KR20200023916A - Computing device for providing prediction information for bone density - Google Patents

Abstract

Disclosed is a computer program stored in a computer-readable recording medium, which includes encoded commands for providing prediction information about a bone density. According to one embodiment of the present invention, when being executed by one or more processors, the computer program allows the one or more processors to execute the following operations of: receiving a bone density data set of a user; determining a feature point of one or more pieces of bone density data included in the bone density data set and identifying a point of time when the feature point exists; receiving first health inspection data of the user corresponding to the point of the time and second health inspection data of the user of a different point of time; comparing the first and second health inspection data to determine a bone density change factor; extracting the bone density data of the user from the first and second health inspection data and generating bone density change data based on the extracted bone density data; matching the bone density change factor with the bone density change data to generate labeled training data; and training one or more neural networks with the labeled training data to generate a bone density variance prediction model for outputting the bone density change data based on the bone density change factor.

Description

COMPUTER DEVICE FOR PROVIDING PREDICTION INFORMATION FOR BONE DENSITY}

The present disclosure relates to a computing device that provides bone density prediction information, and more particularly, to a computing device that provides prediction about bone density through examination data of a user.

As the number of elderly people around the world increases and income levels rise, interest in health grows. In general, the balance of the production of bone in humans is balanced, but in older people, the secretion of estrogen decreases, thereby lowering the bone absorption function, especially in postmenopausal women, the incidence of osteoporosis increases. This osteoporosis rarely exhibits any apparent external symptoms, and once lost bone mass can be very difficult to recover. Accordingly, there is an increasing demand for bone condition analysis. X-ray absorptiometry, ultrasound, clove computed tomography, etc. exist to analyze various bone conditions. Currently, X-ray absorption measurement is most widely used to analyze the condition of bone, and as part of this technology, Korean Patent Publication No. 10-0721007 is disclosed.

However, the X-ray absorption measurement method has a problem in that it must be equipped with a facility according to the inconvenience of the large size of the equipment and radioactivity to handle the radiation. In other words, since it is necessary to measure by a professional operator who has a license to handle radiation, there is a limit to being able to receive medical treatment only in a large hospital at a general hospital level. This can lead to an economic burden on the measurement. In addition, since it is necessary to make repeated measurements in order to predict bone density, the prior art using radiation may inevitably present a risk of repeatedly being exposed to radiation.

Therefore, there is a need in the art for a computing device that is not harmed to the human body due to repeated exposure to radiation and that predicts the future bone density of the user with higher precision.

The present disclosure is directed to the background art described above, and relates to a computing device that provides prediction information about bone density.

DETAILED DESCRIPTION The present disclosure is devised in response to the foregoing background, and is a computer program stored in a computer readable storage medium comprising encoded instructions, the computer program being executed by one or more processors of a computing device. The above processor causes the following operations to be performed, the operations comprising: receiving a user's bone density data set, determining a singularity of one or more bone density data included in the bone density data set, and when the singularity is present Identifying the information, receiving the first health examination data of the user and the second health examination data at a time different from the first health examination data, the first health examination data and the second health. Compare the screening data to determine the changes in bone mineral density And extracting the bone density data of the user from each of the first and second health examination data and generating the bone density change data based on the extracted bone density data, the bone density change factor and the bone density. Generating a labeled learning data by matching change data and generating a bone density change prediction model for outputting the bone density change data based on the bone density change factor by training one or more neural networks using the labeled learning data. It may include.

Alternatively, the bone density data set, the first medical examination data and the second medical examination data may be an electronic health record (EHR), an electronic medical record (EMR) of at least one of a hospital server and a government server. : may be received from at least one of an electronic medical record and a health examination DB or from a bone density measuring apparatus.

Alternatively, the singularity of the bone density data may include at least one of when each of the one or more bone density data included in the bone density data set is outside the threshold of normal bone density values and when the rate of bone density change exceeds a threshold bone density change rate. May include cases.

