Medical information processing equipment

Abstract

To foresee a possibility that an infection becomes severe at an early stage.SOLUTION: A medical information processing device according to the present embodiment comprises: a first calculation unit; a second calculation unit; an estimation unit; and a display control unit. The first calculation unit is configured to calculate a function rate quantitatively indicating whether a portion related to a symptom of an object patient normally functions or not on the basis of first medical information including a current symptom thereof. The second calculation unit is configured to calculate a current state of the object patient as a first state on the basis of the function rate. The estimation unit is configured to estimate a future state of the object patient as a second state on the basis of a function rate calculated based on, of time-series second medical information on a similar patient having a tendency similar to the first medical information, the second medical information from a time when the similarity to the first medical information is confirmed on. The display control unit is configured to cause a display device to display the first state and second state.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The embodiments disclosed in this specification relate to a medical information processing device. [Background technology]

[0002] In recent years, interest in infectious diseases has increased significantly due to the global spread of COVID-19. Medical institutions are taking various measures to prevent infectious diseases, and one of the most important measures is to keep the mortality rate of infectious disease patients low.

[0003] For this reason, there is a strong demand in the medical field for technology that can determine at an early stage whether an infectious disease will become severe. For example, technology has been proposed that calculates the probability of contracting a disease (infectious disease) from findings in medical images.

[0004] However, while conventional technology can tell the probability of contracting an infectious disease, it cannot predict whether the infection will become severe, and therefore cannot be used to take measures to reduce the number of cases that lead to death. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Republished Patent No. 2016 / 006042 Summary of the Invention [Problem to be solved by the invention]

[0006] One of the problems that the embodiments disclosed herein are intended to solve is to predict the possibility that an infectious disease will become severe at an early stage. However, the problems solved by the embodiments disclosed herein are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be considered as other problems solved by the embodiments disclosed herein. [Means for solving the problem]

[0007] The medical information processing device according to the present embodiment includes a first calculation unit, a second calculation unit, an estimation unit, and a display control unit. The first calculation unit calculates a functional ratio that quantitatively indicates the degree to which a part related to the symptom is functioning normally based on first medical information including the current symptom of the target patient. The second calculation unit calculates the current state of the target patient as a first state based on the functional ratio. The estimation unit estimates the future state of the target patient as a second state based on a functional ratio calculated based on second medical information from the time series of second medical information of a similar patient having a tendency similar to the first medical information after the time point when the similarity with the first medical information was confirmed. The display control unit causes the display device to display the first state and the second state. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a medical information processing apparatus according to an embodiment, together with a hospital information system. [Diagram 2] FIG. 2 is a diagram showing an example of a display screen of the severity and severity level according to the embodiment. [Diagram 3] FIG. 3 is a diagram showing an example of a display screen of the severity and severity level according to the embodiment. [Figure 4] FIG. 4 is a flowchart showing an example of processing executed by the medical image processing apparatus according to the embodiment. [Diagram 5] FIG. 5 is a diagram showing an example of a pixel value threshold setting screen in the first modified example. [Figure 6] FIG. 6 is a diagram showing an example of medical image information according to the second modification. [Figure 7] FIG. 7 is a diagram showing an example of a display screen of the severity and the severity degree according to the third modification. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the medical information processing device will be described in detail with reference to the drawings. In the following embodiments, parts with the same reference numerals perform the same operation, and duplicated explanations will be omitted as appropriate. Note that the medical information processing device according to the present application is not limited to the following embodiments.

[0010] The overall configuration of a medical information processing apparatus according to an embodiment will be described below. Fig. 1 is a diagram showing an example of the configuration of a medical information processing apparatus 1 according to an embodiment, together with a hospital information system (HIS) 3.

[0011] The medical information processing device 1 is connected to a hospital information system 3 via a communication network 31. First, the hospital information system 3 related to the medical information processing device 1 will be described, and then the medical information processing device 1 will be described. The hospital information system 3 has an electronic medical record system 33, a radiology department information system 35, a medical image diagnostic device 37, a medical image management system 39, a clinical examination department information system 41, and other department systems 43.

[0012] The electronic medical record system 33, the radiology department information system 35, the medical image diagnostic equipment 37, the medical image management system 39, the clinical testing department information system 41, and the other department systems 43 are connected to each other via a wired or wireless communication network 31.

[0013] The electronic medical record system 33 is an information system that manages electronic medical records for recording medical treatment details. The electronic medical record system 33 includes an electronic medical record server device and an electronic medical record terminal device.

[0014] The electronic medical record server device is a computer device that executes processes related to the management of electronic medical records. The electronic medical record server device is composed of one to multiple computer devices depending on the scale of the system. The electronic medical record terminal device is used by doctors, nurses, etc. who input and refer to the electronic medical records, and one to multiple devices are provided depending on the scale of the system. The electronic medical record server device and the electronic medical record terminal device are connected to a communication network 31.

[0015] The electronic medical record terminal device inputs an examination order for a patient according to a user's instruction. If the examination order is for imaging of a patient for image diagnosis, the examination order includes information on the examination contents such as the examination site (imaging target), disease name, examination purpose, and modality. The examination order may also include information such as the patient ID, patient name, examination ID, and examination date of the patient undergoing the examination.

[0016] In response to the input of an examination order, the electronic medical record server device transmits information regarding the examination order (hereinafter referred to as order information) together with the patient information of the patient in the order information to the radiology department information system 35. If the examination order is for the performance of a clinical test on the patient, the electronic medical record server device transmits the order information to the clinical laboratory department information system 41 in response to the input of the examination order.

[0017] The test results for all tests listed in the order information are linked to the patient's electronic medical record. At this time, the clinician who requested the test makes a diagnosis based on the test results and records the diagnosis in the patient's electronic medical record. The diagnosis includes information such as the diagnosis name, stage, and severity of the disease. The stage and severity of the disease are defined according to a predetermined definition document.

[0018] The electronic medical record terminal device inputs diagnosis results based on various tests performed on the patient, for example, in response to instructions from a clinician. The diagnosis results are stored in the electronic medical record server device.

[0019] The Radiology Information System (RIS) 35 is an information system that manages information in the radiology department in the hospital, and includes a radiology department information server device and a radiology department information terminal device. The radiology department information server device is a computer device that executes processes related to the management of information in the radiology department.

[0020] The radiology department information server device is composed of one to multiple computer devices depending on the scale of the system. The radiology department information terminal device is used by medical technicians in the radiology department, and one to multiple devices are provided depending on the scale of the system. The radiology department information server device and the radiology department information terminal device are connected to a communication network 31.

