WO2025169773A1 - Development month age estimation device, development month age estimation method, and development month age estimation program - Google Patents

Abstract

Relating to the estimation of the development month age of a child, the present invention obtains an estimation result with high objectivity regarding the development month age while increasing the degree of freedom of inspection items. [Solution] A development month age estimation device 3 comprises: a processor 15 for executing processing for estimating the development month age of a child which is the subject of inspection on the basis of inspection results for a plurality of inspection items; and a storage device 14 for storing data on a continuous probability distribution indicating a passage rate where month ages in a predetermined range are taken as a variable, for each of the plurality of inspection items. The processor 15 generates a development month age estimation function on the basis of data on a continuous probability distribution for two or more implementation inspection items used for inspection among the plurality of inspection items and an inspection result for each of the implementation inspection items, estimates the development month age of a child which is the subject of inspection on the basis of the mode value of a distribution of passage rates obtained by the development month age estimation function, and outputs an estimation result of the development month age.

Description

Developmental age estimation device, developmental age estimation method, and developmental age estimation program

This disclosure relates to a developmental age estimation device, a developmental age estimation method, and a developmental age estimation program that estimate a child's developmental age.

It has long been known that there are individual differences in the development of children (including infants and toddlers) in terms of motor, language, intelligence, and emotions. Caregivers may wish to compare their child's current developmental status with the standard developmental status of other children of the same age (or to understand any deviations from the standard developmental status). Furthermore, appropriately estimating a child's developmental age (i.e., a scale based on the number of months that indicates the developmental status of the child being tested) is also important in the fields of medicine and education.

One known technique for estimating a child's developmental age is to use a test sheet that displays multiple test items (such as movements and exercises that a child of the age being tested can perform) that are set according to the child's developmental stage, and have the tester (a medical or educational professional, etc.) use tools to perform tests on the child being tested for each test item, determine whether the child passed or failed each test item, and then graphically represent the pass/fail results to estimate the child's developmental age (see Non-Patent Document 1).

"Tojoji Method: Infant Differential Development Testing Method" by Tojoji Munenori, Keio University Press, 2022 (revised and reprinted first edition, 7th printing)

Incidentally, in addition to the test disclosed in Non-Patent Document 1 above, various other testing methods have been developed for estimating a child's developmental age, and each method estimates the developmental age using its own unique set of test items. Therefore, with such conventional technology, tests for estimating a child's developmental age must be conducted in accordance with the test items specified for each testing method, regardless of the merits or suitability of the test items. For this reason, with conventional technology, it is difficult to estimate a child's developmental age using standardized tests with a high degree of freedom in test items.

Furthermore, with conventional technology such as that described in Non-Patent Document 1, the estimation of the developmental age based on the test results is performed by the person in charge of the test (i.e., a human), and the test results may vary depending on the subjectivity of the person in charge. Furthermore, with conventional technology, since the final estimation of the developmental age requires human judgment, there is also the problem of a lack of speed in obtaining test results.

After extensive research, the inventors of the present application discovered that by using data on the pass rate for each test item (i.e., the proportion of children in a certain age group who achieve that test item), it is possible to obtain highly objective estimates of developmental age while increasing the freedom of test items (i.e., flexibility in selection). Note that in the above-mentioned Non-Patent Document 1, the pass rate for each test item is only used for selecting and rearranging test items, and is not used directly for estimating developmental age.

In light of the above background, the present disclosure aims to provide a developmental age estimation device, a developmental age estimation method, and a developmental age estimation program that enable highly objective estimation results regarding developmental age while increasing the degree of freedom in test items.

