Patent Publication Number: US-2023139818-A1

Title: Blood purification system, controlling method, controlling program, learning device, and learning method

Description:
FIELD 
     The present disclosure relates to a blood purification system, a control method, a control program, a learning apparatus and a learning method. 
     BACKGROUND 
     Blood purification systems used for dialysis are constructed with multiple systems in a dialysis room provided in a medical facility such as a hospital, with blood purification being carried out for many patients in the dialysis room. In such a dialysis room, a server (central control means) is installed, which stores management data for patients undergoing blood purification (such as patient body weight and blood pressure), with the management data being sent to individual blood purification systems for display. 
     When a patient is to undergo dialysis, first the pre-dialysis body weight and blood pressure are measured using a weight scale or sphygmomanometer, and are sent to the central control means and stored as unique information for the patient. Based on the patient&#39;s unique information, the central control means determines the condition of the patient by computation with a predetermined formula, and the determined condition is transmitted to a blood purification system to allow optimal blood purification for each patient. 
     PTL 1, for example, discloses a blood purification system having multiple monitoring devices installed in the dialysis room of a medical facility such as a hospital. 
     CITATION LIST 
     Patent Literature 
     
         
         Patent Literature 1: Japanese Unexamined Patent Publication No. 2012-249748 
       
    
     SUMMARY 
     Achieving more efficient purification of blood is a desired goal for blood purification systems. 
     An object of a blood purification system, a control method, a control program, a learning apparatus or a learning method is to enable more achieve more efficient purification of blood. 
     According to some embodiments, a blood purification system includes a line through which a liquid containing blood or filtrate flows, a plasma separation device to separate a plasma component from blood flowing through the line, a factor separating device to separate a factor component which is a pathogenic factor from the plasma component, a detector to detect blood information relating to blood flowing through the line, a liquid control mechanism to control flow of liquid in the line based on control parameters, a parameter acquisition module to input the blood information detected by the detector into a learning model trained to output predetermined control parameters when predetermined blood information is input, and acquire control parameters output from the learning model, and a control module to control the liquid control mechanism based on the control parameters acquired by the parameter acquisition module. The blood information includes at least one from among a degree of blood flow turbulence, blood vorticity, degree of cardiac murmur, blood pressure, blood pressure loss, plasma pressure, filtrate pressure, blood flow, plasma flow, filtrate flow, fouling of the plasma separation device, fouling of the factor separating device, blood hematocrit value, filtrate albumin concentration, degree of hemolysis and blood pressure of a patient connected to the line. 
     Preferably, the learning model has an action value function using the blood information as a state, and control based on the control parameter as an action, and the action value function is updated based on a reward set so as to be greater as change in the blood information is smaller. 
     Preferably, the liquid control mechanism includes a magnetic regulator to apply a magnetic field to blood in a predetermined direction, a pump to control the flow of liquid in the line, or a resistance member to apply resistance to the line, and the control parameters include at least one from among magnetic field strength applied by the magnetic regulator, drive amount of the pump and a value of resistance applied by the resistance member. 
     Preferably, the blood purification system includes a storage to store the learning model in association with product data for the plasma separation device, data relating to blood flowing through the line, clearance data by the blood purification system, causative substance removal, or data related to antithrombogenicity. 
     Preferably, the blood purification system includes a generation module to generate the learning model based on the blood information detected by the detector before and after controlling the liquid control mechanism based on a specific control parameter, and the specific control parameter. 
     Preferably, the blood purification system is an extracorporeal circulation blood purification system. 
     Preferably, the blood purification system carries out apheresis by double filtration plasmapheresis. 
     According to some embodiments, a control method of a blood purification system including a line through which a liquid containing blood or filtrate flows, a plasma separation device to separate a plasma component from blood flowing through the line, a factor separating device to separate a factor component which is a pathogenic factor from the plasma component, a detector to detect blood information relating to blood flowing through the line, a liquid control mechanism to control flow of liquid in the line based on control parameters, includes inputting the blood information detected by the detector into a learning model trained to output predetermined control parameters when predetermined blood information is input, and acquire control parameters output from the learning model, and controlling the liquid control mechanism based on the acquired control parameters. The blood information includes at least one from among a degree of blood flow turbulence, blood vorticity, degree of cardiac murmur, blood pressure, blood pressure loss, plasma pressure, filtrate pressure, blood flow, plasma flow, filtrate flow, fouling of the plasma separation device, fouling of the factor separating device, blood hematocrit value, filtrate albumin concentration, degree of hemolysis and blood pressure of a patient connected to the line. 
     According to some embodiments, a control program of a computer included in a blood purification system including a line through which a liquid containing blood or filtrate flows, a plasma separation device to separate a plasma component from blood flowing through the line, a factor separating device to separate a factor component which is a pathogenic factor from the plasma component, a detector to detect blood information relating to blood flowing through the line, a liquid control mechanism to control flow of liquid in the line based on control parameters, causes the computer to execute a process. The process includes inputting the blood information detected by the detector into a learning model trained to output predetermined control parameters when predetermined blood information is input, and acquire control parameters output from the learning model, and controlling the liquid control mechanism based on the acquired control parameters. The blood information includes at least one from among a degree of blood flow turbulence, blood vorticity, degree of cardiac murmur, blood pressure, blood pressure loss, plasma pressure, filtrate pressure, blood flow, plasma flow, filtrate flow, fouling of the plasma separation device, fouling of the factor separating device, blood hematocrit value, filtrate albumin concentration, degree of hemolysis and blood pressure of a patient connected to the line. 
     According to some embodiments, a learning apparatus includes a data acquisition module to acquire, in a blood purification system including a line through which a liquid containing blood or filtrate flows, a plasma separation device to separate a plasma component from blood flowing through the line, a factor separating device to separate a factor component which is a pathogenic factor from the plasma component, a detector to detect blood information relating to blood flowing through the line, a liquid control mechanism to control flow of liquid in the line based on control parameters, a plurality of sets of the blood information and the control parameters, a generation module to generate a learning model trained to output predetermined control parameters when predetermined blood information is input, using the sets acquired by the a data acquisition module, and an output control module to output information relating to the learning model. The blood information includes at least one from among a degree of blood flow turbulence, blood vorticity, degree of cardiac murmur, blood pressure, blood pressure loss, plasma pressure, filtrate pressure, blood flow, plasma flow, filtrate flow, fouling of the plasma separation device, fouling of the factor separating device, blood hematocrit value, filtrate albumin concentration, degree of hemolysis and blood pressure of a patient connected to the line. 
     Preferably, the learning apparatus further includes a communication device to communicate with multiple blood purification systems, and the data acquisition module acquires the sets by receiving it from the multiple blood purification systems via the communication device. 
     Preferably, the data acquisition module controls the liquid control mechanism based on specific control parameters, and acquires blood information detected by the detector before and after control of the liquid control mechanism, based on the specific control parameter, to acquire the sets. 
     According to some embodiments, a learning method includes acquiring, by a computer, in a blood purification system including a line through which a liquid containing blood or filtrate flows, a plasma separation device to separate a plasma component from blood flowing through the line, a factor separating device to separate a factor component which is a pathogenic factor from the plasma component, a detector to detect blood information relating to blood flowing through the line, a liquid control mechanism to control flow of liquid in the line based on control parameters, a plurality of sets of the blood information and the control parameters, generating, by the computer, a learning model trained to output predetermined control parameters when predetermined blood information is input, using the acquired sets, and outputting, by the computer, information relating to the learning model. The blood information includes at least one from among a degree of blood flow turbulence, blood vorticity, degree of cardiac murmur, blood pressure, blood pressure loss, plasma pressure, filtrate pressure, blood flow, plasma flow, filtrate flow, fouling of the plasma separation device, fouling of the factor separating device, blood hematocrit value, filtrate albumin concentration, degree of hemolysis and blood pressure of a patient connected to the line. 
     According to some embodiments, a control program of a computer, causes the computer to execute a process. The process includes acquiring in a blood purification system including a line through which a liquid containing blood or filtrate flows, a plasma separation device to separate a plasma component from blood flowing through the line, a factor separating device to separate a factor component which is a pathogenic factor from the plasma component, a detector to detect blood information relating to blood flowing through the line, a liquid control mechanism to control flow of liquid in the line based on control parameters, a plurality of sets of the blood information and the control parameters, generating a learning model trained to output predetermined control parameters when predetermined blood information is input, using the acquired sets, and outputting information relating to the learning model. The blood information includes at least one from among a degree of blood flow turbulence, blood vorticity, degree of cardiac murmur, blood pressure, blood pressure loss, plasma pressure, filtrate pressure, blood flow, plasma flow, filtrate flow, fouling of the plasma separation device, fouling of the factor separating device, blood hematocrit value, filtrate albumin concentration, degree of hemolysis and blood pressure of a patient connected to the line. 
     The blood purification system, the control method, the control program, the learning apparatus and the learning method can achieve more efficient purification of blood. 
