Automatic analysis device

Abstract

The present invention provides an automatic analysis device that improves the reliability of the determination of diseases and the subsequent examinations to be performed. The automatic analysis device includes a memory that stores patient data for each patient, the patient data including at least one of past measurement data obtained by an analysis unit, a past medical history, a medication history, and a family medical history. When measurement data is input from the analysis unit, based on the identification data read by a reading device, patient data corresponding to the measurement data from the analysis unit is read from the memory. Based on the measurement data from the analysis unit and the corresponding patient data, at least one of the determination of diseases and the examinations to be performed is executed, and the determination result is output to a monitor.

Description

Technical Field

[0001] The present invention relates to an automatic analysis device. Background Art

[0002] In regular health checkups and outpatient visits, screening tests are usually performed. When an abnormality is detected in a screening test, a detailed examination is performed to determine the disease and decide on a treatment plan. After starting treatment, tests required to confirm the treatment effect are also performed.

[0003] As a system for assisting such examinations, the following system is known: a single examination is performed on a biological sample of a patient, and when a disease is suspected based on a comparison of the measured value with an examination level (appropriate value), items for diagnosing the suspected disease name are selected and a secondary examination is performed (Patent Document 1).

[0004] However, in the case of simply determining the measurement items of a detailed examination (secondary examination) by comparing the measurement results of a preliminary examination (primary examination) with an appropriate value as in the system of Patent Document 1, it may not be possible to select appropriate measurement items for the detailed examination. This is because even for the same examination measurement results, they are different due to the influence of the patient's personal circumstances such as the medication history.

[0005] Patent Document 1: Japanese Patent Laid-Open No. 8-114600 Summary of the Invention

[0006] An object of the present invention is to provide an automatic analysis device capable of improving the reliability of the determination of a disease and the next examination to be performed.

[0007] In order to achieve the above object, the present invention provides an automatic analysis device, which includes: an analysis unit that analyzes a sample; an operation device; a control device that controls the analysis unit based on an input from the operation device; and a monitor that displays measurement data output from the analysis unit. The analysis unit is configured to include: a sample dispensing mechanism that dispenses a sample into a reaction vessel; a reagent dispensing mechanism that dispenses a reagent into the reaction vessel; a measurement unit that measures the reaction of the sample and the reagent inside the reaction vessel; and a reading device that reads identification data given to a sample container. The control device includes a memory that stores patient data for each patient, and the patient data includes at least one of past measurement data of the analysis unit, medical history, medication history, and family history. When measurement data is input from the analysis unit, based on the identification data read by the reading device, patient data corresponding to the measurement data from the analysis unit is read from the memory, and based on the measurement data from the analysis unit and the corresponding patient data, at least one of a determination of a disease and an examination to be performed is executed, and the determination result is output to the monitor.

[0008] According to the present invention, the reliability of the determination of a disease and the next examination to be performed can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a schematic diagram showing an automatic analysis device according to a first embodiment of the present invention.

[0010] Figure 2 is Figure 1 a functional block diagram of a control device 3 included in the shown automatic analysis device.

[0011] Figure 3 is a diagram showing Figure 1 an example of a flowchart of a determination of a disease or the like executed by the shown automatic analysis device.

[0012] Figure 4 represents Figure 1 an example of a list screen of measurement results of the shown automatic analysis device.

[0013] Figure 5 represents Figure 1 an example of a reference screen of patient data of the shown automatic analysis device.

[0014] Figure 6 represents an example in which a diagnosis process is displayed in the reference screen of patient data in Figure 5 the above.

[0015] Figure 7This is a flowchart showing an example of disease determination performed in the automatic analysis device according to the second embodiment of the present invention.

[0016] Figure 8 This is an example of a screen that shows the result of analysis performed when a disease or the like cannot be determined in the automatic analysis device according to the second embodiment of the present invention.

[0017] Figure 9 This is an example of a reference screen for analysis data of the automatic analysis device according to the second embodiment of the present invention.

[0018] Figure 10 This is a flowchart showing an example of disease determination performed in the automatic analysis device according to the third embodiment of the present invention.

[0019] Figure 11 This shows an example of a screen that shows Figure 10 the determination result (scoring result) of DIC performed in the flowchart of

[0020] Figure 12 This shows an example of a screen that shows Figure 11 the determination result (scoring result) of DIC when the diagnostic criteria applied in the screen of

[0021] Figure 13 This shows an example of a setting screen used in setting options for diagnostic criteria in the automatic analysis device according to the third embodiment of the present invention.

[0022] Figure 14 This is a functional block diagram of the control device included in the automatic analysis device according to the fourth embodiment of the present invention.

[0023] Figure 15 This is a conceptual diagram of learning data.

[0024] Figure 16 This shows the first variation of the composite automatic analysis device to which the present invention can be applied.

[0025] Figure 17 This shows the second variation of the composite automatic analysis device to which the present invention can be applied. Detailed Embodiments

[0026] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0027] <First Embodiment>

[0028] (Automatic Analysis Device)

[0029] Figure 1It is a schematic diagram of an automatic analysis device showing the first embodiment of the present invention. In the first embodiment, a composite automatic analysis device is exemplified as an application object, and this composite automatic analysis device has functions of biochemical analysis, blood coagulation analysis (blood coagulation fibrinolytic markers, blood coagulation time measurement, etc.), and immunoassay.

[0030] Figure 1 The automatic analysis device 100 shown includes: an analysis unit 1 for analyzing samples, an operation device 2, a control device 3 for controlling the analysis unit 1 based on an input from the operation device 2, and a monitor 4 for displaying and outputting the measurement data of the analysis unit 1. The samples analyzed in the automatic analysis device 100 are specimens such as a patient's blood and urine.

[0031] (Analysis Unit)

[0032] The analysis unit 1 is configured to include: a reaction disk 10, a sample disk 20, reagent disks 30A and 30B, a sample dispensing mechanism 40, reagent dispensing mechanisms 50A and 50B, measurement units 60A - 60C, and reading devices 70A - 70C.

[0033] Reaction Disk

[0034] The reaction disk 10 is a disk-shaped unit that can rotate around a vertical axis and holds a plurality of reaction vessels (reaction units) 11 made of a light-transmitting material. The reaction vessel 11 is a container for mixing a sample and a reagent and allowing them to react, and a plurality of reaction vessels 11 are arranged annularly in the reaction disk 10. When the automatic analysis device 100 is operating, the reaction vessels 11 are kept at a predetermined temperature (for example, about 37°C) in the thermostatic bath 12 of the reaction disk 10. In addition, the reaction disk 10 is provided with a stirring mechanism 13 and a reaction vessel cleaning mechanism 14. The stirring mechanism 13 is a device for stirring the liquid contained in the reaction vessel 11. The reaction vessel cleaning mechanism 14 is a device for cleaning the inside of the used reaction vessel 11.

[0035] Sample Disk

[0036] The sample disk 20 is a disk-shaped unit that can rotate around a vertical axis and is used to hold a plurality of sample containers 21 containing samples. In this figure, a structure is exemplified in which the sample containers 21 can be arranged in two concentric circles in the sample disk 20.

[0037] Reagent Disk

[0038] The first reagent tray 30A is a disc-shaped unit that can rotate around a vertical axis and is used to hold a plurality of first reagent bottles 31A. A plurality of reagent bottles 31A are arranged annularly on the reagent tray 30A. Similarly, the second reagent tray 30B is a disc-shaped unit that can rotate around a vertical axis and is used to hold a plurality of second reagent bottles 31B. A plurality of reagent bottles 31B are arranged annularly on the reagent tray 30B. Reagent liquids corresponding to the analysis items analyzed by the automatic analyzer 100 are accommodated in these reagent bottles 31A and 31B. For example, the first reagent for biochemistry or scattering or the coagulation reagent is accommodated in each reagent bottle 31A of the reagent tray 30A, and the second reagent for biochemistry or scattering is accommodated in each reagent bottle 31B of the reagent tray 30B.

[0039] Sample dispensing mechanism

[0040] The sample dispensing mechanism 40 has a pipette tip and aspirates and discharges samples through the pipette tip. The sample dispensing mechanism 40 is located between the sample tray 20 and the reaction tray 10. The sample dispensing mechanism 40 aspirates a predetermined amount of sample from the inside of the sample container 21 at the dispensing position (aspiration position) 20a of the sample tray 20 and discharges the aspirated sample into the inside of the reaction container 11 at the dispensing position (discharge position) 10a of the reaction tray 10.

[0041] Reagent dispensing mechanism

[0042] The reagent dispensing mechanisms 50A and 50B each have a pipette tip and aspirate and discharge reagents through the pipette tip. The first reagent dispensing mechanism 50A is located between the reagent tray 30A and the reaction tray 10. The second reagent dispensing mechanism 50B is located between the reagent tray 30B and the reaction tray 10. The reagent dispensing mechanism 50A aspirates the reagent from the inside of the reagent bottle 31A corresponding to the test item at the dispensing position (aspiration position) 30Aa of the reagent tray 30A and discharges (dispenses) the reagent into the inside of the target reaction container 11 at the dispensing position (discharge position) 10b of the reaction tray 10. Similarly, the reagent dispensing mechanism 50B aspirates the reagent from the inside of the reagent bottle 31B corresponding to the test item at the dispensing position (aspiration position) 30Ba of the reagent tray 30B and discharges (dispenses) the reagent into the inside of the target reaction container 11 at the dispensing position (discharge position) 10c of the reaction tray 10. The reagent discharged into the reaction container 11 is stirred by the stirring mechanism 13 and mixed with the sample.

[0043] Measurement unit

[0044] The measurement units 60A - 60C are units for measuring the target items of the sample, and include a light source that irradiates light onto the mixture of the sample and the reagent inside the reaction container, and a photometer that detects the light transmitted through the mixture and outputs a measurement value. By measuring the reaction between the sample and the reagent inside the reaction container, the target items of the sample are measured.

