Information processing method, program, and information processing system

Abstract

To enable new growth-related parameters including body height and body weight to be obtained specifically by a new method using a reference mathematical model.SOLUTION: Provided is an information processing method executed by an information processing device, the method including: a fitting step for having a reference mathematical model fitted to measured value information including a measured value of the body height and / or the weight of a subject and information on an elapsed period from birth that is linked to the measured value information; and a result output step for outputting result information including one or more growth-related parameters included in a result function that is obtained by the fitting.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates to an information processing method, a program, and an information processing system. [Background technology]

[0002] Traditionally, human developmental assessment has been carried out in the fields of pediatric medicine, maternal and child health, school health, etc. Although it is dealt with in child welfare and various other fields, the general method is "standard The evaluation is limited to evaluating the deviation from the standard height, weight, etc. (how far it deviates from the average). This is often the case.

[0003] In this context, for example, Patent Document 1 describes a technology for evaluating growth: Multiple growth patterns are registered in advance, and the corresponding growth patterns are selected based on the analysis of the child's growth data. A technology is disclosed for determining a growth pattern and predicting weight, etc. based on the determined growth pattern. It is being done. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 7002087 Summary of the Invention [Problem to be solved by the invention]

[0005] However, as disclosed in Patent Document 1, there is a method of storing growth patterns in advance. The method has limitations, and precise evaluation requires the preparation of a large number of growth patterns. In order to achieve more accurate growth assessment, it is necessary to predict height and weight at a certain point in time. There is a need to develop indicators that can evaluate the detailed developmental status.

[0006] Therefore, the present invention is particularly directed to estimating growth-related parameters including height and weight using a reference mathematical model. The present invention aims to realize a method for obtaining data. [Means for solving the problem]

[0007] According to one aspect of the present invention, there is provided an information processing method executed by an information processing device, comprising: Measurement information including at least one of the height or weight of the person, and the measurement information The standard mathematical model is applied to the input information that includes at least the period since birth linked to the birth information. a fitting step of fitting the model obtained by the fitting; A result output screen that outputs result information including one or more growth-related parameters included in the result function. Includes Tep. Effect of the Invention

[0008] According to the present invention, in particular, a new method using a reference mathematical model is used to obtain a variance including height and weight. Growth-related parameters can be determined. [Brief description of the drawings]

[0009] [Figure 1] 1 is a block diagram showing an information processing system according to a first embodiment of the present invention. [Diagram 2] 2 is a functional block diagram showing the server 100 of FIG. 1. [Diagram 3] 1 is a table illustrating growth-related parameters. [Figure 4] 2 is a functional block diagram showing the user terminal 200 of FIG. 1. [Diagram 5] FIG. 2 is a diagram showing an example of subject data stored in the server 100. [Figure 6] FIG. 2 is a diagram showing an example of user data stored in the server 100. [Figure 7]4 is an example of a flowchart relating to an information processing method according to the first embodiment of the present invention. [Figure 8] 6 is another example of a flowchart relating to the information processing method according to the first embodiment of the present invention. [Figure 9] FIG. 13 is a diagram showing an example of a screen for outputting result information of an application, which is displayed on a user terminal. [Figure 10] FIG. 13 is a diagram showing another example of a screen for outputting result information of an application, which is displayed on a user terminal. [Figure 11] FIG. 13 is a diagram showing another example of a screen for outputting result information of an application, which is displayed on a user terminal. [Figure 12] FIG. 13 is a diagram showing another example of a screen for outputting result information of an application, which is displayed on a user terminal. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The embodiments are not intended to unduly limit the scope of the present disclosure as defined in the claims. All of the components shown in the embodiments are not necessarily essential components of the present disclosure. The features shown in each embodiment may be applied to other embodiments as long as they are not mutually inconsistent. It is Noh.

[0011] (Embodiment 1) <Configuration> FIG. 1 is a block diagram showing the configuration of an information processing system according to a first embodiment of the present invention. The system 1 includes a user terminal 200 operated by a user who uses the system 1, and The system is configured by a user terminal 200 and a server 100 connected via a network NW. In this embodiment, the user uses the user terminal 200 to access the server 100. However, the server 100 is configured as a standalone server. Alternatively, the server 100 itself may have a function for allowing a user to perform operations.

[0012] The server 100 and the user terminal 200 are connected via a network NW. The network network includes, for example, the Internet, an intranet, and wireless LAN (Local Area Network). It is composed of a wide area network (WAN), a private network (IP), etc.

[0013] The server 100 receives information on the target person (including the user) registered from the user terminal 200. Based on the registered information, the reference mathematical model is provided to the user terminal 200. The device has at least a function of outputting result information including growth-related parameters for the subject using the An information processing device that provides applications for It may be a general-purpose computer such as a personal computer, or it may be a cloud computer. In the present embodiment, the above-described embodiment may be realized logically by using a For convenience, one server is shown as an example, but the present invention is not limited to this and multiple servers may be used. The system may also include servers with different roles, such as an authentication server and a database server.

