WO2024057436A1 - Dispositif de prédiction de style de vie, système de prédiction de style de vie, procédé de prédiction de style de vie, programme de prédiction de style de vie et support d'enregistrement - Google Patents

Dispositif de prédiction de style de vie, système de prédiction de style de vie, procédé de prédiction de style de vie, programme de prédiction de style de vie et support d'enregistrement Download PDF

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WO2024057436A1
WO2024057436A1 PCT/JP2022/034376 JP2022034376W WO2024057436A1 WO 2024057436 A1 WO2024057436 A1 WO 2024057436A1 JP 2022034376 W JP2022034376 W JP 2022034376W WO 2024057436 A1 WO2024057436 A1 WO 2024057436A1
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data
lifestyle
brain
index value
user
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PCT/JP2022/034376
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English (en)
Japanese (ja)
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秀明 鈴木
聡 安田
彰 樋口
アイジャン イマンクロヴァ
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国立大学法人東北大学
株式会社CogSmart
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Priority to JP2022570215A priority Critical patent/JP7266807B1/ja
Priority to PCT/JP2022/034376 priority patent/WO2024057436A1/fr
Publication of WO2024057436A1 publication Critical patent/WO2024057436A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

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  • the present disclosure relates to a lifestyle prediction device, a lifestyle prediction system, a lifestyle prediction method, a lifestyle prediction program, and a recording medium.
  • Patent Document 1 A system for preventing dementia has been disclosed (for example, see Patent Document 1). According to Patent Document 1, subjects are divided into groups regarding the progression of dementia, questions are determined based on the group to which the subjects belong, and the determined questions are presented to the subjects.
  • one of the objectives of the present disclosure is to provide a lifestyle prediction device that can accurately predict future lifestyle habits.
  • a lifestyle prediction device is a lifestyle prediction device for predicting future lifestyle habits of a user.
  • the lifestyle prediction device includes a brain state image data acquisition unit that acquires brain state image data that is image data regarding the morphology of the user's brain or image data regarding the function of the user's brain;
  • a brain volume related data extraction unit extracts data related to the user's brain volume from the user's brain state image data, and data related to the brain volume is extracted from the data related to the user's brain volume extracted by the brain volume related data extraction unit.
  • a lifestyle index value data deriving unit that derives lifestyle index value data of a future user based on correlation data between the data and lifestyle index value data that is data related to lifestyle habits.
  • the correlation data includes first relational expression data representing the relationship between data on brain volume and lifestyle index value data, which are created based on past brain volume data and lifestyle index value data actually measured in the past. , is the difference between the lifestyle index value data actually measured at the first time point in the past and the predicted data in which the lifestyle index value data is predicted from the data regarding the brain volume at the first time point based on the first relational expression data.
  • the first difference data the second difference data which is the difference between the lifestyle index value data actually measured at the first time point and the lifestyle index value data actually measured at the second time point after the first time point;
  • Second relational expression data representing a relationship between the difference data and the second difference data.
  • the lifestyle index value data derivation unit derives lifestyle index value data of the future user from data regarding brain volume based on the second relational expression data.
  • future lifestyle habits can be accurately predicted.
  • FIG. 1 is a diagram schematically showing the appearance of a lifestyle prediction system according to the first embodiment.
  • FIG. 2 is a block diagram showing the configuration of the lifestyle prediction system shown in FIG. 1.
  • FIG. 3 is a conceptual diagram showing detailed data included in the correlation data.
  • Figure 4 shows the relationship between data regarding the ratio of total brain gray matter volume to intracranial volume, which is data regarding past brain volume, and systolic blood pressure value data, which is one of the lifestyle index values actually measured in the past. This is a graph showing.
  • FIG. 5 is a graph showing the relationship between the actually measured SBP value data at the first time point and the SBP value data predicted using the first relational expression from the data regarding the volume ratio of whole brain gray matter at the first time point. be.
  • FIG. 6 is a graph showing the relationship between the first difference data and the second difference data.
  • FIG. 7 is a flowchart showing a schematic process of deriving lifestyle index value data of a future user using the lifestyle prediction system according to the first embodiment.
