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

Abstract

To present predictions of the progression of a patient's function in the event that disease-modifying treatment is not administered. [Solution] The medical information processing device according to the embodiment includes: a patient information acquisition unit that acquires factor information of a target patient relating to a plurality of factors that affect the disease of the target patient; a similar patient extraction unit that extracts similar patients that are similar to the target patient with respect to the plurality of factors; a template acquisition unit that acquires template data showing the progress of the functional changes due to the disease of the similar patients; a template modification unit that modifies the template data of the similar patients based on the factor information of the target patient, the factor information of the similar patients, and the degree of influence indicating the extent to which each factor affects the disease; and an information output unit that outputs prediction information showing the predicted progress of the functional changes due to the disease of the target patient in the case where the disease-modifying treatment is not received, based on the modified template data.

Description

Technical Field

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

Background Art

[0002] For patients such as those with dementia, disease-modifying treatment using disease-modifying drugs that act on the cause to suppress progression may be performed. Regarding disease-modifying drugs, an effect of slowing down the decline in cognitive function has been confirmed.

[0003] However, for patients who have received a diagnosis of dementia or their relatives, if they do not receive treatment, they may not know how and to what extent the function of the patient will decline.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Non-Patent Documents

[0005]

Non-Patent Document 1

Non-Patent Document 2

[0006] One of the problems that the embodiments disclosed in this specification and drawings aim to solve is to provide a prediction of the progression of a patient's function in the absence of disease-modifying treatment. However, the problems that the embodiments disclosed in this specification and drawings aim to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0007] The medical information processing device according to the embodiment includes: a patient information acquisition unit that acquires factor information of a target patient relating to a plurality of factors that affect the disease of the target patient; a similar patient extraction unit that extracts similar patients that are similar to the target patient with respect to the plurality of factors; a template acquisition unit that acquires template data of the similar patients, which is template data showing the progress of functional changes due to the disease; a template modification unit that modifies the template data of the similar patients based on the factor information of the target patient, the factor information of the similar patients, and the degree of influence indicating the extent to which each factor affects the disease; and an information output unit that outputs prediction information showing the predicted progress of functional changes due to the disease of the target patient in the case where the disease-modifying treatment is not received, based on the modified template data. [Brief explanation of the drawing]

[0008] [Figure 1]A diagram showing an example of the configuration of a medical information processing system according to an embodiment. [Figure 2] A block diagram showing an example of the configuration of a medical information processing device according to an embodiment. [Figure 3] A diagram showing an example of a factor database according to an embodiment. [Figure 4] A diagram showing an example of a weight coefficient database according to an embodiment. [Figure 5] A block diagram showing an example of the configuration of a terminal device according to an embodiment. [Figure 6] A diagram for explaining the extraction of similar patients according to the first embodiment. [Figure 7] A diagram showing the comparison of factors between a target patient and the extracted similar patients. [Figure 8] A flowchart diagram for explaining the medical information processing method according to the first embodiment. [Figure 9] A diagram showing an example of a screen displayed on a terminal device according to the first embodiment. [Figure 10] A flowchart diagram for explaining the medical information processing method according to the second embodiment. [Figure 11] A diagram for explaining the extraction of similar patients according to the second embodiment. [Figure 12] A diagram showing an example of a screen displayed on a terminal device according to the second embodiment. [Figure 13] A flowchart diagram for explaining the medical information processing method according to the third embodiment.

Embodiments for Carrying Out the Invention

[0009] Hereinafter, embodiments of a medical information processing system, a medical information processing device, and a medical information processing method will be described with reference to the drawings. In the following description, components having substantially the same functions and configurations will be denoted by the same reference numerals, and duplicate explanations will be made only when necessary.

[0010] <Medical Information Processing System 1> FIG. 1 shows an example of the schematic configuration of the medical information processing system 1 according to the embodiment.

[0011] As shown in FIG. 1, the medical information processing system 1 includes a medical information processing device 10, a terminal device 20, and a medical information storage device 30. These devices are communicably connected to each other via an in-hospital network. Note that at least one of these devices may be arranged outside the hospital and connected to other devices via a network such as the Internet.