In order to solve the above problem, one aspect of the present disclosure is a developmental age estimation device that estimates a child's developmental age, and includes a processor that executes processing to estimate the developmental age of a child being tested based on the test results for a plurality of test items, and a storage device that stores data regarding a continuous probability distribution indicating the pass rate for each of the plurality of test items, with age within a predetermined range as a variable. The processor generates a developmental age estimation function based on the data regarding the continuous probability distribution for two or more implemented test items used in the test of the child being tested out of the plurality of test items, and the test results for each of the implemented test items, and estimates the developmental age of the child being tested based on the mode of the distribution of pass rates obtained by the developmental age estimation function, and outputs the estimated developmental age.

In this embodiment, a developmental age estimation function is generated based on data regarding the continuous probability distribution of the implemented test items and the test results for each of those implemented test items, and the developmental age of the child being tested is estimated based on that developmental age estimation function. This makes it possible to obtain highly objective estimation results regarding developmental age while increasing the degree of freedom in the test items.

In the above aspect, the processor may acquire data on multiple ages in months and the pass rates corresponding to each of the multiple test items, and generate data on the continuous probability distribution based on the pass rate data.

According to this aspect, data regarding continuous probability distributions can be easily obtained based on passage rate data corresponding to multiple lunar ages.

In the above aspect, the processor may determine two parameters of a beta distribution probability density function based on the passage rate data, and generate data regarding the continuous probability distribution based on a cumulative distribution function obtained by integrating the beta distribution probability density function.

According to this aspect, it is possible to obtain data relating to a continuous probability distribution suitable for estimating a child's developmental age in months, based on the probability density function of the beta distribution.

In the above aspect, the processor may set age ranges for the pass rate data for each of the plurality of test items and convert the age ranges into a range from 0 to 1.

According to this aspect, data regarding continuous probability distributions can be appropriately obtained based on the probability density function of the beta distribution.

In the above aspect, the processor may set a probability function of the pass rate for each of the performed test items based on the data regarding the continuous probability distribution and the test results, and the developmental age estimation function may include a product of the probability functions for each of the performed test items.

In this manner, the developmental age of the child being tested can be easily estimated based on the test results and data regarding the continuous probability distribution for each test item.

In the above aspect, it is preferable to further include a display device that displays the estimated developmental age.

In this manner, the user of the developmental age estimation device can easily check the estimated developmental age.

In order to solve the above problem, one aspect of the present disclosure is a developmental age estimation method using a developmental age estimation device that estimates a child's developmental age, wherein the developmental age estimation device generates a developmental age estimation function based on data relating to continuous probability distributions for two or more implemented test items used in testing the child to be tested out of multiple test items for estimating the developmental age of the child to be tested, and the test results for each of the implemented test items, estimates the developmental age of the child to be tested based on the mode of the distribution of pass rates obtained by the developmental age estimation function, and outputs the estimated developmental age result.

In this embodiment, a developmental age estimation function is generated based on data regarding the continuous probability distribution of the implemented test items and the test results for each of those implemented test items, and the developmental age of the child being tested is estimated based on that developmental age estimation function. This makes it possible to obtain highly objective estimation results regarding developmental age while increasing the degree of freedom in the test items.

In order to solve the above problem, one aspect of the present disclosure is a developmental age estimation program that causes a computer to execute a developmental age estimation process to estimate a child's developmental age, wherein the developmental age estimation process includes the steps of: generating a developmental age estimation function based on data relating to continuous probability distributions for two or more implemented test items used in testing the child to be tested out of multiple test items for estimating the developmental age of the child to be tested, and the test results for each of the implemented test items; estimating the developmental age of the child to be tested based on the mode of the distribution of pass rates obtained by the developmental age estimation function; and outputting the estimated developmental age result.

In this embodiment, a developmental age estimation function is generated based on data regarding the continuous probability distribution of the implemented test items and the test results for each of those implemented test items, and the developmental age of the child being tested is estimated based on that developmental age estimation function. This makes it possible to obtain highly objective estimation results regarding developmental age while increasing the degree of freedom in the test items.

The above aspects allow for greater flexibility in test items while providing highly objective estimates of developmental age.

