Medical information processing device, medical information processing method, and program

Abstract

To provide a medical information processing device, medical information processing method, and program, which allow for contributing to diagnostic accuracy improvement.SOLUTION: A medical information processing device according to an embodiment comprises a first acquisition unit and a second acquisition unit. The first acquisition unit acquires first risk information on a first disease in a subject. When the acquired first risk information satisfies predetermined conditions, the second acquisition unit acquires second risk information on a second disease different from the first disease in the subject.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The embodiments disclosed in the present specification and drawings relate to a medical information processing device, a medical information processing method, and a program. [Background technology]

[0002] In recent years, technologies have become known that automatically diagnose specific diseases that are the subject of examination by analyzing medical data such as medical image data and vital signs. In addition, technologies have been proposed that use supplementary information (such as patient information) of the medical data acquired for diagnosing the disease that is the subject of examination to present diagnostic results for diseases that are not the subject of examination. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2010-284175 Summary of the Invention [Problem to be solved by the invention]

[0004] In situations where the number of normal cases is extremely high compared to the number of abnormal cases, applying automatic diagnosis to all medical data can result in an unacceptable number of cases where normal cases are diagnosed as abnormal (false positives). This can result in a decrease in the reliability of the automatic diagnosis, leading to situations where doctors and patients no longer trust the diagnostic results. To avoid this situation, it is desirable to narrow down the patients who are the targets of automatic diagnosis.

[0005] Furthermore, when using the additional information of conventional medical data to automatically diagnose diseases other than those targeted by the test, the additional information does not necessarily reflect the risk of each disease. Therefore, this conventional method does not necessarily narrow down the patients targeted for automatic diagnosis to those at high risk for the disease, which may result in an increase in the number of false positives.

[0006] The problem to be solved by the embodiments disclosed in this specification and the drawings is to contribute to improving the accuracy of diagnosis. However, the problem to be solved by the embodiments disclosed in this specification and the drawings is not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0007] The medical information processing device of the embodiment has a first acquisition unit and a second acquisition unit. The first acquisition unit acquires first risk information related to a first disease of the subject. The second acquisition unit acquires second risk information related to a second disease different from the first disease of the subject when the acquired first risk information satisfies a predetermined condition. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an example of a usage environment and functional blocks of a medical image processing apparatus 1 according to a first embodiment. [Figure 2] FIG. 2 is a diagram for explaining an example in which the medical information processing apparatus 1 according to the first embodiment is applied to a diagnostic flow. [Figure 3] 4 is a flowchart showing an example of the processing flow of the medical information processing apparatus 1 according to the first embodiment. [Figure 4] FIG. 10 is a diagram for explaining an example in which the medical image processing apparatus 1 according to the second embodiment is applied to a diagnostic flow. [Figure 5] 10 is a flowchart showing an example of the flow of processing by the medical information processing apparatus 1 according to the second embodiment. [Figure 6] FIG. 10 is a diagram showing an example of a usage environment and functional blocks of a terminal device 5 according to a modified example. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, a medical information processing device, a medical information processing method, and a program according to an embodiment will be described with reference to the drawings. The medical information processing device according to the embodiment acquires first risk information related to a first disease of a patient (subject), and if the acquired first risk information satisfies a predetermined condition, acquires second risk information related to a second disease different from the first disease of the subject. In this embodiment, the disease refers to specific illnesses such as diabetes, liver fibrosis, liver cirrhosis, cancer, myocardial infarction, and stroke. This disease may also include pre-disease states, which are not yet diseased but are still healthy. Risk information refers to information related to a disease, such as the presence or absence of the disease, the severity of the disease, and the time of onset of the disease. The risk information includes measurement data measured using a measurement device, index values ​​calculated based on the measurement data, and diagnostic results based on the measurement data.

[0010] In an embodiment, the first disease and the second disease have a predetermined relationship. For example, the second disease is a disease that hinders treatment of the first disease. For example, the second disease is liver dysfunction (liver fibrosis, liver cirrhosis, etc.), and the first disease is diabetes. Some treatment methods for diabetes (for example, treatment methods using tolbutamide) are known to impair liver function. For this reason, when treating diabetes (the second disease), if a patient suffers from liver dysfunction (the first disease), a treatment method that may impair liver function cannot be applied. In this way, the first disease and the second disease have a relationship, for example, regarding treatment methods.