[0012] The medical information processing device 10, which will be described in detail later, is configured as a personal cognitive function change prediction device that generates prediction information regarding the transition prediction of the cognitive function due to the disease of the target patient and causes the terminal device 20 to display it.

[0013] The terminal device 20 is a terminal device used by medical staff such as doctors (hereinafter also referred to as users). For example, it is a desktop personal computer or a notebook personal computer installed in an examination room. Note that the terminal device 20 may be a portable information terminal such as a smartphone or a tablet carried by medical staff. The user selects a target patient or displays information regarding the target patient via the GUI (Graphical User Interface) displayed on the terminal device 20.

[0014] The terminal device 20 displays information regarding the patient selected by the user. Specifically, the terminal device 20 acquires the patient's name, age, gender, disease, examination information, past history, etc. from the electronic medical record stored in the medical information storage device 30 and displays them on the display. Further, the terminal device 20 displays the prediction information and the like received from the medical information processing device 10.

[0015] The medical information storage device 30 is a device that stores patient electronic medical record information, medical information, etc. Medical information may include medical images generated by modalities and / or the results of examinations that evaluate the severity of a disease. Modalities are medical devices such as CT (Computed Tomography) devices, MRI (Magnetic Resonance Imaging) devices, and ultrasound diagnostic devices.

[0016] When the medical information storage device 30 receives a request from the medical information processing device 10 or the terminal device 20, it transmits information corresponding to the request. For example, the medical information storage device 30 transmits the patient's disease name, age, BMI value, MMSE score value, etc., to the requesting device.

[0017] The medical information storage device 30 may be composed of multiple devices (for example, a first information processing device that stores electronic medical record information and a second information processing device that stores test result information).

[0018] Furthermore, the medical information storage device 30 may partially or entirely comprise at least one of the following: an information sharing service, a pathway service for checking the patient's medical status and history, an electronic medical record system, and a PACS (Picture Archiving and Communication System).

[0019] Next, the medical information processing device 10 and the terminal device 20 will be described in detail.

[0020] <Medical Information Processing Device 10> Referring to Figure 2, the medical information processing device 10 according to the embodiment will be described in detail.

[0021] The medical information processing device 10 includes a communication interface 11, a memory circuit 12, and a processing circuit 13. The details of each component are described below.

[0022] The communication interface 11 communicates with other components of the medical information processing system 1 (terminal device 20, medical information storage device 30, etc.) via the communication network in accordance with various communication protocols.

[0023] The memory circuit 12 is connected to the processing circuit 13 and stores various types of information used by the processing circuit 13. The memory circuit 12 stores various programs necessary for the processing circuit 13 to perform its various functions, as well as various types of data processed by these programs. The memory circuit 12 can be implemented using, for example, a semiconductor memory element such as RAM (Random Access Memory) or flash memory, a hard disk, or an optical disc. The various types of data dealt with in this specification are typically digital data.

[0024] The memory circuit 12 stores a factor database 12a, a weight coefficient database 12b, and a template database 12c. Note that at least one of these databases may be stored in the memory circuit of an information processing device other than the medical information processing device 10.

[0025] Factor Database 12a is a database that stores factors that influence diseases. Figure 3 shows an example of Factor Database 12a. In this example, factors are stored for each disease. Specifically, factors that influence Alzheimer's disease include age, BMI (Body Mass Index), and MMSE (Mini Mental State Examination) score, while factors that influence Parkinson's disease include sleep duration, education, and DAT (Dopamine Transporter) scan tests. By searching Factor Database 12a using the disease name as the search key, it is possible to identify the factors that influence that disease.

[0026] The weight coefficient database 12b is a database that stores numerical values ​​of weight coefficients that indicate the degree of influence of factors affecting a disease. Figure 4 shows an example of the weight coefficient database 12b. In this example, for Alzheimer's disease, values ​​such as 0.01 as the weight coefficient (α) for factor 1 (age) and 0.02 as the weight coefficient (β) for factor 2 (BMI) are stored. The weight coefficients are constants calculated based on past cases.