[0014] The user terminal 200 is a user terminal that uses an application provided by the server 100. For example, personal computers, tablet terminals, and other information processing devices owned by users. However, it may be configured as a smartphone, a mobile phone, a PDA, etc. For the sake of convenience, one user terminal is illustrated in the above example, but the present invention is not limited to this and multiple user terminals may be used. There may be several.

[0015] FIG. 2 is a functional block diagram of the server 100 in FIG. The device includes a power supply unit 10, a storage unit 120, and a control unit 130.

[0016] The communication unit 110 is a communication unit for communicating with the user terminal 200 via the network NW. An interface, for example TCP / IP (Transmission Control Protocol / Interface Communication is carried out according to communication protocols such as the IEEE 802.11n Protocol and closed network protocols.

[0017] The storage unit 120 stores programs for executing various control processes and each function in the control unit 130. , input data, etc. are stored in RAM (Random Access Memory), ROM (Read Only Memory), The storage unit 120 is configured with a memory unit (memory unit) and a memory unit (memory unit). A subject data storage unit 121 stores various data related to the mathematical model. A mathematical model data storage unit 122 stores various data related to users. The storage unit 120 also includes a storage unit 123. The various data stored in the storage unit 120 can be temporarily stored. Instead of or in addition to the storage unit 120, a database (see FIG. In addition, the various storage units in the storage unit 120 are merely examples. The present invention is not limited to the above embodiment.

[0018] The control unit 130 executes a program stored in the storage unit 120 to It controls the overall operation of the server 100, and is composed of a CPU (Central Processing Unit) and The control unit 130 is configured with a GPU (Graphics Processing Unit) and other components. an attachment unit 131, a data management unit 132, a fitting unit 133, a result output unit 134, etc. The reception unit 131, the data management unit 132, the fitting unit 133, and the result output unit 134 are included. The unit 134 is started by a program stored in the storage unit 120 and controls the computer ( The program is executed by a server 100, which is a computer.

[0019] The reception unit 131 is provided by the server 100 and is executed by a web browser in the user terminal 200. Or via a user interface such as a screen displayed via an application, When the user performs a predetermined input, the user terminal 200 transmits instructions or Accepts various types of information.

[0020] The data management unit 132 manages various subject data related to the user (such as growth-related parameters, etc. We manage various data such as data on the results of the simulations, mathematical model data, and user data, and The data storage section 110 performs predetermined processing such as registering the data in the corresponding data storage section and reading the data.

[0021] The fitting unit 133 measures at least one of the height and weight of the subject. and information on the period since birth linked to the measurement information, The reference mathematical model is fitted to input information including, for example, If the subject is a child, the age must be 6 months (elapsed time information) and the height must be 65 cm (measurement information). Information such as age 7 years and 2 months (elapsed time information) and height 110 cm (measurement information). It is possible.

[0022] The measurement information includes at least one of the subject's height and weight measurements. For example, only the subject's height, only the subject's weight, or only the subject's height and weight. The measurements are not limited to height and weight, and other measurements may be used. It may include head circumference, chest circumference, sitting height, etc.

[0023] The elapsed period information is information indicating the period from birth to the time of measurement of each measurement value, and is expressed in units of time. The elapsed period information may be expressed in any unit such as days, months, or years. The measurement data is stored in association with the measurement data, and the data is stored as a time when the measurement corresponding to the measurement data was performed from birth. The information may be information indicating a period until the timing.

[0024] The data (information) used is the subject's height according to the time since birth or It must be a weight measurement, but may only be for one subject (e.g., a child). Data on specific groups of people based on characteristics such as gender, nationality, illness, or child abuse The unit of measurement time for the subject's height or weight may be age or month. Regardless of age or number of days since birth, the measurement must be made at least 0 seconds after birth and by the time the person reaches the age of 20. If the information is positive, it is the age at the time of measurement, the number of measurements, and their There are no restrictions on the combinations. In addition, the units in which height or weight is measured do not matter. .

[0025] The reference mathematical model is a function (f(t)) that represents the growth curve model that shows the growth of height or weight. In the present embodiment, as an example, a function (f By integrating '(t), we obtain the function (f(t)) that represents the growth curve model, but we are limited to this. An example is shown below.

[0026] First, the function (f'(t)) that represents the growth rate of the subject's height or weight is It can be expressed as a composite function of the velocity function (g(t)) and the late growth rate function (h(t)). That is, it can be expressed as in the following formula 1. Here, the prime symbol (') indicates the first derivative. Also, t indicates the time elapsed since the child's birth (regardless of the unit of age, month, etc.). In addition, parameters were estimated using data from children in the younger age range for whom late development is not assumed. When implementing this, a mathematical model is used in which the function h(t) relating to late development is removed from the following equation 1. It may be simplified as follows.

[0027]

number

[0028] Next, an example of a function (g(t)) representing the early growth rate is shown.