[0021] As an example of its operation, the radiology department information server device receives order information from the electronic medical record system 33 and acquires information on the medical image diagnostic device 37 from the order information. The radiology department information server device transmits the order information to the medical image diagnostic device 37 specified by the order information.

[0022] When the order information is transmitted to the medical image diagnostic apparatus 37, the radiology department information server device may add information such as a patient ID and an examination date to a DICOM (Digital Imaging and Communications in Medicine) tag or the like.

[0023] The medical image diagnostic apparatus 37 is an apparatus for acquiring medical images, such as an X-ray diagnostic apparatus, an X-ray CT (Computed Tomography) apparatus, and a magnetic resonance imaging apparatus. A plurality of medical image diagnostic apparatuses 37 may be connected via the communication network 31. The medical image diagnostic apparatus 37 performs imaging of a patient in response to an operation by a technician or the like to start imaging, based on an examination order from the radiology department information system 35.

[0024] By performing imaging, the medical image diagnostic device 37 collects data indicating the state of the patient's internal tissues. The medical image diagnostic device 37 then generates a medical image based on the data. The medical image diagnostic device 37 transmits the generated medical image together with order information to the medical image management system 39.

[0025] The medical image management system (PACS: Picture Archiving and Communication Systems) 39 is an information system that manages medical images such as X-ray CT images, MR images, etc. The medical image management system 39 may manage not only image data after reconstruction but also data before reconstruction (raw data).

[0026] The medical image management system 39 includes a medical image management server device. The medical image management server device is a computer device that executes processes related to management of medical images. The medical image management server device is composed of one or more computers depending on the scale of the system. The medical image management server device is connected to a communication network 31.

[0027] Upon receiving the medical images and order information from the medical image diagnostic apparatus 37, the medical image management server device stores the medical images and the order information in association with each other.

[0028] A laboratory information system (LIS) 41 is an information system that manages information in the clinical laboratory of a hospital, and includes a clinical laboratory information server device, a clinical laboratory information terminal device, and testing equipment.

[0029] In the clinical laboratory information system 41, a clinical laboratory technician performs specimen testing and physiological function testing based on the order information sent from the electronic medical record system 33. The specimen testing may be outsourced to an external testing organization such as a hygiene laboratory. The clinical laboratory information server device is a computer device that executes processes related to the management of information in clinical testing.

[0030] The clinical testing department information server device is composed of one to multiple computer devices depending on the scale of the system. The clinical testing department information terminal device is used by laboratory technicians in the clinical testing department, and one to multiple devices are provided depending on the scale of the system. The clinical testing department information server device and the clinical testing department information terminal device are connected to a communication network 31.

[0031] Testing equipment is equipment related to specimen testing, in which blood, urine, and other specimens obtained from patients are analyzed by clinical laboratory technicians, and physiological function testing, which measures the patient's electroencephalogram and electrocardiogram, etc. Examples of specimen testing include pathological testing, blood and biochemistry testing, general testing of urine and stool, immunoserological testing, microbiological testing, and tests related to blood transfusion and organ transplantation. Examples of physiological function testing include electroencephalogram testing, respiratory function testing, cardiac testing, and fundus photography testing.

[0032] A plurality of testing devices are installed according to the type of specimen test and the type of physiological function test. At least a part of the specimen test may be performed by an external testing institution. In this case, the clinical testing department information server device receives and stores the test results related to the specimen test from the external testing institution. The clinical testing department information server device also transmits the test results to the electronic medical record system 33.

[0033] The other department system 43 is, for example, a surgery department system, a rehabilitation department system, or a dialysis department system.

[0034] Similar to the radiology department information system 35 and clinical laboratory department information system 41 described above, the other department system 43 also creates surgery reports, rehabilitation reports, etc. in response to orders issued by doctors at electronic medical record terminal devices. The other department system 43 transmits the created surgery reports, rehabilitation reports, etc. to the electronic medical record system 33.

[0035] The hospital information system 3 stores and manages the medical information of each patient held in the electronic medical record system 33, medical image management system 39, clinical laboratory information system 41, etc., in association with the patient ID. Specifically, the hospital information system 3 stores and manages the medical information related to each patient ID in the chronological order of when imaging and examinations were performed. In other words, the medical information of each patient can be referenced in units of a point in time or period (hereinafter simply referred to as a point in time). Note that a "point in time" refers to a unit of consultation, such as the first visit or the second visit.

[0036] The medical information processing device 1 is connected to a hospital information system 3 via a communication interface 11 and a communication network 31. The medical information processing device 1 may be incorporated into the hospital information system 3. The medical information processing device 1 may also be used as a report creation device 36.

[0037] 1 includes a communication interface 11, a memory 13 (storage unit), a display 15 (display unit), an input interface 17 (input unit), and a processing circuit 19 (processing unit). Data communication between the communication interface 11, the memory 13, the display 15, the input interface 17, and the processing circuit 19 is performed via a bus (BUS).

[0038] The communication interface 11 performs data communication with the hospital information system 3. The standard for communication between the communication interface 11 and the hospital information system 3 may be any standard, for example, HL7 (Hearth Level 7), DICOM, or both.

[0039] The memory 13 is a storage device such as an HDD (Hard disk Drive), an SSD (Solid State Drive), or an integrated circuit storage device that stores various information. The memory 13 stores data generated by executing, for example, an acquisition function 193, an extraction function 195, a calculation function 197, a search function 199, and an estimation function 201. The generated data and these functions will be described later.

[0040] The memory 13 also stores, for example, a first severity calculation table for calculating a first severity level, which will be described later, and a second severity calculation table for calculating a second severity level, which will be described later.

[0041] The memory 13 may be a drive device that reads and writes various information between a portable storage medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a flash memory, or a semiconductor memory element such as a RAM (Random Access Memory), in addition to an HDD or SSD. The memory 13 also stores the control program according to this embodiment.

[0042] The display 15 displays various types of information. For example, the display 15 outputs a GUI (Graphical User Interface) for receiving various operations from a user. The display 15 is an example of a display device.

[0043] For example, the display 15 may be a liquid crystal display (LCD), a cathode ray tube (CRT) display, an organic electroluminescence display (OELD), a plasma display, or any other display, as appropriate.

[0044] The display 15 may be a desktop type, or may be configured as a tablet terminal or the like capable of wireless communication with the medical information processing device 1 main body.