[0027] Note that the weight coefficients may also be values ​​obtained by a function. For example, the weight coefficient α related to age may be a value obtained from a function that takes at least one of the following as arguments: age, BMI value, and MMSE score value. In this case, the function will be stored in the weight coefficient database 12b.

[0028] The template database 12c is a database that stores patient template data, for example, patient identification information and template data are stored in association. Here, template data refers to data showing the progression of function (in this case, cognitive function) due to the disease, and is data for patients who do not receive disease-modifying treatment. In the case of Alzheimer's disease, the template data includes actual cognitive function values ​​for each elapsed time (e.g., 1 month, 3 months, 6 months, 1 year, etc.).

[0029] The "function" mentioned above refers to, for example, cognitive function in the case of Alzheimer's disease, and to, for example, motor function in the case of Parkinson's disease. Furthermore, the function may be a physical function or a mental function.

[0030] The processing circuit 13 is an arithmetic circuit that performs various calculations and controls the operation of the medical information processing device 10. The processing circuit 13 has a patient information acquisition function 13a, a similar patient extraction function 13b, a template acquisition function 13c, a template modification function 13d, and an information output function 13e.

[0031] The patient information acquisition function 13a is an example of a patient information acquisition unit in the claims. The similar patient extraction function 13b is an example of a similar patient extraction unit in the claims. The template acquisition function 13c is an example of a template acquisition unit in the claims. The template modification function 13d is an example of a template modification unit in the claims. The information output function 13e is an example of an information output unit in the claims.

[0032] In this embodiment, each processing function executed by the patient information acquisition function 13a, similar patient extraction function 13b, template acquisition function 13c, template modification function 13d, and information output function 13e is stored in the memory circuit 12 in the form of a program that can be executed by a computer. The processing circuit 13 is composed of a processor and realizes the functions corresponding to each program by reading and executing the program from the memory circuit 12. In other words, the processing circuit 13 in the state in which each program has been read will have the functions shown in the processing circuit 13 of Figure 2.

[0033] In Figure 2, the patient information acquisition function 13a, similar patient extraction function 13b, template acquisition function 13c, template modification function 13d, and information output function 13e are shown to be implemented by a single processing circuit 13, but the embodiments are not limited to this. For example, the processing circuit 13 may be configured as a combination of multiple independent processors, with each processor executing its respective program to implement each processing function. Each processing function of the processing circuit 13 may be implemented by being appropriately distributed or integrated across one or more processing circuits.

[0034] <Terminal device 20> Next, with reference to Figure 5, the terminal device 20 according to this embodiment will be described in detail.

[0035] The terminal device 20 includes a communication interface 21, a memory circuit 22, an input interface 23, a display 24, and a processing circuit 25.

[0036] The communication interface 21 communicates with other components of the medical information processing system 1 (medical information processing device 10, medical information storage device 30, etc.) via the communication network in accordance with various communication protocols.

[0037] The memory circuit 22 is connected to the processing circuit 25 and stores various types of information used by the processing circuit 25. The memory circuit 22 can be implemented using, for example, a semiconductor memory element such as RAM (Random Access Memory) or flash memory, a hard disk, or an optical disk.

[0038] The input interface 23 receives various input operations from the user, converts the received input operations into electrical signals, and outputs them to the processing circuit 25. This input interface 23 can be implemented using, for example, a mouse, keyboard, touch panel, trackball, manual switch, foot switch, button, joystick, etc.

[0039] The display 24 is a display unit for presenting information to the user of the terminal device 20. For example, the display 24 may display a GUI for selecting a patient, or display predictive information for that patient.

[0040] The processing circuit 25 is an arithmetic circuit that performs various calculations and controls the operation of the terminal device 20. The processing circuit 25 has an information reception function 25a and a display control function 25b. The information reception function 25a receives information input from the user via the input interface 23. For example, the information reception function 25a receives operations on the GUI displayed on the screen of the terminal device 20 (such as patient selection) and information input via the GUI (such as medical interview results). The received information is stored in the memory circuit 22 or transmitted to the medical information storage device 30 or the like via the communication interface 21.