  • Figure 8 shows Log10 data of the ratio of total brain gray matter volume to intracranial volume, which is data related to past brain volume, and systolic blood pressure (SBP), which is one of the lifestyle index values actually measured in the past.
  • SBP systolic blood pressure
  • FIG. 9 is a graph showing the relationship between actually measured SBP value data at the first time point and SBP value data predicted from Log10 data of the volume ratio of whole brain gray matter at the first time point in this case.
  • FIG. 10 is a graph showing the relationship between the first difference data and the second difference data in this case.
  • a lifestyle prediction device is a lifestyle prediction device for predicting future lifestyle habits of a user.
  • the lifestyle prediction device includes a brain state image data acquisition unit that acquires brain state image data that is image data regarding the morphology of the user's brain or image data regarding the function of the user's brain;
  • a brain volume related data extraction unit extracts data related to the user's brain volume from the user's brain state image data, and data related to the brain volume is extracted from the data related to the user's brain volume extracted by the brain volume related data extraction unit.
  • the correlation data includes first relational expression data representing the relationship between data on brain volume and lifestyle index value data, which are created based on past brain volume data and lifestyle index value data actually measured in the past. , is the difference between the lifestyle index value data actually measured at the first time point in the past and the predicted data in which the lifestyle index value data is predicted from the data regarding the brain volume at the first time point based on the first relational expression data.
  • the first difference data the second difference data which is the difference between the lifestyle index value data actually measured at the first time point and the lifestyle index value data actually measured at the second time point after the first time point;
  • Second relational expression data representing a relationship between the difference data and the second difference data.
  • the lifestyle index value data derivation unit derives lifestyle index value data of the future user from data regarding brain volume based on the second relational expression data.
  • lifestyle index value data such as future systolic blood pressure value data and smoking amount value data as much as possible. Accurate prediction is desirable. Therefore, the present inventors thought as follows. First, we examined the lifestyle index value data and found that there is a correlation between the user's lifestyle index value data and the brain state image data, which is image data related to the user's brain morphology or brain function. . Furthermore, rather than simply deriving future lifestyle index value data from image data regarding brain morphology and image data regarding brain function, we also derive future lifestyle index value data based on actually measured lifestyle index value data and brain state image data before measurement. If it is possible to modify the predicted lifestyle index value data by considering the relationship with the predicted lifestyle index value data, more accurate lifestyle index value data of future users can be derived, and accurate lifestyle index value data can be derived. I thought it would be possible to predict.
  • the inventors of the present invention have conducted extensive studies to determine the relationship between the user's brain state image data and future lifestyle index value data, and determined the relationship between the user's brain state image data and future lifestyle index value data. Correlation between the deviation from the habit index value data (first deviation) and the deviation between the lifestyle index value data actually measured in the past and the lifestyle indicator value data actually measured after that time (second deviation) We found that if we corrected the predicted lifestyle index value data based on this correlation, we could more accurately derive future lifestyle index value data, and thus conceived the structure of the present invention. It's arrived.
  • the lifestyle index value data derivation unit extracts data related to brain volume and data related to lifestyle from the data related to the user's brain volume extracted by the brain volume related data extraction unit.
  • Lifestyle index value data of future users is derived based on correlation data with lifestyle index value data.
  • the correlation data is a first relational expression that expresses the relationship between brain volume data and lifestyle index value data, which is created based on past brain volume data and lifestyle index value data actually measured in the past. The difference between the data, the lifestyle index value data actually measured at the first time point in the past, and the predicted data that predicts the lifestyle index value data from the data regarding the brain volume at the first time point based on the first relational expression data.
  • the user's brain condition image data can be used to predict future lifestyles by reflecting corrections that take into account the first difference data corresponding to the first deviation and the second difference data corresponding to the second deviation.
  • Habit index value data can be derived. Therefore, according to the lifestyle prediction device, future lifestyle habits can be accurately predicted.
  • the image data related to brain morphology may include at least one of brain MRI (Magnetic Resonance Imaging) image data and brain X-ray CT (Computed Tomography) image data.
  • Image data on brain function is at least at least of the image data of the brain PET (POSITRON EMISSION TOMOGRAPHY) image data and the SPECT (Single Photon Emission Computed TOMPUTED TOMGRAPY) image data. It may include a deviation.