[0045] The measurement units 60A and 60B are measurement units for biochemical analysis and immunoassay. The first measurement unit 60A is configured to include a first light source 61A and a first photometer 62A. The second measurement unit 60B is configured to include a second light source 61B and a second photometer 62B. The light sources 61A and 61B are arranged on the inner peripheral side of the reaction disk 10, and irradiate light on the reaction vessel 11 from the inner peripheral side of the reaction disk 10. The photometers 62A and 62B are arranged on the outer peripheral side of the reaction disk 10, and are opposed to the light sources 61A and 61B respectively across the annular row of the reaction vessels 11. The photometers 62A and 62B are respectively located on the optical axes of the light sources 61A and 61B. The light irradiated from the light source 61A passes through the reaction vessel 11 and is measured by the photometer 62A. Similarly, the light irradiated from the light source 61B passes through the reaction vessel 11 and is measured by the photometer 62B. Each time each reaction vessel 11 passes through the measurement units 60A and 60B along with the rotation of the reaction disk 10, photometry is performed on the reaction liquid (a mixed liquid of a sample and a reagent) accommodated inside each reaction vessel. The used reaction vessels 11 are cleaned by the reaction vessel cleaning mechanism 14 and reused.

[0046] The third measurement unit 60C is a blood coagulation time measurement unit. The measurement unit 60C is configured to include: a reaction vessel accommodation part 63, a reaction vessel transfer mechanism 64, a sample dispensing station 65, a reaction vessel temperature adjustment module 66, a reagent dispensing mechanism 67, and a measurement channel 68.

[0047] In the measurement unit 60C, a plurality of disposable reaction vessels 60a are accommodated in the reaction vessel accommodation part 63. These reaction vessels 60a are transferred to the sample dispensing station 65 by the reaction vessel transfer mechanism 64. The sample dispensing station 65 is arranged with the sample disk 20 separated by the sample dispensing mechanism 40. The sample dispensing mechanism 40 sucks the sample from the sample container 21 and discharges (dispenses) the sample into the reaction vessel 60a at the sample dispensing station 65.

[0048] The reaction vessel 60a into which the sample has been dispensed in this way is transferred from the sample dispensing station 65 to the reaction vessel temperature adjustment module 66 by the reaction vessel transfer mechanism 64 and heated to about 37°C. In addition, the reagent is kept cold in the reagent disk 30A. The reagent dispensing mechanism 50A sucks the reagent from the reagent bottle 31A corresponding to the test item and discharges the reagent into the predetermined empty reaction vessel 11 provided on the reaction disk 10, and heats it to about 37°C.

[0049] After a certain period of time, the reagent dispensing mechanism 67 with a reagent heating function sucks the reagent kept warm inside the reaction vessel 11 and further heats it to about 40°C in the reagent dispensing mechanism 67. During this period, the reaction vessel 60a that keeps the sample warm at about 37°C is transferred by the reaction vessel transfer mechanism 64 to any measurement channel 68 of the measurement unit 60C. The measurement unit 60C has a plurality of measurement channels 68 each having a light source and a photometer. After that, the heated reagent is discharged (dispensed) from the reagent dispensing mechanism 67 into the reaction vessel 60a in the measurement channel 68, and the blood coagulation reaction between the sample and the reagent starts inside the reaction vessel 60a.

[0050] In the measurement channel 68, after the reagent is dispensed into the reaction vessel 60a, measurement data is output from the photometer at a predetermined time interval (for example, a cycle of 0.1 second). If the measurement is completed, the used reaction vessel 60a is transferred by the reaction vessel transfer mechanism 64 and discarded into the reaction vessel disposal section 69.

[0051] As described above, in the measurement units 60A - 60C, the analog signal of the transmitted light or scattered light measured by the photometer is converted into a digital signal proportional to the light quantity by the AD converter 79 and input to the control device 3.

[0052] Reading device

[0053] The reading devices 70A - 70C are devices for reading the identification data attached to the containers. Regarding the identification data, barcodes or RFID etc. can be used. In this example, an example of using a barcode is described. That is, the reading devices 70A - 70C are barcode readers.

[0054] The first reading device 70A reads the barcode pasted on the reagent bottle 31A during reagent registration. The identification data of the reagent read by the reading device 70A and the position data of the reagent bottle 31A with this identification data in the reagent tray 30A are sent to the control device 3 and stored in the memory 6.

[0055] The second reading device 70B reads the barcode pasted on the reagent bottle 31B during reagent registration. Similarly to the reading device 70A, the identification data of the reagent read by the reading device 70B and the position data of the reagent bottle 31B with this identification data in the reagent tray 30B are sent to the control device 3 and stored in the memory 6.

[0056] The third reading device 70C reads the barcode pasted on the sample container 21 at the time of sample registration. Data such as the sample ID, patient ID, and sample type are converted into barcodes. Similar to the reading devices 70A and 70B, the identification data of the sample read by the reading device 70C and the position data of the sample container 21 with the attached identification data in the sample tray 20 are sent to the control device 3 and stored in the memory 6.

[0057] Figure 1 The automatic analysis device 100 is equipped with a function for preventing the occurrence of remaining samples or reagents. In the case where a sample or reagent remains, a cleaning operation is added during the measurement to reduce or prevent the remaining. It is possible to perform a cleaning operation for preventing remaining on each pipette tip of the reagent dispensing mechanisms 50A and 50B and the sample dispensing mechanism 40, and the reaction vessel 11.

[0058] (Operating device)

[0059] The operating device 2 is a device operated by an operator when inputting measurement commission data (described later) into the computer 7 or when causing the monitor 4 to display various data. The operating device 2 can typically use a keyboard and a mouse, but a touch panel and other operating devices can also be applied.

[0060] (Control device)

[0061] The control device 3 is configured to include an interface 5, a memory 6, a computer (first computer) 7, a control computer (second computer) 8, and a server (third computer) 9.

[0062] Interface

[0063] The interface 5 is an input / output unit for data of the computer 7 of the analysis unit 1. In Figure 1 which, the computer 7 and the interface 5 are respectively illustrated, but the interface 5 may sometimes be integrated with the computer 7. Data of the analysis items for the analysis unit 1 are input from the computer 7 to the control computer 8 via the interface 5. In addition, the measurement data of the measurement units 60A - 60C output from the analysis unit 1 via the A / D converter 79 are input to the computer 7 and the memory 6 via the interface 5. The identification data read by the reading devices 70A - 70C are also input to the computer 7 and the memory 6 via the interface 5.

[0064] Memory

[0065] The memory 6 is a storage device such as an HDD or an SSD. In Figure 1An external storage device connected to computer 7 via interface 5 is illustrated, but an internal storage device of computer 7 can also be applied. Data such as reagent identification information, sample identification information, analysis parameters, measurement order data, calibration results, and measurement data are stored in memory 6. The measurement order data includes at least a sample ID and a measurement item, and may also include other information such as a patient ID as needed.

[0066] computer

[0067] Computer 7 is a control device used by an operator. This computer 7 has the following functions: generating measurement order data according to the operator's operation and outputting it to control computer 8, or displaying and outputting a screen corresponding to the operator's operation on monitor 4 based on measurement data from analysis unit 1, etc. In addition, a program for assisting in the judgment of diagnosis or examinations to be performed on doctors and other medical practitioners based on the measurement values of analysis unit 1 and patient data stored in memory 6 is stored in the ROM of computer 7. Specifically, when new (i.e., current or this time) measurement data is input from analysis unit 1 to computer 7, based on the identification data read by reading device 70C, computer 7 reads the patient data of the patient ID corresponding to the new measurement data from memory 6. Then, computer 7 also performs a determination of the disease or the determination of the examinations to be performed next on the patient based on the new measurement data and the read patient data, and outputs the determination result to monitor 4. Doctors, etc. can confirm the determination result of computer 7 through monitor 4 and refer to it when making a judgment on the disease or the examinations to be performed thereafter.

[0068] In Figure 1 a structure in which only one analysis unit 1 is connected to computer 7 is illustrated, but there is also a case where multiple analysis units 1 are connected to one computer 7 via interface 5. When connecting analysis units 1 in multiple facilities, there is also a case where a network (such as a LAN) is connected to interface 5 and multiple analysis units 1 are connected to one computer 7 via the network. It is an automatic analysis device having multiple analysis units for one control device.

[0069] control computer

[0070] Control computer 8 is a control device that outputs an instruction signal to analysis unit 1 according to the measurement order data input from computer 7 and drives the analysis. It is assumed that control computer 8 is integrally formed with analysis unit 1 (installed inside the main body of analysis unit 1), but in Figure 18 and the analysis unit 1 are shown in the figure respectively. The instruction objects of the control computer 8 are working devices (movable devices) such as the sample disk 20, the reagent disks 30A, 30B, the sample dispensing mechanism 40, the reagent dispensing mechanism 50A, 50B, the reaction container transfer mechanism 64, and the reagent dispensing mechanism 67. This embodiment is an example in which the control computer 8 drives the analysis unit 1 comprehensively, but it can also be configured so that each working device is equipped with a dedicated control computer, and each control computer drives the corresponding working device according to the input from the computer 7.

[0071] server

[0072] The server 9 is connected to the computer 7. In this figure, the computer 7 and the server 9 are connected not via the interface 5, but there is also a case where the server 9 is connected to the computer 7 via the interface 5. The memory 9M of the server 9 stores patient data (described later) for each patient ID.

[0073] (Monitor)

[0074] The monitor 4 is connected to the computer 7 and is a display device for displaying and outputting a graphical user interface and various data when operating the computer 7. The various data displayed and output on the monitor 4 include the measurement data of the analysis unit 1, the determination results of the computer 7, the patient data, etc. Based on the operation of the operating device 2 by the operator, the desired data is displayed on the monitor 4 according to the signal input from the computer 7.

[0075] (Basic Actions-Biochemical Examination)

[0076] An example of basic operation using the automatic analyzer 100 will be described. Here, an analysis operation of the first measurement item related to blood coagulation and fibrinolysis markers such as D-dimer and FDP in a biochemical test of a sample and a blood coagulation test using the photometer 62A will be described.