[0041] The display control function 25b controls the display 24 to display desired information. For example, the display control function 25b controls the display 24 to display the predicted function transitions indicated by the predictive information received from the medical information processing device 10. The predicted transitions are displayed on the display 24, for example, in graph or tabular format.

[0042] <Details of the medical information processing device 10> Next, we will describe in detail each processing function of the processing circuit 13 of the medical information processing device 10.

[0043] The patient information acquisition function 13a acquires information about the target patient from the medical information storage device 30. In this embodiment, the patient information acquisition function 13a acquires factor information of the target patient. Factor information is information about multiple factors that affect the target patient's disease (such as Alzheimer's disease). For example, factor information includes information on the numerical values ​​of multiple factors (such as the target patient's age, BMI value, and test score). Factor information may also include categorization of the numerical values ​​of the factors (such as good, normal, or bad) or information indicating the judgment result of the test results.

[0044] The similar patient extraction function 13b extracts similar patients. Here, a similar patient is a patient who has the same disease as the target patient and is similar to the target patient in terms of factors that affect the disease. In addition, a similar patient may be a patient who is similar to the target patient in terms of factors that affect the disease and has not received disease-modifying treatment for the disease. In this embodiment, the similar patient extraction function 13b extracts similar patients by searching the medical information storage device 30.

[0045] In detail, the similar patient extraction function 13b calculates the distance between the target patient and other patients (candidate similar patients) based on the disease factor information of the target patient, and extracts the patient with the shortest distance as the similar patient. Here, "distance" refers to the distance between multiple factors, and is the distance in the space (factor space) based on each factor. Note that the distance is not limited to the Euclidean distance, but may also be other distances such as the Mahalanobis distance.

[0046] Here, we will specifically explain the method for extracting similar patients with reference to Figure 6. Figure 6 shows the positions of patients (target patient P and other patients P1-P6) in a three-dimensional space based on three factors (age, BMI, and MMSE score). For simplicity, we assume here that only age, BMI, and MMSE score are factors that influence Alzheimer's disease. The distance D between target patient P and patients P1-P6 is calculated, for example, using equation (1).

[0047]

number

[0048] In the example shown in Figure 6, patient P3 is selected as a similar patient because the distance between the target patient P and patient P3 is the shortest. Figure 7 is a table showing numerical examples of each factor for the target patient and the similar patient. In this example, the target patient is 5 years younger than the similar patient, has a BMI that is 2 points higher, and an MMSE score that is 3 points higher.

[0049] The template acquisition function 13c retrieves template data for similar patients from the template database 12c. If no template data for similar patients exists, the template acquisition function 13c may retrieve template data for the patient with the second shortest distance from the target patient.

[0050] The template modification function 13d modifies the template data of similar patients based on the factor information of the target patient, the factor information of similar patients, and the degree of influence. Here, the degree of influence refers to information indicating the degree to which each factor has an impact on the disease. In this embodiment, the factor information includes the weight coefficients (α, β, γ...) of each factor.

[0051] In this embodiment, the template modification function 13d modifies the template data by changing the numerical values ​​representing cognitive function included in the template data by multiplying the difference in factors between the target patient and similar patients by a weighting coefficient. Specifically, it changes the values ​​of the template data by the difference in cognitive function obtained by equation (2).

[0052]

number

[0053] The template modification function 13d obtains the weight coefficients of each factor from the weight coefficient database 12b, and then calculates the value of ΔCogAbility using formula 2.

[0054] The information output function 13e outputs various types of information to an external device. The information output function 13e outputs predictive information based on the modified template data. This predictive information shows the predicted progression of the patient's function due to the disease in the event that disease-modifying treatment is not administered. The predictive information is transmitted to the terminal device 20 via the communication interface 11. The predictive information may also be output to the patient's terminal device (smartphone, tablet, etc.). This allows the patient to understand the predicted progression of their function due to the disease in the event that disease-modifying treatment is not administered.

[0055] <Medical information processing method according to the first embodiment> Next, an example of a medical information processing method according to the first embodiment will be described with reference to the flowchart in Figure 8.