Alternatively, the threshold value of the normal bone density value is predetermined based on one or more items included in the user's biometric information extracted from at least one of the user's first and second health examination data. And may be information based on the one or more bone density data of the user.

Alternatively, the bone density change rate is based on the time interval information corresponding to the amount of bone density change calculated by comparing each of the one or more bone density data included in the bone density data set and the viewpoint information included in each of the one or more bone density data. Can be generated.

Alternatively, the bone density change prediction model is generated through machine learning, and includes the one or more neural networks, each of the one or more neural networks comprises at least one of an input layer, one or more hidden layers, and an output layer. Wherein the input layer comprises one or more input nodes, each of the one or more hidden layers comprises one or more hidden layers, and the output layer comprises one or more output nodes, and each of the layers included in each layer Nodes are each connected via links with one or more nodes of different layers, each of which may be weighted.

Alternatively, the method may further include extracting a highly related item highly related to the bone density change data among one or more items of the bone density change factor, and generating risk avoidance information for changing the highly related item. .

Alternatively, extracting a highly related item among the one or more items of the bone density change factor, which is highly related to the bone density change data, is output by outputting the output of the bone density change prediction model based on the bone density change factor. and extracting the highly related items based on the data output through the reverse projection.

Alternatively, the method may further include generating bone density prediction data based on the bone density change data, and the bone density prediction data includes information for predicting future bone density in response to the bone density change rate calculated from the bone density change data. As such, a time point corresponding to each of the future predicted bone densities may be matched and generated.

In another embodiment of the present disclosure, in a method for providing bone density change data, receiving a bone density data set of a user, determining a singularity of bone density data included in the bone density data set, and a point in time at which the singularity exists Identifying the first medical examination data and the second medical examination data at a time point different from the first medical examination data, the first medical examination data and the second health Comparing the examination data to determine the factors of the change in bone density, extracting the bone density data of the user from each of the first health examination data and the second health examination data, and generating bone density change data based on the extracted bone density data And the bone density change factor and the bone density change data. Generating a labeled training data and generating a bone density change prediction model for training one or more neural networks using the labeled training data to output the bone density change data based on the bone density change factor. Can be.

In another embodiment of the present disclosure, a computing device for providing bone density change data is disclosed. The computing device includes a processor including one or more cores, a memory for storing program codes executable on the processor, and a network portion for transmitting and receiving data to and from an external server, wherein the processor receives a user's bone density data set, Determine a singularity of the bone density data included in the bone density data set, identify a time point at which the singularity exists, and generate a first point of time different from the first health check data and the first health check data of the user corresponding to the time point. Receiving 2 medical examination data, comparing the first medical examination data and the second medical examination data to determine a change factor of bone density, and the bone density of the user in each of the first medical examination data and the second medical examination data Extract the data, the extracted bone density Generating bone density change data based on the data, matching the bone density change factor with the bone density change data to generate labeled learning data, and learning one or more neural networks using the labeled learning data to cause the bone density change factor It is possible to generate a bone density change prediction model to output the bone density change data based on.

The present disclosure may provide a computing device that provides prediction information about a bone density of a user.

Various aspects are now described with reference to the drawings, wherein like reference numerals are used to refer to like components throughout. In the following examples, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. However, it will be apparent that such aspect (s) may be practiced without these specific details.
1 is a conceptual diagram illustrating a system of a computing device for providing change data for bone density associated with one embodiment of the present disclosure.
2 illustrates a block diagram of a computing device providing change data for a user's bone density associated with one embodiment of the present disclosure.
3 is a schematic diagram illustrating a network function associated with one embodiment of the present disclosure.
4 is an exemplary view showing a model for predicting bone density change according to an embodiment of the present disclosure.
FIG. 5 is an exemplary diagram for describing singularities of bone density data included in a bone density data set related to an embodiment of the present disclosure.
FIG. 6 is an exemplary diagram for explaining a rate of change in bone density and its specificity based on bone density data included in a bone density data set related to an embodiment of the present disclosure.
7 illustrates a flowchart for generating a bone density change prediction model that outputs bone density change data associated with one embodiment of the present disclosure.
8 illustrates a means for providing bone density change data of a user associated with an embodiment of the present disclosure.
9 is a diagram illustrating a module for providing bone density change data of a user associated with one embodiment of the present disclosure.
FIG. 10 illustrates logic for providing bone density change data of a user associated with an embodiment of the present disclosure.
FIG. 11 illustrates a circuit for providing bone density change data of a user associated with an embodiment of the present disclosure.
12 shows a brief general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In the present specification, various descriptions are presented to provide an understanding of the present disclosure. It may be evident, however, that such embodiments may be practiced without these specific details.