[0011] The medical information processing device is configured to acquire second risk information only when the first risk information satisfies a predetermined condition, thereby narrowing down the targets for acquiring second risk information. This reduces the number of false positives. Furthermore, by treating the second disease before the first disease worsens and requires treatment, the possibility of applying an appropriate treatment for the first disease can be increased.

[0012] (First embodiment) [Configuration of medical information processing device] FIG. 1 is a diagram showing an example of a usage environment and functional blocks of a medical information processing device 1 according to the first embodiment. The medical information processing device 1 is placed in, for example, a medical institution such as a hospital. The medical information processing device 1 is operated by an operator such as a doctor. The medical information processing device 1 may be, for example, a workstation, a server, or a console device of a medical image diagnostic device. The medical information processing device 1 is connected to, for example, at least one monitoring device 3, a terminal device 5, an analysis device 7, a diagnostic information database DB, etc. via a communication network NW so as to be able to transmit and receive data. Either the medical information processing device 1 or the terminal device 5, or a combination of both, is an example of a "medical information processing device."

[0013] The communication network NW refers to all information and communication networks that use telecommunications technology. It includes wireless / wired LANs such as hospital backbone LANs (Local Area Networks), the Internet, telephone communication networks, optical fiber communication networks, cable communication networks, and satellite communication networks.

[0014] The monitoring device 3 periodically measures disease-related monitoring data from a patient. The monitoring device 3 is placed, for example, in a medical institution such as a hospital, the patient's home, etc. The monitoring device 3 may be constantly worn by the patient to perform measurements. The monitoring device 3 periodically measures, for example, monitoring data from a patient regarding a first disease. The monitoring data items include at least one or more items that are related to the disease and that are desirably measured periodically to check the status of the disease. For example, if the first disease is diabetes, the monitoring data items include weight, BMI (Body Mass Index), index values ​​related to exercise habits (number of steps, calories burned, etc.), blood pressure, blood test data, etc. Monitoring devices include, for example, a weighing scale, an electrocardiograph, a heart rate monitor, a pedometer, a blood test device, etc. The monitoring device 3 may be, for example, a single device equipped with measurement functions for multiple items, such as a smart watch.

[0015] The monitoring device 3 transmits the measured data (hereinafter referred to as "monitoring data") to the medical information processing device 1, terminal device 5, etc. via the communication network NW. The monitoring data is saved in any format in any storage device of each device. The monitoring data may be numerical data, image data, text data, voice data, image data of handwritten notes by the patient on paper, etc.

[0016] The measurement frequency by the monitoring device 3 may be equal to or greater than the frequency recommended for each monitoring item related to the first disease. Furthermore, if there are multiple monitoring items related to the first disease, the measurement frequencies for each monitoring item may differ. In this case, interpolation or filtering may be performed on measurement items with low measurement frequencies to align the data intervals and number of data with those of measurement items with high measurement frequencies. Furthermore, data thinning or filtering may be performed on measurement items with high measurement frequencies to align the data intervals and number of data with those of measurement items with low measurement frequencies. Measurement values ​​determined to be abnormal may be excluded or corrected if the acquired measurement value is significantly different from the previous measurement result or is a physiologically or physically impossible value. The monitoring method is not limited. The monitoring device 3 may be provided to the patient, and the patient may measure and record their own data. Alternatively, the patient may be called to a medical institution or a space with equivalent facilities, as in the case of a regular health checkup, and monitoring data may be measured and recorded by a doctor, technician, or the like.

[0017] The terminal device 5 is, for example, a mobile terminal such as a tablet or smartphone owned by the patient, or a personal computer. The terminal device 5 is operated, for example, by the patient to be monitored or a caregiver. The terminal device 5 stores the monitoring data measured by the monitoring device 3 and transmits the monitoring data to the medical information processing device 1. The terminal device 5 also starts a dedicated application program or a browser, etc., and notifies the patient of various information provided by the medical information processing device 1.

[0018] The analysis device 7 automatically analyzes a specific disease using the diagnostic information and outputs the analysis results. The analysis device 7 analyzes a second disease using, for example, medical image data stored in the diagnostic information database DB. Note that each function of the analysis device 7 may be incorporated into the medical information processing device 1.