  • the data regarding brain volume may include data regarding the ratio of the volume of whole brain gray matter to intracranial volume.
  • future lifestyle index value data of the user can be derived more accurately and future lifestyle habits can be predicted.
  • the lifestyle index value data includes systolic blood pressure (SBP) value data, diastolic blood pressure (DBP) value data, smoking amount value data, and alcohol consumption value.
  • the data may include at least one of data and BMI (Body Mass Index) value data. Since these lifestyle index values are closely related to lifestyle-related diseases that are likely to be contracted in the future, it is important to promote lifestyle changes based on more effective lifestyle predictions. can.
  • At least one of the first relational expression data and the second relational expression data may include a linear relational expression.
  • the lifestyle prediction device may further include a storage unit that stores at least one of first difference data, second difference data, first relational expression data, and second relational expression data. By doing so, future lifestyle index value data can be efficiently derived using the data stored in the storage unit.
  • a lifestyle prediction system includes a server and is a lifestyle prediction system for predicting future lifestyle habits of a user.
  • the server includes a brain condition image data acquisition unit that acquires brain condition image data, which is image data related to the form of the user's brain or image data related to the function of the user's brain, from the outside, and a brain condition image data acquisition unit that A brain volume related data extraction unit extracts data related to the user's brain volume from the brain state image data of the user; A lifestyle index value data derivation unit that derives lifestyle index value data of a future user based on correlation data with lifestyle index value data that is data related to lifestyle habits.
  • the correlation data includes first relational expression data representing the relationship between data on brain volume and lifestyle index value data, which are created based on past brain volume data and lifestyle index value data actually measured in the past. , is the difference between the lifestyle index value data actually measured at the first time point in the past and the predicted data in which the lifestyle index value data is predicted from the data regarding the brain volume at the first time point based on the first relational expression data.
  • the first difference data the second difference data which is the difference between the lifestyle index value data actually measured at the first time point and the lifestyle index value data actually measured at the second time point after the first time point;
  • Second relational expression data representing a relationship between the difference data and the second difference data.
  • the lifestyle index value data derivation unit derives lifestyle index value data of the future user from data regarding brain volume based on the second relational expression data.
  • a lifestyle prediction method is a lifestyle prediction method for predicting a user's future lifestyle, and includes image data regarding the shape of the user's brain or image data regarding the function of the user's brain.
  • the method includes a step of deriving lifestyle index value data of a future user based on correlation data with lifestyle index value data that is data related to lifestyle habits.
  • the correlation data includes first relational expression data representing the relationship between data on brain volume and lifestyle index value data, which are created based on past brain volume data and lifestyle index value data actually measured in the past. , is the difference between the lifestyle index value data actually measured at the first time point in the past and the predicted data in which the lifestyle index value data is predicted from the data regarding the brain volume at the first time point based on the first relational expression data.
  • the first difference data the second difference data which is the difference between the lifestyle index value data actually measured at the first time point and the lifestyle index value data actually measured at the second time point after the first time point;
  • Second relational expression data representing a relationship between the difference data and the second difference data.
  • the future user's lifestyle index value data is derived from the data regarding the brain volume based on the second relational expression data.
  • the lifestyle prediction program is a lifestyle prediction system program that includes a server and is used in a lifestyle prediction system for predicting future lifestyle habits of a user.
  • a brain state image data acquisition unit that acquires brain state image data that is image data related to brain function or brain function of the user;
  • a brain volume related data extraction unit that extracts data related to volume, and data related to the user's brain volume extracted by the brain volume related data extraction unit, data related to brain volume and lifestyle index value data that is data related to lifestyle habits.
  • the lifestyle index value data derivation unit functions as a lifestyle index value data derivation unit that derives lifestyle index value data of a future user based on correlation data with the user.
  • the correlation data includes first relational expression data representing the relationship between data on brain volume and lifestyle index value data, which are created based on past brain volume data and lifestyle index value data actually measured in the past. , is the difference between the lifestyle index value data actually measured at the first time point in the past and the predicted data in which the lifestyle index value data is predicted from the data regarding the brain volume at the first time point based on the first relational expression data.