As used herein, the terms “component”, “module”, “system” and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or the execution of software. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a processor and / or thread of execution. One component can be localized within one computer. One component may be distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components may be connected via a network such as the Internet and other systems via, for example, signals with one or more data packets (e.g., data and / or signals from one component interacting with other components in a local system, distributed system). Data may be communicated via local and / or remote processes.

In addition, the term “or” is intended to mean an implicit “or” rather than an exclusive “or”. That is, unless specified otherwise or unambiguously in context, "X uses A or B" is intended to mean one of the natural implicit substitutions. That is, X uses A; X uses B; Or where X uses both A and B, "X uses A or B" may apply in either of these cases. In addition, the term "and / or" as used herein is to be understood to refer to and include all possible combinations of one or more of the listed related items.

In addition, the terms "comprises" and / or "comprising" should be understood to mean that the corresponding features and / or components are present. It is to be understood, however, that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Also, unless otherwise specified or in the context of indicating a singular form, the singular in the specification and claims should generally be construed as meaning "one or more."

Those skilled in the art will further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be electronic hardware, computer software, or a combination of both. It should be appreciated that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in a variety of ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

In one embodiment of the present disclosure, the server may include other configurations for performing the server environment of the server. The server may include all types of devices. The server is a digital device, and may be a digital device having a computing power with a processor and a memory such as a laptop computer, a notebook computer, a desktop computer, a web pad, a mobile phone, and the like. The server may be a web server that handles services. The type of server described above is merely an example and the present disclosure is not limited thereto.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those of ordinary skill in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present invention is not limited to the embodiments presented herein. The invention should be construed in the broadest scope consistent with the principles and novel features set forth herein.

1 is a conceptual diagram illustrating a system of a computing device for providing bone density prediction information related to an embodiment of the present disclosure.

According to one embodiment of the present disclosure, computing device 100 and external server 200 may transmit and receive information via interconnections via wireless and / or wired.

According to an embodiment of the present disclosure, the computing device 100 may generate the bone density prediction data based on the bone density change data generated through the bone density change prediction model. More specifically, the processor 110 may generate bone density prediction data including information on future bone density of the user corresponding to the bone density change rate calculated from the bone density change data. In this case, the bone density prediction data may be generated by matching a time point corresponding to each of the future prediction bone density data. Therefore, the user may be provided with information about future bone density data through the history of bone mineral density change according to the individual from the computing device 100, and systematically perform health care according to his future bone density through more accurate bone density prediction data. Can be.

According to an embodiment of the present disclosure, the computing device 100 may generate bone density change data. More specifically, the computing device 100 may generate a bone density change prediction model for learning one or more neural networks using the labeled learning data to output bone density change data based on bone density change factors. At this time, the labeled learning data may be generated by matching the bone density change factor and the bone density change data. That is, bone density change data can be generated by using the bone density change factor as an input of the learned bone density change amount prediction model.