[0029] The function g(t) expressing the early growth velocity is composed of a term expressing the basic growth velocity immediately after birth and a term expressing the initial growth velocity. The basic growth rate is represented by a constant, and the initial growth rate is represented by an exponential function. In this case, the function g(t) representing the early growth rate can be expressed as follows:

[0030]

number

[0031] where a is the basic growth rate when given as a constant, and ωk t When written as an exponential function Here, ω is the initial growth velocity at birth, and k is the decay coefficient of the initial growth velocity. The basic growth rate does not have to be a constant, but may be a function of time (t). The initial growth rate does not have to be expressed as an exponential function, but rather as a function that has a decreasing tendency over time. If the function is simple, it may be written as a hyperbolic function, for example as follows:

[0032]

number

[0033] Next, an example of a function (h(t)) representing the later growth rate is shown.

[0034] The function h(t) expressing the late growth rate is the growth in height or weight associated with secondary sexual characteristics, i.e., the growth rate of the adult It is a function that represents the long-term cumulative growth rate, and represents the late growth progression rate i(t) and inhibition rate j(t). This can be expressed as a function:

[0035]

number

[0036] Here, u is the upper limit of the late growth rate. The function that represents the rate of late growth i(t) is called the sigma function. When expressed as an id function, it may be written as follows:

[0037]

number

[0038] In addition, if the function representing the inhibition rate of late growth j(t) is expressed as a sigmoid function, it can be written as follows: It may be stated.

[0039]

number

[0040] Here, α is a parameter that contains information about the onset of late growth, and β is a parameter that contains information about the onset of late growth. δ is a parameter that contains information about the rate of growth progression. This is a parameter related to the period (time lag) until the occurrence of the The function expressing the rate of progression and inhibition of growth does not have to be in the form of a sigmoid function, but can be any other function, such as A smooth normalization function may be used.

[0041] Regarding the function f'(t) that represents the growth rate of the subject's height or weight as shown in Equation 2 to Equation 6, As mentioned above, the basic growth rate is the constant a, the initial growth rate is described by an exponential function, and the subsequent When the progression rate function and inhibition rate function of growth in the second trimester are described by a sigmoid function, the subject's height or The function f(t) (the so-called growth curve) that shows the tendency of weight increase or decrease over time from birth is f'( t) is expressed by the following equation:

[0042]

number

[0043] Here, C is an integral constant. Also, at the time of birth (t=0), the effect of later development is not assumed. It is difficult. (-α) ≪1, and e (-(α+δ)) It is assumed that ≪1. Therefore, As mentioned above, the basic growth rate is a constant a, and the initial growth rate is described by an exponential function. When the progression rate function and the inhibition rate function of the late growth are described by sigmoid functions, from Eq. 6, at least At time t=0, the following two terms relating to late growth are close to zero:

[0044]

number

[0045] From Equation 7 and Equation 8, when the subject's height or weight at birth (t=0) is Φ, the above As mentioned above, the basic growth rate is expressed as a constant a, the initial growth rate is expressed as an exponential function, and the progress of the later growth is expressed as When the activation rate function and the inhibition rate function are described by a sigmoid function, the integral constant C is expressed as follows: can be approximated as follows:

[0046]

number

[0047] The function f(t) shown in Equation 7 is used when the measurement time (elapsed period) such as age or month is discrete. If the growth rate is obtained in minutes, it may be approximated using the growth rate curve f'(t) as follows:

[0048]

number

[0049] In addition, the function f'(t) that represents the growth rate of the subject's height or weight can be differentiated as , can be converted into a function f''(t) that represents the growth acceleration of the subject's height or weight. As shown above, the basic growth rate is a constant, the initial growth rate is expressed as an exponential function, and the progress of the later growth rate is expressed as When the rate of growth and inhibition functions are described by sigmoid functions, the growth of children's height or weight The function f''(t) representing the acceleration is given by:

[0050]

number

[0051] Such a basis is a function (f(t)) that represents a growth curve model that shows the growth of height or weight. The quasi-mathematical model is fitted by the fitting unit 133 with at least a factor of the subject's height or weight. Measurement value information including either one of the measurements and information on the period since birth linked to the measurement value information The fitting is performed on input information including at least the above information.

[0052] Regarding the method for estimating parameters from data (i.e., fitting method), If there are no particular constraints on the objectives and a theoretically or practically appropriate solution can be obtained, Any method for optimizing a function may be used, for example Markov chain Monte Carlo. Bayesian estimation using the Carlo method, Hamiltonian Monte Carlo method, etc., nonlinear least squares method , variational inference, EM algorithm, maximum likelihood estimation method, MAP estimation method, etc. Regarding the method of applying the algorithm, we fitted a standard mathematical model to each individual and set the parameters. In addition to the method of estimating the population size and predicting growth, we also fit a model to the population. A method for estimating parameters of the entire group and predicting growth, and for simultaneously estimating parameters of individuals and groups Depending on the purpose, you can choose any method, such as a method to predict growth by analyzing the growth rate (called a hierarchical model). It is possible.

[0053] In addition, when estimating various parameters using the subject's height or weight data, This may be implemented with a statistical model using a rate distribution as follows:

[0054]

number

[0055] Here, Y t is the height or weight measurement (Y) at time t since birth, It has been shown that f(t) follows a normal distribution with mean and standard deviation. The probability distribution does not have to be normal, and other probability distributions may be assumed. For example, a gamma distribution If cloth is used, it will look like this:

[0056]

number

[0057]

number

[0058]

number

[0059] In this case, the parameter (here, σ) that determines the variance of the probability distribution is determined by the output, such as age or month. It may be assumed that it varies depending on the time elapsed since birth.