     The object and advantages of the invention will be realized and attained by means of the elements and combinations, in particular, described in the claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory, and are not restrictive of the invention, as claimed. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a diagram showing the general configuration of a management system  100  according to an embodiment. 
         FIG.  2    is a schematic view of a blood purification unit  14 . 
         FIG.  3    is a diagram showing the general configuration of a blood purification system  40 . 
         FIG.  4    is a schematic view showing an example of the data structure of a result table  553 . 
         FIG.  5    is a diagram of the general configuration of a server  80 . 
         FIG.  6    is a flow chart showing an example of operation for learning processing by a control apparatus  50 . 
         FIG.  7    is a flow chart showing an example of operation for control processing by the control apparatus  50 . 
         FIG.  8    is a flow chart showing an example of operation for learning processing by the server  80 . 
         FIG.  9    is a schematic view of another blood purification unit  14 - 2 . 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, a blood purification system, a control method, a control program, a learning apparatus and a learning method according to an embodiment, will be described with reference to the drawings. However, it should be noted that the technical scope of the invention is not limited to these embodiments, and extends to the inventions described in the claims and their equivalents. 
       FIG.  1    is a diagram showing the general configuration of a management system  100  according to an embodiment. 
     As shown in  FIG.  1   , the management system  100  has one or more blood purification systems  40  and a server  80 . Each blood purification system  40  is connected with the server  80  in a mutually communicable manner via a network  70 . Each blood purification system  40  is an extracorporeal circulation blood purification system. Each blood purification system  40  carries out apheresis by Double Filtration Plasmapheresis (DFPP). Each blood purification system  40  has a blood purification unit  14  and a control apparatus  50 . The network  70  is a wired network such as the internet or an intranet. The network  70  may also be a wireless network such as a wireless LAN (Local Area Network). 
       FIG.  2    is a schematic view of a blood purification unit  14  in the blood purification system  40 . 
     As shown in  FIG.  2   , the blood purification unit  14  has a plasma separation device  1 , a blood supply line  3 , a blood return line  4 , a blood pump  5 , a factor separating device  7 , a plasma line  8 , a plasma pump  9 , a filtrate line  10 , a filtration pump  11 , a magnetic regulator  16 , a blood flow detector  17 , a hematocrit detector  18 , an albumin concentration detector  19 , first pressure gauges  21  to  23 , a second pressure gauge  25 , a third pressure gauge  26 , first flow meters  28 ,  29 , a second flow meter  30 , a third flow meter  31 , tubing systems  33 ,  33 ′, (three-way) valves  34 ,  34 ′, tubing systems  35 ,  35 ′ and a clamp  36 . The blood purification unit  14  is intended for use in a plasma separation device  1  in a clinical setting. The blood purification unit  14  carries out double filtration plasmapheresis for the purpose of removing high-molecular pathogens or pathogen-related substances. 
     The blood supply line  3 , blood return line  4 , plasma line  8 , filtrate line  10  and tubing systems  33 ,  33 ′ are examples of lines through which liquids containing blood or filtrate flow, as a blood circuit. Polyvinyl chloride tubes, for example, are used for the blood supply line  3 , blood return line  4 , plasma line  8 , filtrate line  10  and tubing systems  33 ,  33 ′. The blood supply line  3  and blood return line  4  use tubes with lengths and diameters such that the total volume of internally contained liquid is approximately 150 ml, for example. Other members may be used for the blood supply line  3 , blood return line  4 , plasma line  8 , filtrate line  10  and tubing systems  33 ,  33 ′. 
     The tubing system  33  is a blood circuit and has a blood collector  33   a  for collection of blood from a patient (animal or human). The tubing system  33 ′ is a blood circuit and has a blood return unit  33   b  directed toward the patient. The blood supply line  3  sends blood removed from the blood collector  33   a  to the plasma separation device  1  through the blood pump  5 . The blood return line  4  sends blood flowing out from the plasma separation device  1  to the blood return unit  33   b . The plasma line  8  is connected to a plasma outlet port  1   b  of the plasma separation device  1 , and it sends filtrate (liquid containing blood plasma components) that has flowed out from the plasma outlet port  1   b , to the factor separating device  7  through the plasma pump  9 . The filtrate line  10  is connected to the factor separating device  7 , and it sends filtrate that has flowed out from the factor separating device  7 , to the blood return line  4  through the filtration pump  11 . 
     The plasma separation device  1  separates blood plasma components and cell components from blood flowing through each line. The plasma separation device  1  comprises a plasma outlet port  1   a , plasma outlet port  1   b , blood inlet port  1   c , blood outlet port  1   d  and hollow membrane  1   e . The blood inlet port  1   c  is an inlet for blood removed from the patient. The hollow membrane  1   e  is composed of a bundle of numerous hollow fiber membranes through which blood that has flowed in through the blood inlet port  1   c  passes. The blood outlet port  1   d  is an outlet for blood passing through the hollow membrane  1   e  to the outside. The plasma outlet ports  1   a ,  1   b  are outlets for filtrate that has been filtered by the hollow membrane  1   e  and passed through the outer side of the hollow membrane  1   e , to the outside. The plasma outlet port  1   a  is a spare port. The blood plasma components separated by filtration using the hollow membrane  1   e  are discharged from the plasma outlet port  1   a  or  1   b , while the blood with concentrated cell components is discharged from the blood outlet port  1   d . The plasma separation device  1  used may be a plasma separation device comprising a polyethylene hollow fiber membrane having a nominal pore size of 0.3 μm, an inner diameter of 350 μm, a membrane thickness of 50 μm and a membrane area of 0.5 m 2  (“Plasma Flow OP-05” by Asahi Kasei Medical Co., Ltd.). 
     The factor separating device  7  separates factor components which are pathogenic factors from separated blood plasma components. The factor separating device  7  comprises a filtrate outlet port  7   a , filtrate outlet port  7   b , plasma outlet port  7   c , a plasma inlet port  7   d  and hollow membrane  7   e . The plasma inlet port  7   d  is an inlet for filtrate that has flowed out from the plasma outlet port  1   b  of the plasma separation device  1 . The hollow membrane  7   e  is composed of a bundle of numerous hollow fiber membranes through which filtrate that has flowed in through the plasma inlet port  7   d  passes. Adsorbent beads may also be used for the hollow membrane  7   e . The plasma outlet port  7   c  is an outlet for factor components which are pathogenic factors that have passed through the hollow membrane  7   e , to the outside. The filtrate outlet ports  7   a ,  7   b  are outlets for filtrate that has been filtered by the hollow membrane  7   e , to the outside. The filtrate outlet port  7   b  is a spare port. In  FIG.  2    the filtrate line  10  is connected to the filtrate outlet port  7   a , but for effective utilization of the hollow membrane or adsorbent beads in the factor separating device  7  it is more preferably connected to the filtrate outlet port  7   b . The factor separating device  7  used may be a MONET™ by Fresenius Medical Care Co., for example. 
     The blood pump  5  is provided in the blood supply line  3  and controls the flow of blood in the blood supply line  3 . The plasma pump  9  is provided in the plasma line  8  and controls the flow of plasma in the plasma line  8 . The filtration pump  11  is provided in the filtrate line  10  and controls the flow of filtrate in the filtrate line  10 . The blood pump  5 , plasma pump  9  and filtration pump  11  are provided in a manner allowing variation in their respective drive (output) amount based on control by the control apparatus  50 . A roller pump, for example, may be used for the blood pump  5 , plasma pump  9  and filtration pump  11 . Another publicly known pump may also be used for the blood pump  5 , plasma pump  9  and filtration pump  11 . 
     The magnetic regulator  16  is provided in a manner surrounding a predetermined location of the plasma separation device  1 , and it applies a magnetic field on the blood in the plasma separation device  1  in a predetermined direction. The magnetic regulator  16  may be provided in a manner surrounding the body of the patient connected to the blood supply line  3 , blood return line  4  or blood purification unit  14 , and it may apply a magnetic field in a predetermined direction on blood in the blood supply line  3 , blood return line  4  or patient&#39;s body. The magnetic regulator  16  has a cyclic magnet and applies a one-way magnetic field in the region inside the cyclic magnet, either parallel or reverse to the direction of blood flow. The magnetic regulator  16  is provided in a manner allowing variation in the strength of the applied magnetic field based on control by the control apparatus  50 . The magnetic field strength is set to be sufficient enough to reduce the blood viscosity by a predetermined amount and/or to inhibit the blood flow turbulence by a predetermined amount, at the location where the magnetic field is applied. The magnetic regulator  16  used may be an electromagnet, permanent magnet or superconducting magnet, for example. 