A configuration diagram of a developmental age estimation system including a developmental age estimation device according to an embodiment. FIG. 10 is an explanatory diagram showing an example of pass rate data for a plurality of inspection items. FIG. 10 is an explanatory diagram showing an example of (A) a probability distribution and (B) a cumulative distribution of pass rate data. An explanatory diagram showing an example of setting age ranges for each test item FIG. 10 is an explanatory diagram showing an example of display content on an examination item confirmation screen. An explanatory diagram showing an example of a probability distribution of the passage rate based on the developmental age estimation function.

Below, embodiments of a developmental age estimation system, a developmental age estimation device, a method for estimating a developmental age using a developmental age estimation device, and a program for executing the process of estimating a developmental age according to the present disclosure will be described with reference to the drawings.

As shown in FIG. 1, the developmental age estimation system 1 includes a developmental age estimation device 2 that estimates a child's developmental age, and a user terminal 4 that is communicatively connected to the developmental age estimation device 2 via a communication network 3 such as the Internet.

The developmental age estimation device 2 is composed of a computer that executes a process for estimating a child's developmental age (hereinafter referred to as the "developmental age estimation process"). In this embodiment, the developmental age estimation device 2 is composed of a server that executes a process for estimating a child's developmental age in response to a request from a client used by a user (here, the user terminal 4).

The developmental age estimation process by the developmental age estimation device 2 is performed based on test result data for each of multiple test items conducted in the past, and test results for the test items being conducted on the child currently being tested (hereinafter referred to as "conducted test items"). Tests for the conducted test items can be conducted by the tester in the same way as conventional testing methods.

Data on past test results is obtained for each test item set to estimate a child's developmental age. In the developmental age estimation system 1, the test items used to estimate developmental age can be, for example, the test items adopted in the above-mentioned non-patent document 1. However, the developmental age estimation system 1 is not limited to these and can also use other known testing methods (for example, "DENVER II Denver Developmental Assessment Method" by W. K. Frankenburg, M. D., Japan Pediatric Health Association, 2016 (2nd edition, 2nd printing); "Tanaka-Binet Intelligence Test V" by Junko Nakamura, Ichiro Okawa, Rie Nohara, and Nanami Serizawa, Taken Publishing Co., Ltd., 2003; "Revised Edition Suzuki-Binet Intelligence Test" by Harutaro Suzuki, Furuichi Publishing, 200 You can use any test items adopted in the revised Voluntary Motor Development Test, Speech and Linguistic Medicine, 1990, 31: pp. 172-185; New Edition K-Type Development Test Research Group (ed.), "New Edition K-Type Development Test Method 2001 Edition," Nakanishiya Publishing, 2008; Tsumori Makoto and Inage Noriko, "Infant Mental Development Diagnostic Method," Dainippon Tosho, 1961; Tanaka Misato et al., "Revised Edition Voluntary Motor Development Test," Speech and Linguistic Medicine, 1990, 31: pp. 172-185; Satake Tsuneo, "Question-Response Relationship Test," Escoal, 1997).

In particular, the developmental age estimation system 1 should use test items for which the reliability of data on pass rates for multiple ages (for example, the percentage of children of a certain age who achieve that test item) has been established as test results. Furthermore, if data on pass rates for multiple ages for a new test item is obtained, the developmental age estimation system 1 can also adopt that new test item.

In this embodiment, "children" primarily refers to preschool-age infants and school-age children aged 0 to 12 years. However, developmental age estimation using the systems, devices, methods, and programs disclosed herein is not limited to children, and can be applied to people of any chronological age (including adolescents and adults).

The user terminal 4 is composed of an information processing device with communication functions, such as a smartphone, tablet terminal, or PC, used by the user. The user may include, for example, a medical or educational professional, but is not limited to this, and the user may also be someone other than such a professional (for example, a family member of a child). Furthermore, although only one user terminal 4 is shown in Figure 1, the developmental age estimation system 1 may include multiple user terminals.