[0045] The input interface 17 accepts various input operations from a user, converts the accepted input operations into electrical signals, and outputs the electrical signals to the processing circuit 19. For example, the input interface 17 accepts selection instructions, various conditions, etc. from the user. As the input interface 17, for example, a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch pad, a touch panel display, etc. can be used as appropriate.

[0046] In this embodiment, the input interface 17 is not limited to being equipped with physical operation parts such as a mouse, keyboard, trackball, switch, button, joystick, touch pad, and touch panel display.

[0047] For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs the electrical signal to the processing circuit 19 is also included in the examples of the input interface 17. In addition, the input interface 17 may be configured as a tablet terminal or the like capable of wireless communication with the main body of the medical information processing device 1.

[0048] The processing circuitry 19 controls the overall operation of the medical information processing device 1 in response to an electrical signal of an input operation output from the input interface 17 .

[0049] For example, the processing circuitry 19 has, as hardware resources, a processor such as a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or a GPU (Graphics Processing Unit), and a memory such as a ROM (Read Only Memory) or a RAM.

[0050] In addition, the processing circuit 19 may be realized by an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), other complex programmable logic devices (CPLDs), simple programmable logic devices (SPLDs), etc.

[0051] The processing circuitry 19 has a system control function 191 , an acquisition function 193 , an extraction function 195 , a calculation function 197 , a search function 199 , an estimation function 201 and a display control function 203 .

[0052] The system control function 191, the acquisition function 193, the extraction function 195, the calculation function 197, the search function 199, the estimation function 201 and the display control function 203 are each stored in the memory 13 in the form of a program executable by a computer.

[0053] The processing circuit 19 executes a system control function 191, an acquisition function 193, an extraction function 195, a calculation function 197, a search function 199, an estimation function 201, and a display control function 203 by a processor that executes a program loaded in a memory.

[0054] That is, the processing circuitry 19 corresponds to a processor that realizes a function corresponding to each program by reading and executing the programs from the memory 13. In other words, the processing circuitry 19 in a state in which each program has been read has a function corresponding to the read program.

[0055] Each function (191, 193, 195, 197, 199, 201, 203) is not limited to being realized by a single processing circuit. A processing circuit may be configured by combining multiple independent processors, and each processor may execute a program to realize each function (191, 193, 195, 197, 199, 201, 203).

[0056] The calculation function 197 is an example of a first calculation unit and a second calculation unit. The estimation function 201 is an example of an estimation unit. The display control function 203 is an example of a display control unit.

[0057] The system control function 191 controls each function of the processing circuitry 19 based on an input operation received from a user via the input interface 17. Specifically, the system control function 191 reads out a control program stored in the memory 13, expands it on the memory in the processing circuitry 19, and controls each part of the medical information processing device 1 according to the expanded control program.

[0058] The acquisition function 193 acquires various information. For example, the acquisition function 193 acquires patient information of a patient (target patient) who is the subject of diagnosis. The patient information is, for example, information such as a patient ID for identifying each patient, age, sex, height, weight, address, occupation, employment type, medical history, current illness (underlying illness), and current symptoms of the target patient.

[0059] Specifically, the acquisition function 193 acquires patient information of a patient to be diagnosed from the electronic medical record system 33 via, for example, the communication interface 11. The patient information acquired by the acquisition function 193 is stored in the memory 13 as, for example, first medical information.

[0060] The acquisition function 193 also acquires, for example, medical image information related to a disease to be diagnosed (for example, COVID-19, etc.). For the sake of specificity, the following description will be given assuming that the disease to be diagnosed (hereinafter also referred to as the target disease) is COVID-19. The medical image information is information including at least one medical image data obtained from a medical image diagnostic device such as an X-ray CT (Computed Tomography) device.

[0061] Specifically, the acquisition function 193 acquires CT image data of the lung field region of the target patient imaged by the X-ray CT apparatus from the medical image management system 39 via, for example, the communication interface 11. The CT image data acquired by the acquisition function 193 is stored in the memory 13 as, for example, first medical information.

[0062] The acquisition function 193 also acquires, for example, test information related to the target disease. The test information is information including at least one test result of, for example, a specimen test or a physiological function test.

[0063] Specifically, the acquisition function 193 acquires measurement data of the target patient, such as red blood cell count, HbA1c, glycoalbumin, CGM / FGM, etc., from the clinical laboratory information system 41 via, for example, the communication interface 11. The measurement data acquired by the acquisition function 193 is stored in the memory 13 as, for example, first medical information.

[0064] Here, we explain why red blood cell count, HbA1c, glycoalbumin, CGM / FGM, etc. are used as indicators of the severity of COVID-19. Diabetes is known to be one of the underlying diseases that can cause COVID-19 to worsen.

[0065] Diabetic patients may fall into a state of severe hypoglycemia due to taking medications at the wrong dosage, not eating at the right timing, feeling unwell, etc. It is presumed that when diabetic patients fall into a state of severe hypoglycemia, oxygen transport by red blood cells decreases, causing anemia / decreased function of tissues.

[0066] Therefore, in this embodiment, red blood cell count, HbA1c, glycoalbumin, CGM / FGM, etc., which are thought to decrease due to anemia / decreased function of tissues, are used as indicators of the worsening of COVID-19. For example, when the target disease is COVID-19, measurements such as oxygen saturation and heart rate, which are thought to be related to the worsening of COVID-19, may be used as indicators.

[0067] It is possible to arbitrarily set the part and type of medical image and the examination item of the examination information acquired by the acquisition function 193. Specifically, by making it possible to arbitrarily set the medical information acquired by the acquisition function 193 via the input interface 17, etc., when knowledge is obtained about a new disease (e.g., Kawasaki disease, etc.) that leads to the aggravation of COVID-19, the relationship between the disease and COVID-19 can be analyzed by setting medical information related to the disease as the acquisition target.

[0068] The extraction function 195 extracts a target region of a target disease from the medical image information acquired by the acquisition function 193. The extraction function 195 extracts a target region from, for example, CT image data of a target patient.

[0069] Specifically, for example, when the target region in the medical image information is the lung, the extraction function 195 performs a known segmentation process to extract partial regions of the lung (right upper lobe, right middle lobe, right lower lobe, left upper lobe, and left lower lobe) contained in the CT image data.

[0070] The calculation function 197 calculates a functional ratio that quantitatively indicates the degree to which a part related to the patient's current symptoms is functioning normally, based on the first medical information including the patient's current symptoms. The calculation function 197 also calculates the current condition of the target patient as a severity, based on the functional ratio. The severity is an example of the first condition. The first medical information includes medical image information, examination information, and patient information.