  • the first difference data the second difference data which is the difference between the lifestyle index value data actually measured at the first time point and the lifestyle index value data actually measured at the second time point after the first time point;
  • Second relational expression data representing a relationship between the difference data and the second difference data.
  • the lifestyle index value data derivation unit derives lifestyle index value data of the future user from data regarding brain volume based on the second relational expression data.
  • a storage medium is a computer-readable recording medium that includes a server and is used in a lifestyle prediction system for predicting future lifestyle habits of a user, and is a computer-readable recording medium that includes a server and is used in a lifestyle prediction system for predicting a user's future lifestyle.
  • a brain state image data acquisition unit that acquires brain state image data that is image data or image data related to the functions of the user's brain; and a brain state image data acquisition unit that acquires brain state image data of the user that is image data related to the functions of the user's brain.
  • a brain volume-related data extraction unit extracts data related to the user's brain, and data related to the user's brain volume extracted by the brain volume-related data extraction unit extracts data related to the brain volume and lifestyle index value data, which is data related to lifestyle habits.
  • the lifestyle index value data derivation unit functions as a lifestyle index value data derivation unit that derives lifestyle index value data of a future user based on the correlation data.
  • the correlation data includes first relational expression data representing the relationship between data on brain volume and lifestyle index value data, which are created based on past brain volume data and lifestyle index value data actually measured in the past.
  • the lifestyle index value data derivation unit derives lifestyle index value data of the future user from data regarding brain volume based on the second relational expression data.
  • FIG. 1 is a diagram schematically showing the appearance of a lifestyle prediction system according to the first embodiment.
  • FIG. 2 is a block diagram showing the configuration of the lifestyle prediction system shown in FIG. 1.
  • lifestyle prediction system 11 in the first embodiment includes a server 13 and a terminal device 14 connectable to server 13 via network 12.
  • the network 12 may be the Internet, or may be an internal network in a closed environment built for the company, such as an intranet.
  • the network 12 may be wired or wireless.
  • the terminal device 14 is a portable terminal device 14 such as a smartphone, for example. Of course, it may be a tablet PC, a notebook PC, or a stationary desktop PC.
  • the terminal device 14 serves as an interface between a terminal device network interface section 31 for connecting to the network 12, a terminal device control section 32 for controlling the terminal device 14 itself, a terminal device memory 33 for storing data, and a user.
  • a touch panel 15 is included. The terminal device 14 inputs data and information to the server 13 and outputs data and information transmitted from the server 13 via the touch panel 15.
  • the server 13 functions as a lifestyle prediction device.
  • the server 13 includes a server network interface section 21 for connecting to the network 12, a server control section 22 for controlling the server 13 itself, and a server hard disk 23 as a storage section for storing data.
  • Connected to the server 13 are a display for displaying data, and a keyboard and mouse (both not shown) for inputting data, and these serve as an interface for the server 13.
  • the server hard disk 23 stores first difference data, second difference data, first relational expression data, and second relational expression data, which will be described later.
  • the server control unit 22 includes a brain condition image data acquisition unit 41, a brain volume related data extraction unit 42, and a lifestyle index value data derivation unit 43.
  • the brain condition image data acquisition unit 41 acquires brain condition image data that is image data regarding the form of the user's brain or image data regarding the function of the user's brain.
  • the brain volume related data extraction unit 42 extracts data related to the volume of the user's brain from the user's brain state image data acquired by the brain state image data acquisition unit 41.
  • the lifestyle index value data deriving unit 43 calculates a correlation between data regarding the brain volume and lifestyle index value data, which is data regarding lifestyle habits, from the data regarding the user's brain volume extracted by the brain volume related data extraction unit 42. Based on the relational data, lifestyle index value data of the future user is derived.
  • the brain state image data acquired by the brain state image data acquisition unit 41 is brain MRI image data that is image data regarding the morphology of the brain.
  • the data regarding the brain volume extracted by the brain volume related data extraction unit 42 is data regarding the ratio of the volume of the whole brain gray matter to the intracranial volume.
  • correlation data 51 includes first relational expression data 52, first difference data 53, second difference data 54, and second relational expression data 55.
  • the first relational expression data 52 represents the relationship between data regarding brain volume and lifestyle index value data, which is created based on data regarding past brain volume and lifestyle index value data actually measured in the past.