[0019] The diagnostic information database DB stores diagnostic information for multiple patients. The diagnostic information includes, for example, patient identification information (patient ID), medical image data captured in the past, electronic medical records, disease information, patient information such as age and gender, and biological information, etc. Medical image data includes, for example, CT (Computed Tomography) images, ultrasound diagnostic images, MR (Magnetic Resonance) images, and X-ray images. Biological information includes, for example, blood pressure values, pulse rate, respiratory rate, etc. The diagnostic information database DB is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, a hard disk, or an optical disk.

[0020] The medical information processing device 1 includes, for example, a processing circuit 100, a communication interface 110, an input interface 120, a display 130, and a memory 140. The communication interface 110 communicates with external devices such as the monitoring device 3, the terminal device 5, the analysis device 7, and the diagnostic information database DB via a communication network NW. The communication interface 110 includes, for example, a communication interface such as a network interface card (NIC).

[0021] The input interface 120 accepts various input operations from the operator of the medical information processing device 1, converts the accepted input operations into electrical signals, and outputs the electrical signals to the processing circuit 100. For example, the input interface 120 includes a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch panel, etc. The input interface 120 may also be a user interface that accepts audio input from a microphone, etc.

[0022] In this specification, the input interface is not limited to an interface having physical operation parts such as a mouse, keyboard, etc. For example, an example of an input interface also includes 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 this electrical signal to a control circuit.

[0023] The display 130 displays various types of information. For example, the display 130 displays images generated by the processing circuit 100, a GUI (Graphical User Interface) for receiving various input operations from an operator, and the like. For example, the display 130 is an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube) display, an organic EL (Electro Luminescence) display, or the like. When the input interface 120 is a touch panel, the display function of the display 130 may be incorporated into the input interface 120.

[0024] The processing circuit 100 includes, for example, a first acquisition function 101, a second acquisition function 102, an index value calculation function 103, a determination function 104, an output function 105, a display control function 106, and a notification function 107. The processing circuit 100 realizes these functions by, for example, a hardware processor (computer) executing a program stored in a memory 140 (storage circuit).

[0025] The hardware processor refers to a circuit such as a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD) or a complex programmable logic device (CPLD), or a field programmable gate array (FPGA)). Instead of storing the program in the memory 140, the program may be directly embedded in the circuit of the hardware processor. In this case, the hardware processor realizes its function by reading and executing the program embedded in the circuit. The program may be stored in the memory 140 in advance, or may be stored in a non-transitory storage medium such as a DVD or CD-ROM, and installed in the memory 140 from the non-transitory storage medium by inserting the non-transitory storage medium into a drive device (not shown) of the medical information processing device 1. The hardware processor is not limited to being configured as a single circuit, but may be configured as a single hardware processor by combining multiple independent circuits to realize each function, or multiple components may be integrated into a single hardware processor to realize each function.

[0026] The first acquisition function 101 acquires monitoring data related to the first disease from the monitoring device 3, the terminal device 5, or the diagnostic information database DB via the communication network NW. The first acquisition function 101 also acquires a risk index value for the first disease (first risk index value) output from the index value calculation function 103, and outputs it to the determination function 104. The first acquisition function 101 may acquire the first risk index value by searching the memory 140 in which the first risk index value of the patient to be diagnosed is stored, or may acquire one manually input via the input interface 120 by a doctor, the patient, or a person concerned. The first acquisition function 101 may perform filtering or correction on the first risk index value. For example, it is assumed that the first risk index value has already been calculated multiple times for the patient to be diagnosed, and the change over time of the first risk index value has been stored in the memory 140. In this case, the first acquisition function 101 can reduce the influence of outliers caused by measurement errors of the monitoring device 3 and the propagation of those errors by applying a moving average filter to the change over time in the risk index value of this first disease B. The monitoring data or the first risk index value is an example of "first risk information." The first acquisition function 101 is an example of a "first acquisition unit." That is, the first acquisition function 101 acquires first risk information related to the subject's first disease. The first acquisition function 101 acquires monitoring data obtained by monitoring the subject.