     The magnetic regulator  16  can inhibit clogging of blood cell components in the plasma separation device  1  and inhibit pressure increase in the plasma separation device  1  while also reducing blood flow viscosity and blood flow turbulence. The blood purification unit  14  can thus lower patient blood pressure, ameliorate hypertension and reduce incidence of cardiac murmur. A one-way magnetic field strength of less than 0.01 Tesla with the magnetic regulator  16  will produce a lower magnetic force effect on erythrocytes. A magnetic regulator  16  at greater than 100 Tesla, on the other hand, tends to be expensive and difficult to manage. The one-way magnetic field strength of the magnetic regulator  16  is therefore preferably between 0.01 and 100 Tesla, and more preferably between 1 and 10 Tesla. 
     The blood pump  5 , plasma pump  9 , filtration pump  11  and magnetic regulator  16  are examples of liquid control mechanisms to control liquid flow in each line of the blood purification unit  14 . 
     The blood flow detector  17  is provided at a predetermined location of the plasma separation device  1  and detects the direction, speed and/or speed distribution of blood flow in the plasma separation device  1 . The blood flow detector  17  may be provided at a predetermined location of the body of the patient connected to the blood supply line  3 , blood return line  4  or blood purification unit  14 , and it may detect the direction, speed and/or speed distribution of blood flow in the blood supply line  3 , blood return line  4  or patient&#39;s body. The blood flow detector  17  used may be an Aixplorer Ultrasonic Image Diagnosis Device by SuperSonic Imagine Co., for example. The blood purification unit  14  can rapidly and precisely detect blood flow abnormalities using the blood flow detector  17 . 
     The hematocrit detector  18  is provided in the blood return line  4 , and it measures hematocrit in blood in the blood return line  4 . The hematocrit is an index of the blood concentration, represented as the volume ratio of erythrocytes in whole blood. In order to confirm whether or not there is an abnormality in the amount of plasma permeating from the plasma separation device  1 , the hematocrit detector  18  is preferably provided near the blood outlet port  1   d . In order to confirm the difference between the hematocrit in blood flowing into the blood purification unit  14  and the hematocrit in blood flowing out from the blood purification unit  14 , the hematocrit detector  18  is preferably provided between the blood collector  33   a  and the first pressure gauge  23  in the blood supply line  3 , and also between a first flow meter  29  and the blood return unit  33   b  in the blood return line  4 . The hematocrit detector  18  used may be a StatStrip™ Hb/Hct by Nova Biomedical Co., for example. 
     The albumin concentration detector  19  is provided in the filtrate line  10  and measures albumin concentration in the filtrate in the filtrate line  10 . The albumin concentration detector  19  used may be a biochemical analyzer (DRI-CHEM NX700 by FujiFilm Corp.), for example. 
     The first pressure gauge  23  is provided between the blood collector  33   a  and blood pump  5  of the blood supply line  3 , and it measures blood pressure in the blood supply line  3 . The first pressure gauge  21  is provided between the blood pump  5  and plasma separation device  1  of the blood supply line  3 , and it measures blood pressure in the blood supply line  3 . The first pressure gauge  22  is provided between the plasma separation device  1  and blood return unit  33   b  of the blood return line  4 , and it measures blood pressure in the blood return line  4 . The first pressure gauge  21  and first pressure gauge  22  are able to constantly measure pressure input and output for the plasma separation device  1 , the first pressure gauge  21  and first pressure gauge  22  allowing measurement of blood pressure loss by the plasma separation device  1 . The second pressure gauge  25  is provided in the plasma line  8 , and it measures plasma pressure in the plasma line  8 . The third pressure gauge  26  is provided in the filtrate line  10 , and it measures filtrate pressure in the filtrate line  10 . The first pressure gauges  21  to  23 , second pressure gauge  25  and third pressure gauge  26  used may each be a known water pressure gauge such as a semiconductor piezo resistance diffusion pressure sensor, for example. 
     The first flow meter  28  is provided in the blood supply line  3  and measures blood flow in the blood supply line  3 . The first flow meter  29  is provided in the blood return line  4  and measures blood flow in the blood return line  4 . The second flow meter  30  is provided in the plasma line  8  and measures plasma flow in the plasma line  8 . The third flow meter  31  is provided in the filtrate line  10 , and it measures filtrate flow in the filtrate line  10 . With the first flow meters  28 ,  29 , second flow meter  30  and third flow meter  31 , it is possible to determine whether or not the flow rate in each line, controlled with a pump in each line, is a flow rate within a set range. The first flow meters  28 ,  29 , second flow meter  30  and third flow meter  31  used may be publicly known flow meters such as Coriolis mass flow meters, electromagnetic mass flow meters, or ultrasonic mass flow meters. 
     The blood flow detector  17 , hematocrit detector  18 , albumin concentration detector  19 , first pressure gauges  21  to  23 , second pressure gauge  25 , third pressure gauge  26 , first flow meters  28 ,  29 , second flow meter  30  and third flow meter  31  are examples of detectors (sensors) that detect blood information for blood flowing through each line of the blood purification unit  14 . 
     In a clinical setting, the blood purification unit  14  may include an anticoagulant syringe, bubble detector and alarm function. For safer use, the blood purification system  40  preferably comprises an electric power generator or battery to allow operation during disasters or power outages. 
     Blood removed from the patient through the blood collector  33   a  is sent by the blood pump  5  to the plasma separation device  1  through the blood supply line  3 . Blood sent to the plasma separation device  1  flows into the hollow membrane  1   e  from the blood inlet port  1   c , and flows out from the blood outlet port  1   d  into the blood return line  4 . Filtrate filtered by the hollow membrane  1   e  flows out from the plasma outlet port  1   b , through the plasma line  8 , and is sent by the plasma pump  9  to the factor separating device  7 . Filtrate sent to the factor separating device  7  flows from the plasma inlet port  7   d  into the hollow membrane  7   e , and factor components which are pathogenic factors are separated and flow out from the plasma outlet port  7   c . Filtrate filtered by the hollow membrane  7   e  flows out from the filtrate outlet port  7   a , and is sent by the plasma pump  9  to the blood return line  4  through the filtrate line  10 . Blood that has flowed out from the blood outlet port  1   d  to the blood return line  4  and filtrate that has been sent from the filtrate outlet port  7   a  to the blood return line  4  are returned to the patient through the blood return unit  33   b.    
       FIG.  3    is a diagram showing the general configuration of a blood purification system  40 . 
     The blood purification unit  14  of the blood purification system  40  also has a driving device  15 , ECG meter  41 , hemolysis measurement device  42 , heart rate monitor  43 , sphygmomanometer  44  and blood flow meter  45 , in addition to the configuration described above. 
     The driving device  15  includes one or more motors and drives the blood pump  5 , plasma pump  9  and filtration pump  11  based on a control signal from the control apparatus  50 , controlling flow of liquid in each line of the blood purification unit  14 . 
     The ECG meter  41  is mounted on a patient connected to the blood purification unit  14 , the patient electrocardiogram (heart rate waveform) is measured, and the measured electrocardiogram is outputted. The ECG meter  41  used may be a wearable ECG meter such as an Apple Watch (Series 4, 5, 6), for example. An Apple Watch (Series 4, 5, 6) has a crystal and electrodes, and it coordinates with an electrocardiogram application to record an electrocardiogram, similar to a type I induction electrocardiogram. The ECG meter  41  constantly detects heart rhythm and gives a notification when it has detected an irregular heart rhythm, as a sign of atrial fibrillation (AFib). 
     The hemolysis measurement device  42  is connected to a patient connected to the blood purification unit  14  and measures the degree of hemolysis of the patient. Hemolysis is destruction of blood cells, specifically erythrocytes. The hemolysis measurement device  42  used may be a blood leak detector incorporated into any commercially available dialysis monitoring device (such as a TR-3300 M by Toray Co., Ltd.). 
     The heart rate monitor  43  and sphygmomanometer  44  are mounted on patients connected to the blood purification unit  14 , measuring the patient pulse and blood pressure. The blood flow meter  45  is connected to a patient connected to the blood purification unit  14  and measures the circulating blood flow of the patient. The heart rate monitor  43 , sphygmomanometer  44  and blood flow meter  45  may be publicly known measuring devices. 
     The control apparatus  50  is an example of a learning apparatus and may be an information processing device such as a personal computer. The control apparatus  50  has an input device  51 , display device  52 , first communication device  53 , interface device  54 , first storage device  55  and first processing device  56 . The input device  51 , display device  52 , first communication device  53 , interface device  54 , first storage device  55  and first processing device  56  are mutually connected via a CPU (Central Processing Unit) bus. 
     The input device  51  has an input device such as a touch panel-type input device or a keyboard or a mouse and an interface circuit that acquires a signal from the input device, and it outputs an operation signal in response to input from the user. 
     The display device  52  has a display including a liquid crystal or organic EL (Electro-Luminescent) display, and an interface circuit that outputs image data onto the display to display the image data. 