[0060] The result output unit 134 outputs the result function obtained by the fitting by the fitting unit 133. Outputting result information including one or more of the development-related parameters included in the number.

[0061] That is, as described above, the fitting unit 133 fits the reference mathematical model. Based on the result function f(t) obtained by the above, one or more growth-related parameters are calculated from Equations 1 to 11. The result output unit 134 outputs the result information including the extracted growth-related parameters. Output.

[0062] More specifically, the growth-related parameters for the subject's height and weight growth are expressed by Equations 1 to 14. 11, for example, (1) predicted height or weight at birth and its approximate value, (2) birth (3) the initial growth rate at the time of growth, (4) the base growth rate, and (5) the growth rate at the later stage of growth. (6) the amount of growth rate inhibition of late development; (7) information on the time of onset of late development; (8) (9) information on the onset of late growth inhibition; (10) information on the onset of late growth inhibition; (11) the rate of retardation of late growth; (12) the time of maximum acceleration of late growth; (13) (14) The time when the rate of slowing down of late growth is maximum; (15) The time lag between the progression and inhibition of late growth; (16) The time when the rate of growth inhibition exceeds the rate of growth enhancement in the late development stage. (17) the duration of late development; (18) information on the rate of late development; (19) personal and environmental factors Information on the magnitude of the effects of factors such as developmental factors and intervention factors may be extracted. The relevant parameters are illustrated below.

[0063] In addition, the result output unit 134 outputs the target The height or weight of a person at the time it was measured, or inferred information at the time it was not measured, When obtaining a metric, in addition to representative values ​​such as the estimated mean and estimated mode, interval information showing the range of error, etc. Alternatively, the result output unit 134 may output the result information as a value estimated from the data. At the very least, a virtual arbitrary value is given to the parameter, and the subject's height or weight is measured. Simulation at the time when the measurement is performed or when the measurement is not performed (predetermined timing) Forecast information (including representative values ​​such as the estimated mean and estimated mode, and forecast information including interval information) The result output unit 134 may output the predicted height and weight as result information. Using the values, a composite index (and its estimated interval) such as estimated BMI is calculated and the result information is It may be output as information.

[0064] In addition, the result output unit 134 performs prediction on an individual child basis. It may be on an individual level or on the entire target population. In this case, the result output unit 134 may output, for example, a group by country, a group by region inside and outside Japan, a group by gender, Units (transgender may be distinguished based on the subjects' self-reporting) and disease type groups of people with disabilities (including healthy people), groups of people who have experienced child abuse, groups of organizational units (companies, schools, etc.), The subjects were divided into groups of single mothers and single fathers, or the total groups were Predefined attributes based on subject information about the subject, such as a group of sport types, etc. Multiple measurements in a group of units (or a group arbitrarily selected by the user) By fitting a standard mathematical model to the subject, or by using multiple measurements in the subject population, The aggregated value (derived from a predetermined aggregate such as the representative value, average value, mode, maximum value, minimum value, etc.) From the result function obtained by fitting a standard mathematical model to the One or more development-related parameters in the population may be extracted. When one or more development-related parameters in a certain population are extracted and stored in the storage unit 120, In this case, if there is no measurement value for the subject, the result output unit 134 outputs the result in the above-mentioned predetermined group. One or more development-related parameters in the The above growth-related parameters may be presented on the user terminal 200, and may be included in the subject data. Attribute information included in the questionnaire (such as country of birth, region of birth, country of residence, region of residence, gender, type of disease, etc.) (including healthy people), experience of child abuse, organization (company, school, etc.), single mother, One or more groups corresponding to the category of single fathers, type of sports participated in, etc. The relevant growth-related parameters may be displayed on the user terminal 200. In this case, the group for which the growth-related parameters are to be presented is set in advance in the system. The developmental parameters in a given population (especially one or more populations for which a mathematical model is available) The data may be the user (who may be the subject himself or herself, or may be different from the subject) The measured value may be a development-related parameter in a selected population. When the number of measurements is insufficient for estimation (fitting of a reference mathematical model) (especially when As mentioned above, there are various fitting methods for the case where the number of inputs is one. A reference mathematical model may be fitted using Bayesian estimation (Bayesian updating) or In addition to the measurements related to the subjects, the selected population (for selection of the population, see, for example, the developmental (which may be similar to the method of selecting a group for presenting relevant parameters) The aggregated values ​​may be used as a complement to fit a reference mathematical model to these. In addition, for fitting using Bayesian estimation (Bayesian updating), Similarly, if the distribution in a given population is the prior distribution, the posterior distribution when one measurement is given is In other words, the growth-related parameters of a given population can be calculated based on a standard mathematical distribution. The model is fed with one or more measurements (especially when there is only one measurement) to obtain the growth-related parameters. By updating (fitting) the data, even if the number of measurements is small, Group growth parameters can be estimated.