  • Figure 4 shows data on the ratio of total brain gray matter volume to intracranial volume, which is data on past brain volume, and systolic blood pressure (hereinafter simply referred to as "SBP value").
  • SBP value systolic blood pressure
  • the graph shown in FIG. 4 shows the first relational expression.
  • the horizontal axis shows the volume ratio (%) of the whole brain gray matter
  • the vertical axis shows the actually measured SBP value (mmHg).
  • the percentage of total brain gray matter volume has been extracted from brain MRI image data. That is, it is calculated from the ratio of the volume of the whole brain gray matter to the intracranial volume in the MRI image data.
  • the past whole brain gray matter volume ratio and the actually measured SBP value data have a correlation whose slope is represented by line ⁇ 1 and whose intercept is represented by e 1 .
  • This correlation is a linear relational expression, and is expressed by Equation 1 below.
  • the data of this equation (1) becomes the first relational expression data 52 included in the correlation data 51.
  • the p value in the graph shown in FIG. 4 is, for example, 4.01 ⁇ 10 ⁇ 24 . This value is sufficiently small and highly reliable.
  • the first difference data 53 is a prediction obtained by predicting the lifestyle index value data from the data regarding the brain volume at the first time point based on the lifestyle index value data actually measured at the first time point in the past and the first relational expression data 52. This is the difference between the data and That is, the predicted data in which the lifestyle index value data is predicted from the data regarding the brain volume at the first time point based on the first relational expression data 52 is calculated using the above-mentioned formula (1) using the slope ⁇ 1 and the intercept e1. This is predicted data of SBP value.
  • FIG. 5 shows the relationship between the actually measured SBP value data at the first time point and the SBP value data predicted using the first relational expression data 52 from the data regarding the volume ratio of whole brain gray matter at the first time point. It is a graph.
  • the horizontal axis shows the predicted SBP value (mmHg)
  • the vertical axis shows the actually measured SBP value (mmHg).
  • the predicted SBP value is calculated using the above equation (1).
  • first difference data is expressed by the following equation (2).
  • the data calculated by this equation (2) becomes the first difference data 53 included in the correlation data 51.
  • the p value in the graph shown in FIG. 5 is, for example, 9.82 ⁇ 10 ⁇ 8 . This value is sufficiently small and highly reliable.
  • the second difference data 54 is the difference between the lifestyle index value data actually measured at the first time point and the lifestyle index value data actually measured at the second time point after the first time point.
  • the second difference data 54 actually measured SBP value (second time point) - actually measured SBP value (first time point). Note that from this relational expression, it is derived that the actually measured SBP value (second time point) is the actually measured SBP value (first time point)+second difference data 54.
  • the second relational expression data 55 represents the relationship between the first difference data 53 and the second difference data 54.
  • FIG. 6 is a graph showing the relationship between the first difference data 53 and the second difference data 54.
  • the graph shown in FIG. 6 shows the second relational expression.
  • the horizontal axis shows the first difference data 53 (mmHg)
  • the vertical axis shows the second difference data 54 (mmHg).
  • the first difference data 53 and the second difference data 54 have a correlation whose slope is represented by line ⁇ 2 and whose intercept is represented by e 2 .
  • This correlation is a linear relational expression, and is expressed by the following equation (3).
  • Second difference data first difference data ⁇ ⁇ 2 + e 2 (3)
  • the data of this equation (4) becomes the second relational expression data 55 included in the correlation data 51.
  • the p value in the graph shown in FIG. 6 is, for example, 4.86 ⁇ 10 ⁇ 9 . This value is sufficiently small and highly reliable.
  • FIG. 7 is a flowchart showing a schematic process for deriving lifestyle index value data of a future user using the lifestyle prediction system 11 according to the first embodiment.
  • the brain condition image data acquisition unit 41 acquires brain condition image data that is image data regarding the morphology of the user's brain or image data regarding the function of the user's brain (in FIG. YES in S11 (hereinafter, "step" will be omitted).
  • the brain condition image data acquisition unit 41 acquires brain MRI image data as image data regarding the morphology of the user's brain.
  • brain MRI image data transmitted from the terminal device 14 is acquired.