In this embodiment, the user terminal 4 is configured to cause the developmental age estimation device 2 to execute the developmental age estimation process, but this is not limited to this, and the user terminal 4 may also function as the developmental age estimation device (i.e., execute the developmental age estimation process). In other words, the developmental age estimation device 2 is not limited to a server, and may be configured as an information processing device such as a smartphone, tablet terminal, or PC, just like the user terminal 4. In this case, the developmental age estimation device 2 does not necessarily need to be connected to a communications network, and can be operated directly by the user. Furthermore, the developmental age estimation process in the developmental age estimation system 1 may be realized by the cloud (i.e., a collection of virtual resources and services).

The developmental age estimation device 2 has a communication unit 11, an input unit 12, a display unit 13, a memory unit 14, and a control unit 15.

The communication unit 11 includes an antenna, communication circuitry, etc., and performs wireless or wired communication with other devices (such as the user terminal 4 in this case) via the communication network 3 in accordance with a known communication protocol.

The input unit 12 includes a known input device (e.g., a keyboard) for inputting operation commands, etc. to the developmental age estimation device 2. However, if operation commands, etc. are input to the developmental age estimation device 2 from another device (e.g., the user terminal 4), the input unit 12 may be omitted.

The display unit 13 includes a known device (for example, a display device such as an LCD display) for displaying (i.e., outputting) information used by the developmental age estimation device 2 and information generated by the developmental age estimation device 2. However, if information from the developmental age estimation device 2 is output to another device (for example, the user terminal 4), the display unit 13 may be omitted. Furthermore, such information may be output by voice (i.e., from a speaker not shown).

The memory unit 14 includes a storage or other storage device for storing data and information necessary for processing by the developmental age estimation device 2. The memory unit 14 does not need to be provided integrally with the developmental age estimation device 2, and may be configured as an external storage device connected to the developmental age estimation device 2 via a network or the like. As will be described later, the memory unit 14 stores test item data 21, distribution function data 22, test result data 23, and developmental age estimation function data 24, etc.

The control unit 15 includes one or more processors (CPU, MPU, etc.), and the processor executes a predetermined control program (an example of a developmental age estimation program) to perform the processing required to estimate the developmental age. The control unit 15 also has overall control over the operation of the developmental age estimation device 2.

In the control unit 15, the test item data collection unit 31 collects data on multiple test items performed in the past from data stored in other databases (not shown), etc., and stores this data in the memory unit 14 as test item data 21. For example, as shown in Figure 2, the test item data 21 includes data on the pass rates for multiple ages for multiple test items performed in the past.

Figure 2, for example, shows the pass rate for test item No. 134, "walk two to three steps." More specifically, Figure 2 shows that for "walk two to three steps," 44.2% of 11-month-old children achieved this (i.e., they were able to "walk two to three steps"), 68.3% of 13-month-old children achieved this, 89.5% of 15-month-old children achieved this, and 98.0% of 17-month-old children achieved this. The pass rates for other test items are similar to that for test item No. 134.

As described above, the test item data 21 includes data on test items used in known test methods. The test item data 21 may also include data obtained on new test items. Note that at least a portion of the test item data 21 stored in the memory unit 14 may be data entered in advance by an administrator of the developmental age estimation system 1, etc.

In the control unit 15, the distribution function estimation unit 32 generates a probability density function and a cumulative distribution function for the pass rate for each test item based on the test item data 21.

In this embodiment, a beta distribution is used as the continuous probability distribution. The probability density function f(x;α,β) for the pass rate of each test item is expressed by the following equation (1). As will be explained in detail later, when using the beta distribution, the age ranges in months for which the pass rate for each test item was obtained are converted to correspond to the range of the random variable between 0 and 1.

　  

where:
x: random variable (0≦x≦1)
α, β: positive real numbers

However, the beta function B(α, β) in equation (1) is expressed as follows:

　 

Furthermore, by integrating the probability density function f(x;α,β) of the beta distribution shown in equation (1), we obtain the cumulative distribution function F(x;α,β) of the beta distribution expressed by the following equation (2).