[0065] The result output unit 134 also stores the extracted one or more growth-related parameters and the storage unit 130 The comparison results and The evaluation criteria information may be output as result information based on the evaluation criteria information. It defines the range of results to be used to judge the evaluation. For example, the higher the number, the greater the growth. Growth-related parameters that indicate early growth (i.e., lower values ​​indicate slower growth) (e.g. , height, weight, etc.), the growth-related parameter is set to a reference value A (e.g., standard If the growth rate is lower than the lower limit of the range, the evaluation criteria information is referred to and the growth rate is judged to be "slow" and the standard If the value is higher than value A and lower than standard value B (e.g., the upper limit of the standard range), refer to the evaluation standard information. If the growth is judged to be "average" and the value is higher than standard value B, refer to the evaluation criteria information. The evaluation result "fast growth" may be output as the result information. Using values ​​based on aggregate values ​​such as the average value or representative value of the group formed in the above-mentioned specified attribute unit The result output unit 134 may also output the extracted one or more growth-related parameters and a predetermined The result is information showing the relative position of the attribute group compared to the aggregate value of the attribute group. This may be output as information.

[0066] In addition, the result output unit 134 extracts growth-related parameters and outputs the height or body size of the subject. When outputting forecast information for a specific time period, the information may include age, It can be plotted on a graph with age in months on the horizontal axis and height or weight measurement units on the vertical axis. In this case, information on the baseline population (e.g., standard growth curves, the number of children born prematurely, etc.) Growth curve information, etc.) or actual measurement data may be overlaid on the plot. The horizontal axis is age. The unit may be the age in months, the number of days since birth, or a division of these units. What is the predicted value, centimeters, meters, grams, kilograms, etc.? The results should be presented in a table or text format rather than in a graph. This is also fine.

[0067] For each parameter shown in Equation 1 to Equation 11, individual factors (genetics, disease, etc.) Information such as nationality and living environment), environmental factors (information such as nationality and living environment), implementation of interventions (health guidance, lifestyle guidance, A coefficient representing the effect of medical interventions (such as growth hormone administration) may be provided. Using information such as individual factors, environmental factors, and intervention implementation, each of the factors shown in Equations 1 to 11 is Statistical models that predict parameters (generalized linear models, generalized linear mixed models, etc.) and machine learning Defined in a format that incorporates learning models (deep learning models, neural network models, etc.) Furthermore, for the functions shown in Equation 1 to Equation 11, individual factors (genetic information on health and disease), environmental factors (information on nationality and living environment), implementation of interventions (health guidance, growth A new term (constant or function) that assumes the effects of factors such as medical interventions (e.g., hormone administration) can be added in the form of addition or subtraction. Also, the new term added here (constant or A predictive model such as a statistical model or a machine learning model may be incorporated into the function. For example, the basic growth rate is a constant, and information on individual factors, environmental factors, and intervention implementation is X. When the weight vector of the information contained in the matrix X is w, the basic growth rate a is expressed as In rule form (e.g., using a logarithmic link),

[0068]

number

[0069] FIG. 4 is a functional block diagram showing the user terminal 200 of FIG. The device includes a communication unit 210, a display operation unit 220, a storage unit 230, a camera 240, and a control unit 2 It has 50.

[0070] The communication unit 210 is a communication interface for communicating with the server 100 via the network NW. It is an interface through which communication is carried out using communication protocols such as TCP / IP.

[0071] The display operation unit 220 receives an instruction from the user and, in response to the input data from the control unit 250, A user interface used to display text, images, etc. If the terminal 200 is configured as a personal computer, a display and keyboard, The user terminal 200 is a smartphone or a tablet terminal. When the display operation unit 220 is a memory unit 23, the display operation unit 220 is configured with a touch panel or the like. It is a computer (electronic calculator) that is started by a control program stored in The user terminal 200 executes the process. Through the display operation unit, the user can select the aptitude test to be provided. For the keyboard test, the keystrokes are pressed, and for the mouse test, the mouse cursor is used. If you have a touch panel, you can tap, swipe, pinch, etc. Cut.

[0072] The storage unit 230 stores programs for executing various control processes and functions in the control unit 250. The memory section stores input data, etc. and is composed of RAM, ROM, etc. 230 temporarily stores the contents of communication with the server 100.

[0073] The camera 240 has a function of capturing an image of a part of the subject's body, for example.

[0074] The control unit 250 executes a program stored in the storage unit 230 to obtain the It controls the overall operation of the user terminal 200 and is composed of a CPU, GPU, etc.

[0075] The server 100 may be configured to have a display and operation unit function. In this case, The configuration may not include the terminal 200.

[0076] FIG. 5 is a diagram showing an example of subject data stored in server 100. As shown in FIG.

[0077] The subject data 1000 shown in FIG. 5 stores, for example, various data related to the subject. In the following, the subject will be described as an example of a child. For the sake of convenience, in FIG. This is an example of a child identified by the subject ID "10001", but it is possible to store information on multiple subjects. Various data relating to children can be stored, for example, Other information (child's name, address, contact information such as email address, gender, age, school name, grade level) , teacher's name, school attendance status, tag information, etc.), parent information (parent's name, ID, address, contact information, Gender, etc.), related party information (name, ID, address, contact information, gender, etc. of related party), family group information Information (family group name, ID, group name description, group members (past marital status, (including guardians in common-law marriages), information on related institutions (name, ID, type (medical This may include personal information (such as name, telephone number, telephone number, etc.), address, contact details, etc.

