to the above configuration, and may be configured as a single piece of hardware (SoC: System On a Chip) that integrates a processor, memory, storage, communication interface, etc. The control unit 21 may also have functions such as a clock that outputs date and time information, a timer that measures the elapsed time from when an instruction to start measurement is given until when an instruction to end measurement is given, and a counter that counts numbers.

[0038] The storage unit 22 includes storage such as a memory and a hard disk. The storage unit 22 stores various computer programs executed by the control unit 21, data necessary for executing the computer programs, etc. The computer programs stored in the storage unit 22 include application programs for using the services provided by the server device 10.

[0039] The programs stored in the storage unit 22 may be provided by a non-transitory storage medium on which the programs are readably recorded. The storage medium is, for example, a portable memory such as a CD-ROM, a USB memory, or an SD card. The control unit 21 reads the various programs from the storage medium using a reading device (not shown) and installs the read programs in the storage unit 22. The programs stored in the storage unit 22 may also be provided by communication. In this case, the control unit 21 acquires the various programs through the communication unit 23 and installs the acquired programs in the storage unit 22.

[0040] The communication unit 23 includes a communication interface for connecting to the communication network NW. The communication interface included in the communication unit 23 is a communication interface conforming to a communication standard such as Wi-Fi (registered trademark), LAN, Bluetooth (registered trademark), ZigBee (registered trademark), 3G, 4G, 5G, LTE, etc. The communication unit 23 transmits various information to be notified to the outside and receives various information transmitted from the outside to the device itself.

[0041] The operation unit 24 is equipped with input devices such as a touch panel and operation buttons, and receives various types of operation information and setting information. The operation unit 24 outputs the received operation information and setting information to the control unit 21. The control unit 21 performs appropriate control based on the operation information input from the operation unit 24, and stores the setting information in the storage unit 22 as necessary.

[0042] The display unit 25 includes a display device such as a liquid crystal display or an organic EL display, and displays information to be presented to the patient based on a control signal output from the control unit 21 .

[0043] 4 is a block diagram illustrating the internal configuration of the doctor terminal 30. The doctor terminal 30 is a computer such as a personal computer, a tablet terminal, or a smartphone, and includes a control unit 31, a storage unit 32, a communication unit 33, an operation unit 34, and a display unit 35.

[0044] The control unit 31 includes, for example, a CPU, a ROM, a RAM, etc. The CPU of the control unit 31 controls the operation of the various hardware components described above by loading various programs stored in advance in the ROM or the storage unit 32 into the RAM and executing them, thereby causing the entire device to function as the doctor terminal 30 of the present disclosure.

[0045] The control unit 31 is not limited to the above configuration, and may be configured as a single piece of hardware (SoC: System On a Chip) that integrates a processor, memory, storage, communication interface, etc. The control unit 31 may also have functions such as a clock that outputs date and time information, a timer that measures the elapsed time from when an instruction to start measurement is given until when an instruction to end measurement is given, and a counter that counts numbers.

[0046] The storage unit 32 includes storage such as a memory and a hard disk. The storage unit 32 stores various computer programs executed by the control unit 31, data necessary for executing the computer programs, etc. The computer programs stored in the storage unit 32 include application programs for using the services provided by the server device 10.

[0047] The programs stored in the storage unit 32 may be provided by a non-transitory storage medium on which the programs are readably recorded. The storage medium is, for example, a portable memory such as a CD-ROM, a USB memory, or an SD card. The control unit 31 reads the various programs from the storage medium using a reading device (not shown) and installs the read programs in the storage unit 32. The programs stored in the storage unit 32 may also be provided by communication. In this case, the control unit 31 acquires the various programs through the communication unit 33 and installs the acquired programs in the storage unit 32.

[0048] The communication unit 33 includes a communication interface for connecting to the communication network NW. The communication interface included in the communication unit 33 is a communication interface conforming to a communication standard such as Wi-Fi (registered trademark), LAN, Bluetooth (registered trademark), ZigBee (registered trademark), 3G, 4G, 5G, LTE, etc. The communication unit 33 transmits various information to be notified to the outside and receives various information transmitted from the outside to the device itself.