In addition, the BMD of the user may be extracted from each of the first and second health examination data of the computing device 100, and the bone density change data may be generated based on the extracted bone density data. In addition, the computing device 100 may determine the factor of change in bone density by comparing the first medical examination data and the second medical examination data. In this case, the first health examination data may be health examination data of a user corresponding to a time point when a singularity exists in at least one bone density data included in the bone density data set received by the computing device 100, and the second health examination data may be The medical examination data may be different from the first medical examination data. For example, when the first health examination data is examination data corresponding to the point in time when a singularity is found in the user's bone density, and the user's body includes health examination data including a factor that changes the bone density, the second health examination The data is screening data at a point in time different from when a singularity is found in the user's bone density, and may be a screening data in a healthy state or a normal state without a factor changing bone density in the user's body. That is, the bone density change data may be generated by comparing the first health examination data at the time when the user's bone density is changed (that is, the singularity of the bone density data) with the first health examination data and the second health examination data at a different time point. And determine the cause of bone mineral density change. The above descriptions of the first medical examination data and the second medical examination data are only examples, and the present disclosure is not limited thereto.

In addition, the computing device 100 may determine a singularity of one or more bone density data included in the bone density data set, and identify a time point at which the singularity exists. Specifically, the computing device 100 receives the bone density data set from the external server 200, and calculates at least one of one or more bone density data included in the received bone density data set and a bone density change rate calculated based on the bone density data. Through the singularity of the bone density data can be determined. In more detail, the computing device 100 may determine at least one of cases where each of the one or more bone density data included in the bone density data set is outside the threshold of the normal bone density value and when the rate of bone density change exceeds the threshold bone density change rate. It can be determined by the singularity of the bone density data. In addition, when determining the bone density data as the singularity, the computing device 100 may identify a time point corresponding to the singularity of the determined bone density data. That is, the computing device 100 may determine a singularity on the bone density data of the user and identify the viewpoint of the bone density data in which the singularity exists, and thus, the first and second medical examination data and the first medical examination data corresponding to the viewpoint may be used. Second health check data may be received at different time points.

FIG. 2 illustrates a block diagram of a computing device providing prediction information about a user's bone density associated with one embodiment of the present disclosure.

The components of computing device 100 shown in FIG. 2 are exemplary. Only some of the components shown in FIG. 2 may configure the computing device 100. In addition to the components shown in FIG. 2, additional component (s) may be included in the computing device 100. In addition, computing device 100 may include any type of device, including, for example, computing power with a processor and memory, such as a laptop computer, notebook computer, desktop computer, web pad, mobile phone, etc. It may be a digital device equipped with. In addition, the computing device 100 may be a web server that calculates bone density change information.

In an embodiment of the present disclosure, the computing device 100 may distribute and process the model by using at least one of the CPU, the GPGPU, and the TPU. In addition, in an embodiment of the present disclosure, the computing device 100 may distribute and process the model along with other computing devices.

In the present specification, the network function may be used interchangeably with an artificial neural network and a neural network. The network function herein may include one or more neural networks, in which case the output of the network function may be an ensemble of the outputs of one or more neural networks.

In this specification, the model may include a network function. The model may include one or more network functions, in which case the output of the model may be an ensemble of outputs of one or more network functions.

As shown in FIG. 2, the computing device 100 may include a processor 110, a memory 120, and a network unit 130.

According to an embodiment of the present disclosure, the processor 110 may include one or more cores, and may include a central processing unit (CPU) and a general purpose graphics unit (GPGPU) of the computing device 100. and a processor 110 for data analysis and deep learning such as a processing unit (TPU) and a tensor processing unit (TPU). The processor 110 may read the computer program stored in the memory 120 to calculate bone density change information according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the processor 110 may perform calculation for learning a neural network. The processor 110 may be configured to process neural networks such as processing input data for learning in deep learning (DN), feature extraction from input data, error calculation, weight update of neural networks using backpropagation, and the like. You can perform calculations for learning.

In addition, at least one of the CPU, GPGPU, and TPU of the processor 110 may process the training of the model. For example, the CPU and GPGPU can work together to train the model and compute bone density data using the model. In addition, in an embodiment of the present disclosure, the processor 110 of the plurality of computing devices may be used together to process the learning of the model and the calculation of the bone density data through the bone density measurement model. In addition, the computer program executed in the computing device according to an embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

Hereinafter, a method of outputting bone density change data of a user according to an exemplary embodiment of the present disclosure will be described.