　  

With this type of beta distribution probability density function f(x;α,β), the distribution function estimation unit 32 can appropriately control the peak and variance of the probability density distribution by determining two parameters α and β, as shown in Figure 3(A), for example. The distribution function estimation unit 32 then determines the parameters α and β so as to fit the pass rate values of each test item in the test item data 21 (i.e., actual measured values obtained from past test results), thereby obtaining an appropriate continuous probability distribution for the pass rate of each test item based on the cumulative distribution function F(x;α,β), as shown in Figure 3(B), for example.

For example, the distribution function estimation unit 32 can determine the parameters α and β so that the sum of squared residuals between the pass rate value for each test item in the test item data 21 and the pass rate at the corresponding age in months (more precisely, the predetermined value of the random variable converted from the age in months) calculated using the cumulative distribution function F(x;α,β) is minimized.

In addition, a general-purpose scientific calculation library may be used to determine the parameters α and β. For example, the BFGS method (Broyden-Fletcher-Goldfarb-Shanno method) or the L-BFGS-B method (Limited-memory Broyden-Fletcher-Goldfarb-Shanno method with Box constraints) may be used in the minimize function of the optimize module in the Scipy library for Python to determine the parameters α and β.

Then, the distribution function estimation unit 32 inversely converts the range of the random variable from 0 to 1 into the lunar age interval, and obtains data on the cumulative distribution function F(x;α,β) of the passage rate with lunar age as a variable (an example of data related to a continuous probability distribution).

The data for the probability density function f(x;α,β) and cumulative distribution function F(x;α,β) of the beta distribution obtained for each test item are sequentially stored in the memory unit as distribution function data 22.

The distribution function estimation unit 32 can generate the probability density function and cumulative distribution function described above when a new test item is added to the test item data 21 or when pass rate data for an existing test item is updated.

Here, referring to Figure 4, we will explain the process of converting the age intervals in which a significant change occurs in the pass rate for each test item so that they correspond to the range of a random variable between 0 and 1 (hereinafter referred to as the "age conversion process"). Note that in addition to the processing method described below, different processing methods may be used to determine the age intervals, as long as they include all ages corresponding to the pass rate data and can determine appropriate parameters α and β. Furthermore, the age intervals may be redefined by referencing the parameters α and β that have already been obtained, and new parameters α and β may be determined. Furthermore, these processing methods do not necessarily have to be applied uniformly to all test items; as long as it is possible to define age intervals in which sufficiently appropriate parameters α and β can be determined for each test item, it is acceptable to use different processing methods.

In the age-in-months conversion process, the distribution function estimation unit 32 first identifies the minimum value x_min and maximum value x_max of age-in-months X in the data group of the test item to be processed in the test item data 21. Here, the data group of the test item is assumed to be composed of ages in months and passage rates corresponding to the two white circles (○) shown in Figure 4. Similarly, the distribution function estimation unit 32 identifies the minimum value y_min and maximum value y_max of passage rate Y in the data group of that test item.

The distribution function estimation unit 32 therefore creates a straight line passing through (x_min, y_min) and (x_max, y_max) on the XY plane (i.e., see the solid line connecting the circles (○) in Figure 4 and the dashed line extending from that), and calculates the X coordinate of the intersection of this line with the line y=0, y=1 (see the square (■) in Figure 4).

Next, the distribution function estimation unit 32 calculates the X coordinates of both ends of interval 2W, which is obtained by doubling interval W, whose ends are the X coordinates of the intersection points, and widening it around the midpoint of the interval (see the black circle (●) in Figure 4). Furthermore, the distribution function estimation unit 32 can determine the smallest interval that includes these X coordinates and has integers on both ends as the age interval to be subjected to the age conversion process for the target test item. The distribution function estimation unit 32 can convert the determined age interval so that its ends are 0 and 1, respectively.