  • the brain volume related data extraction unit 42 extracts data related to the brain volume from the acquired brain state image data, in this case, brain MRI image data (S12).
  • the brain MRI image data not only intracranial volume data is extracted, but also whole brain gray matter data is extracted. Then, the ratio of the total brain gray matter volume to the intracranial volume data is calculated and extracted as brain volume related data.
  • the lifestyle index value data derivation unit 43 calculates the correlation between the data on the brain volume and the lifestyle index value data, which is data on lifestyle habits, from the extracted brain volume related data that is data on the user's brain volume. Based on the relational data 51, lifestyle index value data of the future user is derived (S13). In this case, the lifestyle index value data derivation unit 43 derives the future user's lifestyle index value data from the data regarding the brain volume based on the second relational expression data 55. Specifically, for example, the current percentage of the user's total brain gray matter is calculated from MRI image data, and from this percentage of the total brain gray matter, predictive data of SBP value data, which is lifestyle index value data, is calculated using equation (1). Derive.
  • future lifestyle index value data can be derived from the user's brain state image data by reflecting corrections that take into account the first difference data and the second difference data. . Therefore, according to the lifestyle prediction system 11, future lifestyle habits can be accurately predicted.
  • the user's Future lifestyle index value data can be derived from brain condition image data. Therefore, according to the lifestyle prediction device, future lifestyle habits can be accurately predicted.
  • the server 13 includes the server hard disk 23 that stores the first difference data, the second difference data, the first relational expression data, and the second relational expression data. Therefore, future lifestyle index value data can be efficiently derived using the data stored in the server hard disk 23.
  • the data regarding the brain volume includes data regarding the ratio of the volume of the whole brain gray matter to the intracranial volume.
  • the brain condition image data acquisition unit acquires brain MRI image data as image data regarding the morphology of the brain, but the brain condition image data acquisition unit is not limited to this.
  • the image data regarding the morphology of the brain acquired by the method may include at least one of brain MRI image data and brain X-ray CT image data.
  • the image data regarding brain functions acquired by the brain state image data acquisition unit may include at least one of brain PET image data and brain SPECT image data.
  • the lifestyle index value data includes SBP value data, but the lifestyle index value data is not limited to this, and the lifestyle index value data may include diastolic blood pressure (DBP) value data,
  • DBP diastolic blood pressure
  • the information may include at least one of smoking amount value data, drinking amount value data, and BMI (Body Mass Index) value data. Since these lifestyle index values are closely related to lifestyle-related diseases that are likely to be contracted in the future, it is important to promote lifestyle changes based on more effective lifestyle predictions. can.
  • both the first relational expression data and the second relational expression data have linear relational expressions; however, the first relational expression data and the second relational expression data are not limited to this.
  • At least one of the data may include a linear relational expression. By doing so, it is possible to simplify at least one of the first relational expression data and the second relational expression, and to easily derive the lifestyle index value data.
  • at least one of the first relational expression data and the second relational expression data may be nonlinear relational expression data. Specifically, for example, one may be linear relational expression data and the other may be nonlinear relational expression data. By using nonlinear relational expression data, it is possible to refine the relational expression and derive more accurate lifestyle index value data. Note that such a relational expression can be derived by deeply analyzing each parameter using deep learning.
  • Figure 8 shows Log10 data of the ratio of total brain gray matter volume to intracranial volume, which is data related to past brain volume, and systolic blood pressure (SBP), which is one of the lifestyle index values actually measured in the past.
  • SBP systolic blood pressure
  • the horizontal axis indicates the Log 10 of the value obtained by multiplying the volume ratio (%) of the whole brain gray matter by 100, that is, the common logarithm of the value multiplied by 100 times the volume ratio (%) of the whole brain gray matter.
  • the vertical axis shows the actually measured SBP value (mmHg).
  • the common logarithm value of the volume ratio (%) of whole brain gray matter is derived based on brain MRI image data. Note that the p value in the graph shown in FIG. 8 is, for example, 8.88 ⁇ 10 ⁇ 24 . This value is sufficiently small and highly reliable.