According to an embodiment of the present disclosure, the processor 110 may receive a user's bone density data set from the external server 200. More specifically, the processor 110 may receive the bone density data set from at least one of an electronic health record, an electronic medical record, and a medical examination DB of at least one server of a hospital server and a government server or from a bone density measuring apparatus. .

In addition, the processor 110 may determine a singularity of one or more bone density data included in the bone density data set received from the external server 200, and identify a time point when the determined singularity exists in the bone density data. In this case, when the processor 110 determines the bone density data as a singularity in the user's bone density data set, each of the one or more bone density data included in the bone density data set is outside the threshold 510 of the normal bone density value and the speed of change in the bone density May be at least one of the cases where the critical bone density change rate is exceeded.

According to one embodiment of the present disclosure, the singularity of the bone density data determined by the processor 110 may be a case where each of the one or more bone density data included in the bone density data set is outside the threshold 510 of the normal bone density value. In detail, the threshold 510 of the normal bone density value may be predetermined based on one or more items included in the user's biometric information extracted from the user's health examination data. May be information. In this case, the biometric information of the user may include gender, age, height, and weight. That is, the threshold value 510 of the normal bone density value may be a predetermined value based on at least one of the sex, age, height, and weight of the user, and may have a threshold value 510 of the normal bone density value different for each user. For example, when the user is in their 40s, the maximum value of the threshold of normal bone density may be lower than the maximum of the threshold of normal bone density when the user is in 20s. For another example, when the gender of the user is male, the peak of the normal BMD may be higher than when the gender of the user is female, and the threshold of the BMD may be narrower than when the range is female. The foregoing detailed description of normal bone density values is illustrative only, and the present disclosure is not limited thereto.

Referring to FIG. 5, the threshold 510 of a normal bone density value of any user may have a range of 1.103 to 1.115 (g / cm 2). In this case, it can be seen that the bone density data 520 measured in September of the arbitrary user deviates from the threshold of the normal bone density value. That is, the processor 110 may determine a case where the user's bone density data is out of the threshold 510 of the normal bone density value as the singularity of the bone density data. In addition, the processor 110 may identify the time point of the bone density data of the user outside the threshold 510 of the normal bone density value. For example, for any of the users of FIG. 5, processor 110 may identify the point in time at which the user's bone density data deviates from threshold 510 of normal bone density values as September. Accordingly, the processor 110 may set a threshold 510 of normal bone density values corresponding to individual user characteristics, thereby determining a singularity of individual bone density data, and identifying a time point at which the singularity is determined. Specific numerical values for the threshold of normal bone density values of any of the users described above are exemplary only, and the present disclosure may include thresholds of various normal bone density values according to the user's personal characteristics (ie, biometric information).

According to an embodiment of the present disclosure, the processor 110 may calculate a rate of change in bone density. More specifically, the processor 110 may obtain time interval information corresponding to a change amount of one or more bone density data and view information included in each of the one or more bone density data in order to calculate a rate of change in bone density.

The processor 110 may calculate the amount of change in bone density by comparing each of one or more bone density data included in the bone density data set. For example, as shown in FIG. 6A, when the bone density data measured in April and May of the user are 1.111 (g / cm 2) and 1.113 (g / cm 2), respectively, the processor 110. ) Can calculate the amount of change in bone density between April and May of the user as 0.002 (g / cm2). In another example, if the bone density data measured in a user's July and August is 1.098 (g / cm2) and 1.020 (g / cm2), respectively, the processor 110 may determine the user's bone density between July and August. The change amount can be calculated as 0.078 (g / cm 2).