[0078] FIG. 6 is a diagram showing an example of user data stored in the server 100. As shown in FIG.

[0079] The user data 3000 shown in FIG. 6 stores various data related to the user. For the sake of convenience, we will use the example of one user (identified by user ID "30001") as an example. However, information on multiple users can be stored. For example, basic information about you (your name, address, email address or phone number) Contact information such as phone number, affiliation information, job title information, access authority information, etc.), Information on the child (user responds, or responding child ID, information on the accepted child) The linked child ID, the child ID linked to the provided child information, etc., especially the list information ) etc. User data may be collected for each employee or other person affiliated with a user institution. An ID (account) may be registered, but instead of or in addition to this, A common user ID (account) may be registered for each contact.

[0080] <Processing flow> With reference to FIG. 7, the information processing method executed by the information processing system 1 of this embodiment will be described. The flow of the information processing method according to the first embodiment of the present invention will be described. This is an example of a route.

[0081] In order to use the system 1, a user (e.g., a staff member of a child consultation center, etc.) Using the web browser or application of each user terminal 200, If you are accessing 00 and using the service for the first time, you will need to enter your user data to register as a new user. Register basic information on the Data 3000 and obtain a user account. If you have already obtained the ID and password, you can log in by entering the required authentication information. After this authentication, the service can be used. A predetermined user interface screen is provided via the above, and step S101 shown in FIG. Proceed to.

[0082] First, in step S101, the reception unit 131 of the control unit 130 of the server 100 Basic information such as subject information on the subject is transmitted from the user terminal 200 via the communication unit 110. The data management unit 132 of the control unit 130 of the server 100 receives the registration of the information. The basic information is stored in the subject data storage unit 121 of the storage unit 120 as subject data 1000. The information is stored in association with the subject ID.

[0083] As shown in FIG. 8, the reception unit 131 receives the following basic information in no particular order: For example, the child's name, address, contact information such as email address, gender, age, school name, and grade Accepts registration of information about children, such as teacher's name, school attendance status, tag information, etc. (Status TOP S201), information about the guardian, such as the guardian's name, ID, address, contact information, and gender The registration of information is accepted (step S202), followed by registration of uncles, aunts, friends of the child's older brother, Information regarding the child's relatives, such as the name, ID, address, contact information, and gender of the mother's partner or other relatives The registration of related party information is accepted (step S203), and the family group name, ID, group Description of group name, group members (children, siblings, past marriages, common-law marriages, etc.) We accept registration of family group information about family groups, such as parents and guardians who are involved in the family group registration process. Step S204), and the name, ID, type (medical institution, police, educational institution, etc.), address, etc. of the relevant institution. Registration of related organization information such as the location, contact information, etc. of related organizations is accepted (step S205 ) can be done.

[0084] Next, in step S102, the reception unit 131 receives a measurement First, the user selects an application displayed on the user terminal 200. The measurement information and elapsed period information are input on the application screen. Not only can users input data by keyboard operation, but also the target information can be retrieved from a linked database. Alternatively, measurement information regarding the above may be acquired and input.

[0085] Next, in step S103, the fitting unit 133 of the control unit 130 Measurement information including at least one of the height or weight of the person, and the measurement A standard mathematical model is applied to the input information that includes at least the period since birth associated with the information. Fitting the Dell.

[0086] Next, in step S104, the result output unit 134 One or more growth factors included in the result function obtained by fitting by the fitting unit 133 The result information including the related parameters is transmitted to the user terminal 200, and the application of the user terminal 200 The result information is displayed in a predefined format on the application's user interface screen. .

[0087] Here, the result output unit 134 outputs the graphs for each of the functions in Equation 1 to Equation 11. As shown in Figure 9, the horizontal axis represents the age or months of the person, and the horizontal axis represents the elapsed time. The measurements and growth forecast ( In this case, the information of the reference population (e.g., standard The long curve may be plotted over the actual measured values.

[0088] As shown in Figure 10, the horizontal axis represents the elapsed time such as age or months, while the vertical axis represents the height and weight. As shown in FIG. 11, The measurement values ​​and growth forecast are displayed on a graph with the horizontal axis representing the elapsed time (age or months) and the vertical axis representing the growth rate. When drawing the outcome function, reference information (explanatory text or formula) showing each growth-related parameter is provided. Character information such as the above, and graphic information such as arrows and dotted lines may be overlaid and drawn.

[0089] As shown in FIG. 12, the horizontal axis represents the elapsed time such as age or age in months, and the vertical axis represents acceleration. On the graph, we can plot the function f''(t) that shows the growth acceleration curve. When plotting the growth acceleration curve, the function f''(t) is used as a composite function. It is possible to plot it as it is, but as shown in Fig. 11, the second term on the right hand side of Equation 11 can be plotted as it is. (Progression rate function of late growth) and the third term on the right hand side of Equation 11 (Inhibition rate function of late growth) are plotted separately. It is also possible to do so.