[0077] The operation parameters related to the measurement items that can be analyzed by the automatic analyzer 100 are inputted to the computer 7 by the operator in advance and stored in the memory 6. The operator inputs the measurement request data for each sample. When the measurement order of a certain sample ID for which the measurement request data is inputted arrives, the operation parameters corresponding to the measurement items of the corresponding measurement request data are read out from the memory 6 and inputted from the computer 7 to the control computer 8. Then, the control computer 8 drives the analysis unit 1 according to the operation parameters.

[0078] Specifically, according to the operation instructions from the control computer 8, first, the reaction disk 10 and the sample disk 20 are driven, and the target reaction container 11 and the sample container 21 are respectively moved to the dispensing positions 10a and 20a. Then, through the sample dispensing mechanism 40, a predetermined amount of sample is aspirated from the target sample container 21 located at the dispensing position 20a and dispensed into the target reaction container 11 located at the dispensing position 10a of the reaction disk 10. The reaction container 11 with the dispensed sample is moved from the dispensing position 10a to the dispensing positions 10b or 10c by the rotating reaction disk 10, and the reagent corresponding to the measurement item is dispensed by the reagent dispensing mechanism 50A or 50B. The dispensing order of the sample and the reagent can also be reversed (compared to dispensing the reagent first and then the sample).

[0079] After that, when the reaction container 11 passes across the measurement unit 60A, the light transmitted through the sample is measured by the photometer 62A. The measurement value obtained by the photometer 62A is converted into a digital signal by the A / D converter 79 and input into the computer 7 via the interface 5. In the computer 7, based on the standard curve data corresponding to the measurement item and the measurement value, the concentration of the mixed solution of the sample and the reagent is calculated as the measurement data. Regarding the standard curve data, it is pre-measured and stored in the memory 6 under the specified analysis method. The measurement data calculated by the computer 7 is displayed and output on the monitor 4 according to the operator's operation or automatically.

[0080] It can also be configured to calculate the measurement data by the control computer 8 instead of the computer 7. In Figure 1 the analysis unit 1 has the following characteristics: by using the reaction disk 10 of the turntable method, the sample can be continuously dispensed through the rotation of the turntable, and the processing ability is excellent.

[0081] (Basic operation - Blood coagulation test)

[0082] Another example of the basic operation of the automatic analyzer 100 is described. Here, the analysis operation related to the measurement of the hemostasis function test item, that is, the measurement of the blood coagulation time, is described. In the measurement of the blood coagulation time, the control computer 8 drives the analysis unit 1 according to the operation parameters.

[0083] Specifically, in the measurement unit 60C, the reaction container 60a housed in the reaction container housing part 63 is transferred to the sample dispensing station 65 by the reaction container transfer mechanism 64. Then, through the sample dispensing mechanism 40, the sample aspirated from the corresponding sample container 21 of the sample disk 20 is dispensed into the reaction container 60a at the sample dispensing station 65. The reaction container 60a with the dispensed sample is transported to the reaction container temperature adjustment module 66 by the reaction container transfer mechanism 64 and heated to 37 °C there.

[0084] On the other hand, the reagent dispensing mechanism 50A discharges the reagent aspirated from the reagent bottle 31A corresponding to the measurement item into a predetermined empty reaction vessel 11 provided on the reaction disk 10. The reagent kept cold in the reagent disk 30A is heated to about 37°C in the reaction disk 10.

[0085] After a certain period of time, the reagent kept warm in the reaction vessel 11 is aspirated by the reagent dispensing mechanism 67 with a reagent heating function and further heated to, for example, 40°C inside the reagent dispensing mechanism 67. During this period, the reaction vessel 60a containing the sample is transferred from the reaction vessel temperature control module 66 to a predetermined measurement channel 68 by the reaction vessel transfer mechanism 64. Then, the heated reagent is dispensed into the reaction vessel 60a in the measurement channel 68 by the reagent dispensing mechanism 67. Through this reagent dispensing, the blood coagulation reaction between the sample and the reagent starts inside the reaction vessel 60a.

[0086] After discharging the reagent in this way, the measured values of light are successively output at a predetermined short measurement time interval (for example, every 0.1 second) in the measurement channel 68. The output measured values are converted into digital signals by the A / D converter 79 and input into the computer 7 via the interface 5. After the photometry is completed, the used reaction vessel 60a is transferred by the reaction vessel transfer mechanism 64 and discarded into the reaction vessel disposal section 69.

[0087] The computer 7 obtains the blood coagulation time based on the measured values input from the analysis unit 1 in this way. Then, based on the standard curve data corresponding to the measurement item and the calculated blood coagulation time, the concentration of the mixture of the sample and the reagent is calculated as the measurement data. The measurement data and the blood coagulation time calculated by the computer 7 are displayed and output on the monitor 4 according to the operation of the operator or automatically.

[0088] In the measurement unit 60C, it is necessary to collect the measured values at regular intervals. Therefore, during this period, only one reaction vessel 60a can be measured in one measurement channel 68. Figure 1 An example shows a structure having six measurement channels 68. However, when there is no free space in the measurement channel 68, the next measurement of the blood coagulation time is not accepted and the device enters a standby state. From the viewpoint of suppressing the occurrence of such a standby state, a structure with more measurement channels 68 is advantageous.

[0089] (Function of the control device)

[0090] Figure 2 is Figure 1 the functional block diagram of the control device 3 included in the automatic analyzer 100 shown. In Figure 2 for the elements corresponding to Figure 1 the same reference numerals as Figure 1 are marked and the description is appropriately omitted. As Figure 2As shown, the control device 3 has the functions of measurement sequence management F1, mechanism control F2, data calculation F3, data management F4, and analysis F5. It is assumed that these functions are shared and executed by multiple computers (computer 7 and control computer 8 in this embodiment) according to a predetermined program, but it can also be configured so that all functions are executed by a single computer. For example, the functions of measurement sequence management F1 and mechanism control F2 can be executed by the control computer 8, and the other three functions can be executed by the computer 7.

[0091] Measurement sequence management

[0092] The measurement order management F1 is a function for setting the measurement order of samples. It is assumed that the function of the measurement order management F1 is executed by the control computer 8, but it can also be executed by the computer 7. The measurement request data set by the operating device 2 in the computer 7 is input from the computer 7 to the control computer 8. For the convenience of explanation, specific measurement request data is designated and recorded as measurement request data X, and the sample whose ID is designated in the measurement request data X is recorded as sample Y. When the measurement request data X is input from the computer 7, the control computer 8 sets the measurement order of which sample to perform measurement after for the sample Y.

[0093] Institutional Control

[0094] The mechanism control F2 is a function for controlling the operation of the analysis unit 1. The function of the mechanism control F2 is executed by the control computer 8. When the measurement order of the sample Y set in the measurement order management F1 arrives, the control computer 8 drives the analysis unit 1 to perform measurement on the sample Y. Specifically, according to the measurement item specified by the measurement request data X, the sample Y set on the sample disk 20 is dispensed into the reaction container 11 or 60a, and is mixed with the reagent according to the measurement item to react as described above.

[0095] Data Operation

[0096] The data calculation F3 is a function of calculating measurement data based on the measurement value input from the analysis unit 1. The function of the data calculation F3 is performed by, for example, the computer 7. When the sample Y reacts with the reagent by the mechanism control F2, the photometric value of the sample Y is input from the measurement unit 60A, 60B or 60C via the A / D converter 79. Based on the measurement value, the measurement data of the measurement item specified for the sample Y is calculated. The measurement data of the sample Y calculated here is stored in the memory 6 together with the measurement value, the reagent identification information, the sample identification information, the analysis parameter, the measurement request data, the calibration result, etc.

[0097] Data Management

[0098] Data management F4 is a function for managing measurement data. The function of this data management F4 is, for example, executed by the computer 7. Specifically, the computer 7 associates the data (such as measurement data) about the sample Y calculated by the data operation F3 with the sample ID of the sample Y. At the same time, for the measurement data of the sample Y stored in the memory 6, it is also associated with the sample ID of the sample Y. The measurement data etc. associated with the sample ID of the sample Y are sent from the computer 7 to the server 9 and stored as inspection information database 9A related to the corresponding patient ID stored in the memory 9M of the server 9. The past measurement data registered in the inspection information database 9A corresponds to the past measurement data in the data included in the patient data. The computer 7 automatically or according to the operator's operation, displays and outputs the measurement data etc. of the sample Y processed by the data management F4 on the monitor 4. In addition, the electronic medical record 9B of each patient ID is stored in the memory 9M of the server 9. The electronic medical record 9B is a database of the findings of the doctor during the examination, the patient's symptoms, past medical history, medication history, family history, etc. In the server 9, the measurement data of the same patient ID registered in the inspection information database 9A is reflected in the patient data, and thus, patient data as a database integrating various data of the patient individual is stored for each patient ID.

[0099] Analysis

[0100] Analysis F5 is a function for analyzing and determining the next examination to be performed in the diagnosis and treatment of the patient based on the newly obtained measurement data and the patient data of the corresponding patient ID. The function of this analysis F5 is, for example, executed by the computer 7. Specifically, the computer 7 downloads the patient data corresponding to the patient ID between the newly obtained measurement data from the server 9 through the data management F4. The computer 7 also infers the disease for the patient corresponding to the sample Y or determines the next examination to be performed in the diagnosis and treatment of the patient based on the new measurement data and the patient data through the analysis F5. In addition, the result of the analysis F5 is sent from the computer 7 to the server 9 and reflected in the patient data of the corresponding patient ID stored in the memory 9M of the server 9. In addition, the computer 7 automatically or according to the operator's operation, displays and outputs the result of the analysis F5 on the monitor 4.

[0101] In this embodiment, one feature is that not only the latest (i.e., current situation) measurement data obtained by the analysis unit 1 is considered, but also the patient data is considered as described above for the determination of the disease or the determination of the examination to be performed. This determination function can not only determine both the disease and the examination, but also can only determine the disease or can only determine the examination to be performed. Hereinafter, a specific example of the analysis of the computer 7 will be described.