[0027] The second acquisition function 102 acquires diagnostic information related to the second disease from the diagnostic information database DB via the communication network NW and stores it in the memory 140. The second acquisition function 102 may acquire diagnostic information manually input via the input interface 120 by a doctor, a patient, or a related party. Furthermore, if the first risk information satisfies a predetermined condition, the second acquisition function 102 acquires, via the network NW, analysis results based on the diagnostic information related to the second disease by the analysis device 7. The diagnostic information related to the second disease or the analysis results based on the diagnostic information related to the second disease is an example of "second risk information." The second acquisition function 102 is an example of a "second acquisition unit." That is, if the acquired first risk information satisfies a predetermined condition, the second acquisition function 102 acquires second risk information related to a second disease different from the subject's first disease. The second acquisition function 102 acquires second risk information, which is the analysis result of the second disease, from the analysis device 7. The second acquisition function 102 acquires past diagnostic information about the subject, and acquires second risk information obtained by analyzing the past diagnostic information.

[0028] The index value calculation function 103 calculates a first risk index value based on the monitoring data related to the first disease acquired by the first acquisition function 101. The index value calculation function 103 may calculate multiple first risk index values. The index value calculation function 103 may be provided in a processing device separate from the medical information processing device 1. The type of first risk index value may be any index value calculated based on the monitoring data related to the first disease measured by the monitoring device 3. The first risk index value includes, for example, the probability of future onset of the first disease, severity, urgency, treatment priority, survival rate within a certain period, overall survival time, etc. The index value calculation function 103 calculates the first risk index value using any method. For example, the index value calculation function 103 may predict the risk index value using regression analysis, a neural network, a decision tree, a naive Bayes classifier, etc., using measurement values ​​of multiple monitoring items as input. The index value calculation function 103 may also use only one monitoring item to calculate the first risk index value. For example, if a certain monitoring item is strongly correlated with at least one first risk index value, the index value calculation function 103 may use the measurement value of that monitoring item itself as the first risk index value. Alternatively, the index value calculation function 103 may calculate the first risk index value by multiplying the measurement value of the monitoring item by a coefficient and normalizing it. The first risk index value may be a continuous value. Alternatively, the first risk index value may be a value obtained by classifying the risk index value of the first disease by size. For example, the first risk index value may be expressed by characters indicating the degree of risk, such as "low," "medium," or "high," symbols such as "+," "-," "▲," or "▼," or by discontinuous values. The index value calculation function 103 is an example of an "index value calculation unit." That is, the index value calculation function 103 calculates first risk information, which is an index value related to the first disease, based on the acquired monitoring data.

[0029] The determination function 104 determines whether or not to perform an analysis of the second disease based on the first risk index value calculated by the index value calculation function 103. For example, the determination function 104 sets a threshold for the first risk index value and determines whether or not to perform an analysis of the second disease based on a comparison between the first risk index value and the threshold (for example, based on whether the first risk index value is equal to or greater than the threshold). Furthermore, when there are multiple types of first risk index values, the determination function 104 may determine whether or not to perform an analysis of the second disease using regression analysis, a convolutional neural network, a decision tree, a naive Bayes classifier, or the like. The determination function 104 is an example of a "determination unit." That is, the determination function 104 determines whether or not to perform an analysis of the second disease based on whether the acquired first risk information satisfies a predetermined condition. The determination function 104 determines whether or not to perform an analysis of the second disease based on a comparison between the first risk information and a predetermined threshold.

[0030] The output function 105 outputs the diagnostic information related to the second disease acquired by the second acquisition function 102 to the analysis device 7. The analysis device 7 analyzes the second disease using the diagnostic information acquired from the output function 105 and outputs the analysis results to the medical information processing device 1, the diagnostic information database DB, etc. The output function 105 is an example of an "output unit." That is, when the determination function 104 determines that an analysis related to the second disease should be performed, the output function 105 outputs instruction information instructing the analysis of the second disease using the diagnostic information related to the subject to the external analysis device 7.

[0031] The display control function 106 controls the display of various information on the display 130, such as monitoring data, the first index value, diagnostic information regarding the second disease, analysis results by the analysis device 7, and a GUI for accepting various input operations from the operator.