[0056] Step S11: The patient information acquisition function 13a acquires factor information of the target patient. For example, the patient information acquisition function 13a acquires the target patient's age, BMI value, and MMSE score value. More specifically, the patient information acquisition function 13a identifies the disease from the target patient's electronic medical record information and searches the factor database 12a using the disease as a search key to identify the factors that affect the disease. Subsequently, the patient information acquisition function 13a acquires the numerical values ​​of the identified factors (age, BMI, MMSE score, etc.) from the weight coefficient database 12b.

[0057] Step S12: The similar patient extraction function 13b extracts one similar patient. For example, based on the factor information obtained in step S11, the similar patient extraction function 13b calculates the distance between the target patient and other patients using formula (1), and extracts the patient with the shortest distance as a similar patient. Thus, in this embodiment, the similar patient extraction function 13b extracts one similar patient who is most similar to the target patient in terms of factors.

[0058] Step S13: The template acquisition function 13c acquires template data of similar patients extracted in step S12 from the template database 12c.

[0059] Step S14: The template modification function 13d modifies the template data of similar patients obtained in step S13. For example, the template modification function 13d calculates the difference in cognitive function ΔCogAbility using equation (2), with the weight coefficients α, β, and γ corresponding to age, BMI, and MMSE score respectively obtained from the weight coefficient database 12b, and the difference in age ΔAge, BMI value ΔBMI, and MMSE score value ΔScore between the target patient and similar patients. ΔCogAbility is calculated for each elapsed time in the template data (e.g., 1 month, 3 months, 6 months, 1 year, etc.).

[0060] Step S15: The information output function 13e outputs predictive information for the target patient based on the template data modified in step S14. This predictive information shows the predicted progression of cognitive function in the target patient with Alzheimer's disease if disease-modifying treatment is not administered.

[0061] The prediction information output in step S15 is transmitted to the terminal device 20. The display control function 25b of the terminal device 20 controls the display 24 to display the trend prediction indicated by the prediction information received from the medical information processing device 10. As a result, the trend prediction for the target patient is presented to the user on the display 24 of the terminal device 20.

[0062] Figure 9 shows an example of a screen displayed on the terminal device 20. In this example screen, the trend prediction is displayed as a graph. The horizontal axis of the graph represents time (elapsed time), and the vertical axis represents cognitive function. The solid line in the graph shows the trend curve (actual trend) based on template data of similar patients, and the dashed line in the graph shows the trend prediction (prediction result, baseline) for the target patient.

[0063] Furthermore, as shown in Figure 9, the predicted progress of the target patient and the actual progress of similar patients may be displayed in a comparable manner. That is, the display control function 25b may control the display 24 to display the predicted progress indicated by the prediction information and the actual progress of similar patients in a comparable manner. This makes it easy for the user to compare the target patient with similar patients.

[0064] The display control function 25b may also control the display 24 to show the basis for predicting the patient's progress. For example, as shown in Figure 9, the basis for predicting the patient's progress may be displayed in a callout format. In the example screen in Figure 9, the basis for the prediction is shown as "5% upward adjustment due to age difference," "7% upward adjustment due to MMSE score difference," and "2% downward adjustment due to BMI difference." In this example, regarding age, the patient is 70 years old, and the similar patient is 75 years old, meaning the patient is 5 years younger than the similar patient. Since the age weighting coefficient α is 0.01, ΔCogAbility has increased by 0.05 (5%). Similar calculations are used to show the basis for other factors. This basis is generated, for example, by the template modification function 13d based on the weighting coefficient corresponding to the factor and the difference between the patient's factor and the similar patient's factor, as described above. The generated basis information is transmitted from the medical information processing device 10 to the terminal device 20 along with the prediction information.

[0065] <Medical information processing method according to the second embodiment> Next, an example of a medical information processing method according to the second embodiment will be described with reference to the flowchart in Figure 10.

[0066] Step S21: The patient information acquisition function 13a acquires factor information of the target patient. This step is the same as step S11 described above, and for example, the target patient's age, BMI value, and MMSE score value are acquired.