     The first communication device  53  is an example of a communication device. The first communication device  53  has a wired communication interface circuit following a communication protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol). The first communication device  53  is connected in a communicable manner with a network  70  conforming to a communication standard such as Ethernet™. The first communication device  53  sends data received from the server  80  to the first processing device  56  via the network  70 , and transmits data received from the first processing device  56  through the network  70  to the server  80 . The first communication device  53  may have an antenna that sends and receives wireless signals and a wireless communication interface circuit following a communication protocol such as wireless LAN, and it may be connected in a communicable manner with the network  70  conforming to a communication standard such as wireless LAN. 
     The interface device  54  has an interface circuit compatible with a serial bus such as a USB (Universal Serial Bus). The interface device  54  is connected to a driving device  15 , magnetic regulator  16 , blood flow detector  17 , hematocrit detector  18 , albumin concentration detector  19 , first pressure gauges  21  to  23 , second pressure gauge  25 , third pressure gauge  26 , first flow meters  28 ,  29 , second flow meter  30 , third flow meter  31 , ECG meter  41 , hemolysis measurement device  42 , heart rate monitor  43 , sphygmomanometer  44  and blood flow meter  45  of the blood purification unit  14 , being provided in a communicable manner with each device with which it is connected. The interface device  54  sends data received from each connected device to the first processing device  56 , and transmits the data received from the first processing device  56  to each connected device. The interface device  54  may have an interface circuit compatible with a short range wireless communication standard such as Bluetooth, for connection in a wireless communicable manner with each member of the blood purification unit  14 . 
     The first storage device  55  is an example of a storage. The first storage device  55  has a memory device such as a RAM (Random Access Memory) or ROM (Read Only Memory), a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or optical disk. A computer program, database and table to be used for processing by the control apparatus  50  are stored in the first storage device  55 . The computer program may be installed in the first storage device  55  from a computer readable portable storage medium, using a well-known set-up program. The portable storage medium may be a CD-ROM (Compact Disc Read Only Memory) or DVD-ROM (Digital Versatile Disc Read Only Memory), for example. The computer program may also be installed from a designated server. 
     A learning model  551 , product data  552  and a result table  553 , etc., are stored as data in the first storage device  55 . The learning model  551  is a model for controlling the flow of blood in the blood purification unit  14 . The learning model  551  is generated by the first processing device  56  or received from the server  80 . The product data  552  are data relating to the blood purification unit  14 , and they indicate the separation performance (filtration performance) for blood plasma components and cell components by the plasma separation device  1  and separation performance (filtration performance) for factor components by the factor separating device  7 . The product data  552  is preset by the user using an input device  51 . The results of blood purification for each blood purification by the blood purification system  40  are stored in the result table  553 . The result table  553  will be described in detail below. 
     The first processing device  56  operates based on a program prestored in the first storage device  55 . The first processing device  56  is a CPU, for example. The first processing device  56  used may be a DSP (digital signal processor), LSI (large scale integration), ASIC (Application Specific Integrated Circuit) or FPGA (Field-Programmable Gate Array). The first processing device  56  is connected to an input device  51 , display device  52 , first communication device  53 , interface device  54  and first storage device  55 , and controls each of the devices. The first processing device  56  generates a learning model  551  and uses the generated learning model  551  to control the flow of blood in the blood purification unit  14 . 
     The first processing device  56  reads the computer program stored in the first storage device  55  and operates according to the read-out computer program. The first processing device  56  thus functions as a first data acquisition module  561 , a first generation module  562 , a first output control module  563 , a parameter acquisition module  564  and a control module  565 . The first data acquisition module  561 , first generation module  562  and first output control module  563  are examples of a data acquisition module, a generation module and an output control module, respectively. 
       FIG.  4    is a schematic view showing an example of the data structure of a result table  553 . 
     The relative ID No. (measurement ID), learning model, patient data, product data, blood data, clearance data, causative substance removal amount and antithrombogenicity are stored in the result table  553  for each blood purification by the blood purification system  40 . The learning model is a learning model  551  used for blood purification. The identification information or storage address of the learning model  551  used for blood purification may also be stored for the learning model. Patient data is data relating to the patient including the name, body height and body weight of the patient undergoing blood purification. The product data is product data  552  that has been preset by the user. 
     The blood data is data relating to blood flowing into each line of the blood purification unit  14 , and it includes the pulse, blood pressure, circulating blood flow, blood hematocrit or degree of hemolysis of the patient before blood purification. The clearance data is data relating to blood that has been purified by the blood purification system  40 , and it includes the pulse, blood pressure, circulating blood flow, blood hematocrit or degree of hemolysis of the patient after blood purification. The clearance data may also include blood pressure, blood pressure loss, plasma pressure, filtrate pressure, blood flow, plasma flow, filtrate flow or filtrate albumin concentration, which are measured during blood purification. Causative substance removal is the amount of causative substances removed by the blood purification unit  14 . Antithrombogenicity is the ability to control blood clotting activation, and it is the degree of thrombus formed in blood purified by the blood purification unit  14 . 
       FIG.  5    is a diagram showing the general configuration of a server  80 . 
     The server  80  is high-end computer of the control apparatus  50 , and is an example of a learning apparatus. The server  80  has a second communication device  81 , second storage device  82  and second processing device  83 . The second communication device  81 , second storage device  82  and second processing device  83  are mutually connected through a CPU bus. 
     The second communication device  81  is an example of a communication device for communication with multiple blood purification systems  40 . The second communication device  81  has a wired communication interface circuit following a communication protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol). The second communication device  81  is connected in a communicable manner with a network  70  conforming to a communication standard such as Ethernet™ The second communication device  81  sends data received from the control apparatus  50  to the second processing device  83  via the network  70 , and transmits data received from the second processing device  83  through the network  70  to the control apparatus  50 . The second communication device  81  may have an antenna that sends and receives wireless signals and a wireless communication interface circuit following a communication protocol such as wireless LAN, and it may be connected in a communicable manner with the network  70  conforming to a communication standard such as wireless LAN. 
     The second storage device  82  has a memory device such as a RAM or ROM, a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or optical disk. A computer program, database and table to be used for processing by the server  80  are stored in the second storage device  82 . The computer program may be installed in the second storage device  82  from a computer readable portable storage medium, using a well-known set-up program. The portable storage medium may be a CD-ROM or DVD-ROM, for example. The computer program may also be installed from a designated server. 
     A learning model  821 , etc., are stored as data in the second storage device  82 . The learning model  821  is generated by the second processing device  83  or received from the control apparatus  50 . 
     The second processing device  83  operates based on a program prestored in the second storage device  82 . The second processing device  83  is a CPU, for example. The second processing device  83  used may be a DSP, LSI, ASIC or FPGA. The second processing device  83  is connected to a second communication device  81  and second storage device  82 , and controls each of the devices. The second communication device  81  generates a learning model  821  and sends it to each control apparatus  50 . 
     The second processing device  83  reads the computer program stored in the second storage device  82  and operates according to the read-out computer program. The second processing device  83  thus functions as a second data acquisition module  831 , a second generation module  832  and a second output control module  833 . 
       FIG.  6    is a flow chart showing an example of operation for learning processing by a control apparatus  50 . 
     An example of operation for learning processing by the control apparatus  50  will now be explained with reference to the flow chart in  FIG.  6   . The flow of operation described below is carried out in cooperation with each of the elements of the control apparatus  50  primarily by the first processing device  56 , based on a program prestored in the first storage device  55 . 
     First, the first data acquisition module  561  acquires blood information relating to blood flowing through each line of the blood purification unit  14  (step S 101 ). The blood information includes the degree of blood flow turbulence, blood vorticity, degree of cardiac murmur, blood pressure, blood pressure loss, plasma pressure, filtrate pressure, blood flow, plasma flow, filtrate flow, fouling of the plasma separation device  1 , fouling of the factor separating device  7 , blood hematocrit, filtrate albumin concentration or degree of hemolysis, pulse, blood pressure or circulating blood flow of a patient. The blood information includes one or more of the aforementioned parameters. 
     The first data acquisition module  561  acquires the direction of blood flow in the blood purification unit  14  and its speed and/or speed distribution from the blood flow detector  17  via the interface device  54 , and calculates the degree of blood flow turbulence and blood vorticity based on the acquired information. For example, the first data acquisition module  561  calculates the Reynolds number for each blood flow detected by the blood flow detector  17 , and determines whether each blood flow is turbulent or laminar based on whether or not the calculated Reynolds number is equal to or more than a predetermined threshold value. The first data acquisition module  561  calculates the degree of turbulent flow as the ratio of the number of turbulent blood flows with respect to the total number of blood flows detected by the blood flow detector  17 . The first data acquisition module  561  also calculates the speed vector of blood flow based on the direction and speed of blood flow acquired from the blood flow detector  17 , and calculates the vorticity as the rotation of the vector field formed by the calculated speed vector. Vorticity is the state of rotating flow expressed as a quantity. 