The processor 110 may obtain time interval information corresponding to view information included in each of the one or more bone density data. For example, as shown in FIG. 6A, the bone density data of the user may be measured monthly, and in this case, the time interval information corresponding to the viewpoint information included in each of the bone density data may be one month ( That is, 30 days). That is, the processor 110 may calculate a change in bone density through one or more bone density data included in a user's bone density data set, and obtain time interval information according to each change in bone density. In addition, the processor 110 may calculate a bone density change rate based on the bone density change amount and the time interval information. In detail, the processor 110 may calculate the rate of change in the bone density through the degree to which the user's bone density has changed (ie, the amount of change in bone density) and the period in which the bone density has changed (ie, time interval information). For example, as shown in (a) of FIG. 6, it may be determined that the bone density change amount is 0.001 (g / cm2) based on the bone density data of April and May, and the time interval information is 30 days. can do. That is, as shown in (b) of FIG. 6, the slope between the monthly data on the graph may be a rate of change in bone density. In addition, the singularity of the bone density data determined by the processor 110 may be a case where the rate of change in bone density calculated based on the user's bone density data exceeds a predetermined threshold bone density change rate. In detail, when the rate of change of the bone density of the user calculated based on the user's bone density data is faster than the predetermined threshold bone density change rate, that is, when the slope on the graph is large, the processor 110 may determine the singularity of the user's bone density data. You can judge. In this case, the predetermined threshold bone density change rate may be for detecting that the user's bone density data rapidly increases or decreases. For example, referring to FIG. 6B, the processor 110 may determine a section in which the slope sharply increases (that is, a section in which the bone density change amount is large, 610) as the section in which the bone density singularity of the user occurs. have. The above-described detailed description of the amount of bone density change, time interval information, and the rate of change in bone density is only an example to help understanding of the present disclosure, but is not limited thereto.

According to an embodiment of the present disclosure, when the processor 110 identifies the viewpoint of the bone density data from which the singularity is determined among one or more bone density data included in the bone density data set of the user, the processor 110 corresponds to the viewpoint of the determined singularity. 1 We can receive medical examination data. For example, when the time point of the bone density data from which the singularity is determined among one or more bone density data of the user is May 2017, the difference between the time points of the health examination data of the user measured before and after May 2017 of the user may be The smallest checkup data (ie, first checkup data) can be received. In addition, the processor 110 may receive second medical examination data at a different time point than the first medical examination data. More specifically, the second health examination data may be health examination data of the user measured at a different time point than the first health examination data for which the singularity of the bone density data is determined. In this case, the first health check data and the second health check data received by the processor 110 may be an electronic health record, an electronic medical record, and a health check of at least one of an external server 200, that is, a hospital server and a government server. It can be received from at least one of the DB.

In addition, the processor 110 may determine a change factor of bone density by comparing the first and second health examination data of the user. In detail, it is possible to determine a factor that changes the user's bone density through comparison between the first health examination data and the second health examination data, which is the examination records at different time points. For example, the first medical examination data corresponding to the time point when the singularity is determined in the bone density data is measured in January 2018, and the second medical examination data at a time different from the first medical examination data is determined in May 2017. When measured, the medical records of the users of January 2018 and May 2017 may be compared to identify different items among the plurality of items included in the records. For example, when an item related to “pregnancy”, which is an item recorded in the first health examination data, is not included in the second health examination data, the processor 110 may determine a factor that changed the bone density data of the user. Can be judged. In another example, if the "hyperthyroidism" recorded in the first medical examination data is not included in the second medical examination data, i.e., the hyperthyroidism found in the January 2018 medical examination results is If not found in the month (eg, before onset), the processor 110 may determine that the factor causing the change in bone density data is "hyperthyroidism". In addition, factors that may change the bone mineral density data may include smoking, drinking, childbirth, various diseases and taking medications for various diseases. Specific description of the above-described factors for changing bone density data is only an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 extracts the bone density data of the user from each of the first health examination data and the second health examination data of the user, and extracts the bone density change data based on the extracted bone density data. Can be generated. More specifically, the processor 110 extracts bone density data of the user included in each of the first and second health examination data of the user, and compares the extracted bone density data to calculate a change amount to calculate the bone density change data. Can be generated. For example, when the bone density data of the user extracted from each of the first health examination data and the second health examination data is 1.1 (g / cm 2) and 1.3 (g / cm 2), the processor 110 may determine a change amount of bone density by 0.2 ( g / cm2). Specific description of the numerical value for calculating the above-described bone density change amount is only an example, the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may generate the labeled learning data by matching the bone density change factor and the bone density change data. The processor 110 may generate learning data using the user's bone density change factor as input of the training data and the bone density change data as the label of the training data. For example, user B's learning bone density change factor (pregnancy, hyperthyroidism, etc.) is input to learning data, and user A's learning bone density change data (0.2 (g / cm2) in this example) Can be used to create training data. The description about the generation of the above-described learning data is only an example, and the present disclosure is not limited thereto.