[0049] The operation unit 34 is equipped with input devices such as a keyboard and a mouse, and receives various types of operation information and setting information. The operation unit 34 outputs the received operation information and setting information to the control unit 31. The control unit 31 performs appropriate control based on the operation information input from the operation unit 34, and stores the setting information in the storage unit 32 as necessary.

[0050] The display unit 35 includes a display device such as a liquid crystal display or an organic EL display, and displays information to be presented to medical personnel based on a control signal output from the control unit 31 .

[0051] The operation of the peritoneal management support system will be described below. When creating a dialysis prescription, the peritoneal management support system according to this embodiment acquires patient information from the patient terminal 20 used by the patient. The patient information is input by the patient himself / herself through the patient information input screen shown below.

[0052] Fig. 5 is a schematic diagram showing an example of a patient information input screen on the patient terminal 20. The patient information input screen shown in Fig. 5 shows an example of a screen that is displayed on the display unit 25 when the server device 10 is accessed from the patient terminal 20 and a predetermined operation is performed. This patient information input screen accepts information regarding edema, urine volume, and amount of water removed.

[0053] The state of edema is measured by the patient themselves by checking the indentation. Specifically, the patient applies pressure to the area where the bone, such as the tibia, is subcutaneously located with the thumb or the second through fourth fingers, and then visually and palpably examines the skin after releasing the pressure. An edema level of "0" indicates a normal state with no indentation. An edema level of "1+" indicates a slight indentation, but no visible distortion, and the indentation disappears rapidly. An edema level of "2+" indicates a moderately deep indentation that disappears within 10 to 15 seconds. An edema level of "3+" indicates a deep indentation that may last for more than one minute. An edema level of "4+" indicates a very deep indentation that may last for more than two to five minutes. The patient information input screen in Figure 5 allows the patient to input the state of edema, categorizing it into three categories: "0-1+," "2+," and "3+-4+." Alternatively, the state of edema may be input by classifying it into five states from "0" to "4+".

[0054] The amount of urine is measured by the patient themselves using a measuring cup or the like. The amount of urine (mL) per day is entered on the patient information input screen in Figure 5. The amount of fluid removed is calculated as the infusion amount minus the drainage amount. Here, the infusion amount represents the amount of dialysis fluid injected into the patient's body and is measured by weighing the bag containing the dialysis fluid before and after injection. The drainage amount represents the amount of dialysis fluid discharged from the patient's body and is measured by weighing the bag containing the drainage fluid. The amount of fluid removed (mL) per day is entered on the patient information input screen in Figure 5.

[0055] Figure 5 illustrates an example of a patient information input screen that accepts information regarding edema, urine volume, and water removal volume, but it may also be configured to accept any one or two of edema, urine volume, and water removal volume.

[0056] Furthermore, the patient information input screen may accept information on dry mouth or weight. Dry mouth is subjectively rated by the patient using an NRS (numerical rating scale) of 0 to 10. Alternatively, dry mouth may be rated on a two-level scale: dry mouth / no dry mouth. Weight is measured by the patient using a weighing scale.

[0057] Furthermore, some of the patient information may be entered by a medical professional. Fig. 6 is a schematic diagram showing an example of a patient information input screen on the doctor terminal 30. The patient information input screen shown in Fig. 6 is an example of a screen displayed on the display unit 35 when the doctor terminal 30 accesses the server device 10 and performs a predetermined operation. This patient information input screen accepts information regarding hANP, cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion.

[0058] hANP is a human atrial natriuretic peptide, and its concentration is measured by a blood test. The IRMA (immunoradiometric assay) method is used for concentration measurement. The reference value for hANP is, for example, 43.0 pg / mL. Because hAMP is secreted in response to atrial expansion stimulation, it exhibits abnormally high values ​​in diseases or pathological conditions that cause increased atrial pressure or increased body fluid volume. In the example screen shown in Figure 6, the hAMP concentration is entered as a numerical value (pg / mL).