[0067] Step S22: The similar patient extraction function 13b extracts multiple similar patients. For example, based on the factor information obtained in step S21, the similar patient extraction function 13b calculates the distance between the target patient and other patients using formula (1), and extracts a predetermined number of similar patients starting with the patient with the shortest distance. In the example shown in Figure 11, in addition to similar patient A, who is most similar to the target patient, similar patient B, who is the second most similar, and similar patient C, who is the third most similar, are extracted. In this way, the similar patient extraction function 13b extracts multiple similar patients who are similar to the target patient in terms of factors.

[0068] Step S23: The template acquisition function 13c acquires multiple template data corresponding to each of the multiple similar patients extracted in step S22 from the template database 12c.

[0069] Step S24: The template modification function 13d modifies each of the multiple template data obtained in step S23. The method for modifying each template data is the same as in step S14 described above, so the explanation is omitted.

[0070] Step S25: The information output function 13e outputs multiple prediction information based on the multiple template data modified in step S24. For example, three prediction information corresponding to similar patients A, B, and C are output.

[0071] The prediction information output in step S25 is transmitted to the terminal device 20. The display control function 25b of the terminal device 20 controls the display 24 to display the trend prediction indicated by the prediction information received from the medical information processing device 10. As a result, the trend prediction for the target patient is presented to the user on the display 24 of the terminal device 20.

[0072] Figure 12 shows an example of a screen displayed on the terminal device 20. In this example screen, the projected progress of the target patient and the actual progress of similar patients A, B, and C are displayed as graphs. For example, the user of the terminal device 20 selects one similar patient from similar patients A, B, and C by clicking on the actual progress of similar patients A, B, and C. The display control function 25b controls the display 24 to display the projected progress of the target patient corresponding to the selected similar patient (i.e., the projected progress shown by the prediction information generated based on the template data of the selected similar patient). In the example screen in Figure 12, similar patient A was selected, so the projected progress generated based on the template data of similar patient A is displayed. In addition, similar to the first embodiment, the display control function 25b may also control the display 24 to show the basis for the projected progress of the target patient. In this case, for example, the basis corresponding to the similar patient selected by the user is displayed on the display 24.

[0073] <Medical information processing method according to the third embodiment> Next, an example of a medical information processing method according to the third embodiment will be described with reference to the flowchart in Figure 13.

[0074] Step S31: The patient information acquisition function 13a acquires factor information of the target patient. This step is the same as steps S11 and S21 described above.

[0075] Step S32: The similar patient extraction function 13b extracts multiple similar patients. This step is the same as step S22 described above.

[0076] Step S33: The template acquisition function 13c acquires multiple template data corresponding to each of the multiple similar patients extracted in step S32 from the template database 12c.

[0077] Step S34: The template modification function 13d integrates the multiple template data obtained in step S33 to create a single integrated template data. For example, the template modification function 13d may integrate the multiple template data by performing statistical processing to calculate statistical values ​​such as the mean and median of the values ​​at each elapsed time.

[0078] The method of integrating template data is not limited to the above. For example, the template modification function 13d may integrate the multiple acquired template data by considering the distance between the target patient and similar patients. Specifically, the weight coefficient may be increased for template data of similar patients that are relatively close to the target patient, and decreased for template data of similar patients that are relatively long to the target patient, and the multiple template data may be integrated.

[0079] Step S35: The template modification function 13d modifies the integrated template data created in step S34. The method for modifying the integrated template data is the same as in step S14, so the explanation is omitted.

[0080] Step S36: The information output function 13e outputs predictive information based on the integrated template data modified in step S35.

[0081] The prediction information output in step S36 is transmitted to the terminal device 20. The display control function 25b of the terminal device 20 controls the display 24 to display the trend prediction indicated by the prediction information received from the medical information processing device 10. As a result, the trend prediction for the target patient is presented to the user on the display 24 of the terminal device 20. For example, in the example screen shown in Figure 9, the display 24 displays a screen in which the curve of the template data for similar patients has been replaced with the curve of the integrated template data. In this embodiment as well, the basis for the prediction may be displayed on the display 24. If an integrated template is created by averaging, the basis for the prediction, such as "3% upward adjustment due to the difference in average age," will be displayed.