[0071] Specifically, the calculation function 197 calculates, for example, a first functionality ratio for each of the areas of the right upper lobe, the right middle lobe, the right lower lobe, the left upper lobe, and the left lower lobe extracted by the extraction function 195, based on medical image information, the first functionality ratio quantitatively indicating the degree to which the area is functioning normally or not.

[0072] As an example, the first functional rate can be calculated by dividing the difference between the number of pixels in a normal region indicating a region that functions normally and the number of pixels in an abnormal region indicating a region that functions abnormally by the number of pixels in the entire region, and multiplying the result by 100. In other words, the first functional rate is a value indicating the ratio of normal regions to abnormal regions. For example, when no abnormal region exists in a specific partial region, the calculation function 197 calculates the functional rate of the partial region as 100 percent.

[0073] Here, a normal region is a region made up of pixels whose pixel values ​​(brightness values) are equal to or greater than a predetermined threshold, and an abnormal region is a region made up of pixels whose pixel values ​​are less than a threshold. The threshold pixel value is, for example, the average or median value of all pixels in the region.

[0074] Incidentally, for example, it is thought that there will be a difference in the extent to which a decline in overall lung function will be caused when the functional rate of the right upper lobe becomes 50 percent and when the functional rate of the left lower lobe becomes 50 percent.

[0075] Therefore, the calculation function 197 determines a correction coefficient (also called a weight) representing the degree of contribution of each lung region to the function of the entire lung, and multiplies the first performance ratio by the correction coefficient to calculate the corrected first performance ratio.

[0076] Then, the calculation function 197 calculates the first functionality ratio of the entire lung from the corrected first functionality ratios of each lung region. For example, the calculation function 197 determines correction coefficients for each region so that the total sum is 1, and adds up the corrected first functionality ratios of each region to calculate the first functionality ratio of the entire lung.

[0077] In this embodiment, the calculation function 197 is configured to divide the target area into partial areas and calculate the first functionality rate, but this is not limited to the above, and the first functionality rate may be calculated for the entire target area (e.g., the entire lungs) without dividing the target area.

[0078] Furthermore, when the calculation function 197 calculates the first functional rate of the target region, it refers to a first severity calculation table that associates the first functional rate with the first severity based on medical image information, and calculates (specifies) the first severity corresponding to the functional rate of the target region. It calculates it as the first severity. The first severity calculation table is a table in which the first functional rate and the first severity are associated with each other so that, for example, the lower the first functional rate, the higher the first severity.

[0079] In the present embodiment, the calculation function 197 is configured to calculate the first severity using the first severity calculation table, but is not limited thereto, and the calculation function 197 may calculate the first severity using a relational expression that defines the relationship between the first functional rate and the first severity. Also, the calculation function 197 may calculate the first severity for each partial region from the first functional rate of each partial region.

[0080] Furthermore, the calculation function 197 calculates a second severity based on the test information based on the test value of a test item related to the disease of the target patient, among the test values ​​of a plurality of test items included in the test information.

[0081] Specifically, the calculation function 197, for example, obtains an observation value from the test data, and calculates a second functional ratio based on the test information based on the observation value. Here, the observation value is a value calculated by subtracting a functional limit value from a measured value. The functional limit value is a value below which (or above which) the target area will no longer function. The functional limit value is set to a different value for each test.

[0082] Incidentally, various tests (specimen tests) are performed in medical facilities, and some test items are important in diagnosing a target disease, while other test items do not require special attention. Therefore, the calculation function 197 determines a correction coefficient representing the importance of each test item, and multiplies the second functional rate by the correction coefficient to calculate the corrected second functional rate. Note that since the importance of test items differs depending on the target disease, the above correction coefficient is determined for each target disease.

[0083] The calculation function 197 calculates a comprehensive second functional rate from the corrected functional rates for each test. Specifically, the calculation function 197 calculates, for example, an average value of the corrected second functional rates for each test as the comprehensive second functional rate. Then, the calculation function 197 refers to a second severity calculation table that associates the second functional rate with the second severity, and calculates the second severity corresponding to the calculated second functional rate as the second severity.

[0084] The second severity calculation table is a table in which the second functional rate and the second severity are associated with each other so that the lower the second functional rate, the higher the second severity. In the present embodiment, the calculation function 197 is configured to calculate the second severity using the second severity calculation table, but is not limited to this. The calculation function 197 may calculate the second severity using a relational expression that specifies the relationship between the second functional rate and the second severity. The calculation function 197 may also calculate the second functional rate for each test item from the second functional rate for each test item.

[0085] The calculation function 197 calculates the third severity, which is the overall (final) severity of the target patient, based on the first severity and the second severity. For example, the calculation function 197 calculates the average value of both and rounds off the decimal point to calculate the third severity. Also, for example, the calculation function 197 calculates the larger value of the first severity and the second severity as the third severity. Also, for example, the calculation function 197 may calculate the third severity after determining a correction coefficient indicating which of the medical image information and the examination information is to be emphasized.

[0086] The search function 199 searches for similar patients whose condition is similar to that of the target patient at the time of examination. Specifically, the search function 199 calculates a similarity indicating the degree of similarity between the first medical information of the target patient and the medical information of the patient contained in the hospital information system 3, and searches for patients who have tendencies similar to the first medical information (hereinafter also referred to as similar patients) based on the similarity.

[0087] For example, the search function 199 searches the hospital information system 3 (the storage devices of each device included in the system) and calculates the similarity of medical image information, examination information, and patient information for each patient. The search function 199 calculates an overall similarity for each patient from these similarities. The search function 199 then determines a patient whose overall similarity exceeds a threshold as a similar patient. Note that if there is no patient whose overall similarity exceeds the threshold, the patient with the highest similarity is determined to be a similar patient.

[0088] The medical information of similar patients found in the search by the search function 199 is stored as second medical information, for example, in the memory 13. The memory 13 is an example of a storage device.

[0089] The prediction function 201 predicts the future condition of the target patient as a degree of severity based on a function rate calculated based on the second medical information of the similar patient's time series from the point in time when similarity with the first medical information was confirmed.

[0090] The severity level is an example of the second condition. The second medical information may or may not include the third severity level, but in this embodiment, the second medical information is described as not including the third severity level.

[0091] Specifically, the prediction function 201 predicts the future condition of the target patient using the second medical information (medical image information, examination information, patient information) of similar patients in time series stored in the memory 13, from the point in time when similarity with the first medical information was confirmed.