[0059] The cardiothoracic ratio is an index that represents the ratio of the cardiac transverse diameter to the thoracic diameter, and is measured using a chest X-ray. A ratio of 50% or less is considered appropriate for men and 55% or less for women. When the extracellular fluid volume increases, the amount of water in the blood vessels also increases, causing the heart to become larger, resulting in an increase in the cardiothoracic ratio. In the example screen shown in Figure 6, the cardiothoracic ratio is entered as a numerical value (%).

[0060] Bioimpedance is the resistance (impedance) of a living body and is measured by a body composition analyzer. Body composition analyzers use a bioelectrical impedance analysis (BIA) method that takes advantage of the fact that almost no current flows through fat tissue, but that current flows easily through lean tissue containing water and electrolytes. By using this BIA method, a body composition analyzer can measure an indicator of body fluid surplus / deficiency (OH). The reference value for OH is -1.1 to 1.1 L. In the example screen shown in Figure 6, the indicator of body fluid surplus / deficiency is input as a numerical value (L).

[0061] The transperitoneal sodium removal amount is measured, for example, by collecting the dialysate (effluent) discharged from the patient's body during peritoneal dialysis and analyzing the components of the collected effluent. In the example screen of Figure 6, the transperitoneal sodium removal amount is input as a value (g) converted to salt.

[0062] The amount of urinary sodium removal is measured, for example, by collecting urine from a patient and analyzing the components of the collected urine. In the example screen of Figure 6, the amount of urinary sodium removal is input as a value (g) converted to salt.

[0063] 6 illustrates an example of a patient information input screen that accepts information on hANP, cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion, but the screen may accept any one or more of these pieces of information. Furthermore, it is not necessary to accept all of the information on a single input screen, and each piece of information may be accepted individually.

[0064] Furthermore, the patient information input screen may accept information regarding at least one of antihypertensive drugs, diuretics, blood pressure, urinary cortisol, urinary creatinine, skin sodium, and Na-MRI.

[0065] The patient information input to the patient terminal 20 and the doctor terminal 30 is transmitted to the server device 10 via the communication network NW. The server device 10 generates a dialysis prescription for the patient based on the patient information acquired from the patient terminal 20 and the doctor terminal 30. In this embodiment, the patient information is input to the learning model LM1, and a dialysis prescription is generated based on prescription information obtained by performing calculations using the learning model LM1.

[0066] 7 is a schematic diagram showing an example of the configuration of the learning model LM1. When patient information is input, the learning model LM1 is trained to output prescription information for the patient. Known learning models such as CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), LSTM (Long Short Term Memory), and autoencoder are used for the learning model LM1.

[0067] The learning model LM1 includes an input layer, an intermediate layer, and an output layer. Each layer includes one or more nodes. Patient information is input to the nodes of the input layer. Information regarding at least one of edema, urine volume, and fluid removal volume is input to the nodes of the input layer. The information input to the nodes of the input layer may further include information regarding at least one of hANP, cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion. Furthermore, the information input to the nodes of the input layer may include information regarding at least one of the patient's weight, dry mouth, antihypertensive medication, diuretic medication, and blood pressure. Furthermore, the information input to the nodes of the input layer may include information regarding at least one of urinary cortisol, urinary creatinine, skin sodium, and Na-MRI.

[0068] The intermediate layer of the learning model LM1 is composed of one or more layers. Each node in the intermediate layer is connected to the nodes in the previous and next layers. The activity level of each node and the strength of the connections between nodes (weighting coefficients) are determined during the learning process. The nodes in each layer output values ​​calculated based on the weighting coefficients and activity levels to the next node.

[0069] The output layer calculates probabilities using a softmax function based on values ​​input from the intermediate layer and outputs information (prescription information) based on the calculated probabilities. For example, the output layer calculates the probability that the type of peritoneal dialysis fluid to be prescribed is "solution A," "solution B," ..., or "solution X," and outputs information based on the calculated probabilities. Alternatively, the output layer may calculate the probability that the number of peritoneal dialysis sessions on the Nth day (N is a natural number) is one, two, ..., or n (n is, for example, 4), and output information based on the calculated probabilities. The output layer may also output both information related to the type of peritoneal dialysis and information related to the number of peritoneal dialysis sessions per day.