[0090] As described above, according to the present embodiment, in particular, a new method using a reference mathematical model is used. , growth-related parameters including height and weight can be calculated.

[0091] In addition, it is important to consider what external factors will be considered, or what kind of target (group) data will be collected. Depending on which device you use, the height or weight of the subject that can be output at a given time can be The prediction results and various growth-related parameters can be used for the following purposes:

[0092] (1) Medical field (1-1) "Test result reports for the diagnosis of developmental retardation and for determining medical intervention" Growth-related parameters, such as basal growth rate and the onset of late growth, are estimated, Comparisons are made with aggregate values ​​for a given attribute group (e.g., group of countries, gender, etc.) and within that group Based on the relative positioning and comparison results (and evaluation criteria information), the target children's growth deficiencies and Risk information suggesting the possibility of coexisting other diseases may also be transmitted.

[0093] (1-2) "Epidemiological surveys of childhood development and medical research on specific diseases related to child development" "Kiku" Growth-related parameters, such as basal growth rate and onset of late growth, were determined based on a specific Estimates are made for groups of disease units (such as Down's syndrome) and basic epidemiological information linked to the subjects Refer to the information on interventions such as transmission and treatment for specific diseases (including information on the timing of the intervention) In light of this, we investigated how growth-related parameters such as basal growth velocity and the onset of later growth differed before and after the intervention. Alternatively, intervention effect evaluation information regarding how the condition has changed may be transmitted.

[0094] (2) Health, welfare and education (2-1) "Growth assessment in infant health checkups, etc." Growth-related parameters, for example, predict the future height or weight of the target child and The results of the comparison with the aggregated values ​​of the specified attribute groups to which the target children belong (such as gender) (and evaluation criteria information) Based on this, we will provide information on the results of growth evaluations, including future predictions, and indicate the need for interventions such as health and lifestyle guidance. The results of the evaluation (e.g., average The transmission may be made in response to a test result indicating that the value is lower than the standard value or representative value. stomach.

[0095] (2-2) "Developmental assessments related to child abuse assessments and health and welfare intervention and guidance implementation decisions" " Growth-related parameters, for example, predict the future height or weight of the target child and Comparison results with aggregated values ​​of the specified attribute groups to which the target children belong, such as gender, and the number of children who have suffered child abuse Comparison results (and evaluation) with the aggregated values ​​of the child group (especially the group that died due to malnutrition, etc.) Based on the standard information, information suggesting the need for welfare intervention or medical intervention is provided. The risk information to be evaluated is lower than the results of the evaluation (e.g., the average or representative value, etc.). The notification may be sent depending on the result of the test.

[0096] (2-3) "Growth assessment for school health examinations, etc." Growth-related parameters, for example, predict the future height or weight of the target child and Based on the results of comparison with the aggregated values ​​of the specified attribute groups to which the target children belong, such as gender, future predictions are also made. The results of the developmental assessment, including the results of the evaluation, and information suggesting the need for interventions such as health and lifestyle guidance, as well as medical advice, are also provided. Risk information suggesting the need for intervention is provided as a result of the assessment (e.g., lower than the average or representative value). Alternatively, the measurement may be performed in response to an evaluation of the measurement result. Growth parameters such as basic growth velocity and the onset of late growth were compared for each age group. Transmit information on standard developmental changes by generation. Good too.

[0097] (2-4) "Evaluation of the effectiveness of food education interventions" Growth-related parameters, such as basal growth rate and the onset of late growth, are estimated, Please also refer to the information on the dietary education intervention linked to the subject (especially including information on the time when the dietary education intervention was implemented). By comparing the results, it is possible to see how the food education intervention changed before and after the intervention, or how the food education intervention changed the food intake. The difference in the above growth-related parameters between the group and the group without the treatment was evaluated. The evaluated intervention effect evaluation information may be transmitted.

[0098] (3) Sports, fashion, and other (3-1) "Diet management and training menu setting in the field of sports (exercise program) "Evaluation of the effectiveness of interventions such as setting the standard deviation in grams, and prediction of growth in height or weight as a result of interventions." As an example, the growth-related parameters may be predicted to predict future height or weight and to predict a given sporting event. Groups in the field (groups by sport, sports experience group, professional group, amateur group, academic group) A group consisting of one or more units such as a student unit group, a gender group, or a age group. Based on the comparison results with the total score of the group (combined), the results information including the evaluation and the target value are sent to the user's terminal. Intervention suggestions to reach development-related parameters or goals Includes information on the effectiveness of interventions that evaluate the difference from the target progress for achieving growth-related parameters. The result information may be transmitted.

[0099] (3-2) "Growth prediction for subjects (especially children) involved in clothing sales, etc." Growth-related parameters are used to predict future height (or weight) and the purchased size is used to determine the size. When the user sizes out and purchases the next size, the purchase is based on the predicted size on the user's device. Recommendation information may be transmitted.