Referring again to FIG. 1, in the control unit 15, the test item setting unit 33 sets test items to be performed from among the test items included in the test item data 21. For example, the test item setting unit 33 can set multiple test items selected by the tester (e.g., input from the user terminal 4) as test items to be performed.

Here, the developmental age estimation device 2 can pre-calculate the age range for each test item (i.e., the age range in which a significant change occurs in the pass rate for each test item) based on the cumulative distribution function F(x;α,β) obtained for each of the multiple test items. The developmental age estimation device 2 can then generate a test item confirmation screen showing the age range calculated for each test item, as shown in Figure 5, for example. When the tester selects the test item to be performed, the developmental age estimation device 2 can send the test item confirmation screen to an external device (here, the user terminal 4) and display it on its display.

The test item confirmation screen displays a list of each test item (see test item numbers 313 to 382 in Figure 5) along with a graphic (here, a bar for displaying the age range, hereafter referred to as the "age range display bar") indicating the range of the age range covered by each test item. The length of the age range display bar is set to correspond to a specific pass rate range (for example, 2.5% to 97.5%). The age range display bar also displays a symbol (here, a black circle (●)) indicating the age at which the pass rate is greatest.

By referring to the length of the age range display bar and the horizontal axis values indicating age in months shown below it, test personnel can determine the range of age ranges that each test item covers (i.e., that are effective for estimating developmental age). Test personnel can also determine whether the test item they are about to select is appropriate for the current test by checking the length of the age range display bar and the position of the symbol indicating the age at which the pass rate is highest. Test personnel can set the desired test item (e.g., letter) as the test item to be performed by selecting it (e.g., by touching the touch panel display) on the test item confirmation screen. Alternatively, test items can be evaluated in advance using a similar method, and only those that are concluded to be appropriate can be used for subsequent processing.

Note that the test items to be performed do not necessarily have to be selected by the tester. For example, the test item setting unit 33 may randomly set the test items to be performed from among the test items included in the test item data 21, or may use a specific algorithm that sequentially selects the most appropriate test items to be performed in accordance with the estimated developmental age.

In the control unit 15, the test result acquisition unit 34 acquires the test results for each test item selected for testing the child being tested. For example, an examiner can conduct a test for each test item and input the test results into the developmental age estimation device 2 from the user terminal 4. However, the test results may also be input by an automatic testing device instead of an examiner. For example, the automatic testing device is equipped with a trained model prepared in advance for the test, and can acquire test results for each test item based on video footage of the child being tested's movements. The test results acquired by the test result acquisition unit 34 are sequentially stored in the memory unit as test result data 23.

Test result data 23 includes, for example, an identifier indicating "achieved" or "not achieved" for each implemented test item. However, test result data 23 may also include an identifier indicating "partial achievement" (i.e., a state in which it is difficult to determine whether the test item has been completely achieved) for each implemented test item.

In the control unit 15, the developmental age estimation function generation unit 35 generates a developmental age estimation function based on data on the cumulative distribution function F(x;α,β) for two or more implemented test items used in the test of the child being tested, and the test results for each implemented test item.

In generating the developmental age estimation function, the estimated pass rate distribution function Ei(m) for each test item by age is defined as follows:

　

however,
m: Age in months M _lower : Lower limit of age in months in the age range targeted by each test item (integer value)
M _upper : Upper limit of age in months for each test item (integer value)

Furthermore, the probability function Pi(m) of the pass rate according to the test results is defined as follows. However, this may be omitted in the case of "partial achievement."

　

however,
i: Index number of each test item

Based on Bayes' theorem, the developmental age estimation function P(m) can be expressed as the following equation (3) using the probability function Pi(m) described above.