[0032] The notification function 107 notifies the terminal device 5, smart watch, or the like owned by the patient of the analysis results related to the second disease output by the analysis device 7 and instruction information (support information) instructing the patient to start treatment for the second disease via the communication network NW. The notification function 107 is an example of a "notification unit." That is, the notification function 107 notifies the support information related to the first or second disease based on the acquired first or second risk information. The notification function 107 notifies the support information instructing the patient to start treatment for the second disease based on the acquired second risk information.

[0033] The memory 140 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, a hard disk, or an optical disk. These non-transitory storage media may be realized by other storage devices connected via a communication network NW, such as a network-attached storage (NAS) or an external storage server device. The memory 140 may also include other non-transitory storage media such as a read-only memory (ROM) or a register. The memory 140 stores, for example, monitoring data, a first index value, diagnostic information related to the second disease, analysis results related to the second disease, etc. In addition, the memory 140 stores programs, parameter data, and other data used by the processing circuit 100.

[0034] [Processing flow] Next, an example of a processing flow of the medical information processing device 1 according to the first embodiment will be described. FIG. 2 is a diagram illustrating an example in which the medical information processing device 1 according to the first embodiment is applied to a diagnosis flow. FIG. 3 is a flowchart illustrating an example of a processing flow of the medical information processing device 1 according to the first embodiment. In the following description, an example in which the first disease is "diabetes" and the second disease is "liver fibrosis" will be described. Furthermore, it is assumed that a patient to be diagnosed has undergone a CT scan of organs around the liver (e.g., a pneumonia scan) within the past year, and the CT images from that scan are stored in the diagnostic information database DB. Furthermore, it is assumed that mild liver fibrosis was observed in the liver at the time of the CT scan, but the patient has no subjective symptoms and has not developed cirrhosis, so no liver fibrosis testing or analysis has been ordered. It is also assumed that the patient's liver fibrosis abnormality worsens day by day.

[0035] As shown in FIG. 2, a patient undergoes regular (daily to monthly) diabetes monitoring. For example, the patient transmits monitoring data measured by the patient using a monitoring device 3 to a medical information processing device 1 every day, and receives information on the diabetes index value (first index value) from the medical information processing device 1 via a terminal device 5 or the like. As long as the diabetes index value shows no abnormalities ("low" risk), no special treatment is required, and thus this monitoring is carried out continuously. The progression level of liver fibrosis is defined, for example, by a five-level index from F0 to F4 (indicating progression of fibrosis from F0 to F4), and it is assumed that the progression level of the patient's liver fibrosis is maintained at F1 during this period.

[0036] On the other hand, as time passes and the diabetes index value indicates an abnormal trend (medium risk), the liver fibrosis analysis process is first performed. The liver fibrosis analysis process involves analysis based on the patient's past CT images stored in the diagnostic information database (DB). If this analysis reveals progression of liver fibrosis (e.g., if the liver fibrosis level is F2), the patient is administered treatment to control the progression of liver fibrosis (prevent or slow the progression of liver fibrosis). As time passes, when the diabetes index value indicates an abnormality (high risk), a diabetes diagnosis is initiated. If this diagnosis identifies the patient as having diabetes, the patient is administered diabetes treatment. Even if a treatment method that may impair liver function (e.g., a treatment method using tolbutamide) is performed to prevent the progression of liver fibrosis by controlling liver fibrosis prior to diabetes treatment, it can still be used to treat diabetes. In other words, treating liver fibrosis before the diabetes worsens and requires treatment can increase the likelihood of applying a better diabetes treatment.

[0037] Next, the processing flow of the medical information processing device 1 under the conditions assumed in Fig. 2 will be explained using Fig. 3. The flowchart shown in Fig. 3 is executed at a predetermined timing (for example, once per day) based on the monitoring conditions, etc. First, the first acquisition function 101 of the medical information processing device 1 acquires monitoring data related to diabetes of the patient to be diagnosed from the monitoring device 3 or the terminal device 5 via the communication network NW (step S101).

[0038] Next, the index calculation function 103 calculates a risk index value for diabetes of the patient to be diagnosed based on the monitoring data related to diabetes acquired by the first acquisition function 101 (step S103).

[0039] Next, the determination function 104 determines whether or not a liver fibrosis analysis has already been performed on the patient to be diagnosed (step S105). The determination function 104 determines whether or not a liver fibrosis analysis has already been performed based on, for example, whether or not a liver fibrosis analysis result is stored in the diagnostic information database DB. If it is determined that a liver fibrosis analysis has already been performed (step S105; YES), the processing of this flowchart ends.