[0070] Such a learning model LM1 is generated by acquiring a data set containing patient information and a dialysis prescription (correct answer data) prescribed by a doctor for a patient who has undergone appropriate peritoneal dialysis, and using the acquired data set as training data to learn with an existing learning algorithm. The trained learning model LM1 is stored in the memory unit 12 of the server device 10.

[0071] When the control unit 11 of the server device 10 acquires patient information from the patient terminal 20 or the doctor terminal 30, it inputs the acquired patient information into the learning model LM1, performs calculations, and acquires prescription information as the calculation result by the learning model LM1.

[0072] The control unit 11 of the server device 10 generates a dialysis prescription for the patient based on the prescription information output from the learning model LM1. Fig. 8 is a schematic diagram showing an example of a dialysis prescription generated by the server device 10. Fig. 8 shows the progress of the dialysis prescription over the course of peritoneal dialysis therapy. In the example of Figure 8, peritoneal dialysis using a "low-glucose concentration peritoneal dialysis solution" is performed twice a day during period A (a period when urine output is sufficient), the number of peritoneal dialysis sessions using the "low-glucose concentration peritoneal dialysis solution" is increased to three during period B (a period when the amount of fluid removed needs to be increased due to a tendency toward fluid overload), the number of peritoneal dialysis sessions using the "low-glucose concentration peritoneal dialysis solution" is increased to four during period C (a period when urine output is slightly decreasing), the "low-glucose concentration peritoneal dialysis solution" is gradually replaced with a "medium-glucose concentration peritoneal dialysis solution" during period D (a period when the decrease in the amount of fluid removed is accelerating), and the first "medium-glucose concentration peritoneal dialysis solution" is replaced with an "icodextrin-containing peritoneal dialysis solution" during period E (a period when urine output is insufficient and the amount of fluid removed is insufficient with the medium-glucose concentration peritoneal dialysis solution alone). The example also shows an estimated "present" time point within a long-term peritoneal dialysis therapy period. In the example of Figure 8, the transition of the amount of urine and the amount of water removed per day is also shown. These values ​​are estimated values ​​when peritoneal dialysis therapy is performed as shown in the figure, and represent values ​​calculated by the control unit 11 according to the type of peritoneal dialysis fluid and the amount of fluid injected.

[0073] The control unit 11 generates a dialysis prescription as shown in Fig. 8 as a document with images and outputs the generated dialysis prescription to an external device. The output destination may be the display unit 15 of the information processing device 10, the patient terminal 20, or the doctor terminal 30. The dialysis prescription shown in Fig. 8 represents a dialysis prescription conforming to incremental peritoneal dialysis (PD). Incremental PD is a stepwise peritoneal dialysis introduction method in which renal replacement therapy is initiated with a prescription for fewer PD bag exchanges in the early stages of introduction while residual renal function is maintained, and the amount of dialysis is increased according to the degree of decline in residual renal function.

[0074] 8 shows an example of a dialysis prescription including the type of peritoneal dialysis solution to be prescribed and the number of peritoneal dialysis sessions per day, the control unit 11 may generate a dialysis prescription including either the type of peritoneal dialysis solution to be prescribed or the number of peritoneal dialysis sessions per day. Alternatively, the control unit 11 may generate a dialysis prescription (see FIG. 12 ) including replacing the currently prescribed peritoneal dialysis solution with a different type of peritoneal dialysis solution, instead of the above-mentioned dialysis prescription, and output the generated dialysis prescription.

[0075] 9 is a flowchart illustrating the procedure of the process executed by the server device 10. The server device 10 acquires patient information from the patient terminal 20 and the doctor terminal 30 via the communication network NW (step S101). The patient information acquired by the server device 10 includes information on at least one of edema, urine volume, and water removal volume. In addition to this information, information such as hANP, cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion may also be acquired.

[0076] The control unit 11 of the server device 10 inputs the acquired patient information into the learning model LM1 and executes calculations using the learning model LM1 (step S102). The control unit 11 acquires prescription information as a calculation result using the learning model LM1 (step S103).