　

however,
n: Number of test items M _lower : Minimum lower limit of age in months for each test item (integer value)
M _upper : The maximum upper limit of the age range for each test item (integer value)

In the control unit 15, the developmental age estimation unit 36 can estimate the developmental age of the child being tested based on the mode of the distribution of values obtained by the developmental age estimation function P(m) (i.e., the conditional probability of obtaining given test items and their implementation results at each age). In the probability distribution based on the developmental age estimation function P(m), a peak (i.e., the mode) occurs, as shown in Figure 6, for example, and the developmental age estimation unit 36 can estimate the developmental age of the child being tested based on the age value corresponding to that peak. When indicating the estimated developmental age, the developmental age estimation unit 36 may output the age value corresponding to the peak, or may output a predetermined range that includes the age value corresponding to the peak (for example, the age value corresponding to the peak is used as the median).

Note that the position of the peak in the probability distribution based on the developmental age estimation function P(m) depends on the likelihood function corresponding to the numerator in equation (3). Therefore, the developmental age estimation function P(m) shown in equation (3) may be expressed using only the likelihood function, omitting the denominator.

Furthermore, if the test results for all implemented test items are "achieved," it is not possible to obtain the peak estimated value of the developmental age or the upper limit of the estimated range, but it is possible to obtain the lower limit of the estimated range of the developmental age. If the test results for all implemented test items are "not achieved," it is not possible to obtain the peak estimated value of the developmental age or the lower limit of the estimated range, but it is possible to obtain the upper limit of the estimated range of the developmental age. In either case, the developmental age estimation unit 36 can output the above results to the display unit 13 or user terminal 4, but may also output an error message stating that an appropriate estimation result could not be obtained.

Furthermore, the developmental age estimation unit 36 can output an error message to the display unit 13 or the user terminal 4 if it determines that there is a contradiction in the test results. For example, in the test items shown in FIG. 5, if the result of an implemented test item targeting a certain age range is "not achieved" and the result of an implemented test item targeting a higher age range than the not-achieved implemented test item is "achieved," the developmental age estimation unit 36 can determine that there is a contradiction in the test results.

At least some of the functions of each of the units 31-36 in the control unit 15 can be realized by one or more processors executing a predetermined control program. The control unit 15 can also comprehensively control the operation of the developmental age estimation device 2.

In the developmental age estimation system 1 having the above configuration, the examiner can access the developmental age estimation device 2 from the user terminal 4 and execute the developmental age estimation process. In this developmental age estimation process, the examiner can input (i.e., select) the desired test items (i.e., implemented test items) into the developmental age estimation device 2 from the user terminal 4. Furthermore, the examiner can input the test results of each implemented test item into the developmental age estimation device 2 from the user terminal 4. At this time, the examiner can input the test results into the developmental age estimation device 2 while sequentially implementing multiple implemented test items on the child being tested (i.e., while sequentially obtaining the test results of each implemented test item).

Once the tester has completed inputting the test results, the developmental age estimation device 2 generates a developmental age estimation function based on the data for the cumulative distribution function F(x;α,β) for each implemented test item and the test results for each implemented test item. Next, the developmental age estimation device 2 estimates the developmental age of the child being tested based on the most frequent value of the probability distribution obtained by the generated developmental age estimation function. Furthermore, the developmental age estimation device 2 transmits the estimated developmental age results to the user terminal 4 and displays them on the display of the user terminal 4.

If the user terminal 4 functions as a developmental age estimation device, the above-mentioned developmental age estimation process can be performed entirely by the user terminal 4.

In this way, the developmental age estimation system 1 generates a developmental age estimation function P(m) based on data on the cumulative distribution function F(x;α,β) of the pass rate for the implemented test items, with age as a variable, and the test results for each of those implemented test items, and estimates the developmental age of the child being tested based on that developmental age estimation function P(m).This makes it possible to obtain highly objective estimation results regarding developmental age while increasing the degree of freedom in the test items.