     The first data acquisition module  561  acquires an electrocardiogram of the patient connected to the blood purification unit  14  from the ECG meter  41  via the interface device  54 , and calculates the degree of cardiac murmur based on the acquired electrocardiogram. Cardiac murmur is a disturbed temperament of heart noise out of harmony with the heart beat. Heart noise is a characteristic abnormal sound produced when blood flows through a cardiac valve or blood vessel near the heart (valve opening or closing). Abnormal sound is mainly produced due to a faulty cardiac valve. The first data acquisition module  561  calculates the degree of cardiac murmur as the occurrence rate of irregular heart rhythm, suggesting atrial fibrillation, in a heart rhythm represented in an acquired electrocardiogram. For example, the control apparatus  50  prestores in the first storage device  55  a waveform pattern for heart rhythm of atrial fibrillation. The first data acquisition module  561  uses publicly known pattern matching technology to detect waveforms similar to the heart rhythm for atrial fibrillation within a heart rhythm represented in the acquired electrocardiogram. The first data acquisition module  561  may also detect the P wave corresponding to each R wave in the heart rhythm represented in the acquired electrocardiogram, and based on the number of disappeared P waves, may detect occurrence of heart rhythms suggesting atrial fibrillation. 
     The first data acquisition module  561  also acquires blood pressure from first pressure gauges  21  to  23  via the interface device  54 . The first data acquisition module  561  also calculates blood pressure loss as the value of the blood pressure acquired from the first pressure gauge  21  minus the blood pressure acquired from the first pressure gauge  22 . The first data acquisition module  561  also acquires plasma pressure and filtrate pressure from the second pressure gauge  25  and third pressure gauge  26 , respectively, via the interface device  54 . The first data acquisition module  561  also acquires blood flow, plasma flow and filtrate flow from the first flow meters  28 ,  29 , second flow meter  30  and third flow meter  31 , respectively, via the interface device  54 . 
     The first data acquisition module  561  also calculates filtrate volume of the plasma separation device  1  per unit time from the start of operation of the blood purification unit  14  to the current time, based on the plasma flow acquired from the second flow meter  30 . The first data acquisition module  561  also calculates the current pressure difference between the plasma pressure acquired from the second pressure gauge  25  and the blood pressure acquired from the first pressure gauge  21 . The first data acquisition module  561  further calculates fouling of the plasma separation device  1 , as the value calculated for the filtrate volume divided by the calculated pressure difference, and further divided by unit time. Likewise, the first data acquisition module  561  calculates filtrate volume of the factor separating device  7  per unit time from the start of operation of the blood purification unit  14  to the current time, based on the filtrate flow acquired from the third flow meter  31 . The first data acquisition module  561  also calculates the current pressure difference between the filtrate pressure acquired from the third pressure gauge  26  and the plasma pressure acquired from the second pressure gauge  25 . The first data acquisition module  561  further calculates fouling of the factor separating device  7 , as the value calculated for the filtrate volume divided by the calculated pressure difference, and further divided by unit time. 
     The first data acquisition module  561  acquires the blood hematocrit value from the hematocrit detector  18 , via the interface device  54 . The first data acquisition module  561  also acquires the filtrate albumin concentration from the albumin concentration detector  19 , via the interface device  54 . The first data acquisition module  561  further acquires the degree of hemolysis, pulse, blood pressure and circulating blood flow of the patient connected to the blood purification unit  14 , from the hemolysis measurement device  42 , heart rate monitor  43 , sphygmomanometer  44  and blood flow meter  45 , respectively, via the interface device  54 . 
     The first data acquisition module  561  then acquires control parameters relating to the flow of liquid in the blood purification unit  14  (step S 102 ). The control parameters include the magnetic field strength applied to the blood by the magnetic regulator  16  in a predetermined direction, or the drive amount of the blood pump  5 , plasma pump  9  or filtration pump  11 . The control parameters include one or more of the aforementioned parameters. 
     For example, the control apparatus  50  stores various usable values in the first storage device  55  for each control parameter type, and the first data acquisition module  561  acquires the control parameters by successively reading the values stored in the first storage device  55 . The first data acquisition module  561  may also acquire control parameters by generating a random number value in usable ranges for each of the control parameter types. The control apparatus  50  may also store optimum values for the different control parameters in a first storage device  55  for each of the control parameter types, and the first data acquisition module  561  may acquire control parameters with fine adjustment of the optimum values. 
     The first data acquisition module  561  then controls the magnetic regulator  16 , or controls the blood pump  5 , plasma pump  9  or filtration pump  11  via the driving device  15 , based on the acquired control parameters (step S 103 ). 
     The first data acquisition module  561  subsequently acquires blood information in the same manner as for the processing in step S 101  (step S 104 ). The first data acquisition module  561  thus controls the liquid control mechanisms of the blood purification unit  14  based on the control parameters acquired in step S 102 , and acquires blood information detected by the detectors before and after control of the liquid control mechanisms, based on the control parameters. The first data acquisition module  561  thereby acquires sets of blood information and control parameters. 
     The first generation module  562  next sets a reward for the learning model  551  based on the blood information acquired by the first data acquisition module  561  (step S 105 ). 
     The learning model  551  used by the blood purification system  40  is learned by reinforcement learning, for example. Reinforcement learning by the learning model  551  may be Q-learning, for example. The learning model  551  has an action value function that determines the value of control based on different control parameters, with the blood purification unit  14  as the environment, the control apparatus  50  as the agent, blood information as the condition and control of the liquid control mechanism based on the control parameter as the action. Each item of blood information is a physical quantity that varies with change in each control parameter. That is, a correlation exists between each item of blood information and each control parameter, and the first generation module  562  can generate the learning model  551  allowing suitable control parameters to be set, corresponding to the state of the blood purification unit  14 . 
     With Q-learning, the environment state “s” and the action “a” selected in that state “s” are used as independent variables for learning of the action value function Q(s, a), which represents the value of action with selection of action “a” at state “s”. In Q-learning, learning begins in a state with unknown correlation between state “s” and action “a”, and selection of different action “a” values for a given state “s” is repeated by trial and error for repeated update of the action value function Q. The configuration for Q-learning is such that a reward r is obtained when an action “a” has been selected for a certain state “s”, the action value function Q being learned so as to select actions “a” that give higher rewards r. Updating of the action value function Q is represented by the following formula. 
     
       
         
           
             
                 
               
                 
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     In this formula, s t  and a t  are, respectively, the state and action at each time t, the state with action a t  varying from s t  to s t+1 . The value of r t+1  is the reward obtained when the state changes from s t  to s t+1 . The variable maxQ is the value of Q when carrying out action “a” which gives the maximum value of Q at time t+1 (as assumed at time t). The variables α and γ are the learning coefficient and discount factor, respectively, which are arbitrarily set to 0&lt;α≤1 (normally 0.9 to 0.99) and 0&lt;γ≤1 (normally about 0.1). 
     This update formula indicates that Q(s t , a t ) is increased if, compared to the evaluation value Q(s t , a t ) for action a t  at a given state s t , the evaluation value Q(s t+i , maxa t+1 ) for the optimal action maxa t+1  at the next state s t+1  is greater, and Q(s t , a t ) is decreased if it is smaller. In other words, with this update formula the value of a given action in a given state can approach the value of the optimal action at the next step. The action value function is therefore updated so as to increase the action value for action (operation conditions) to create the most optimal state for operation of the blood purification unit  14 , i.e. to increase the action value of the optimal action (operation conditions) of the blood purification unit  14 . 
     The initial value for each action value function Q is arbitrarily set. The first generation module  562  uses the number of executions of step S 106  as the time in the formula. The first generation module  562  identifies the blood information before control acquired in step S 101  as state s t , specifies control based on the control parameter acquired in step S 102  as action a t , and specifies the blood information after control acquired in step S 104  as state s t+1 . 
     The first generation module  562  sets a reward r based on the change in blood information (state) before and after control. The first generation module  562  sets the reward r so that the reward r is greater as change (difference) in the blood information is smaller after control with respect to the blood information before control, and the reward r is smaller as change (difference) is larger. For example, setting one or more thresholds in advance for each blood information item, the first generation module  562  sets a reward r by comparing the amount of change in each blood information item with the respective threshold. When the amount of change is greater than the threshold maximum, the first generation module  562  may set the reward r to be 0. The first generation module  562  thus sets the reward r so that, when the blood purification unit  14  has been controlled based on a specific control parameter, the reward r is higher with a more stable state of the blood in the blood purification unit  14 . This allows the first generation module  562  to generate a learning model  551  trained so as to select a control parameter that results in a more stable blood state in the blood purification unit  14  with respect to the current blood state. 
     The first generation module  562  then updates the action value function based on the sets of the blood information acquired by the first data acquisition module  561  and the control parameters, and the set reward (step S 106 ). The first generation module  562  updates the action value function Q(s t , a t ) according to the aforementioned formula based on the specific state s t , action a t , state s t+1  and set reward r. 