[0040] On the other hand, if it is determined that liver fibrosis analysis has not been performed (step S105; NO), the determination function 104 determines whether or not to perform liver fibrosis analysis based on a comparison between the diabetes risk index value and a threshold value (step S107). The determination function 104 determines whether or not to perform liver fibrosis analysis, for example, based on whether or not the diabetes risk index value is equal to or greater than a threshold value. For example, if the diabetes risk index value is "low", liver fibrosis analysis is not performed. On the other hand, for example, if the diabetes risk index value is "medium" or "high", liver fibrosis analysis is performed. If it is determined that liver fibrosis analysis is not to be performed (step S107; NO), the processing of this flowchart ends.

[0041] On the other hand, if it is determined that liver fibrosis analysis is to be performed (step S107; YES), the second acquisition function 102 acquires diagnostic information (CT images) of the patient to be diagnosed from the diagnostic information database DB via the communication network NW (step S109).

[0042] Next, the output function 105 outputs the diagnostic information (CT image) of the patient to be diagnosed acquired by the second acquisition function 102 to the analysis device 7 (step S111). As a result, the analysis device 7 starts an automatic analysis of the liver region using the diagnostic information acquired from the output function 105, and notifies the patient or a person related to the patient (such as a doctor) of the analysis results. The patient who has been notified of the presence of liver fibrosis will visit a medical institution and begin treatment for the liver fibrosis. This completes the processing of this flowchart.

[0043] According to the first embodiment described above, narrowing down the diagnostic target for the second disease can improve the accuracy of analysis and diagnosis by the analysis device 7. Furthermore, treating the second disease before the first disease worsens and requires treatment can increase the likelihood of applying a better treatment method for the first disease. For example, when the risk index value for the first disease (e.g., diabetes) reaches a threshold value or higher (e.g., "medium"), an analysis of the second disease (e.g., liver fibrosis) is performed based on diagnostic information (e.g., CT images) previously captured by the patient. As a result, if the patient has developed the second disease at the time of a previous CT scan, the presence of the second disease can be detected before treatment for the first disease is initiated, allowing treatment for the second disease to be initiated early. Some therapeutic drugs for the first disease affect functions (e.g., liver function) affected by the second disease, and some are contraindicated for patients with liver dysfunction due to severe liver disease (including advanced liver fibrosis, cirrhosis, etc.). Therefore, if treatment for the second disease can be initiated before treatment for the first disease becomes necessary, it becomes possible to consider the application of treatments for the first disease that affect other functions, broadening the scope of treatment. As a result, patients can proceed with treatment for the first disease using the most appropriate treatment method. Treatment for the second disease (liver fibrosis) (liver fibrosis control) includes exercise and dietary guidance. These treatment methods are also effective in preventing the first disease (diabetes). As a result, it is expected to have the secondary effect of reducing the patient's risk of developing diabetes and the risk of diabetes becoming severe.

[0044] In the first embodiment, it is assumed that the analysis of the second disease (e.g., liver fibrosis) is automatically performed based on previously acquired diagnostic information. The patient does not need to undergo a new diagnosis (e.g., CT imaging) to diagnose liver fibrosis. Furthermore, because the analysis is performed automatically by the analysis device 7, a doctor does not need to order an examination or take the trouble of selecting images. On the other hand, it is conceivable that the stage of liver fibrosis at the time of a CT examination of organs surrounding the liver differs from the stage of liver fibrosis at the time the diabetes risk index value becomes "medium." Therefore, the diagnostic result may be adjusted by adjusting parameters such as weights when analyzing liver fibrosis in the analysis device 7, depending on the time interval between the stage of liver fibrosis at the time of a CT examination of organs surrounding the liver and the time the diabetes risk index value becomes "medium."

[0045] (Second embodiment) The second embodiment will be described below. The difference from the first embodiment described above is that processing is performed under conditions where no previously acquired diagnostic information (e.g., CT images) exists for the patient to be diagnosed. In the following description, differences from the first embodiment will be mainly described, and explanations of points in common with the first embodiment will be omitted. In the description of the second embodiment, parts that are the same as those in the first embodiment will be described with the same reference numerals.