  • FIG. 9 is a graph showing the relationship between actually measured SBP value data at the first time point and SBP value data predicted from Log10 data of the volume ratio of whole brain gray matter at the first time point in this case.
  • the horizontal axis shows the predicted SBP value (mmHg)
  • the vertical axis shows the actually measured SBP value (mmHg).
  • the predicted SBP value is data calculated based on the correlation shown in FIG. 8 described above.
  • FIG. 9 is a graph corresponding to FIG. 5 described above. Note that the p value in the graph shown in FIG. 9 is, for example, 5.21 ⁇ 10 ⁇ 8 . This value is sufficiently small and highly reliable.
  • FIG. 10 is a graph showing the relationship between the first difference data and the second difference data in this case.
  • the horizontal axis shows the first difference data 53 (mmHg)
  • the vertical axis shows the second difference data 54 (mmHg).
  • FIG. 10 is a graph corresponding to FIG. 6 described above. Note that the p value in the graph shown in FIG. 10 is, for example, 5.39 ⁇ 10 ⁇ 9 . This value is sufficiently small and highly reliable.
  • Log 10 which is 100 times the past total brain gray matter volume ratio
  • the actually measured SBP value data have a correlation expressed by a linear relational expression.
  • the past volume ratio of whole brain gray matter and the actually measured SBP value data have a correlation expressed by a nonlinear relational expression.
  • the first difference data and the second difference data also have a correlation expressed by a non-linear relational expression.
  • a lifestyle prediction method is a lifestyle prediction method for predicting a user's future lifestyle, and includes image data regarding the shape of the user's brain or image data regarding the function of the user's brain.
  • the method includes a step of deriving lifestyle index value data of a future user based on correlation data with lifestyle index value data that is data related to lifestyle habits.
  • the correlation data includes first relational expression data representing the relationship between data on brain volume and lifestyle index value data, which are created based on past brain volume data and lifestyle index value data actually measured in the past. , is the difference between the lifestyle index value data actually measured at the first time point in the past and the predicted data in which the lifestyle index value data is predicted from the data regarding the brain volume at the first time point based on the first relational expression data.
  • the first difference data the second difference data which is the difference between the lifestyle index value data actually measured at the first time point and the lifestyle index value data actually measured at the second time point after the first time point;
  • Second relational expression data representing a relationship between the difference data and the second difference data.
  • the future user's lifestyle index value data is derived from the data regarding the brain volume based on the second relational expression data.
  • the lifestyle prediction program is a lifestyle prediction system program that includes a server and is used in a lifestyle prediction system for predicting future lifestyle habits of a user.
  • a brain state image data acquisition unit that acquires brain state image data that is image data related to brain function or brain function of the user;
  • a brain volume related data extraction unit that extracts data related to volume, and data related to the user's brain volume extracted by the brain volume related data extraction unit, data related to brain volume and lifestyle index value data that is data related to lifestyle habits.
  • the lifestyle index value data derivation unit functions as a lifestyle index value data derivation unit that derives lifestyle index value data of a future user based on correlation data with the user.
  • the correlation data includes first relational expression data representing the relationship between data on brain volume and lifestyle index value data, which are created based on past brain volume data and lifestyle index value data actually measured in the past. , is the difference between the lifestyle index value data actually measured at the first time point in the past and the predicted data in which the lifestyle index value data is predicted from the data regarding the brain volume at the first time point based on the first relational expression data.
  • the first difference data the second difference data which is the difference between the lifestyle index value data actually measured at the first time point and the lifestyle index value data actually measured at the second time point after the first time point;
  • Second relational expression data representing a relationship between the difference data and the second difference data.
  • the lifestyle index value data derivation unit derives lifestyle index value data of the future user from data regarding brain volume based on the second relational expression data.
  • a storage medium is a computer-readable recording medium that includes a server and is used in a lifestyle prediction system for predicting future lifestyle habits of a user, and is a computer-readable recording medium that includes a server and is used in a lifestyle prediction system for predicting a user's future lifestyle.
  • a brain state image data acquisition unit that acquires brain state image data that is image data or image data related to the functions of the user's brain; and a brain state image data acquisition unit that acquires brain state image data of the user that is image data related to the functions of the user's brain.