[0092] More specifically, the prediction function 201 first calculates a function rate at each time point from the second medical information in the time series after the time point when the similarity with the first medical information is confirmed, in cooperation with the calculation function 197. Then, the prediction function 201 derives the predicted severity based on the function rate at each time point in the time series as a severity degree indicating the future condition of the target patient.

[0093] For example, the prediction function 201 derives the severity calculated based on the functionality at the time when the functionality is the lowest (the time when the functionality is the most severe) after the time when the similarity with the first medical information is confirmed as the severity. Note that the prediction function 201 may derive the average severity at each time point in the time series as the severity, or may derive the range between the severity at the time when the functionality is the minimum and the severity at the time when the functionality is the maximum as the severity.

[0094] When there are multiple similar patients, the prediction function 201 derives the severity from the function rate calculated for each similar patient at each time point after the similarity with the first medical information is confirmed.

[0095] For example, the prediction function 201 may derive the severity level as the average of the severity levels calculated based on the functional rates at each time point calculated for each similar patient, or may derive the severity level as the maximum of the severity levels calculated based on the functional rates at each time point calculated for each similar patient.

[0096] Furthermore, the prediction function 201 may derive, as the severity level, a range between the minimum severity level and the maximum severity level among the severity levels calculated based on the function rate at each time point calculated for each similar patient.

[0097] The display control function 203 causes a screen displaying the severity and severity level of the target patient to be displayed on the display 15. Here, the display of the severity and severity level will be described with reference to Fig. 2 and Fig. 3. Fig. 2 and Fig. 3 are diagrams showing an example of a screen displayed by the display control function 203.

[0098] As shown in Figure 2, the display screen 15a has a first medical image display area I1, a second medical image display area I2, a schematic diagram display area R1, a first severity display area R2, a second severity display area R3, a third severity display area R4, a similarity display area R5, a severity display area R6, a first underlying disease list display area T1, a first test item display area T2, a first test result display area T3, a second underlying disease list display area S1, a second test item display area S2, and a second test result display area S3.

[0099] The first medical image display area I1 is an area for displaying a medical image of a target patient (e.g., a CT image of a lung field area). In the first medical image display area I1, for example, the latest medical image or a medical image that is the subject of calculation of the first severity level is displayed.

[0100] The second medical image display area I2 is an area for displaying medical images of similar patients. In the second medical image display area I2, medical images determined to be similar to the target patient and medical images that are the subject of calculation of the severity are displayed. When there are multiple similar patients, for example, the medical image of the similar patient most similar to the target patient may be displayed in the second medical image display area I2, or the medical image of one similar patient selected from a similar patient list L3 described later may be displayed.

[0101] In this way, the display control function 203 displays the medical image of the target patient and the medical image of the similar patient side by side on the display screen 15a, allowing the user to easily compare the medical image of the target patient with the medical image of the similar patient.

[0102] Furthermore, the display control unit 205 displays the normal and abnormal regions determined by the calculation function 197 in a distinguishable manner in the displayed medical images of the target patient and similar patient. For example, the display control unit 205 changes the display colors of the normal and abnormal regions to display both regions in a distinguishable manner. Note that FIG. 2 shows an example in which the normal and abnormal regions are displayed in a distinguishable manner by changing the line type of hatching. This makes the normal and abnormal regions clear, so that the user can easily compare the region where the abnormal region exists (target region) and the size of the normal and abnormal regions between the target patient and similar patients.

[0103] The schematic diagram display area R1 is an area for displaying a schematic diagram of a target area related to a target disease. Figure 2 shows an example of a schematic diagram in which a lung field area is divided into partial areas of the right upper lobe, right middle lobe, right lower lobe, left upper lobe, and left lower lobe, and is displayed in the schematic diagram display area R1.

[0104] Furthermore, when the display control function 203 receives a selection operation of a partial region displayed in the schematic diagram display region R1, it displays detailed information such as the first functional rate of the target patient calculated for the selected partial region. Specifically, when the display control function 203 receives a selection from the user of any one of the right upper lobe, the right middle lobe, the right lower lobe, the left upper lobe, and the left lower lobe, it displays detailed information of the selected partial region.

[0105] For example, when a selection input of the right middle lobe is received from the user, the display control function 203 displays detailed information F1 regarding the right middle lobe in association with the right middle lobe displayed in the schematic diagram display area R1, as shown in Fig. 3. The detailed information F1 includes, for example, the name of the partial area (right middle lobe), the first functional ratio of the right middle lobe calculated by the calculation function 197, and the first severity calculated from the first functional ratio of the right middle lobe.

[0106] Also, for example, when a selection input of the left upper lobe is received from the user, the display control function 203 displays detailed information F2 of the left upper lobe in association with the left upper lobe displayed in the schematic diagram display area R1, as shown in Fig. 3. The contents are the same as above, so a description is omitted. Although not shown, the display control function 203 also performs the same processing as above when a selection input of another partial area is received from the user. This allows the user to easily check the first functional rate and the first severity for each partial area calculated for the target patient.

[0107] Note that the method of selecting a partial region is not particularly limited. For example, the selection may be accepted by a single click, a mouse-over operation, or the like. Furthermore, when the display control function 203 accepts an input from the user to erase the display of the detailed information (for example, a single click or a mouse-out operation while the detailed information is displayed), the display of the detailed information is erased.

[0108] Returning to Fig. 2, the explanation will be continued. The first severity display area R2 is an area for displaying the first severity of the target patient calculated based on the medical image information. The display control function 203 causes the first severity of the target patient calculated by the calculation function 197 from the medical image information to be displayed in the first severity display area R2.

[0109] Furthermore, the display control function 203 displays a list of medical images used to calculate the first severity level according to an instruction from the user. Specifically, when the display control function 203 receives a selection operation of the first severity level display area R2 from the user, the display control function 203 displays the medical image list L1 in association with the first severity level display area R2 as shown in FIG.

[0110] This allows the user to easily confirm what medical image the first severity level is based on.

[0111] When the display control function 203 receives a selection input of a medical image in the medical image list L1 from the user, the display control function 203 may display the selected medical image in the first medical image display area I1. When the display control function 203 receives an input to erase the display of the medical image list L1 from the user (for example, a single click or mouse out operation while the medical image list LI is displayed), the display control function 203 erases the display of the medical image list L1.

[0112] Returning to Fig. 2, the explanation will be continued. The second severity display area R3 is an area for displaying the second severity of the target patient calculated based on the examination information. The display control function 203 causes the second severity of the target patient calculated by the calculation function 197 to be displayed in the second severity display area R3.