[0100] The contents of the embodiments of the present invention will be listed and explained again. The processing method, the program, and the information processing system have the following configuration. [Item 1] An information processing method executed by an information processing device, Measurements including at least one of the following: height or weight, head circumference, chest circumference, and sitting height of the subject Input including at least fixed value information and information on the period of time since birth associated with the measurement value information a fitting step of fitting a reference mathematical model to the information; One or more growth-related parameters included in the result function obtained by the fitting A result output step of outputting result information including Information processing methods. [Item 2] The growth-related parameters include at least height or weight at birth, head circumference, chest circumference, and sitting height. 2. The method according to item 1, comprising either one of the predicted values. [Item 3] The growth-related parameter is at least a height or weight at a predetermined time in the future. 3. The method according to any of items 1 or 2, comprising either one of the predictive values. [Item 4] The growth-related parameters include at least one of the following items (1) to (18): 4. A method according to any one of 1 to 3. (1) Growth velocity at birth, (2) Decay rate of the initial growth velocity, (3) basic growth rate; (4) The upper limit of the growth rate of the late developmental stage; (5) Growth rate suppression amount of late development, (6) information regarding the onset of later development; (7) Information on the rate of enhancement and inhibition of late development (8) Information on the start of late growth suppression (9) Progression rate of late development (10) Rate of progression of late growth inhibition, (11) The period when the acceleration of late development is greatest, (12) The time when the rate of slowing down in late development is maximum, (13) The time lag between the progression and inhibition of late development; (14) The time during which the rate of inhibition of late development exceeded the rate of enhancement, (15) The period of highest growth rate in late development. (16) the period of late development, (17) Information regarding the rate of progression of late growth; (18) The extent of the influence of individual factors, environmental factors, and / or intervention factors Information [Item 5] In the information processing device, Measurements including at least one of the following: height or weight, head circumference, chest circumference, and sitting height of the subject Input including at least fixed value information and information on the period of time since birth associated with the measurement value information a fitting step of fitting a reference mathematical model to the information; One or more growth-related parameters included in the result function obtained by the fitting A result output step of outputting result information including the result information. Hmm. [Item 6] An information processing device having a control unit, The control unit is Measurements including at least one of the subject's height or weight, head circumference, chest circumference, and sitting height and input information including at least the measurement value information and information on the period of time since birth associated with the measurement value information. a fitting unit for fitting a reference mathematical model to the information; One or more growth-related parameters included in the result function obtained by the fitting A result output unit that outputs result information including the Information processing device.

[0101] Although the embodiments of the present disclosure have been described above, these may be embodied in various other forms. It is possible to implement the invention with various omissions, substitutions and modifications. These embodiments and modifications, as well as omissions, substitutions and alterations, are intended to be understood as including the spirit and scope of the present invention as claimed. are within the technical scope of the above and their equivalents. [Explanation of symbols]

[0102] 1 Information processing system, 100 servers, 110 Communications Department, 120 storage section, 130 control section, 200 user terminals, NW Network

Claims

1. An information processing method executed by an information processing device, Measurements including at least one of the following: height or weight, head circumference, chest circumference, and sitting height of the subject Input including at least fixed value information and information on the period of time since birth associated with the measurement value information a fitting step of fitting a reference mathematical model to the information; One or more growth-related parameters included in the result function obtained by the fitting A result output step of outputting result information including Information processing methods.

2. The growth-related parameters include at least height or weight at birth, head circumference, chest circumference, and sitting height. The method of claim 1 , further comprising either one of the predicted values.

3. The growth-related parameter is at least a height or weight at a predetermined time in the future. The method of claim 1 , further comprising predicting either the first or second of the first and second predictions.

4. The growth-related parameters include at least one of the following (1) to (18): Item 1. The method according to item 1. (1) Growth velocity at birth, (2) Decay rate of initial growth velocity, (3) Basic growth rate, (4) the upper limit of the growth rate of the late stage of development; (5) a growth rate inhibitory amount for late development; (6) information regarding the onset of later development; (7) Information on the rate of enhancement and inhibition of late development (8) Information on the start of late growth suppression (9) Progression rate of late development (10) rate of progression of late growth inhibition, (11) The period when the acceleration of late development is maximum, (12) The time when the rate of slowing down in late development is maximum; (13) The time lag between the progression and inhibition of late development; (14) The time when the inhibition rate exceeds the enhancement rate of late development; (15) The period of highest growth rate in late development; (16) the period of late development, (17) information regarding the rate of progression of late growth; (18) The magnitude of the influence of individual factors, environmental factors, and / or intervention factors. Information

5. In the information processing device, Measurements including at least one of the following: height or weight, head circumference, chest circumference, and sitting height of the subject Input including at least fixed value information and information on the period of time since birth associated with the measurement value information a fitting step of fitting a reference mathematical model to the information; One or more growth-related parameters included in the result function obtained by the fitting A result output step of outputting result information including the result information. Hmm.

6. An information processing device having a control unit, The control unit is Measurements including at least one of the subject's height or weight, head circumference, chest circumference, and sitting height and input information including at least the measurement value information and information on the period of time since birth associated with the measurement value information. a fitting unit for fitting a reference mathematical model to the information; One or more growth-related parameters included in the result function obtained by the fitting A result output unit that outputs result information including the Information processing device.

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