[0046] [Processing flow] An example of a processing flow of the medical information processing device 1 according to the second embodiment will be described. FIG. 4 is a diagram illustrating an example in which the medical information processing device 1 according to the second embodiment is applied to a diagnostic flow. FIG. 5 is a flowchart illustrating an example of a processing flow of the medical information processing device 1 according to the second embodiment. As in the above description of the processing flow in the first embodiment, the following description will be given by taking an example in which the first disease is "diabetes" and the second disease is "liver fibrosis." It is assumed that the patient to be diagnosed has not undergone an examination of organs around the liver (e.g., an examination for pneumonia) within the past year, and no diagnostic information is stored in the diagnostic information database DB. Alternatively, it is assumed that diagnostic information is stored in the diagnostic information database DB, but the stored diagnostic information does not meet the input conditions of the analysis device 7 and cannot be used for analysis.

[0047] As shown in Figure 4, a patient undergoes regular (daily to monthly) diabetes monitoring. As long as the patient's diabetes index values ​​are normal (low risk), no special treatment is required, and so monitoring alone continues. As time passes, when the patient's diabetes index values ​​begin to show signs of abnormality (medium risk), liver fibrosis analysis is performed for the first time. Because past CT images of the patient are not stored in the diagnostic information database (DB), new CT images must be acquired. Therefore, the patient or a person related to the patient (the patient's physician in this case) is notified that a liver fibrosis test is required. Upon receiving the notification, the patient undergoes, for example, a simple liver fibrosis test. If the simple test is positive, a detailed liver fibrosis test is performed. Alternatively, the physician who received the notification may recommend or order a liver fibrosis test for the patient, and the patient undergoes the simple or detailed liver fibrosis test as instructed by the physician. The test data on liver fibrosis obtained by the test is analyzed by the analysis device 7, and the analysis results are notified to the patient or the like.

[0048] If the results of the above analysis indicate progression of liver fibrosis (for example, if the level of liver fibrosis is F2), the patient will be given treatment to control the progression of liver fibrosis (prevent the progression of liver fibrosis). As time passes, when the diabetes index values ​​become abnormal (high risk), a diagnosis for diabetes will be initiated. If this diagnosis identifies the patient as having diabetes, the patient will be given treatment for diabetes. Even if a treatment method that involves the control of liver fibrosis prior to diabetes treatment prevents the progression of liver fibrosis and may result in a decline in liver function (for example, a treatment method using tolbutamide), it can still be used to treat diabetes.

[0049] Next, the processing flow of the medical information processing device 1 under the conditions assumed in Fig. 4 will be explained using Fig. 5. The flowchart shown in Fig. 5 is executed at a predetermined timing (for example, once per day) based on the monitoring conditions, etc. First, the first acquisition function 101 of the medical information processing device 1 acquires monitoring data related to diabetes from the monitoring device 3 or the terminal device 5 via the communication network NW (step S201).

[0050] Next, the index calculation function 103 calculates a diabetes risk index based on the diabetes-related monitoring data acquired by the first acquisition function 101 (step S203).

[0051] Next, the determination function 104 determines whether or not the patient to be diagnosed has already been analyzed for liver fibrosis (step S205). If it is determined that the patient has already been analyzed for liver fibrosis (step S205; YES), the process of this flowchart ends.

[0052] On the other hand, if it is determined that the analysis of liver fibrosis has not been performed (step S205; NO), the determination function 104 determines whether or not to perform the analysis of liver fibrosis based on the comparison between the diabetes risk index value and the threshold value (step S207). If it is determined that the analysis of liver fibrosis will not be performed (step S207; NO), the process of this flowchart ends.

[0053] On the other hand, if it is determined that liver fibrosis analysis is to be performed (step S207; YES), the notification function 107 notifies the terminal device 5, smart watch, etc. owned by the patient via the communication network NW that a liver fibrosis test is required (step S209). In addition to or instead of notifying the patient, the notification function 107 notifies a doctor, etc. by displaying on the display 130, under the control of the display control function 106, that a liver fibrosis test is required. For example, the patient who receives the notification undergoes a liver fibrosis test. The liver fibrosis test data acquired in the test is analyzed by the analysis device 7, and the patient, etc. is notified of the analysis results. Thereafter, a process for controlling the progression of liver fibrosis is performed on the patient as necessary. This completes the processing of this flowchart.