     The first generation module  562  then determines whether or not the conditions for completion of learning have been satisfied (step S 107 ). The conditions for completion of learning are set beforehand, as being when the total number of updates of the action value function is equal to or more than a predetermined number, or when the maximum or minimum number of updates for each action value function is equal to or more than a predetermined number. When the conditions for completion of learning have not been satisfied, the first data acquisition module  561  and first generation module  562  return to the processing of step S 101  and repeat the processing of steps S 101  to S 107 . The first data acquisition module  561  thus acquires a plurality of sets of blood information and control parameters, and the first generation module  562  uses each set acquired by the first data acquisition module  561  to update the action value function. From the second processing onward, the processing in step S 101  may be omitted and the first data acquisition module  561  may use the blood information after control that was previously acquired in step S 104 , as the blood information before control. 
     When the conditions for completion of learning have been satisfied, the first generation module  562  generates a learning model  551  having sets (an action value table) of each of the finally updated Q(s, a) values (action values), and stores it in the first storage device  55  (step S 108 ). The learning model  551  is trained so that when predetermined blood information is input as the state, it outputs control parameters corresponding to action with the highest action value in that state, i.e. control parameters that allow maximum stabilization of the blood information. The learning model  551  may also be trained so that when predetermined blood information is input as the state, it outputs the action value for each control parameter (each value) in that state. 
     This allows the blood purification system  40  to automatically create the optimal operation conditions for each time period elapsed after beginning operation of the blood purification unit  14 . As a result, the blood purification system  40  can derive the optimal operation conditions for apheresis using double filtration plasmapheresis, for example. 
     The first output control module  563  then outputs the learning model  551  generated by the first generation module  562  by sending it to the server  80  via the first communication device  53  (step S 109 ), and the series of steps is complete. The learning model  551  is an example of information relating to the learning model. After the learning model  551  has been received from the control apparatus  50  via the second communication device  81 , the server  80  stores the received learning model  551  in the second storage device  82  as a learning model  821 , while also sending it to the other control apparatus  50  via the second communication device  81 . After the learning model  821  has been received from the server  80  via the first communication device  53 , the other control apparatus  50  stores the received learning model  821  in the first storage device  55  as a learning model  551 . The management system  100  can share the learning model among multiple blood purification systems  40 , thereby increasing generation efficiency by the learning model. The processing of step S 109  can be omitted, and the control apparatus  50  may use the learning model  551  generated by its own device only in its own device. 
       FIG.  7    is a flow chart showing an example of operation for control processing by the control apparatus  50 . 
     An example of operation for control processing by the control apparatus  50  will now be explained with reference to the flow chart in  FIG.  7   . The flow of operation described below is carried out in cooperation with each of the elements of the control apparatus  50  primarily by the first processing device  56 , based on a program prestored in the first storage device  55 . Control processing is carried out when the user has used the input device  51  to indicate that blood purification is to begin. 
     First, the parameter acquisition module  564  acquires from the blood purification unit  14  the blood information detected by the detector of the blood purification unit  14 , in the same manner as treatment in step S 101  of  FIG.  6   , and stores it in the first storage device  55  (step S 201 ). 
     The parameter acquisition module  564  then inputs the acquired blood information into the learning model  551  stored in the first storage device  55  and acquires the control parameter output from the learning model  551  (step S 202 ). 
     The control module  565  then controls the magnetic regulator  16 , or controls the blood pump  5 , plasma pump  9  or filtration pump  11  via the driving device  15 , based on the control parameters acquired by the parameter acquisition module  564  (step S 203 ). 
     The first data acquisition module  561  subsequently acquires blood information in the same manner as for the processing in step S 104  of  FIG.  6   , and stores it in the first storage device  55  (step S 204 ). 
     Next, the first generation module  562  determines a reward for the learning model  551  in the same manner as for the processing in step S 105  of  FIG.  6    (step S 205 ). The first generation module  562  sets the reward based on the change in the blood information acquired in step S 201  and the blood information acquired in step S 204 . 
     Next, the first generation module  562  updates the action value function in the same manner as for the processing in step S 106  of  FIG.  6    (step S 206 ). The first generation module  562  updates the action value function based on the blood information acquired in step S 201 , the blood information acquired in step S 204 , the control parameter acquired in step S 202  and the reward set in step S 206 . The processing of steps S 204  to S 206  may be omitted, and the first generation module  562  does not need to update the learning model  551  in the control processing. 
     The control module  565  then determines whether or not the user has used the input device  51  to indicate that the blood purification is complete (step S 207 ). If it has not yet been indicated that blood purification is complete, the control module  565  returns to the processing of step S 201  and repeats the processing of steps S 201  to S 207 . 
     If it has been indicated that blood purification is complete, the control module  565  stores the results of the control processing in the result table  553  (step S 208 ). 
     The control module  565  reads out from the first storage device  55  the learning model  551  used for the current blood purification. The control module  565  also receives the input of patient data by the user using the input device  51 . The control module  565  further reads out the product data  552  of the plasma separation device  1  and factor separating device  7  from the first storage device  55 . The control module  565  further reads out from the first storage device  55  the blood information that was initially stored in step S 201 , and acquires it as blood data relating to blood flowing through each line of the blood purification unit  14 . The control module  565  further reads out from the first storage device  55  each blood information item that has been stored in step S 204 , and acquires it as clearance data for the blood purification system  40 . The control module  565  may also read out only the blood information that is finally stored in step S 204  as the clearance data for the blood purification system  40 . 
     The control module  565  also receives the input of causative substance removal from the patient, input by the user using the input device  51 . A meter for measurement of causative substance removal may also be connected to the patient who is connected to the blood purification unit  14 , and the control module  565  may acquire the causative substance removal information for the patient from the meter via an interface  50 . 
     The control module  565  also receives the input of patient antithrombogenicity by the user using the input device  51 . For example, after blood purification is complete and once the blood and plasma components have been discharged from the blood purification unit  14 , the fixing solution including glutaraldehyde for fixing of thrombi formed in the hollow membrane  1   e  is recirculated for a certain time in the blood supply line  3  and blood return line  4 . The plasma separation device  1  is then removed from the blood purification unit  14 , and the hollow membrane  1   e  at the inlet of the removed plasma separation device  1  is visually observed to evaluate the presence of thrombus formation. The state of thrombi inside the hollow membrane  1   e  at a predetermined site is observed using a scanning electron microscope. For example, the hollow membrane  1   e  is divided into the  3  regions of filtration side, center and outside (the side opposite the filtration side) in the longitudinal direction and divided into the  3  regions of inlet, center and outlet in the transverse direction, and the following evaluation is made for each 3×3 region. 
     For example, about 15 hollow fiber membranes are randomly extracted per site, the cross-sectional area of thrombus-formed sections is calculated for each hollow fiber membrane, and the average value for cross-sectional area of the thrombus-formed sections per hollow fiber membrane is calculated for each site. The ratio of the average cross-sectional area of thrombus-formed sections with respect to the average cross-sectional area of the hollow fiber membranes is calculated as the thrombus formation rate, for each site. The thrombus formation rate is used as an index for evaluation of the antithrombogenicity of the plasma separation device  1 . For example, the thrombus formation rate is divided into 5 levels and assigned points (score), for each site. Zero points are assigned if the thrombus formation rate is less than 1%, 1 point is assigned if the thrombus formation rate is ≥1% and &lt;25%, 2 points are assigned if the thrombus formation rate is ≥25% and &lt;50%, 3 points are assigned if the thrombus formation rate is ≥51% and &lt;75%, and 4 points are assigned if the thrombus formation rate is ≥76%. The average point score may also be calculated for each region in the longitudinal direction or for each region in the transverse direction. This allows the direction of thrombus formation in the hollow membrane  1   e  to be determined for different regions. The average number of points at all of the sites may also be used as the evaluation score for antithrombogenicity for the plasma separation device  1  as a whole. A lower evaluation score is evaluated as higher antithrombogenicity. 
     The control module  565  mutually associates the learning model  551 , patient data, product data  552 , blood data, clearance data, causative substance removal and antithrombogenicity, assigning new measurement IDs and storing them in the result table  553 . 
     The first output control module  563  then outputs the results of blood purification by displaying them on the display device  52  (step S 208 ), and the series of steps is complete. The results of blood purification are an example of information relating to the learning model. The user can thereby confirm the effect of the learning model  551 , so that the learning model  551  exhibiting a high effect can be identified and shared with another control apparatus  50 . 
     The control apparatus  50  may also use a learning model stored in the server  80  to acquire the control parameter, instead of using the learning model stored in its own device. In this case the parameter acquisition module  564  sends blood information to the server  80  via the first communication device  53  in step S 202 . The second processing device  83  of the server  80  receives blood information from the control apparatus  50  via the second communication device  81 , inputs it into the learning model  821  stored in the second storage device  82 , and acquires the control parameters outputted from the learning model  821 . The second processing device  83  sends the acquired control parameters to the control apparatus  50  via the second communication device  81 , and the parameter acquisition module  564  acquires the control parameters by receiving them from the server  80  via the first communication device  53 . 