  • a brain volume-related data extraction unit extracts data related to the user's brain, and data related to the user's brain volume extracted by the brain volume-related data extraction unit extracts data related to the brain volume and lifestyle index value data, which is data related to lifestyle habits.
  • the lifestyle index value data derivation unit functions as a lifestyle index value data derivation unit that derives lifestyle index value data of a future user based on the correlation data.
  • the correlation data includes first relational expression data representing the relationship between data on brain volume and lifestyle index value data, which are created based on past brain volume data and lifestyle index value data actually measured in the past.
  • the lifestyle index value data derivation unit derives lifestyle index value data of the future user from data regarding brain volume based on the second relational expression data.
  • 11 Lifestyle prediction system 12 Network, 13 Server, 14 Terminal device, 15 Touch panel, 21 Server network interface section, 22 Server control section, 23 Server hard disk, 31 Terminal device network interface section, 32 Terminal device control section, 33 Terminal device Memory, 41 Brain condition image data acquisition unit, 42 Brain volume related data extraction unit, 43 Lifestyle index value data derivation unit, 51 Correlation data, 52 First relational expression data, 53 First difference data, 54 Second difference data , 55 Second relational expression data.

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  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

La présente invention concerne un dispositif de prédiction de style de vie qui comprend une unité d'acquisition de données d'image d'état cérébral, une unité d'extraction de données de volume cérébral et une unité de dérivation de données de valeur d'indice de style de vie. Les données de corrélation comprennent : des premières données d'expression relationnelle représentant une relation entre des données concernant le volume du cerveau et des données de valeur d'indice de style de vie; des premières données différentielles qui désignent la différence entre des données de valeur d'indice de style de vie réellement mesurées à un premier instant dans le passé, et des données prédites prédisant des données de valeur d'indice de style de vie à partir de données concernant le volume du cerveau au premier instant sur la base des premières données d'expression relationnelle; des deuxièmes données différentielles qui désignent la différence entre les données de valeur d'indice de style de vie réellement mesurées au premier instant et des données de valeur d'indice de style de vie réellement mesurées à un deuxième instant postérieur au premier instant; et des deuxièmes données d'expression relationnelle représentant une relation entre les premières données différentielles et les deuxièmes données différentielles. L'unité de dérivation de données de valeur d'indice de style de vie déduit des données de valeur d'indice de style de vie futures d'un utilisateur à partir des données concernant le volume du cerveau, sur la base des deuxièmes données d'expression relationnelle.
PCT/JP2022/034376 2022-09-14 2022-09-14 Dispositif de prédiction de style de vie, système de prédiction de style de vie, procédé de prédiction de style de vie, programme de prédiction de style de vie et support d'enregistrement WO2024057436A1 (fr)

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JP2022570215A JP7266807B1 (ja) 2022-09-14 2022-09-14 生活習慣予測装置、生活習慣予測システム、生活習慣予測方法、生活習慣予測プログラムおよび記録媒体
PCT/JP2022/034376 WO2024057436A1 (fr) 2022-09-14 2022-09-14 Dispositif de prédiction de style de vie, système de prédiction de style de vie, procédé de prédiction de style de vie, programme de prédiction de style de vie et support d'enregistrement

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2018191722A (ja) * 2017-05-12 2018-12-06 株式会社Ｓｐｌｉｎｋ サーバシステム、サーバシステムによって実行される方法及びプログラム
JP2021099608A (ja) * 2019-12-20 2021-07-01 株式会社Ｓｐｌｉｎｋ 認知症リスクの提示システムおよび方法
JP7116445B1 (ja) * 2021-10-01 2022-08-10 株式会社CogSmart 認知症予防支援装置、認知症予防支援プログラムおよび認知症予防支援方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2018191722A (ja) * 2017-05-12 2018-12-06 株式会社Ｓｐｌｉｎｋ サーバシステム、サーバシステムによって実行される方法及びプログラム
JP2021099608A (ja) * 2019-12-20 2021-07-01 株式会社Ｓｐｌｉｎｋ 認知症リスクの提示システムおよび方法
JP7116445B1 (ja) * 2021-10-01 2022-08-10 株式会社CogSmart 認知症予防支援装置、認知症予防支援プログラムおよび認知症予防支援方法

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