[0102] (Examples of disease etc. determination)

[0103] Figure 3 This is a flowchart showing an example of a disease determination performed by the automatic analysis device 100. In this example, the process of diagnosing and examining diabetes will be used as an example for explanation.

[0104] In the computer 7, when the measured value of the subject patient ID is input through the processing of the institutional control F2 for diabetes examination under the measurement entrustment data, the Figure 2 flowchart starts. If the flowchart starts, the computer 7 calculates the measured data of blood glucose level and HbA1c (S101), and stores the measured data files of blood glucose level and HbA1c in the memory 6 (S102). Figure 3 Then, the computer 7 determines whether the measured data of the blood glucose level is abnormal (whether it exceeds the reference range) (S103). If the measured data of the blood glucose level is abnormal, the computer 7 further determines whether the measured data of HbA1c is abnormal (whether it exceeds the reference range) (S104). At this time, the computer 7 notes an alarm such as "exceeds the reference range" for the measured data of the blood glucose level.

[0105] When the measured data of HbA1c is abnormal in the determination of S104, the computer 7 determines that the patient has diabetes, prompts continuous measurement in the future and ends the

[0106] flowchart (S105). At this time, the computer 7 notes an alarm such as "exceeds the reference range" and a comment such as "recommended to continue the examination" for the measured data of HbA1c, and then sends it to the server 9 in association with the patient ID and reflects it in the patient data. Figure 3 When it is determined in the previous S104 that the measured data of HbA1c is normal (within the reference range), the computer 7 downloads the patient data of the corresponding patient ID from the server 9 (S106), and determines whether there are no associated symptoms of diabetes based on the past records (S107). If there is any one of the typical symptoms of diabetes (thirst, excessive drinking, excessive urination, weight gain, etc.) and diabetic retinopathy, the computer 7 determines that the patient has diabetes in step S107, prompts continuous measurement in the future and ends the

[0107] flowchart (S105). At this time, the computer 7 notes a comment such as "recommended to continue the examination" for the measured data, sends it to the server 9 in association with the patient ID and reflects it in the patient data. Figure 3 If it is determined in S107 that there are no typical symptoms of diabetes and diabetic retinopathy, the computer 7 prompts to recheck the blood glucose level and HbA1c within one month in the future (S108), and temporarily retains

[0108] the Figure 3At this time, the computer 7 adds a note to the measurement data such as "re-examination recommended (within one month)", associates it with the patient ID, sends it to the server 9, and reflects it in the patient data.

[0109] After step S108, if the patient is re-examined (measurement request data is input) within one month, the procedure is restarted. Figure 3 If the process is restarted, the computer 7 calculates the measured data of the re-examination and stores it in the memory 6 in the same way as steps S101 and S102 (S109, S110), and determines whether the blood sugar level and HbA1c value are both normal (S111). Here, if at least one of the blood sugar level and HbA1c value is abnormal, the computer 7 determines that the patient has diabetes, prompts the patient to continue the measurement in the future, and ends the test. Figure 3 At this time, the computer 7 annotates the measurement data with an alarm such as "out of reference range" and a comment such as "recommend continued examination" for the corresponding measurement item, associates it with the patient ID, sends it to the server 9, and reflects it in the patient data.

[0110] In S111, if both the blood sugar level and HbA1c value are normal, there is still a doubt that it cannot be determined as diabetes. In this case, the computer 7 determines that it is suspected diabetes (S112), and as a process observation, it is prompted to check the blood sugar level and HbA1c again within 3-6 months, and the process ends. Figure 3 At this time, the computer 7 adds a note such as "re-examination recommended (within 3 to 6 months)" to the measurement data, associates it with the patient ID, sends it to the server 9, and reflects it in the patient data.

[0111] In addition, if the blood sugar level measurement data in the previous S103 is normal, the computer 7 determines whether the HbA1c measurement data is abnormal (S114). If the HbA1c measurement data is abnormal, it is prompted to check the blood sugar level again within one month (S115), and the computer 7 temporarily holds the blood sugar level measurement data. Figure 3 At this time, the computer 7 adds a warning about HbA1c "out of standard range" and a comment "re-examination recommended (within one month)" to the measurement data, associates it with the patient ID, sends it to the server 9, and reflects it in the patient data.

[0112] After step S115, if the patient is re-examined (measurement request data is input) within one month, the procedure is restarted. Figure 3process. If restarting the process, the computer 7 operates on the measurement data re-checked and stores it in the memory 6 (S116, S117), and determines whether the blood glucose value is normal during the re-check (S118). Here, if the blood glucose value is abnormal, the computer 7 determines that the patient has diabetes, prompts continuous measurement in the future, and ends Figure 3 process (S105). At this time, the computer 7 attaches an alarm of "outside the reference range" related to the blood glucose value and a note of "recommended re-check (within one month)" to the measurement data, associates it with the patient ID, sends it to the server 9, and reflects it in the patient data.

[0113] In S118, if the blood glucose value is normal, there remains a suspicion that diabetes cannot be determined. In this case, the computer 7 determines it as suspected diabetes (S119), and as a process observation, prompts a re-check of the blood glucose value and HbA1c within 3 to 6 months in the future, and ends Figure 3 process (S120). At this time, the computer 7 attaches a note such as "recommended re-check (within 3 to 6 months)" to the measurement data, associates it with the patient ID, sends it to the server 9, and reflects it in the patient data.

[0114] When both the blood glucose value and HbA1c value are normal in the initial examination (i.e., when the HbA1c value is normal in step S114), the computer 7 determines that the patient does not have diabetes and ends Figure 3 process (S121).

[0115] (Example of screen display)

[0116] Figure 4 An example of a list screen showing measurement results, Figure 5 An example of a reference screen for patient data. Including Figure 4 and Figure 5 The screens described in this application specification are all displayed and output by the computer 7 on the monitor 4 according to the operator's operations.

[0117] When the label 401 displayed as "measurement result" in a predetermined screen displayed on the monitor 4 is selected (clicked), the Figure 4 screen is displayed. In this Figure 4In the screen, a measurement status display area 400a for each sample and a result display area 400b divided by item are shown. The result display area 400b divided by item shows the results of each measurement item of the sample selected in the measurement status display area 400a. In the result column of each sample in the measurement status display area 400a, a button 402 is shown. This button 402 is used to view the detailed information of the patient ID associated with the sample ID, that is, patient data. When the button 402 in any column in the measurement status display area 400a is selected and operated (clicked), the patient data of the patient ID associated with the selected sample ID is downloaded from the server 9 and displayed Figure 5 reference screen

[0118] As an example, it shows that in Figure 5 the exemplified reference screen, there is a display column 411 for the patient ID and patient name (name) for identifying the patient, a measurement result display area 410a for displaying the measurement results, and a patient data display area 410b for displaying patient data such as examination findings

[0119] Regarding diabetes, the diagnosis can change according to the presence or absence of chronic hyperglycemia. In this embodiment, as described in Figure 3 step S107, the patient is determined whether to have diabetes by referring to the patient data and considering the past examination results

[0120] In the measurement result display area 410a, the past measurement data 413 of the same patient ID is displayed together with the latest (this measurement) measurement data 412. In this example, for the measurement data 414 that deviates from the reference range, it is displayed in a way that can be seen at a glance, such as by changing the text color, displaying in bold, adding hatching, or a display method that combines them

[0121] When the label 415 displayed as "finding" is selected (clicked) in the patient data display area 410b, the patient symptoms 416, symptom level 417, and remarks 418 with special explanations are displayed. The symptom level 417 is an index for evaluating the severity of symptoms based on the symptom state and occurrence frequency. The relationship between the symptom state, occurrence frequency, and the severity of symptoms is obtained, for example, by statistically processing the symptom data corresponding to each patient accumulated in the server 9. In this example, the degree of symptoms is evaluated in 5 stages (level 1 - 5), with level 1 being normal, and the larger the value of the level, the worse the symptoms

[0122] In addition, by selecting the labels 419, 420, 421 in the patient data display area 410b, information such as the patient's family history, diagnosis process, and charts can be viewed. It shows in Figure 6 in Figure 5An example of a diagnostic process is displayed on a reference screen of patient data. By displaying the diagnostic process, for example, it is possible to confirm the entire process of patient diagnosis and which status is currently in the process. For example, the current stage in the process is surrounded by a thick line, or as Figure 6 shown by color display, whereby the progress of the diagnostic process can be confirmed.

[0123] By downloading or manually editing the diagnostic guideline that is the basis of the process, the Figure 6 diagnostic process can be updated, and the Figure 6 process update performed on the screen can also be reflected in the Figure 3 flowchart. That is, by updating the process on the Figure 6 screen, the order executed by the computer 7 can be changed.

[0124] (Effect)

[0125] The retrieval and collection operations of various information such as the patient's past measurement data, medical history, and family history are burdensome tasks for busy doctors or other medical practitioners. In addition, important data may be missed when dealing with a large amount of information.

[0126] In contrast, the automatic analysis device 100 of the present embodiment does not simply determine a disease based on the measurement data measured by the analysis unit 1 this time, but considers the patient's personal data, that is, patient data, determines a disease and the next examination to be performed based on the measurement data of the analysis unit 1, and presents the result. In this way, by performing the determination considering the patient data, the reliability of the determination of the disease and the next examination to be performed can be improved. In addition, it is possible to reduce the burden on doctors or other medical practitioners when making a diagnosis or examination judgment, and it is also possible to prevent data from being missed, and it is also possible to contribute to rapid diagnosis and examination in a medical facility.