[0077] The control unit 11 generates a dialysis prescription for the patient based on the acquired prescription information (step S104). The calculation result by the learning model LM1 includes information on at least one of the type of peritoneal dialysis solution to be prescribed for the patient and the number of peritoneal dialysis sessions per day. Therefore, the control unit 11 can generate a dialysis prescription based on this information. In one example, the control unit 11 can generate a dialysis prescription as shown in FIG. 8.

[0078] The control unit 11 outputs the generated dialysis prescription (step S105). For example, the control unit 11 can display the generated dialysis prescription on the display unit 15. The control unit 11 may also transmit the generated dialysis prescription to the patient terminal 20 or the doctor terminal 30. The dialysis prescription transmitted from the server device 10 reaches the patient terminal 20 or the doctor terminal 30 via the communication network NW. When the patient terminal 20 receives the dialysis prescription transmitted from the server device 10, it displays the received dialysis prescription on the display unit 25. When the doctor terminal 30 receives the dialysis prescription transmitted from the server device 10, it displays the received dialysis prescription on the display unit 35.

[0079] As described above, in this embodiment, a dialysis prescription for a patient can be presented to the patient himself / herself or a medical professional. By checking the screen displayed on the display unit 25 of the patient terminal 20, the patient can understand the appropriate dialysis prescription according to his / her bodily fluid status. By checking the screen displayed on the display unit 35 of the doctor terminal 30, medical professionals can use it as a community tool to provide guidance on appropriate peritoneal management.

[0080] In this embodiment, the dialysis prescription is sent directly from the server device 10 to the patient terminal 20, but it may also be sent to the doctor terminal 30 before being sent to the patient terminal 20, and then sent to the patient terminal 20 after receiving approval from the doctor.

[0081] In this embodiment, the server device 10 is configured to execute calculations using the learning model LM1 and generate a dialysis prescription using the calculation results of the learning model LM1, but the patient terminal 20 and the doctor terminal 30 may each execute calculations using the learning model LM1 and generate a dialysis prescription using the calculation results of the learning model LM1. In this case, the prescription generation program PG1 and the learning model LM1 are installed in the patient terminal 20 and the doctor terminal 30, respectively.

[0082] Furthermore, a language generation model may be used as the learning model LM1. The learning model LM1 based on the language generation model is trained to output sentences related to a peritoneal dialysis prescription in response to input patient information. Such a learning model LM1 is generated, for example, by fine-tuning an existing large-scale language model. In response to input patient information, the learning model LM1 based on the language generation model outputs sentences such as, "At this time, please perform dialysis using a low-glucose concentration peritoneal dialysis solution once a day. Since urine volume is tending to decrease, please consider performing dialysis using a low-glucose concentration peritoneal dialysis solution twice a day at the next outpatient visit..." The control unit 11 of the server device 10 generates a dialysis prescription based on the sentences output from the learning model LM1 and outputs the dialysis prescription by displaying it on the display unit 15 or transmitting it to the patient terminal 20 or the doctor terminal 30.

[0083] (Embodiment 2) In embodiment 2, a configuration will be described in which an image showing the patient's bodily fluid status is generated and output together with a dialysis prescription. The overall configuration of the peritoneal management support system and the internal configuration of each device constituting the system are the same as those in embodiment 1, and therefore a description thereof will be omitted.

[0084] The server device 10 according to the second embodiment generates an image representing the bodily fluid status of a patient based on patient information. For example, when the server device 10 receives patient information, the server device 10 can generate an image representing the bodily fluid status using a learning model LM2 that has been trained to output information relating to the patient's bodily fluid status.