[0054] According to the second embodiment described above, narrowing down the diagnostic target for the second disease can improve the accuracy of analysis and diagnosis by the analysis device 7. Also, by treating the second disease before the first disease worsens and requires treatment, the possibility of applying a better treatment method for the first disease can be increased. Furthermore, since the test ordered in response to a notification from the medical information processing device 1 is a test for diagnosing the second disease (e.g., liver fibrosis), the accuracy of analysis of the second disease can be improved compared to the first embodiment in which diagnostic information captured for the purpose of diagnosing another disease is used secondary.

[0055] (Variation) Fig. 6 is a diagram showing an example of the usage environment and functional blocks of a terminal device 5 according to a modified example. As shown in Fig. 6, the only difference from the first and second embodiments described above is that the functions of the processing circuit 100 of the medical information processing device 1 are implemented in the terminal device 5 owned by the patient. With this configuration, it becomes possible to perform diagnostic processing using only the equipment in the patient's home.

[0056] According to at least one of the embodiments described above, by providing a first acquisition unit that acquires first risk information regarding a first disease of a subject, and a second acquisition unit that acquires second risk information regarding a second disease of the subject that is different from the first disease when the acquired first risk information satisfies a predetermined condition, it is possible to contribute to improving the accuracy of diagnosis.

[0057] The above-described embodiment can be expressed as follows. processing circuitry; The processing circuitry obtaining first risk information regarding a first disease in a subject; If the acquired first risk information satisfies a predetermined condition, second risk information regarding a second disease of the subject that is different from the first disease is acquired. Medical information processing equipment.

[0058] Although several embodiments 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 within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0059] 1 Medical information processing device 3 Monitoring equipment 5 Terminal Devices 7 Analysis device DB Diagnostic information database 100,50 Processing circuit 110,60 Communication Interface 120,70 Input Interface 130,80 display 140,90 memory 101,51 First acquisition function 102,52 Second acquisition function 103,53 Index value calculation function 104,54 Judgment function 105,55 Output Function 106,56 Display control function 107,57 Notification function

Claims

1. a first acquisition unit that acquires first risk information related to a first disease of a subject; a second acquisition unit that acquires second risk information regarding a second disease of the subject that is different from the first disease when the acquired first risk information satisfies a predetermined condition; A medical information processing device comprising:

2. a determination unit that determines whether to perform an analysis on the second disease based on whether the acquired first risk information satisfies a predetermined condition; The medical information processing device according to claim 1 .

3. an output unit that outputs, when the determination unit determines that an analysis regarding the second disease is to be performed, instruction information instructing an analysis of the second disease using diagnostic information regarding the subject to an external analysis device; the second acquisition unit acquires the second risk information, which is an analysis result of the second disease, from the analysis device; The medical information processing device according to claim 2 .

4. the determination unit determines whether to perform an analysis on the second disease based on a comparison between the first risk information and a predetermined threshold value; The medical information processing device according to claim 2 or 3.

5. the second acquisition unit acquires past diagnostic information regarding the subject, and acquires the second risk information obtained by analyzing the past diagnostic information; The medical information processing device according to claim 1 .

6. a notification unit that notifies support information related to the first or second disease based on the acquired first or second risk information; The medical information processing device according to claim 1 .

7. The notification unit notifies the support information instructing the start of treatment for the second disease based on the acquired second risk information. The medical information processing device according to claim 6 .

8. the first acquisition unit acquires monitoring data obtained by monitoring the subject; further comprising an index value calculation unit that calculates the first risk information, which is an index value related to the first disease, based on the acquired monitoring data; The medical information processing device according to claim 1 .

9. the second disease is a disease that hinders treatment of the first disease; The medical information processing device according to claim 1 .

10. The computer of the medical information processing device obtaining first risk information regarding a first disease of a subject; If the acquired first risk information satisfies a predetermined condition, second risk information regarding a second disease of the subject, which is different from the first disease, is acquired. Medical information processing method.

11. The computer of the medical information processing device obtaining first risk information regarding a first disease of a subject; If the acquired first risk information satisfies a predetermined condition, second risk information regarding a second disease of the subject, which is different from the first disease, is acquired. program.

Images