     The control apparatus  50  may use the learning model  821  stored in the server  80  to properly control the blood purification system  40  using the latest learning model  821  as updated by the server  80 . The control apparatus  50  may also use the learning model  551  stored in its own device to properly control the blood purification system  40  even when communicable connection with the server  80  has been severed. 
     As described in detail above, the blood purification system  40  generates a learning model  551  based on blood information relating to blood flowing through the blood purification unit  14  and the control parameters of the blood purification unit  14 , and uses the generated learning model  551  to control the blood purification unit  14 . This allows the blood purification system  40  to learn, analyze and provide optimal operation conditions by itself. The blood purification system  40  can therefore better stabilize the state of blood flowing in the blood purification unit  14 , and can achieve more efficient purification of blood. 
     Specifically, blood information includes the degree of blood flow turbulence or the blood vorticity. The present inventors have found that blood flow turbulence (a fluid with irregular movement, being in a constantly varying disturbed state) and blood vorticity promote platelet production. With large change in the degree of blood flow turbulence and blood vorticity, fouling of the plasma separation device  1  tends to be accelerated. Because operation of the blood purification system  80  takes several hours to several days and there are limits to the extent of visual work and operation by humans, it is difficult for a human to ascertain the optimal conditions for degree of blood flow turbulence and blood vorticity. The blood purification system  40  can use machine learning to acquire the optimal conditions for the degree of blood flow turbulence and the blood vorticity, and particularly the optimal conditions that are difficult for a human to imagine, thereby can reduce fouling of the plasma separation device  1 . 
     Blood information also includes the degree of cardiac murmur. A greater degree of cardiac murmur tends to result in poorer efficiency of blood removal from the patient. For patients using in vitro blood treatment devices such as a plasma separation device  1 , studies have shown that more than half of those diagnosed with ischemic heart disease were previously thought to be without symptoms. Myocardial infarction tends to occur most commonly within the first year of using an in vitro blood treatment device. The blood purification system  40  can use machine learning to acquire the optimal conditions for degree of cardiac murmur, and particularly the optimal conditions that are difficult for a human to imagine, thereby can reduce the potential for ischemic heart disease in patients using the plasma separation device  1 . Moreover, since the blood purification system  80  has an ECG meter  41 , it allows the patient to be aware of ischemic heart disease. 
     Control parameters also include magnetic field strength applied to blood in a predetermined direction. Since even slight changes in magnetic field strength significantly affect the flow of blood, it is difficult to properly control magnetic field strength. After the magnetic field strength has changed, the flow of blood does not change immediately but only after a certain period of time, thus making it difficult to properly control the magnetic field strength. It is difficult to obtain optimal conditions for magnetic field strength by visual work and operation by a human. The blood purification system  40  can use machine learning to acquire the optimal conditions for magnetic field strength, and particularly the optimal conditions that are difficult for a human to imagine. 
     As a result of much experimentation the present inventors have found that optimal operation conditions can be obtained by providing a blood purification system  40  with a self-learning function. In particular, the blood purification system  40  can analyze and provide operation conditions that can increase the antithrombogenicity of the plasma separation device  1  used for apheresis by double filtration plasmapheresis, lengthen its lifetime and reduce fouling. The blood purification system  40  also can analyze and provide operation conditions that can lengthen the lifetime of the factor separating device  7  and reduce its fouling. The blood purification system  40  also can analyze and provide operation conditions that can reduce pathogenic substances. 
     While a period of about 3 days is generally required to obtain suitable operation conditions using a closed circulating test device, the blood purification system  40  can use machine learning technology to obtain suitable operation conditions within a short period of time. The control apparatus  50  can also evaluate the performance of the plasma separation device  1  and factor separating device  7  while conducting filtration with the blood purification unit  14 , allowing more efficient performance evaluation of the plasma separation device  1  and factor separating device  7 . The control apparatus  50  can also set the plasma component and filtration levels in the blood purification unit  14  and properly manage operation of all of the multiple pumps. 
     If the control apparatus  50  controlling the blood purification unit  14  generates a learning model  551 , the blood purification system  40  can generate the learning model  551  using a simple configuration. 
       FIG.  8    is a flow chart for showing an example of operation of learning processing by the server  80  according to another embodiment. For this embodiment, the second data acquisition module  831 , second generation module  832  and second output control module  833  of the server  80  are examples of the data acquisition module, generation module and output control module, respectively. 
     An example of operation for learning processing by the server  80  will now be explained with reference to the flow chart in  FIG.  8   . The flow of operation described below is carried out in cooperation with each of the elements of the server  80  primarily by the second processing device  83 , based on a program prestored in the second storage device  82 . 
     First, the second data acquisition module  831  acquires a plurality of sets of blood information and control parameters, as received from the control apparatus  50 , i.e. the blood purification system  40 , via the second communication device  81  (step S 301 ). The first data acquisition module  561  of the control apparatus  50  repeatedly carries out the processing of steps S 101  to S 104  in  FIG.  6    to acquire a plurality of sets of blood information and control parameters, and sends them to the server  80  via the first communication device  53 . The second data acquisition module  831  acquires a plurality of sets of blood information and control parameters, as received from the control apparatus  50  via the second communication device  81 . Incidentally, the second data acquisition module  831  may also acquire sets of blood information and control parameters received from multiple control apparatuses  50 , i.e. from multiple blood purification systems  40 . This will allow the second data acquisition module  831  to more efficiently acquire learning data of the learning model  821 . 
     Next, the second generation module  832  determines a reward corresponding to each set of blood information and control parameter, in the same manner as the processing in step S 105  of  FIG.  6    (step S 302 ). 
     The second generation module  832  then updates the action value function for each set of blood information and control parameter, based on the set of blood information and control parameter and the set reward, in the same manner as the processing in step S 106  of  FIG.  6    (step S 303 ). 
     The second generation module  832  then generates a learning model  821  having sets (action value table) of each of the finally updated Q(s, a) values (action values), and stores it in the second storage device  82  (step S 304 ), in the same manner as the processing of step S 108  in  FIG.  6   . 
     The second output control module  833  then outputs the learning model  821  generated by the second generation module  832  by sending it to each control apparatus  50  via the second communication device  81  (step S 305 ), and the series of steps is complete. The learning model  821  is an example of information relating to learning model. After the learning model  821  has been received from the server  80  via the first communication device  53 , each control apparatus  50  stores the received learning model  821  in the first storage device  55  as a learning model  551 . 
     As described in detail above, the blood purification system  40  can achieve more efficient purification of blood even if the server  80  generates the learning model  821 . 
     In particular, the management system  100  is capable of unified management of the learning model used by each blood purification system  40  at the server  80 , whereby it can inhibit variation in learning model precision used by the blood purification system  40 . 
     The learning processing represented in  FIG.  8    may also be carried out by each control apparatus  50  instead of the server  80 . That is, each control apparatus  50  may acquire a plurality of sets of blood information and control parameters from another control apparatus  50 , and may use the acquired sets to generate a learning model  551 . 
       FIG.  9    is a schematic diagram showing a blood purification unit  14 - 2  according to another embodiment. 
     The blood purification unit  14 - 2  has each of the parts of the blood purification unit  14  and is used instead of the blood purification unit  14 . However, the blood purification unit  14 - 2  does not have tubing systems  33 ,  33 ′, (three-wave) valves  34 ,  34 ′ or tubing systems  35 ,  35 ′, instead having a blood bag  2 , resistance members  6 ,  37  and opening and closing ports  24 ,  24 ′,  38 ,  39 . The blood purification unit  14 - 2  can be employed when using the plasma separation device  1  for blood flow, blood pressure, plasma volume or filtrate volume under essentially the same conditions as for actual use, but in a non-clinical setting which is not for patients. The blood purification unit  14 - 2  is capable of comparative evaluation of complement activation inhibition of the plasma separation device  1 , antithrombogenicity (blood clotting-activation inhibition) and lifetime of the plasma separation device  1 , as well as evaluation of fouling of the plasma separation device  1 . The blood purification unit  14 - 2  is able to inhibit albumin loss in the factor separating device  7 , to comparatively evaluate the performance for separating pathogenic factors from plasma components and the lifetime of the factor separating device  7 , and to evaluate fouling of the factor separating device  7 . 
     The blood purification unit  14 - 2  has a closed circuit wherein test liquid flows in a circulated manner through the plasma separation device  1  without contacting the atmosphere. Human blood (whole blood) is used as the test liquid. The test liquid to be used is not limited to human blood, and the test liquid may also be another liquid such as animal blood or artificial blood with blood components similar to human blood. 
     The blood supply line  3  and blood return line  4  are connected via a blood bag  2 . 