[0085] FIG. 10 is a schematic diagram showing an example configuration of the learning model LM2. When patient information is input, the learning model LM2 is trained to output information about the patient's bodily fluid status. As with the learning model LM1 described in embodiment 1, the learning model LM2 uses known learning models such as CNN, RNN, LSTM, and autoencoder. The learning model LM2 includes an input layer, an intermediate layer, and an output layer. In response to patient information input to the input layer, the intermediate layer performs calculations, and the output layer outputs information about the bodily fluid status. For example, the output layer calculates the probability that the bodily fluid volume is normal, the probability that it is excessive, and the probability that it is excessive, and outputs information based on the probabilities. For example, the learning model LM2 may output information that determines whether the patient's bodily fluid volume is normal, tends to be excessive, or is excessive, in response to input patient information. Such a learning model LM2 is generated by learning with an existing learning algorithm using, as training data, a dataset including patient information such as edema, urine volume, and amount of water removed obtained from the patient, and actual measurements (ground truth values) of body fluid volume measured using a measuring device such as a body composition analyzer. The memory unit 12 of the server device 10 stores the learned learning model LM2.

[0086] When the control unit 11 of the server device 10 acquires patient information from the patient terminal 20 or the doctor terminal 30, it inputs the acquired patient information into the learning model LM2, executes calculations, and acquires the calculation results from the learning model LM2. In the second embodiment, the calculation results from the learning model LM2 acquire information that determines whether the patient's body fluid volume is normal, tends to be excessive, or is excessive.

[0087] The control unit 11 of the server device 10 generates images representing the patient's bodily fluid status based on the calculation results of the learning model LM2. FIGS. 11A-11C are schematic diagrams showing examples of generated images. Each image depicts a first container V1 containing interstitial fluid, a second container V2 containing plasma, a flow path P1 connecting the first container V1 and the second container, and an excretion path P2 through which fluid is discharged from the second container. The interstitial fluid contained in the first container V1 and the plasma contained in the second container V2 can flow from one container to the other through the flow path P1. The amount of sodium excreted as urine is depicted as the amount of fluid discharged from this excretion path P2. The amount of sodium removed by dialysis is depicted by the number of ">" symbols.

[0088] 11A shows that sodium removal by peritoneal dialysis is sufficient and that body fluid management is good. When the learning model LM2 determines that the patient's body fluid volume is normal, the control unit 11 generates an image as shown in FIG.

[0089] 11B shows that sodium removal by peritoneal dialysis is insufficient, and that fluid management is somewhat poor. When the learning model LM2 determines that the patient's fluid volume is excessive, the control unit 11 generates an image showing that the interstitial fluid volume and plasma volume are somewhat high, as shown in FIG.

[0090] 11C shows that sodium removal by peritoneal dialysis is not working properly, indicating poor fluid management. When the learning model LM2 determines that the patient has an excess amount of fluid, the control unit 11 generates an image showing the first container V1 and the second container V2 filled with interstitial fluid and plasma, respectively, as shown in FIG.

[0091] In this embodiment, patient information is input into learning model LM2 to obtain information regarding the body fluid status, and an image representing the patient's body fluid status is generated based on the obtained information. However, it is also possible to use an image generation learning model such as GAN (Generative Adversarial Network) or PGGAN (Progressive Growing of GANs) to generate an image representing the patient's body fluid status directly from the patient information.

[0092] In the second embodiment, an image representing the patient's body fluid status is generated and output together with the dialysis prescription. FIGS. 12 and 13 are schematic diagrams illustrating output examples in the second embodiment. FIG. 12 shows an example of a dialysis prescription generated for a patient with good body fluid management. This example shows a dialysis prescription that includes replacing the peritoneal dialysis fluid in the current prescription (solution A in the example of FIG. 12 ) with a different type of peritoneal dialysis fluid (a low-Na solution in the example of FIG. 12 ). The example of FIG. 12 also displays a summary of the body fluid management, stating that although there is no fluid overload, the amount of Na accumulation has increased slightly and suggesting replacing solution A with a low-Na solution. This sentence may be a standard phrase or may be generated by a language generation model.

[0093] Fig. 13 shows an example of a dialysis prescription generated for a patient with somewhat poor fluid management. This example shows a dialysis prescription that includes replacing the peritoneal dialysis fluid in the current prescription (liquid B in the example of Fig. 13) with a different type of peritoneal dialysis fluid (icodextrin solution in the example of Fig. 13). The example of Fig. 13 also shows a summary of fluid management, stating that there is a tendency for fluid overload, that sodium accumulation is increasing, and that it is suggested to replace liquid B with icodextrin solution. This sentence may be a standard phrase or may be generated by a language generation model.