The present disclosure has been described above based on specific embodiments, but these embodiments are merely examples, and the present disclosure is not limited to these embodiments. Not all of the components of the developmental age estimation device, developmental age estimation method, and developmental age estimation program shown in the above-described embodiments are necessarily essential, and at least those skilled in the art can select and discard as appropriate within the scope of the present disclosure.

For example, in this disclosure, the "age in months" indicating a child's developmental stage does not necessarily have to be expressed in months, but may instead be expressed in years or years and months. Furthermore, the age intervals do not necessarily have to be integer values, but may include decimal values. Furthermore, in this embodiment, a beta distribution is used as the continuous probability distribution for the pass rate of each test item, but other probability density distribution functions can also be used as alternatives, as long as the parameters specifying the distribution can be uniquely and appropriately determined using existing pass rate data.

1: Developmental age estimation system 2: Developmental age estimation device 3: Communication network 4: User terminal 11: Communication unit 12: Input unit 13: Display unit 14: Memory unit 15: Control unit 21: Test item data 22: Distribution function data 23: Test result data 24: Developmental age estimation function data 31: Test item data collection unit 32: Distribution function estimation unit 33: Test item setting unit 34: Test result acquisition unit 35: Developmental age estimation function generation unit 36: Developmental age estimation unit

Claims (8)

A developmental age estimation device for estimating a child's developmental age, comprising:
a processor that executes a process of estimating the developmental age of a child being tested based on test results for a plurality of test items;
a storage device that stores data relating to a continuous probability distribution indicating a passing rate for each of the plurality of test items, with a predetermined range of age in months as a variable;
Equipped with
The processor:
generating a developmental age estimation function based on data on the continuous probability distribution for two or more implemented test items among the plurality of test items used in testing the child to be tested and the test results for each of the implemented test items;
estimating the developmental age of the test subject child based on the mode of the distribution of the passage rate obtained by the developmental age estimation function;
A developmental age estimation device that outputs the estimation result of the developmental age. The processor:
For each of the plurality of test items, data on a plurality of ages in months and passage rates corresponding to the plurality of ages in months is acquired;
The developmental age estimation device according to claim 1 , wherein data relating to the continuous probability distribution is generated based on data on the passage rate. The processor determines two parameters of a probability density function of a beta distribution based on the passage rate data;
The developmental age estimation device according to claim 2 , wherein data relating to the continuous probability distribution is generated based on a cumulative distribution function obtained by integrating a probability density function of the beta distribution. The processor:
setting an age range for the passage rate data for each of the plurality of test items;
The developmental age estimation device according to claim 3 , wherein the age interval is converted into a range from 0 to 1. The processor:
for each of the test items to be performed, a probability function of the pass rate is set based on the data regarding the continuous probability distribution and the test results;
The developmental age estimating device according to claim 1 , wherein the developmental age estimating function includes a product of the probability functions of the respective test items. The developmental age estimation device of claim 1, further comprising a display device that displays the estimated developmental age. A developmental age estimation method for estimating a child's developmental age by a developmental age estimation device, comprising:
The developmental age estimation device includes:
generating a developmental age estimation function based on data on continuous probability distributions for two or more implemented test items used in testing the child among a plurality of test items for estimating the developmental age of the child to be tested, and test results for each of the implemented test items;
estimating the developmental age of the test subject child based on the mode of the distribution of the passage rate obtained by the developmental age estimation function;
A developmental age estimation method that outputs the estimation result of the developmental age. A developmental age estimation program that causes a computer to execute a developmental age estimation process for estimating a child's developmental age,
The developmental age estimation process includes:
generating a developmental age estimation function based on data on continuous probability distributions for two or more implemented test items used in testing the child among a plurality of test items for estimating the developmental age of the child to be tested, and test results for each of the implemented test items;
estimating the developmental age of the test subject child based on the mode of the distribution of the passage rate obtained by the developmental age estimation function;
A developmental age estimation program including a procedure for outputting the estimation result of the developmental age.