[0113] Furthermore, the display control function 203 displays a list of the test items used to calculate the second severity level according to an instruction from the user. Specifically, when the display control function 203 receives a selection operation of the second severity level display area R3 from the user, the display control function 203 displays the test item list L2 in association with the second severity level display area R3 as shown in FIG.

[0114] This allows the user to easily confirm what test items the second severity level is based on in calculation.

[0115] In addition, when the display control function 203 receives an input from the user to erase the display of the examination item list L2 (for example, a single click or mouse out operation while the examination item list L2 is displayed), it erases the display of the examination item list L2.

[0116] Returning to Fig. 2, the explanation will be continued. The third severity display area R4 is an area for displaying the final severity of the target patient. The display control function 203 causes the final severity calculated by the calculation function 197 to be displayed in the third severity display area R4.

[0117] The similarity display area R5 is an area for displaying the similarity between the target patient and similar patients. The display control function 203 causes the overall similarity calculated by the estimation function 201 to be displayed in the similarity display area R5.

[0118] Furthermore, in response to an instruction from the user, the display control function 203 displays a list of similar patients found in a search performed by the search function 199. Specifically, as shown in Fig. 3, when the display control function 203 receives a selection input from the user of the similarity display area R5, it displays the similar patient list L3 superimposed on the severity and severity display screen 15a.

[0119] The display control function 203 displays in the similar patient list L3 a list of similar patients found in the search performed by the search function 199. Furthermore, when multiple similar patients are displayed in the similar patient list L3, the display control function 203 accepts an input for selecting one similar patient from the user. The processing after the selection of one similar patient will be described later.

[0120] It should be noted that when the display control function 203 receives an input from the user to erase the display of the similar patient list L3 (for example, a single click or mouse out operation while the similar patient list L3 is displayed), it erases the display of the similar patient list L3.

[0121] Furthermore, the display control function 203 displays the degree of progression of symptoms related to the target disease of similar patients in response to an instruction from the user. Specifically, when an instruction to display the degree of progression of one similar patient based on the similar patient list L3 is received, the display control function 203 displays a progress display L4 of the selected similar patient, as shown in Fig. 3.

[0122] The degree of progression is information including, for example, whether similar patients have died, the number of days of hospitalization, the progress of the measurement results of a quantitative antibody test, whether a blood clot has been found (whether Kawasaki disease-like symptoms have been found), the presence or absence of aftereffects, etc. Furthermore, the display control function 203 may display, in addition to the degree of progression, information regarding treatment including the presence or absence of medication (if medication has been administered, the type of medication, the dosage, and the number of administrations), etc., on the degree of progression display L4.

[0123] This allows the user to easily learn about the progress of the target disease in similar patients, and makes it possible to use the obtained information in treating the target patient.

[0124] For example, the instruction to display the progress degree may be in the form of displaying a progress degree display button on the display screen 15a in response to an operation of the progress degree display button. Also, when the display control function 203 receives an input to erase the display of the progress degree display L4 (for example, a single click or a mouse-out operation while the progress degree display L4 is displayed), the display control function 203 erases the display of the progress degree display L4.

[0125] Returning to Fig. 2, the explanation will be continued. The severity display area R6 is an area for displaying the severity of the target patient. The display control function 203 causes the severity calculated by the estimation function 201 to be displayed in the severity display area R6.

[0126] The first underlying disease list display area T1 is an area for displaying information on the underlying diseases of the target patient. Specifically, the display control function 203 displays a list of underlying diseases related to the target disease among the underlying diseases included in the patient information in the first underlying disease list display area T1.

[0127] In the example of FIG. 2 (FIG. 3), an example is shown in which the target patient suffers from diabetes as an underlying disease. In this case, since the patient information of the target patient includes information indicating that the patient suffers from diabetes, the display control function 203 checks and displays diabetes included in the underlying disease list based on the underlying disease in the patient information. This makes it possible to know that the target patient suffers from diabetes. In addition, since the display control function 203 does not check and display underlying diseases such as COPD (chronic obstructive pulmonary disease) that the target patient does not suffer from, it is possible to know that the target patient does not suffer from COPD.

[0128] The first test item display area T2 is an area for displaying test items related to the calculation of the second severity of the target patient. The display control function 203, for example, displays the test items selected by the user in the first test item display area T2.

[0129] Specifically, the display control function 203 displays all test items related to the target disease in the first test item display area T2, and when a user selects a test item, displays a check mark on the selected test item. In the examples of Figures 2 and 3, the display control function 203 displays a check mark on the CGM / FGM displayed in the first test item display area T2.

[0130] The first test result display area T3 is an area that displays the test results of the test items displayed in the first test item display area T2. Specifically, when a check mark is displayed on the CGM / FGM displayed in the first test item display area T2, the display control function 203 displays a graph showing the time series changes in the CGM / FGM measurement results in the first test result display area T3.

[0131] The second underlying disease list display area S1 is an area for displaying information on the underlying diseases of similar patients. The second test item display area S2 is an area for displaying test items that the user pays attention to among the test items used to calculate the severity of similar patients. The second test result display area S3 is an area for displaying the test results of the test items displayed in the second test item display area S2.

[0132] The contents displayed in the second underlying disease list display area S1, the second test item display area S2, and the second test result display area S3 are similar to those in the first underlying disease list display area T1, the first test item display area T2, and the first test result display area T3, and therefore will not be described here. Note that similar patients to be displayed in the second underlying disease list display area S1, the second test item display area S2, and the second test result display area S3 can be selected via, for example, a similar patient list L3.

[0133] In this way, the display control function 203 displays the underlying diseases and the measurement data of each test item of the target patient and the similar patient side by side, allowing the user to easily compare the underlying diseases and the measurement data of the target patient and the similar patient.

[0134] The following describes the processing executed by the above-mentioned medical information processing device 1. Fig. 4 is a flowchart showing an example of the processing executed by the medical information processing device 1.

[0135] First, the acquisition function 193 acquires information such as the patient ID, age, sex, height, weight, address, occupation, working style, medical history, underlying disease name, and symptoms of the target patient as patient information from the hospital information system 3 (step ST1). The patient information acquired by the acquisition function 193 is stored in the memory 13 as the first medical information.

[0136] The acquiring function 193 acquires CT image data of the lung field region of the target patient as medical image information from the electronic medical record system 33 (step ST2). The medical image information acquired by the acquiring function 193 is stored in the memory 13 as first medical information.