[0127] <Second Embodiment>

[0128] Figure 7 is a flowchart showing an example of disease determination and the like performed in the automatic analysis device according to the second embodiment of the present invention. The same point of this embodiment as the first embodiment is that when an abnormality determination is made based on the measurement data, the patient data is referred to determine the disease and the examination to be performed. The difference from the first embodiment is that when it is impossible to determine, the cumulative data is statistically analyzed to determine the disease. Specifically, in this example, when the computer 7 cannot determine the disease, it extracts the diagnostic results that meet the common predetermined conditions with the new measurement data from the patient data of multiple patients (for example, all valid patient data) stored in the memory 9M, statistically analyzes the extraction results, and outputs them to the monitor 4. The hardware structure of the automatic analysis device of the present embodiment is the same as that of the first embodiment.

[0129] (Example of Disease Determination and the Like)

[0130] Use Figure 7 Figure 7 For example, in the case of blood coagulation analysis, the determination of diseases and the like by the automatic analysis device of the present embodiment will be described. Representative diseases in which APTT (activated partial thromboplastin time) becomes abnormal (coagulation time is prolonged) include hemorrhagic diseases and thrombotic diseases. In addition, as the main causes of these diseases, it is possible that there is a decrease in the production of coagulation factors and inhibitors. When PT (prothrombin time) is normal and only APTT is abnormal, the computer 7 starts Figure 7 the flowchart of

[0131] When the process of Figure 7 starts, the computer 7 acquires measurement data in which APTT is abnormal and PT is normal (S201), and stores these measurement data in the memory 6 (S202). Then, the computer 7 downloads the patient data of the corresponding patient ID from the server 9 (S203), and determines whether heparin is taken based on the medication history (S204). In this example, an example of using the medication history among the patient data such as the past medical history, medication history, and family history in the determination is described. However, the data used in the determination varies depending on the examination purpose, and sometimes multiple data are used in the determination.

[0132] When it is determined in S204 that heparin is taken based on the medication history, it is suspected that the abnormal value of APTT may be caused by the mixing of heparin. Therefore, the computer 7 prompts a heparin quantification test (S205), and temporarily suspends Figure 7 the process of

[0133] After the processing of S205, if a heparin quantification test (input measurement request data) is performed, the process of Figure 7 is restarted. When the process is restarted, the computer 7 determines whether the heparin concentration is a value that affects APTT for the measurement data of the heparin quantification test (S206). As a result, if the heparin concentration is a value at a level that affects APTT (a value equal to or higher than the set value), the computer 7 determines that the abnormality of APTT is caused by the influence of heparin, and ends Figure 7 the process of

[0134] When it is determined in the previous S204 that heparin has not been taken based on the medication history, the possibility of heparin intake as the cause of abnormal APTT disappears, and the computer 7 further refers to the patient data to determine whether the bleeding time is normal (S208). In the case where the bleeding time is abnormal (i.e., prolonged), the computer 7 prompts the measurement of von Willebrand factor (VWF) activity (S209). At this time, the computer 7 adds a note such as "Recommended to measure VWF activity" to the measurement data of APTT, associates it with the patient ID, sends it to the server 9, and reflects it in the patient data.

[0135] On the other hand, in the case where it is determined in S208 that the bleeding time is normal, the computer 7 prompts the APTT cross-mixing test (S210), and temporarily holds Figure 7 the process. At this time, the computer 7 adds a note such as "Recommended APTT cross-mixing test" to the measurement data of APTT, associates it with the patient ID, sends it to the server 9, and reflects it in the patient data. The APTT cross-mixing test refers to mixing normal plasma with the test plasma in various ratios and measuring the immediate reaction after mixing and the delayed reaction after heating at 37°C for 2 hours for APTT.

[0136] After the processing of S210, if the APTT cross-mixing test has been performed (measurement commission data has been input), the Figure 7 process is restarted. If the process is restarted, the computer 7 calculates the measurement data of the APTT cross-mixing test (S211), saves the measurement data in the memory 6 (S212). Then, the computer 7 generates a determination mixing curve (not shown) based on the measurement data (S213). The mixing curve generated here has the mixing ratio of normal plasma to the test plasma as the horizontal axis and the APTT measurement value as the vertical axis.

[0137] When the mixing curve generated based on the APTT cross-mixing test in the processing of S213 is convex downward in both the immediate reaction and the delayed reaction, it may be liver insufficiency (decrease in coagulation factor production), congenital hemophilia A, congenital hemophilia B, congenital factor XII or XI deficiency. In this case, in order to determine the disease, the computer 7 prompts the coagulation factor quantification test (S214). At this time, the computer 7 adds a note such as "Recommended coagulation factor quantification test" to at least one of the measurement data of APTT and the APTT cross-mixing test, associates it with the patient ID, sends it to the server 9, and reflects it in the patient data.

[0138] When, in the process of S213, the mixed curve generated based on the APTT cross-mixing test is clearly convex upward in the delayed response compared to the immediate response, acquired hemophilia and inhibitors against each coagulation factor are suspected. For example, the process of S213 can be executed by determining that the average curvature of the delayed response is greater than a set value compared to the curvature of the immediate response. In the case of suspected acquired hemophilia or inhibitors, in order to identify the reduced factor, the computer 7 prompts for a coagulation factor quantification test and waits for the test results (S215). At this time, the computer 7 attaches a note such as "Recommended coagulation factor quantification test" to the measurement data, associates it with the patient ID, sends it to the server 9, and reflects it in the patient data.

[0139] After the process of S215, if a coagulation factor quantification test (input of measurement commission data) is executed, the Figure 7 process is restarted. If the process is restarted, the computer 7 calculates the measurement data of the coagulation factor quantification test (S216) and stores the measurement data in the memory 6 (S217). Based on the results of this coagulation factor quantification test, the computer 7 prompts for the determination of the inhibitor titer relative to the reduced factor (S218). At this time, the computer 7 attaches a note such as "Recommended determination of inhibitor titer" to the measurement data, associates it with the patient ID, sends it to the server 9, and reflects it in the patient data.

[0140] When, in the process of S213, the mixed curve generated based on the APTT cross-mixing test is a straight line or convex upward in both the immediate response and the delayed response, antiphospholipid syndrome (APS) is suspected. In this case, in order to determine whether it is APS, the computer 7 prompts for the examination of LA (lupus anticoagulant) (S219). At this time, the computer 7 attaches a note such as "Recommended examination of LA" to the measurement data, associates it with the patient ID, sends it to the server 9, and reflects it in the patient data.

[0141] If the factor quantification test, inhibitor titer determination, or LA examination prompted in the processes of S214, S218, or S219 is executed, the computer 7 determines whether the result has identified the disease (S220). If the disease can be identified, the Figure 7 process ends.

[0142] In a case where a disease cannot be determined even after performing a factor quantification test, an inhibitor titer measurement, or an LA examination, the computer 7 estimates the disease by statistically analyzing the accumulated data in the server 9 (S221). When it is determined in the process of S206 that the heparin concentration is not a value that affects APTT, the disease cannot be determined either. Therefore, the computer 7 transfers the process to the process of S221 and attempts to estimate the disease by statistically analyzing the accumulated data. The same applies when no abnormality is found in the VWF activity measurement in S209.

[0143] In the process of S221, the computer 7 statistically analyzes a plurality of patient data (for example, all valid patient data) accumulated in the memory 9M of the server 9, and extracts a diagnostic result that meets the common predetermined conditions with the measurement data of the determination object calculated in the process of S201. The computer 7 displays and outputs the extraction result on the monitor 4 (S222). In addition, the extraction result is associated with the patient ID and sent to the server 9 and reflected in the patient data, and then the process ends. Figure 7 of the process.

[0144] (Example of screen display)

[0145] Figure 8 is an example of a screen showing the result of the analysis performed by the computer when the determination cannot be made. Figure 9 is an example of a reference screen for analyzing data. These screens are all displayed and output by the computer 7 on the monitor 4 according to the operation of the operator.

[0146] In Figure 8 the screen, a measurement result display area 600a for displaying the measurement result of the patient ID of the determination object and an analysis display area 600b for displaying the analysis conditions and the analysis result are shown.

[0147] In the measurement result display area 600a, the patient ID 601 for identifying the patient, the patient name 602, the measurement information 603 recorded as the measurement date, the item name 604 as the measurement item name, the result 605 as the measurement data, and the grade 606 are displayed. The grade 606 is an index for indicating the degree of abnormality of the measurement result with respect to the reference range, and is classified by the thresholds of seconds, activity, and concentration. Regarding the thresholds for each grade, they can be input and set to the computer 7, for example, using the operation device 2 in a medical facility using the automatic analysis device 100. In this example, the degree of abnormality is evaluated in five grades (grades 1-5), with grade 1 being normal and the value of the grade increasing as the degree of abnormality worsens.

[0148] In the analysis display area 600b, the analysis conditions 607, the graph 608, the legend 609 of the graph, and the details button 610 for displaying the details of the data selected in the legend 609 are displayed. In Figure 8 the example of Figure 7 , the grades of the current measurement results of the patient ID displayed in the measurement result display area 600a (APTT = 3, PT = 1) are used as the analysis conditions. The computer 7 extracts multiple data that match the analysis conditions from the huge amount of data accumulated in the server 9, including the patient data of other patients, and represents the statistical results of the diagnostic results associated with each of the extracted data in the graph 608. In Figure 8 the process of S221, such a process is executed by the computer 7. For example, in the process of S222, the operator can confirm through the monitor 4 Figure 8 the analysis results as shown. In

[0149] When the Figure 8 details button 610 is operated (clicked), the screen exemplified in Figure 9 is displayed for the selected data. In Figure 9 the screen, the disease name and the corresponding number of cases are shown in the column 611, and the data that match the analysis conditions are displayed in a list in the label 612 displayed as "Data". In the label 612, for each data, the patient ID, gender, APTT, PT, and the details button 613 are displayed. When data is selected and the details button 613 is operated (clicked), the patient data shown previously in Figure 5 can be displayed. When the label 614 displayed as "Recommended Items" is selected, the measurement items required for diagnosing the disease displayed in the column 611 are displayed.

[0150] (Effect)

[0151] According to the present embodiment, in addition to obtaining the same effects as the first embodiment, even when the disease cannot be determined only by the newly obtained measurement data and the corresponding patient data, disease candidates can be screened based on multiple performance data including the data of other patients.