     The resistance member  6  is provided in the blood return line  4 . The resistance member  6  may be provided in the blood supply line  3  either instead of or in addition to the blood return line  4 . The resistance member  37  is provided in the filtrate line  10 . The resistance member  6  and resistance member  37  are used to apply resistance to the blood supply line  3 , blood return line  4  or filtrate line  10  at each installation location, matching the flow rate and pressure of the test liquid (blood) to the use environment. In particular, the resistance member  6  applies aperture resistance to the blood return line  4  to simulate peripheral resistance of the human body, functioning as a vein model for adjustment of flow of the test liquid to simulate a vein in the human body. The resistance member  6  and resistance member  37  use clamps that are able to change the value of force (resistance) applied to each line by driving of a motor, for example. Various other types of devices such as valves may also be used for the resistance member  6  and resistance member  37 . The resistance member  6  and resistance member  37  are examples of liquid control mechanisms. The driving device  15  also has a motor for driving the resistance member  6  and resistance member  37 . 
     The opening and closing ports  24 ,  24 ′ are constructed with stopcocks, for example, that allow switching between an open state that permits supply or discharge of test liquid through the blood supply line  3 , and a closed state which prevents supply or discharge of test liquid. The opening and closing ports  24 ,  24 ′ are set in the open state when harvesting a test liquid sample during testing or when switching test liquids after testing, and are otherwise set in the closed state. When the opening and closing ports  24 ,  24 ′ are set in the closed state, the circuit of the blood purification unit  14 - 2  as a whole is maintained in non-contact with air. Similarly, the opening and closing port  38  is constructed with a stopcock that allows switching between the open state and closed state for the filtrate line  10 . The opening and closing port  39  is constructed with a stopcock that allows switching between the open state and closed state for the plasma line  8 . 
     The blood purification unit  14 - 2  preferably comprises temperature control means to adjust the blood purification device  1 , blood bag  2 , replacement fluid container  7  and blood purification unit  14 - 2 , in order to maintain a constant temperature for each liquid in the blood purification unit  14 - 2 . The temperature control means has a water tank with water stored inside it, and a heater that keeps the water temperature in the water tank at a predetermined temperature. By placing the blood bag  2  and a portion of the blood supply line  3  in the water tank, it is possible to keep the test liquid flowing through the blood supply line  3  and blood return line  4  at a constant temperature similar to human body temperature (about 36 to 37° C.). Similarly, by placing the factor separating device  7 , and a portion of the plasma line  8  and filtrate line  10  in the water tank, it is possible to keep the plasma components flowing through the plasma line  8  and filtrate line  10  at a constant temperature similar to human body temperature (about 36 to 37° C.). The temperature control means may also have a heater that keeps the closed space in which the entire blood purification unit  14 - 2  is housed at the constant temperature. 
     In order to simulate the effects of differential pressure by the level difference in each device provided in the liquid circuit of the blood purification unit  14  used during actual blood filtration, each part of the blood purification unit  14 - 2  is disposed so as to have a similar level difference for creating differential pressure. Each part of the blood purification unit  14 - 2  is also disposed in a manner that also takes into account the effect of gravity by the level difference. For example, the blood pump  5  is disposed at the highest point. The blood pump  5  is disposed at a height of about 600 mm to 700 mm from the installation surface of the blood purification unit  14 - 2 , which is the lowest point. The plasma separation device  1  and factor separating device  7  are held with the blood inlet port  1   c  and plasma inlet port  7   d  at the upper end and the blood outlet port  1   d  and plasma outlet port  7   c  at the lower end. The top portions of the plasma separation device  1  and factor separating device  7  are disposed at a location at a height of about 400 mm to 500 mm from the installation surface. 
     The control apparatus  50  controls the blood purification unit  14 - 2  in a manner similar to control of the blood purification unit  14 . When the blood purification unit  14 - 2  is used, however, the value of resistance applied by the resistance member  6  and resistance member  37  (resistance amounts) are included in the control parameters of the blood purification unit  14 - 2 . When the blood purification unit  14 - 2  is used, the blood information does not include the degree of cardiac murmur, patient blood pressure, degree of hemolysis, pulse, blood pressure or circulating blood flow of patient. 
     A test method for evaluation of antithrombogenicity and evaluation of the lifetime of the plasma separation device  1  using the blood purification unit  14 - 2 , as well as a test method for evaluation of the lifetime of the factor separating device  7 , will now be explained. 
     After the plasma separation device  1  and factor separating device  7  to be tested have been set in the blood purification unit  14 - 2 , the test liquid (blood) is filled into the blood supply line  3  and blood return line  4 . The blood pump  5  is driven, and the plasma pump  9  and filtration pump  11  are properly driven in order depending on the state of arrival of the plasma components and filtrate to the plasma pump  9  and filtration pump  11 . Blood with a pulsatile flow created by the blood pump  5  under the same conditions as an actual use environment passes from the blood bag  2  to the blood pump  5 , and flows into the plasma separation device  1  from the blood inlet port  1   c  and through the hollow membrane  1   e . The blood that has passed through the hollow membrane  1   e  flows from the blood outlet port  1   d  to the blood return line  4  and through the resistance member  6 , returning to the blood bag  2 . 
     The blood pump  5 , plasma pump  9  and filtration pump  11  are driven continuously for a time corresponding to an actual use environment (for example, about 3 hours to 3 days). The first pressure gauges  21  to  23 , second pressure gauge  25 , third pressure gauge  26 , first flow meters  28 ,  29 , second flow meter  30  and third flow meter  31  are periodically monitored during this time, and data relating to periodic changes in the inlet pressure, outlet pressure or differential pressure of the plasma separation device  1  are acquired. Data relating to periodic changes in the inlet pressure and filtrate pressure of the factor separating device  7  are acquired for the factor separating device  7  as well. The control apparatus  50  can determine the lifetime of the plasma separation device  1  and factor separating device  7  based on the acquired data. When the inlet pressure of the plasma separation device  1  and factor separating device  7  have increased to a predetermined value (such as 150 mmHg) from the initial value (70 mmHg, for example), even if the driving time has not elapsed, the blood pump  5 , plasma pump  9  and filtration pump  11  are stopped and the test is completed, and the elapsed time from the start of the test (the operating time) is also acquired as data. 
     During the test, blood, plasma components and filtrate that have flowed through the blood supply line  3 , plasma line  8  and filtrate line  10  are harvested as samples at predetermined intervals, with each of the components being measured each time, and data relating to periodic changes are also acquired for each component. After the test is completed, and once the blood and plasma components have been discharged from the blood purification unit  14 - 2 , the fixing solution including glutaraldehyde for fixing of thrombi formed in the hollow membrane  1   e  is recirculated for a certain time in the blood supply line  3  and blood return line  4 . The driving conditions for the blood pump  5 , plasma pump  9  and filtration pump  11  during this time are set to be the same conditions as for testing with blood. The plasma separation device  1  is also removed from the blood purification unit  14 - 2 , and the antithrombogenicity of the plasma separation device  1  is evaluated for the removed plasma separation device  1 . According to this embodiment, therefore, an evaluation test is carried out for the plasma separation device  1  while maintaining the desired state for the flow rate and pressure and the test liquid components without contact with air, and a test that is essentially the same as under actual use conditions with a patient is carried out in a non-clinical setting without a patient. 
     This embodiment is not limited to the description given above. For example, the control apparatus  50  and blood purification unit  14  may be constructed integrally instead of with separate devices. In this case, each of the devices of the blood purification unit  14  are directly connected to the CPU bus of the control apparatus  50 , sending and receiving information through the CPU bus to and from the first processing device  56  of the control apparatus  50 . 
     The learning model may also learn by a method other than reinforcement learning. For example, the learning model may be trained in advance by supervised learning such as deep learning. In this case the teacher data used are multiple blood information items, and control parameters allowing stabilization of the state of blood that is represented by each blood information item. The learning model learns so that, when each blood information item has been input, it outputs control parameters that allow stabilization of the state of blood represented by each blood information item. The learning model may also learn by unsupervised learning, semi-supervised learning, transduction or multitask learning. 
     REFERENCE SIGNS LIST 
       1  Plasma separation device,  3  blood supply line,  4  blood return line,  5  blood pump,  6  resistance member,  7  factor separating device,  8  plasma line,  9  plasma pump,  10  filtrate line,  11  filtration pump,  14 ,  14 - 2  blood purification unit,  21 - 23  first pressure gauge,  25  second pressure gauge,  26  third pressure gauge,  28 ,  29  first flow meter,  30  second flow meter,  31  third flow meter,  37  resistance member,  40  blood purification system,  50  control apparatus,  53  first communication device,  55  first storage device,  551  learning model,  561  first data acquisition module,  562  first generation module,  563  first output control module,  564  parameter acquisition module,  565  control module,  80  server,  81  second communication device,  821  learning model,  831  second data acquisition module,  832  second generation module,  833  second output control module.