[0137] The acquisition function 193 acquires measurement data such as red blood cell count, HbA1c, glycoalbumin, CGM / FGM, oxygen saturation, and heart rate of the target patient as test information from the electronic medical record system 33 (step ST3). The test information acquired by the acquisition function 193 is stored in the memory 13 as first medical information.

[0138] The extraction function 195 performs a segmentation process to extract partial lung regions (right upper lobe, right middle lobe, right lower lobe, left upper lobe, and left lower lobe) contained in the CT image data of the lung field region of the target patient acquired by the acquisition function 193 (step ST4).

[0139] The calculation function 197 calculates a first functionality rate for each partial region extracted by the extraction function 195. The calculation function 197 calculates a first severity level for the entire lung field region by combining the first functionality rates for each partial region (step ST5).

[0140] The calculation function 197 calculates a second function rate for each of the test items based on the test information acquired by the acquisition function 193. The calculation function 197 calculates a second severity of the entire test information by summing up the second function rates of each test item (step ST6).

[0141] The calculation function 197 calculates a third severity level based on the first severity level calculated in step ST5 and the second severity level calculated in step ST6 (step ST7).

[0142] The search function 199 searches the hospital information system 3 for similar patients that match or are similar to the search key using the medical images and the measurement results of the examination items included in the first medical information as search keys (step ST8). The medical information of similar patients that is found in the search by the search function 199 is stored in the memory 13 as second medical information.

[0143] The estimation function 201 causes the calculation function 197 to calculate the third severity of the similar patient based on the second medical information of the similar patient found in the search by the search function 199, which was obtained after the similarity to the target patient was confirmed (step ST9).

[0144] The prediction function 201 calculates the severity level based on the third severity level of the similar patient calculated by the calculation function 197 (step ST10).

[0145] The display control function 203 causes the display 15 to display the above-mentioned display screen 15a based on the third severity calculated by the calculation function 197 and the severity calculated by the prediction function 201, and ends this process (step ST11).

[0146] According to the medical information processing device 1 of the embodiment described above, the calculation function 197 calculates the condition of the disease suffered by the target patient as the severity based on the first medical information obtained from the target patient, the prediction function 201 predicts the future condition of the target patient as the severity based on the second medical information of a similar patient having elements similar to the first medical information from a storage device that stores second medical information of patients who are undergoing treatment or whose treatment has been completed, and the display control function 203 displays the severity and the severity on the display 15.

[0147] In this way, the calculation function 197 calculates the severity of the target patient, and a user such as a doctor can know the current condition of the target patient as an objective numerical value. In addition, the prediction function 201 can calculate the severity of the target patient if there is the severity of the target patient calculated by the calculation function 197, so that even if the target patient is a first-time patient, for example, the future condition of the target patient can be predicted to some extent.

[0148] That is, the medical information processing device 1 according to this embodiment can predict the possibility that an infectious disease will become severe at an early stage.

[0149] The above-described embodiment can be modified as appropriate by changing a part of the configuration or function of each device. Therefore, some modified examples of the above-described embodiment will be described below as other embodiments. Note that the following mainly describes the differences from the above-described embodiment, and a detailed description of the common points with the contents already described will be omitted. The modified examples described below may be implemented individually or in appropriate combination.

[0150] (Variation 1) In the above-described embodiment, the calculation function 197 calculates the first performance rate by using the average or median pixel value of all pixels in the region as the threshold pixel value, but the threshold pixel value may also be set by the user.

[0151] In this case, the calculation function 197 displays, for example, a histogram for pixels in the partial region, and allows the user to specify a threshold (reference value) for the normal region. Fig. 5 is a diagram showing an example of a threshold setting screen for pixel values. A histogram for pixels in the partial region is displayed by the display control function 203 on the threshold setting screen 15b.

[0152] Furthermore, on the threshold setting screen 15b, a selection cursor C is displayed by the display control function 203. The user moves the selection cursor C to an arbitrary position to input a threshold (reference value). The calculation function 197 calculates the first function rate using the threshold (reference value) determined by the user.

[0153] According to this modified example, the user can check the distribution of pixel values ​​within the region. Therefore, even in situations where it is inappropriate to use the average or median as the threshold, such as when there is a large bias in the distribution of pixel values ​​and using the average or median as the threshold would result in an extremely large increase in abnormal regions, the user can set an appropriate value as the threshold based on the distribution of pixel values.

[0154] (Variation 2) In the above-described embodiment, the extraction function 195 has been described as dividing and extracting partial regions (right upper lobe, right middle lobe, right lower lobe, left upper lobe, and left lower lobe) of the lung field area from the CT image data. However, the extraction function 195 may also be configured to extract areas of underlying diseases such as COPD or areas where characteristic findings are observed as abnormal areas.

[0155] This modification will be described using an example of a subject patient whose CT image data shows emphysema due to COPD in the right upper lobe of the lung and characteristic findings of COVID-19 in the left upper lobe of the lung. FIG. 6 is a diagram showing an example of medical image information according to the modification 2.

[0156] The extraction function 195 extracts, from the CT image data, a region in which emphysema due to COPD is observed as an abnormal region. The extraction function 195 also extracts, from the CT image data, a region in which characteristic findings of COVID-19 are observed as an abnormal region.

[0157] The calculation function 197 calculates a first functional rate for each of an area where emphysema due to COPD is observed and an area where characteristic findings of COVID-19 are observed. In addition, the calculation function 197 calculates a first severity level for each abnormal area individually.

[0158] The display control function 203 displays areas where emphysema due to COPD is observed and areas where characteristic findings of COVID-19 are observed on the CT image data in a distinguishable manner (for example, by displaying each area in a different color).

[0159] Furthermore, the display control function 203 displays detailed display areas F3 and F4. In the detailed display areas F3 and F4, the display control function 203 displays the disease name (finding name), the (first) functional rate, and the (first) severity.

[0160] In this example, in the detailed display area F3, the display control function 203 displays "emphysema due to COPD," "functionality rate: 60%, and severity level: 2." In addition, in the detailed display area F4, the display control function 203 displays "COVID-19," "functionality rate: 20%, and severity level: 3."

[0161] According to this modification, it is possible to grasp the state of the diseased part that is difficult to grasp from the first functional rate and the first severity of each partial region extracted focusing on the morphology. For example, when the diseased part spans a plurality of partial regions, the influence of the disease is dispersed to each partial region, making it difficult to see the influence of the disease. In contrast, in this modification, the calculation function 197 calculates the first functional rate and the first severity focusing on the diseased part, so that the user can accurately grasp the degree to which the function of the diseased part has deteriorated.