[0152] <Third Embodiment>

[0153] An automatic analysis device according to a third embodiment of the present invention will be described. This embodiment is an example in which the following function is further added, which is a function of selecting a diagnostic criterion used in the determination of a disease or the like.

[0154] In this embodiment, a plurality of diagnostic criteria are stored as options in the memory 9M of the server 9. The diagnostic criterion stipulates measurement items for confirming a disease to be determined, reference values for determining measurement values, and the like. Depending on the disease, there may sometimes be a plurality of diagnostic criteria. In this embodiment, in order to be able to use each diagnostic criterion in the determination, the computer 7 digitizes each diagnostic criterion and stores it in the memory 9M. In the memory 9M, more diagnostic criteria than the number of the above options of the diagnostic criterion are stored as registration data. The above options of the diagnostic criterion can be preset by the manufacturer of the automatic analysis device 100, but in this embodiment, they can be arbitrarily selected from the registration data.

[0155] In this embodiment, the computer 7 selects a diagnostic criterion from the above options based on new measurement data input from the analysis unit 1 and patient data corresponding to the measurement data, reads it from the memory 9M, and performs the determination of a disease or the like based on the selected diagnostic criterion. Regarding the hardware structure of the automatic analysis device, this embodiment is the same as the first embodiment.

[0156] Hereinafter, taking the example of determining the diagnostic criterion to be used in the diagnosis of DIC (disseminated intravascular coagulation syndrome), a specific example of the diagnostic criterion selection process in the automatic analysis device 100 of this embodiment will be described. Here, an example of selecting the type of diagnostic criterion based on the DIC diagnostic criterion 2017 version (new criterion) proposed by the Japanese Society of Thrombosis and Hemostasis is given.

[0157] (Example of disease or the like determination)

[0158] Figure 10 It is a flowchart showing an example of the determination of a disease or the like performed in the automatic analysis device according to the third embodiment of the present invention. In Figure 10 this process, the determination until the DIC diagnostic criterion to be used in the DIC diagnosis is determined is explained.

[0159] When a decrease in platelet count, an increase in FDP, a decrease in fibrinogen (Fbg), an extension of PT time, a decrease in antithrombin activity, an increase in TAT, SF or F1+2 are confirmed in the examination findings, the Figure 10 diagnostic process is performed as a suspicion of DIC.

[0160] If Figure 10In the process, the computer 7 obtains the measurement data of FDP or Fbg, PT, antithrombin, TAT or SF, and F1+2 obtained by the analysis unit 1 or other analysis units (S301), and stores the measurement data in the memory 6 (S302).

[0161] Next, the computer 7 downloads the patient data of the patient ID (the patient to be diagnosed) corresponding to the obtained measurement data from the server 9 (S303), and determines whether the patient to be diagnosed is not in the obstetrics field based on the patient data (such as the diagnosis history, etc.) (S304). When it is determined that the patient to be diagnosed is in the obstetrics field, the computer 7 selects the diagnostic reference data for the obstetrics field (S305).

[0162] When it is determined in the process of S304 that the patient to be diagnosed is not in the obstetrics field, the computer 7 determines whether the patient to be diagnosed is not in the neonatal field based on the patient data (such as age, etc.) (S306). When it is determined that the patient to be diagnosed is in the neonatal field, the computer 7 selects the diagnostic reference data for the neonatal field (S307).

[0163] When it is determined that the patient to be diagnosed is neither in the obstetrics field nor in the neonatal field, the computer 7 determines whether there is no hematopoietic disorder (S308). Regarding Figure 10 In the process, whether there is hematopoietic disorder can be input together when inputting the measurement entrustment data, or whether there is hematopoietic disorder can be determined based on the patient data when there is data in the patient data. When it is determined that there is hematopoietic disorder, the computer 7 selects the diagnostic reference data of the hematopoietic disorder type of the 2017 version of the DIC diagnostic criteria (S309).

[0164] When it is determined in the process of S308 that the patient to be diagnosed has no hematopoietic disorder, in order to determine whether there is sepsis, the computer 7 prompts to measure procalcitonin (PCT) by the measurement unit 60B and waits for the result (S310). After the process of S310, if PCT measurement is performed (input measurement entrustment data), the Figure 10 process is restarted. If the process is restarted, the computer 7 calculates the measurement data of the PCT measurement (S311), and stores the measurement data in the memory 6 (S312). Next, the computer 7 determines whether the measurement data of the PCT measurement is within the reference range (S313).

[0165] When it is determined that the measurement data of the PCT measurement deviates from the reference range, the computer 7 selects the diagnostic reference data of the infectious disease type of the 2017 version of the DIC diagnostic criteria (S314). On the other hand, when it is determined that the measurement data of the PCT measurement is within the reference range, the computer 7 selects the basic type of the diagnostic reference data of the 2017 version of the DIC diagnostic criteria (S315).

[0166] Finally, the computer 7 scores the measurement data obtained in the process of S301 by using the diagnostic criteria selected in the processes of S305, S307, S309, S314 or S315, and determines whether the patient to be determined is DIC (S316). Then, the determination result is displayed and output on the monitor 4, and the Figure 10 process ends.

[0167] (Example of screen display)

[0168] Figure 11 It is an example of a screen showing the DIC determination result (scoring result) executed in the Figure 10 flowchart. Figure 12 It is an example of a screen showing the DIC determination result (scoring result) when the diagnostic criteria to be applied are changed in the Figure 11 screen. These screens are all displayed and output by the computer 7 on the monitor 4 according to the operator's operation.

[0169] In the screens illustrated in these figures, patient ID 801, patient name 802, disease name 803, diagnostic criteria 804, date 805, sample ID 806, item name 807, measurement result 808, unit 809, input value 810, score 811, and total 812 are displayed. The disease name 803 is the name of the diagnosed disease, which is DIC in this example. The date 805 is the date when items such as FDP were measured, and the sample ID 806 is the ID of the sample for which items such as FDP were measured. The measurement result 808 is the measurement data of each item, and the unit 809 indicates the unit of the measurement result. The input value 810 is the data input as patient data, and the presence or absence of liver insufficiency is illustrated in the Figure 11 example. In the input value 810, the corresponding data of the patient data downloaded from the server 9 can be reflected in the Figure 11 screen, and the presence or absence of hematopoietic disorder used for determination in the process of S308 in Figure 10 can also be input through the operation device 2 etc. The score 811 represents the scoring result of each item according to the selected diagnostic criteria, and the total 812 represents the total value of the scores of each item. Whether the patient to be diagnosed is DIC is determined by this total 812.

[0170] In the Figure 11 screen, the diagnostic criteria for scoring can be selected (clicked) from the options displayed in the checkbox 800 of the diagnostic criteria 804 through the operation device 2. In Figure 11 it shows the state where the basic type 813 is selected, but by selecting the infectious disease type 814, the screen display becomes Figure 12Like that. The basic type 813 and the infectious disease type 814 are data from the 2017 version of the DIC diagnostic criteria proposed by the Japanese Society on Thrombosis and Hemostasis. In the 2017 version of the DIC diagnostic criteria proposed by the Japanese Society on Thrombosis and Hemostasis, Fbg is an object of scoring in the diagnostic criteria of the basic type. On the other hand, it is not an object of scoring in the diagnostic criteria of the infectious disease type. In Figure 11 and Figure 12 , the check 815 is added to the measurement items that are objects of scoring and becomes shaded display. In contrast, for the measurement items that deviate from the objects of scoring ( Figure 12 Fbg), the check 815 is not added and it does not become shaded display. In addition, the score 811 of the measurement items other than the objects of scoring is not displayed, nor is it reflected in the total 812. Therefore, in Figure 11 and Figure 12 , the value of the total 812 is different.

[0171] In the 2017 version of the DIC diagnostic criteria proposed by the Japanese Society on Thrombosis and Hemostasis, it is stipulated that DIC is diagnosed when the score is 6 or more in the basic type, DIC is diagnosed when the score is 4 or more in the hematopoietic disorder type, and DIC is diagnosed when the score is 5 or more in the infectious disease type. Therefore, even for the same test results, as Figure 11 shows, when the basic type 813 is selected, it is equivalent to DIC, but as Figure 12 is like that, when the infectious disease type 814 is selected, it is not equivalent to DIC. On the screen of Figure 11 , by switching the selection of the diagnostic criteria, the scores for each diagnostic criteria can be easily displayed, and doctors and the like can also refer to the diagnosis.

[0172] (Option setting of diagnostic criteria)

[0173] In Figure 11 and Figure 12 , the multiple diagnostic criteria displayed in the checkbox 800 of the diagnostic criteria 804 on the screen are selectable options applicable to the scoring results displayed below the screen. In addition, it can also be configured such that the diagnostic criteria set in the checkbox 800 is set as an option of the diagnostic criteria that can be selected in the flowchart of Figure 10 as the diagnostic criteria for DIC diagnosis. That is, it is configured that: as a result of performing various determinations in the flowchart of Figure 10 , the computer 7 selects the diagnostic criteria for DIC diagnosis from the options set in the checkbox 800 and executes the process of S316.

[0174] In this example, the options of the diagnostic criteria displayed inside the checkbox 800 can be arbitrarily set. When the computer 7 is connected to multiple analysis units 1, the options of the diagnostic criteria can be arbitrarily set for each analysis unit.

[0175] Figure 13An example of a setting screen showing the diagnostic criteria. Figure 13 The screen. Figure 11 The same configuration as the buttons of the diagnostic benchmark option in the screen Figure 13 In the setting screen of , a plurality of (in this example, 10) buttons (custom buttons) are displayed inside the check box 800. In addition, a plurality of tables displaying these buttons are prepared. Figure 13 In the figure, the tab 901 displayed as "Sheet1" is selected, but a different tab can be selected to use more buttons. Figure 13 In the example of , 5 labels are prepared and a maximum of 50 keys can be used.