[0094] As described above, in the second embodiment, a dialysis prescription is presented to a patient along with an image showing the patient's bodily fluid status, allowing the patient to intuitively understand their own bodily fluid status by checking the image displayed on the display unit 25 of the patient terminal 20. Medical professionals can understand the patient's bodily fluid status by checking the image displayed on the display unit 35 of the doctor terminal 30, and can use it as a community tool for providing guidance on appropriate bodily fluid management.

[0095] The embodiments disclosed herein are illustrative in all respects and should not be considered limiting. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims.

[0096] REFERENCE SIGNS LIST 10 Server device 11 Control unit 12 Memory unit 13 Communication unit 14 Operation unit 15 Display unit 20 Patient terminal 21 Control unit 22 Memory unit 23 Communication unit 24 Operation unit 25 Display unit 30 Doctor terminal 31 Control unit 32 Memory unit 33 Communication unit 34 Operation unit 35 Display unit PG1 Prescription generation program LM1 Learning model

Claims

1. A computer program for causing a computer to execute the following processes: acquiring information about a peritoneal dialysis patient, including at least one of edema, urine volume, and amount of water removed; inputting the acquired information into a learning model trained to output information related to a dialysis prescription for the peritoneal dialysis patient when the information about the peritoneal dialysis patient is input, executing a calculation using the learning model; generating a dialysis prescription based on the information output from the learning model; and outputting the generated dialysis prescription.

2. The computer program according to claim 1, wherein the dialysis prescription includes at least one of the type of peritoneal dialysis fluid to be prescribed and the number of peritoneal dialysis sessions per day.

3. The computer program of claim 1, wherein the dialysis prescription includes substituting a different type of peritoneal dialysis fluid for a currently prescribed peritoneal dialysis fluid.

4. The computer program according to claim 1, for causing the computer to execute the process of: generating an image showing a bodily fluid state of a peritoneal dialysis patient based on the acquired information of the peritoneal dialysis patient; and outputting the generated image.

5. The computer program according to claim 4, for causing the computer to execute a process of generating, as the image, an image that diagrammatically represents the amount of sodium stored and the amount of sodium excreted in the peritoneal dialysis patient.

6. The computer program according to claim 1, wherein the information on the peritoneal dialysis patient further includes information on at least one of hANP (human atrial natriuretic peptide), cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion.

7. The computer program according to claim 6, wherein the information on the peritoneal dialysis patient further includes information on at least one of the weight, dry mouth, antihypertensive drugs, diuretics, and blood pressure of the peritoneal dialysis patient.

8. The computer program according to claim 7, wherein the information on the peritoneal dialysis patient further includes information on at least one of urinary cortisol, urinary creatinine, skin sodium, and Na-MRI (nuclear magnetic resonance).

9. The computer program of claim 1, for causing the computer to execute a process in which the learning model is a language generation model trained to output sentences related to a peritoneal dialysis prescription, and the sentences related to the peritoneal dialysis prescription are output by inputting acquired information into the language generation model.

10. An information processing method in which a computer performs the following processes: acquire information about a peritoneal dialysis patient, including at least one of edema, urine volume, and amount of water removed; input the acquired information into a learning model that has been trained to output information related to a dialysis prescription for the peritoneal dialysis patient when the information about the peritoneal dialysis patient is input, execute a calculation using the learning model, generate a dialysis prescription based on the information output from the learning model, and output the generated dialysis prescription.

11. An information processing device comprising at least one processor, which acquires information on a peritoneal dialysis patient including at least one of edema, urine volume, and water removal volume, inputs the acquired information into a learning model trained to output information related to a dialysis prescription for the peritoneal dialysis patient when the information of the peritoneal dialysis patient is input, executes calculations using the learning model, generates a dialysis prescription based on the information output from the learning model, and outputs the generated dialysis prescription.

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