[0162] (Variation 3) In the above embodiment, the prediction function 201 predicts the future condition of the target patient based on the second medical information, but the prediction function 201 may also predict the future condition of the target patient based on changes over time in the target disease indicated in the first medical information.

[0163] 7 is a diagram showing an example of a display screen according to Modification 3. On a display screen 15c according to Modification 3, a display control function 203 causes a past test result display area T4 to be displayed instead of the second underlying disease list display area S1, the second test item display area S2, and the second test result display area S3. Also, a medical image of a target patient captured in the past is displayed in the second medical image display area I2.

[0164] In the past test result display area T4, past measurement data of the target patient for the test items displayed in the first test item display area T2 is displayed by the display control function 203. By displaying the latest data and past data of the target patient side by side, the user can easily grasp the change over time of the target disease of the target patient.

[0165] The display control function 203 may display two or more pieces of past measurement data in parallel with the latest data.

[0166] According to this modification, the prediction function 201 predicts the future condition of the target patient based on the progress of the target patient's own disease, and therefore it is considered possible to predict the future condition of the target patient with higher accuracy.

[0167] (Variation 4) In the above embodiment, the calculation function 197 calculates the first functional rate based on the ratio of a normal area indicating an area functioning normally, extracted from a target area related to a disease of a target patient, to an abnormal area indicating an area with abnormal function. However, the calculation function 197 may calculate the first functional rate using a trained model generated by deep learning or other machine learning techniques.

[0168] In this case, the trained model is a model trained by associating medical image information with the first functional rate. The calculation function 197 inputs medical image information (one or more CT image data, etc.) to the trained model and calculates the first functional rate output by the trained model as the first functional rate.

[0169] (Variation 5) In the above embodiment, the calculation function 197 calculates the second performance rate based on the test value and a coefficient predetermined for each test item. However, the calculation function 197 may calculate the second performance rate using a trained model generated by deep learning or other machine learning techniques.

[0170] In this case, the trained model is a model trained by associating the test information (one or more pieces of measurement data) with the second function rate. The calculation function 197 inputs the test information (one or more pieces of measurement data, etc.) to the trained model and calculates the second function rate output by the trained model as the second function rate.

[0171] (Variation 6) In the above-described embodiment, the calculation function 197 calculates the first severity based on the first functional rate, calculates the second severity based on the second functional rate, and calculates the third severity based on the first severity and the second severity. However, the calculation function 197 may calculate the third severity using a trained model generated by deep learning or other machine learning techniques.

[0172] In this case, the trained model is a model trained by associating medical image information, examination information, and the third severity. The calculation function 197 inputs medical image information (one or more CT image data, etc.) and examination information (one or more measurement data, etc.) to the trained model, and calculates the third severity output by the trained model as the third severity.

[0173] In addition, the calculation function 197 may calculate the first severity by inputting medical image information into a trained model that has been trained in such a way that the medical image information and the first severity are associated with each other, calculate the second severity by inputting examination information into a trained model that has been trained in such a way that the examination information and the second severity are associated with each other, and calculate the third severity based on the first severity and the second severity.

[0174] According to at least one of the embodiments described above, it is possible to predict the possibility that an infectious disease will become severe at an early stage.

[0175] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0176] 1 Medical information processing device 3. Hospital Information System 11 Communication Interface 13. Memory 15 Display 17 Input Interface 19 Processing circuit 31 Communication Networks 33 Electronic Medical Record System 35 Radiology Department Information System 37 Medical imaging diagnostic equipment 39 Medical Image Management System 41 Clinical Laboratory Department Information System 43 Other department systems 191 System Control Functions 193 Acquisition Function 195 Extraction Function 197 Calculation Function 199 Search function 201 Guessing Function 203 Display control function

Claims

1. a first calculation unit that calculates a functional rate that quantitatively indicates a degree of whether a part related to a current symptom of a subject patient is functioning normally, based on first medical information including the current symptom of the subject patient; A second calculation unit that calculates a current state of the subject patient as a first state based on the functional rate; a prediction unit that predicts a future state of the target patient as a second state based on the functional rate calculated based on the second medical information from a time-series of second medical information of a similar patient having a tendency similar to the first medical information after a time point when the similarity with the first medical information is confirmed; a display control unit that displays the first state and the second state in a comparable manner; A medical information processing device comprising:

2. The first medical information includes medical image information obtained by imaging the target patient, 2. The medical information processing device according to claim 1, wherein the first calculation unit calculates the functional rate based on a ratio of a normal area indicating an area functioning normally and an abnormal area indicating an area having an abnormal function, the normal area being extracted from a target area related to a disease of the target patient represented in the medical image information.

3. The first calculation unit divides the target region into a plurality of partial regions, and calculates the functional ratio based on a ratio between the normal region and the abnormal region calculated for each of the partial regions. The medical information processing device according to claim 2 .

4. The second calculation unit calculates the first state based on the function rate calculated based on the ratio between the normal region and the abnormal region calculated for each of the partial regions by the first calculation unit in the previous period and a coefficient predetermined for each of the partial regions. The medical information processing device according to claim 3 .

5. The first medical information includes examination information obtained by examining the target patient, The first calculation unit calculates the functional rate based on a test value of a test item related to a disease of the target patient among test values ​​of a plurality of test items included in the test information. The medical information processing device according to claim 1 .

6. When there are a plurality of test items related to the disease of the target patient, the second calculation unit calculates the first state based on the functional ratio calculated by the first calculation unit based on test values ​​of the test items related to the disease of the target patient and a coefficient predetermined for each of the test items. The medical information processing device according to claim 5 .

7. the prediction unit predicts the second state based on a change over time of a disease of the target patient indicated in the first medical information when time-series first medical information of the target patient is present; The medical information processing device according to claim 1 .

8. The display control unit displays the medical image information of the target patient and the medical image information of the similar patient in a comparative manner. The medical information processing device according to claim 2 .

9. the display control unit causes the normal region and the abnormal region to be superimposed on the medical image information in a distinguishable manner; The medical information processing device according to claim 2 .

10. the display control unit causes an image representing each of the partial regions to be displayed, and also causes a ratio of the normal region to the abnormal region calculated for each of the partial regions to be displayed in association with the corresponding partial region. The medical information processing device according to claim 3 .

11. The display control unit displays the test information of the target patient and the test information of the similar patient in a comparative manner. The medical information processing device according to claim 5 .

12. The display control unit displays the first medical information of the subject patient in a time series indicating a change over time of a disease of the subject patient. The medical information processing device according to claim 7 .

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