[0176] In the key setting area 900, a diagnostic criterion selection area 900a, a setting button 902, a release button 903, and a configuration selection area 900b are displayed. In the diagnostic criterion selection area 900a, a list of diagnostic criterions 904 pre-registered as registration data in the memory 9M (or the memory 6) is displayed together with a comment 905 input at the time of registration. The data of the diagnostic criterion 904 is not limited to publicly disclosed diagnostic criterions, and may also include diagnostic criterions of local medical facilities. Regarding the method of registering the data of the diagnostic criterion 904, in addition to the method of downloading the data via the network, there is also a method of manually editing and registering.

[0177] When assigning a diagnostic criterion to a button, a desired diagnostic criterion is selected in the diagnostic criterion selection area 900a, a button to which the selected diagnostic criterion is assigned is selected in the configuration selection area 900b, and a setting button 902 is operated (clicked). Thus, the diagnostic criterion selected in the diagnostic criterion selection area 900a is input to the diagnostic criterion 906 of the configuration selection area 900b, and the selected diagnostic criterion is assigned to the corresponding button in the check box 800. In addition, the setting of the button to which the diagnostic criterion has been assigned can be cleared by selecting a button in the configuration selection area 900b and operating (clicking) the release button 903.

[0178] (Effect)

[0179] According to this embodiment, in addition to obtaining the same effects as those of the first embodiment, the following advantages can be obtained.

[0180] In recent years, with the advancement of testing technology, the number of new items that can be measured in the medical field has continued to increase, and the items that can be measured have become more and more diverse. As a result, changes in diagnostic guidelines or standards are not uncommon. In addition, sometimes diagnostic standards for the same disease are proposed from multiple sources. For example, Figures 10 to 13In the description, an example of selecting the type of diagnostic criteria based on the DIC diagnostic criteria 2017 edition (new criteria) proposed by the Japanese Society on Thrombosis and Hemostasis was given. However, in the DIC diagnostic criteria, there are multiple diagnostic criteria in addition to those proposed by the Japanese Society on Thrombosis and Hemostasis. For example, there are the DIC diagnostic criteria (old criteria) proposed by the former Ministry of Health and Welfare, the DIC diagnostic criteria (ISTH criteria) proposed by the International Society on Thrombosis and Hemostasis (ISTH), and the DIC diagnostic criteria (acute phase criteria) proposed by the Japanese Society for Acute Medicine in the acute phase. The scoring methods of these diagnostic criteria are different from each other, so the selection of diagnostic criteria is important in determining the disease. In DIC diagnosis, patient data such as underlying diseases, family history, and pregnancy status also need to be considered separately. It is a heavy burden for doctors, etc. to master such various information for appropriate diagnosis.

[0181] In contrast, in the present embodiment, based on the measurement data and patient data, the diagnostic criteria for diagnosis are automatically selected from multiple options, which can reduce the burden on doctors, etc. when they consider complex conditions to select diagnostic criteria, and can also shorten the time required to determine the disease. In addition, by having the computer 7 refer to the patient data to select the diagnostic criteria, errors caused by missing data, etc. when manually judging the diagnostic criteria can also be suppressed.

[0182] Moreover, the options of the diagnostic criteria can be arbitrarily set. If there are changed diagnostic criteria or newly proposed diagnostic criteria, their information can be reflected. Even in a medical facility as a user, it can flexibly respond to changes and additions of diagnostic criteria. At this time, if the method of downloading the changed or newly proposed diagnostic criteria via the network and registering them in the diagnostic criteria selection area 900a ( Figure 13 ) is adopted, compared with the case of manually changing or adding diagnostic criteria, it does not require labor and time, and can also suppress input errors.

[0183] <Fourth Embodiment>

[0184] Figure 14 is a functional block diagram of the control device included in the automatic analysis device according to the fourth embodiment of the present invention. In the present embodiment, for elements that are the same as or corresponding to those described in the first embodiment, the same reference numerals as the already shown drawings are marked in Figure 14 and the description is appropriately omitted. Hereinafter, the automatic analysis device according to the fourth embodiment of the present invention will be described using Figure 14 .

[0185] This embodiment is an example in which AI is applied to the determination of diseases and examinations to be performed based on the output of the analysis unit 1 and patient data. First, learning data (learning model) 9D is stored in the memory 9M of the server 9. The learning data (learning model) 9D is obtained by pre-learning the relationship between the output of the analysis unit, patient data, and the determination results (i.e., diseases and examinations to be performed).

[0186] In addition, the control device 3 is provided with a processing circuit 9X and an update circuit 9Y. The processing circuit 9X reads the learning data 9D from the memory 9M and, based on the learning data 9D, takes the output of the analysis unit 1 and patient data as inputs and outputs a determination result. After the determination result is presented, the update circuit 9Y reflects the result of the actual diagnosis or the actual examination performed on the patient to be diagnosed in the learning data 9D in the memory 9M to update the learning data 9D.

[0187] As in the first to third embodiments, after the disease name and examinations to be performed are presented by the computer 7, a doctor or other medical practitioner determines the disease name of the patient and the examinations to be performed next, and associates them with the patient ID and stores them, for example, in the memory 9M. If, for example, such a determination result obtained by a doctor or the like is input together with the patient ID through the operation device 2, the computer 7 generates a data set of the input determination result of the doctor or the like, the measurement data of the analysis unit 1 that is the basis for the doctor or the like, and the patient data. It can be configured to store the determination result of the doctor or the like in the memory 9M of the server 9 via, for example, a computer terminal used by the doctor or the like (an unillustrated terminal connected to the server 9 via a network), and then download it to the computer 7. The data set of the measurement data generated by the computer 7 and the diagnosis result of the doctor or the like is sent to the server 9 via the network and input to the update circuit 9Y as training data. In the update circuit 9Y, the learning data 9D is read from the memory 9M, and the training data is reflected to update the learning data 9D. The updated learning data 9D is stored (overwritten) in the memory 9M.

[0188] On the other hand, if the measurement data calculated based on the measurement value of the analysis unit 1 is input to the server 9 together with the patient ID from the computer 7, the disease and examinations to be performed are derived in the processing circuit 9X. The determination result of the processing circuit 9X is output to the computer 7 and displayed on the monitor 4 by the computer 7.

[0189] An example of a structure in which learning data 9D is stored in server 9 and used and updated by server 9 will be described. However, it may also be configured such that learning data 9D is loaded from server 9 to computer 7 and used or updated by computer 7. It may also be configured such that learning data 9D is stored in the memory of computer 7 or memory 6 and used and updated by computer 7.

[0190] Figure 15 It is a conceptual diagram of learning data. The learning data shown in this figure has an input layer, an intermediate layer, and an output layer, and multiple nodes are provided in the input layer and the intermediate layer. Each node in the input layer is linked to each node in the intermediate layer, and each node in the intermediate layer is linked to the node in the output layer. A weight coefficient representing the link strength is set for each node. That is, in the learning data, there is an operation model of the number of combinations of nodes in the input layer, the intermediate layer, and the output layer. This operation model simulates the human brain neural network and is called a neural network.

[0191] An example of applying the Figure 15 learning data to blood coagulation analysis will be described with reference to the second embodiment. When a diagnosis by a doctor or the like is input to update circuit 9Y, various measurement data serving as the basis for the diagnosis are read from memory 9M to update circuit 9Y according to the patient ID, and these measurement data are input as input values to the input layer of learning data 9D. Regarding the measurement data serving as the basis for the diagnosis, for example, are the measured values of APTT, PT, heparin medication history, VWF activity measurement, the mixing curve of the APTT cross-mixing test, the measured values of the coagulation factor quantification test, the measured values of the inhibitor titer measurement, etc.

[0192] When the disease name is prompted before a doctor or the like makes a diagnosis, update circuit 9Y inputs the above input values to the input layer and compares the disease name output from the output layer with the diagnosis made by a doctor or the like. In the case where the comparison result shows that the prompted disease name is different from the diagnosis, update circuit 9Y adjusts the weight coefficients set for each link of the associated nodes to increase the probability of outputting the diagnosis made by a doctor or the like when the above input values are input. Thus, learning data 9D is updated and saved (overwritten) to memory 9M.

[0193] At each time of diagnosis, the update circuit 9Y repeatedly executes such steps to repeat learning, so that the diagnostic accuracy of the learning data 9D can be improved. The function set at the node generally uses an exponential function called the S-shaped function, but is not limited thereto. In addition, a variety of algorithms for adjusting the weighting coefficients during learning are considered. Usually, the backpropagation method is used. For these detailed calculation algorithms, for example, they are described in detail in "NEURAL NETWORKS: a comprehensive foundation - 2nd sd." written by Simon Haykin, published by Prentice-Hall, Inc. in 1999.

[0194] In this example, a case where the learning data 9D is applied to blood coagulation analysis by imitating the second embodiment is illustrated, but the learning data 9D can also be used to suggest other diseases or examinations to be performed. For example, when applied to diabetes as in the first embodiment, the following learning data can be learned. The learning data uses blood glucose value, HbA1c value, presence or absence of thirst, daily water intake and urine output, weight change amount during a predetermined period, presence or absence of diabetic retinopathy, etc. as inputs, and a diagnosis result such as whether it is diabetes or suspected diabetes as an output. For the diagnosis of DIC and other diseases illustrated in the third embodiment, as long as learning data that takes measurement values and patient data as inputs and the diagnosis result as an output is generated and learned in the same manner. If learning data that takes measurement values as inputs and the diagnostic criteria as outputs is generated, it can also be used to select diagnostic criteria.

[0195] In this embodiment, the same effects as those of the first to third embodiments can be obtained, and in addition, it has the advantage that the reliability is higher the more the diagnosis is repeated.

[0196] <Modification Example>

[0197] The embodiments of the present invention are not limited to the above 4 embodiments and can be appropriately changed. For example, in any one of the first to fourth embodiments, a part of the structure can be replaced with the structure of other embodiments, or for example, the structures of other embodiments can be combined in any one of the first to fourth embodiments. In any one of the first to fourth embodiments, structures irrelevant to the gist of the present invention can also be omitted.