[0082] In the above explanation, the template modification function 13d integrated multiple template data, but another function (such as a template integration function) may also perform the processing related to template integration.

[0083] As described above, in this embodiment, factor information of the target patient is obtained regarding multiple factors that affect the target patient's disease, similar patients similar to the target patient are extracted with respect to multiple factors, template data for similar patients is obtained, the template data for similar patients is modified based on the factor information of the target patient, the factor information of similar patients, and the degree of influence indicating the extent to which each factor affects the disease, and based on the modified template data, predictive information showing the predicted progression of the target patient's function due to the disease in the case where disease-modifying treatment is not received is output. This makes it possible to present a prediction of the progression of function (natural course) for the target patient in the case where disease-modifying treatment is not received. For example, predicted values ​​of cognitive function several months later can be presented to the doctor and the target patient.

[0084] Furthermore, according to this embodiment, the basis for predicting trends can be presented. This can improve the understanding and acceptance of the trend predictions by doctors, target patients, and others.

[0085] A more specific example of its use is as follows: It can be used to understand the future course of cognitive function in patients with Alzheimer's disease if they do not receive disease-modifying treatment. This can help patients decide whether or not to receive treatment. The presented trend prediction will serve as a baseline for cognitive function if no treatment is received. This baseline can be used as a placebo for the individual patient.

[0086] Furthermore, comparing baseline cognitive function with current cognitive function during treatment allows for the assessment of the effectiveness of the treatments administered so far. This can then be used to determine whether or not to continue treatment.

[0087] As described above, this embodiment makes it possible to understand how the cognitive function of an individual patient diagnosed with dementia (the target patient) will decline in the future if disease-modifying treatment is not received. As a result, it is possible to support decision-making by doctors, patients, and family members regarding the initiation and continuation of treatment.

[0088] Although the above embodiment was described using Alzheimer's disease as an example, it can be similarly applied to other diseases that are targets of disease-modifying therapy, such as Parkinson's disease, amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS). For example, in the case of Parkinson's disease, sleep duration and other factors may be used to predict the progression of motor function and present this prediction to the user.

[0089] In the above explanation, the term "processor" refers to circuits such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an Application Specific Integrated Circuit (ASIC), or a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). The processor functions by reading and executing a program stored in the memory circuit 12. Alternatively, instead of storing the program in the memory circuit 12, the processor may be configured to directly incorporate the program into its circuitry. In this case, the processor functions by reading and executing the program incorporated into the circuitry. The processor is not limited to being a single circuit; it may also be composed of multiple independent circuits combined to form a single processor and achieve its functions. Furthermore, the multiple components shown in Figure 2 may be integrated into a single processor to achieve its functions.

[0090] Furthermore, the medical information processing method described in Figures 8, 10, and 13 can be implemented by executing a pre-prepared medical information processing program on a computer such as a personal computer or workstation. This medical information processing program can be distributed via a network such as the Internet. Alternatively, this medical information processing program can be recorded on a computer-readable, non-transient recording medium such as a hard disk, flexible disk (FD), CD-ROM, MO, or DVD, and executed by reading it from the recording medium by a computer.

[0091] Although several embodiments have been described above, these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel apparatus and methods described herein can be implemented in a variety of other forms. Furthermore, various omissions, substitutions, and modifications can be made to the embodiments of the apparatus and methods described herein, without departing from the spirit of the invention. The appended claims and equivalents are intended to include such embodiments and modifications that are included in the scope and spirit of the invention. [Explanation of Symbols]

[0092] 1. Medical Information Processing System 10 Medical Information Processing Devices 11 Communication Interface 12 Memory circuit 12a Factor Database 12b Weighting Coefficient Database 12c Template Database 13 Processing Circuit 13a Patient information acquisition function 13b Similar patient extraction function 13c Template acquisition function 13d Template Modification Function 13e Information output function 20 Terminal devices 21 Communication Interface 22 Memory circuit 23 Input Interfaces 24 displays 25 Processing Circuit 25a Information reception function 25b Display control function 30 Medical information storage device

Claims

1. A patient information acquisition unit that acquires factor information of the target patient regarding multiple factors that affect the patient's disease, A similar patient extraction unit extracts similar patients to the target patient with respect to the aforementioned multiple factors, A template acquisition unit acquires template data of similar patients, which shows the progress of function due to the disease. A template modification unit modifies the template data of the similar patient based on the factor information of the target patient, the factor information of the similar patient, and the degree of influence indicating the extent to which each factor affects the disease. An information output unit outputs predictive information showing the predicted progression of the disease in the target patient in the case where the disease-modifying treatment is not administered, based on the modified template data. A medical information processing device equipped with [a specific feature].