[0198] For example, in the first to third embodiments, a structural example in which the determination of diseases and examinations is performed by the computer 7 is illustrated, but it can also be configured such that the determination of diseases and examinations is performed by the server 9. The functional sharing of the computer 7, the control computer 8, and the server 9 can be changed. If it is not necessary to configure the control device 3 with multiple computers, the control device 3 can be configured with a single computer.

[0199] In addition, the determination data of the past of the analysis unit 1, the past medical history, the medication history, and the family history included in the patient data were used as examples for illustration. However, the required patient data vary depending on the determination content, and it is not necessarily required to include all these types of data in the patient data. According to the determination content, it is sufficient to include at least one of the determination data of the past of the analysis unit 1, the past medical history, the medication history, and the family history in the patient data.

[0200] In addition, the structure of the analysis unit 1 is not limited to Figure 1 the structure exemplified. In Figure 1 a composite automatic analysis device was exemplified as an application object, but the present invention can also be applied to an automatic analysis device with only one measurement unit. For example, the present invention can be applied to an automatic analysis device that performs biochemical analysis, blood coagulation analysis, or immunoassay. In addition, there are various composite automatic analysis devices. The composite automatic analysis device referred to in the specification of this application is an automatic analysis device having a plurality of different types of analysis units and detectors respectively provided for these analysis units, and generally refers to an automatic analysis device having a plurality of analysis units among a biochemical analysis unit, a blood coagulation analysis unit, and an immunoassay unit. That is, an automatic analysis device that performs biochemical analysis and blood coagulation analysis, an automatic analysis device that performs biochemical analysis and immunoassay, an automatic analysis device that performs blood coagulation analysis and immunoassay, and an automatic analysis device that performs biochemical analysis, blood coagulation analysis, and immunoassay are typical examples of the composite automatic analysis device. Two variants of the composite automatic analysis device are exemplified below.

[0201] (Variant 1 of the composite automatic analysis device)

[0202] Figure 16 Fig. shows a first variant of the composite automatic analysis device to which the present invention can be applied. In Figure 16 only the analysis unit 1 is illustrated, and the illustration of the control device 3 is omitted. In addition, in the automatic analysis device of this example, for the parts that are the same as or corresponding to those of Figure 1 the automatic analysis device, the same reference numerals as those in Figure 16 are marked and the description is omitted. Figure 1 The main difference between the automatic analysis device shown in

[0203] Figure 16 and the automatic analysis device 100 shown in Figure 1 is that instead of the sample tray 20, a system for transporting the sample container through the sample rack 101 is adopted. One or more sample containers are held in one sample rack 101. In Figure 16 a structure in which up to 5 sample containers can be held in one sample rack 101 is exemplified.

[0204] Figure 16 ​​The automatic analysis device is configured to include: a rack supply unit 102, a rack storage unit 103, a conveyor line 104, a return line 105, a rack waiting unit 106, a waiting unit processing mechanism 107, a rack return mechanism 108, a reading unit (conveyor line) 109, and an analysis unit 110.

[0205] The conveyor line 104 conveys the sample rack 101 to the analysis unit 110. The return line 105 conveys the sample rack 101 in a direction opposite to that of the conveyor line 104. A structure in which the dedicated forward conveyor line 104 and the dedicated return conveyor line 105 are arranged in parallel is illustrated, but the conveyor line 104 and the return line 105 may be replaced by other mechanisms such as a robotic mechanism capable of two-way movement.

[0206] The rack waiting unit 106 stores the sample racks 101 waiting for analysis. The waiting unit processing mechanism 107 introduces the sample racks 101 from the conveyor line 104 and the return line 105 into the rack waiting unit 106. The reading unit (conveyor line) 109 reads identification information such as barcodes assigned to the sample racks 101 in the conveyor line 104.

[0207] The analysis unit 110 corresponds to the part obtained by removing the sample tray 20 and the control device from the constituent elements of the automatic analysis device 100 described in Figure 1 The conveying system of the analysis unit 110 is arranged along the conveyor line 104 and includes a reading unit 111, a rack operating mechanism 112, a dispensing line 113, and a rack operating mechanism 114. The reading unit 111 verifies the analysis order information for the samples. The rack operating mechanism 112 receives the sample rack 101 from the conveyor line 104. The dispensing line 113 can hold the sample rack 101 until the start of dispensing and conveys the sample rack 101 to the sampling area 113a where sample dispensing into the sample container of the sample rack 101 is performed. The rack operating mechanism 114 conveys the sample rack 101 after sample dispensing to the return line 105.

[0208] In the automatic analysis device 100, when an analysis start instruction signal is input from the computer 7, the sample racks 101 arranged in the rack supply unit 102 are transferred to the conveyor line 104. Here, the reading unit 109 reads the individual identification media pasted on the sample rack 101 on the conveyor line 104 and the sample container 21 stored in the sample rack, and identifies the sample rack number and the sample container number.

[0209] If there is a sample rack 101 on the dispensing line 113, the sample read by the reading unit 109 is stored in the rack waiting unit 106 and waits for analysis. At the stage when the sample dispensing on the dispensing line 113 is completed, the waiting sample rack 101 is transported to the analysis unit 110, and the sample rack number and the sample container number are identified by the reading unit 111. Then, it is transported to the dispensing line 113 via the rack operating mechanism 112, and the sample is dispensed by the sample dispensing mechanism 40. At this time, if there is no sample rack 101 on the dispensing line 113, it can also be directly transported to the dispensing line 113 without being stored in the rack waiting unit 106.

[0210] The sample after dispensing is transported to the return line 105 via the rack transport mechanism 114, and is transported to the rack waiting unit 106 via the waiting unit processing mechanism 107. Or it is transported to the rack storage unit 103. Multiple sample racks 101 can be stored in the rack waiting unit 106, and by changing the measurement order, the required sample rack 101 is transferred to the transport line 104 each time, so that it can respond flexibly.

[0211] The present invention can also be publicly used for Figure 16 such an automatic analysis device.

[0212] (Variant 2 of the composite automatic analysis device)

[0213] Figure 17 It shows the second variant of the composite automatic analysis device to which the present invention can be applied. In Figure 17 only unit 1 is shown, and the illustration of the control device 3 is omitted. In addition, in the automatic analysis device of this example, for the parts that are the same as or corresponding to those of the Figure 1 automatic analysis device, the same reference numerals as those in Figure 17 are marked and the description is omitted. Figure 1 The automatic analysis device shown in

[0214] Figure 17 is a composite automatic analysis device equipped with a biochemical analysis unit, a blood coagulation time measurement unit, and a heterogeneous immunoassay unit. In the automatic analysis device shown in this figure, a heterogeneous immunoassay detection unit 81 and a B / F separation mechanism 82 for heterogeneous immunoassay items are arranged within the moving range of the reaction vessel transfer mechanism 64. The heterogeneous immunoassay unit shares the reaction vessel 60a, the reaction vessel storage unit 63, the reaction vessel transfer mechanism 64, the reaction vessel temperature control module 66, and the reaction vessel disposal unit 69 with the measurement unit 60C. In addition, a reagent tray 83 for heterogeneous immunoassay is added within the moving range of the reagent dispensing mechanism 67 with a reagent heating function.

[0215] The present invention can also be publicly used for Figure 17 such an automatic analysis device.

[0216] [Description of reference numerals]

[0217] 1 Analysis unit; 2 Operating device; 3 Control device; 4 Monitor; 6, 9 M memories; 9D Learning data; 40 Sample dispensing mechanism; 50A, 50B, 67 Reagent dispensing mechanisms; 60A - 60C Measuring units; 70A - 70C Reading devices; 100 Automatic analyzer.

Claims

1. An automatic analysis device, comprising: an analysis unit that analyzes a sample of a patient; an operating device; a control device that controls the analysis unit based on an input from the operating device; and a monitor that displays measurement data output from the analysis unit, wherein the analysis unit is configured to include: a sample dispensing mechanism that dispenses a sample into a reaction vessel; a reagent dispensing mechanism that dispenses a reagent into the reaction vessel; a measurement unit that measures the reaction of the sample and the reagent in the reaction vessel; and a reading device that reads identification data given to a sample container, characterized in that the control device includes a memory in which patient data has been accumulated for each patient including other patients, the patient data including at least one of past measurement data obtained by the analysis unit, a medical history, a medication history, and a family history; when measurement data is input from the analysis unit, the control device reads, based on the identification data read by the reading device, patient data corresponding to the measurement data from the analysis unit from the memory; the control device performs a determination of at least one of a disease and an examination to be performed based on the measurement data from the analysis unit and the corresponding patient data, and outputs a determination result to the monitor; when the determination cannot be made, the control device extracts patient data whose analysis conditions match the measurement data from the analysis unit from the patient data accumulated in the memory including patient data of other patients, and outputs a statistical result obtained by statistically processing the diagnosis results associated with the extracted patient data to the monitor; wherein the analysis conditions are grades of the measurement results of the analysis unit for a patient, and the grades are indicators for representing the degree of abnormality of the measurement results with respect to a reference range.

2. The automatic analysis device according to claim 1, characterized in that a plurality of diagnostic criteria are stored in the memory as options, and the control device selects a diagnostic criterion for the determination from the options based on the measurement data from the analysis unit and the patient data, and reads it from the memory.

3. The automatic analysis device according to claim 2, characterized in that more diagnostic criteria than the number of options are stored as registration data in the memory, and the options can be arbitrarily selected from the registration data.

4. The automatic analysis device according to claim 1, characterized in that learning data is stored in the memory, the learning data being obtained by pre-learning the relationship between the output of the analysis unit and the patient data and the determination result, and the control device reads the learning data from the memory and outputs the determination result with the output of the analysis unit and the patient data as inputs based on the learning data.

5. The automatic analysis device according to claim 4, characterized in that after the determination result, the control device reflects the actual diagnosis result or the actual examination performed into the learning data to update the learning data.

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