2. The medical information processing apparatus according to claim 1, further comprising a display control unit that controls a display to display the trend prediction indicated by the aforementioned prediction information.

3. The medical information processing apparatus according to claim 2, wherein the display control unit controls the display to indicate the basis for the trend prediction.

4. The medical information processing device according to claim 3, wherein the basis is generated based on a weight coefficient corresponding to the factor and the difference between the factor of the target patient and the factor of the similar patient.

5. The medical information processing apparatus according to claim 2, wherein the display control unit controls the display to display the trend prediction in graph or tabular format.

6. The medical information processing apparatus according to claim 2, wherein the display control unit controls the display to display the trend prediction indicated by the prediction information and the trend performance of similar patients in a comparable manner.

7. The medical information processing device according to claim 1, wherein the template modification unit modifies the template data by changing the numerical value of the template data by multiplying the difference in the factor between the target patient and the similar patient by the weight coefficient of the factor among the degree of influence.

8. The medical information processing apparatus according to claim 7, wherein the weight coefficient is a constant or a value obtained by a function.

9. The similar patient extraction unit extracts one similar patient who is most similar to the target patient with respect to the multiple factors, The template acquisition unit acquires the template data of the extracted similar patients, The template modification unit modifies the acquired template data, The information output unit outputs predictive information based on the modified template data. The medical information processing device according to claim 1.

10. The similar patient extraction unit extracts a plurality of similar patients that are similar to the target patient with respect to the plurality of factors, The template acquisition unit acquires multiple template data corresponding to each of the extracted multiple similar patients, The template modification unit modifies each of the acquired template data, The information output unit outputs multiple prediction information based on the modified multiple template data. The medical information processing device according to claim 1.

11. The similar patient extraction unit extracts a plurality of similar patients that are similar to the target patient with respect to the plurality of factors, The template acquisition unit acquires multiple template data corresponding to each of the extracted multiple similar patients, The template modification unit then integrates the acquired multiple template data to create integrated template data, and then modifies the integrated template data. The information output unit outputs predictive information based on the modified integrated template data. The medical information processing device according to claim 1.

12. The medical information processing apparatus according to claim 11, wherein the template modification unit integrates the acquired plurality of template data by performing statistical processing.

13. The medical information processing apparatus according to claim 11, wherein the template modification unit integrates the acquired plurality of template data, taking into account the distance between the target patient and each of the similar patients in the factor space.

14. We obtain factor information for the target patient regarding multiple factors that affect the patient's disease, Similar patients to the target patient are extracted with respect to the aforementioned multiple factors. Template data of the aforementioned similar patients, wherein template data showing the progression of function due to the aforementioned disease is obtained, Based on the factor information of the target patient, the factor information of the similar patient, and the degree of influence indicating the extent to which each factor affects the disease, the template data of the similar patient is modified. Based on the modified template data, predictive information is output showing the predicted progression of the disease in the target patient in the case where the disease-modifying treatment is not administered. Medical information processing method.

15. On the computer, We obtain factor information for the target patient regarding multiple factors that affect the patient's disease, Similar patients to the target patient are extracted with respect to the aforementioned multiple factors. Template data of the aforementioned similar patients, wherein template data showing the progression of function due to the aforementioned disease is obtained, Based on the factor information of the target patient, the factor information of the similar patient, and the degree of influence indicating the extent to which each factor affects the disease, the template data of the similar patient is modified. Based on the modified template data, predictive information is output showing the predicted progression of the disease in the target patient in the case where the disease-modifying treatment is not administered. A medical information processing program that performs a task.

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