In addition, when there is a missing item among one or more items of the user's bone density change factor, the processor 110 may set the item value of the missing item as an intermediate value or an average value of the training data set. In addition, when the data of one item among the factors of bone density change of the users of the learning bone density change factor set is insufficient, the processor 110 may delete the column for the item. For example, if the data for hyperthyroidism among the items of the learning bone density change factor are negatively present in the learning biometric data of more than a predetermined percentage of all users, the column for that item (in this example, hyperthyroidism) is deleted. Can be. The description of the item value supplement of the above-described missing item is only an example, and the present disclosure is not limited thereto.

In addition, the processor 110 may generate a bone density variation prediction model including one or more network functions. The processor 110 may generate a bone density variation prediction model including one or more network functions including at least one of an input layer, one or more hidden layers, and an output layer. The processor 110 may generate a network function composed of an input layer including one or more input nodes. The processor 110 may generate a network function such that the hidden layer included in the network function includes one or more hidden nodes. The processor 110 may generate a network function such that an output layer included in the network function includes one or more output nodes. The processor 110 may generate each node included in the layer of the network function to be connected to one or more nodes of another layer through a link. Each weight may be set to each link.

In addition, the processor 110 may input a learning bone density change factor of the training data as an input of the bone density change prediction model. At this time, the learning bone density change factor may include one or more items. The item may mean each piece of data related to the bone density of the user. The processor 110 may input items of the learning bone density change factor to each of one or more input nodes included in the input layer of the bone density change prediction model. For example, when five nodes are included in the input layer of the BMD predictive model, the processor 110 displays five node values such as smoking, drinking, fracture, disease, and pregnancy, which may be an item of the BMD change factor. You can type in each. The description of the input of the above-described bone density change amount prediction model is only an example, and the present disclosure may further include various bone density change factors that may change the user's bone density.

The processor 110 may generate a bone density variation prediction model including an input layer, one or more hidden layers, and an output layer. The layer can include one or more nodes. Nodes can be connected via links with other nodes. The processor 110 may calculate an item inputted to an input node of an input layer of the bone density change prediction model through a link connected to the input node and propagate it to the hidden layer. The operation can include any mathematical operation. For example, the operation may be a product or a compound product, but the foregoing description is merely an example and the present disclosure is not limited thereto. The processor 110 may calculate an item input to an input node of the BMD prediction model through a link connected to the input node and propagate it to the output layer through one or more hidden layers. The processor 110 may generate bone density change data based on a value propagated to the output layer of the bone density change amount prediction model.

In addition, the processor 110 may determine the first node value of the first node of the BMD prediction model, the second node value of the second node included in the previous layer connected to the first node, and the second node included in the previous layer. It may be derived by calculating the first link weight of the link set in the link connecting the node and the first node. The processor 110 calculates a value of a first node of a first node of the BMD prediction model by using a second link weight set on a link connecting a third node included in a next layer connected to the first node to a third node. It can spread.

The processor 110 inputs each item value of an item included in the learning bone density change factor of the training data into one or more input nodes included in the input layer of the bone density change prediction model to generate a bone density change prediction model, and predicts the bone density change The error may be calculated by comparing the bone density change data (ie, the output) calculated in the output layer of the model with the training bone density change data (ie, the correct answer). The processor 110 may adjust the weight of the bone density change prediction model based on the error. The processor 110 may update the weight set for each link by propagating from the output layer included in the one or more network functions of the bone density change prediction model to the input layer based on the error, through the one or more hidden layers.

In generating the BMD predictive model, the processor 110 may set a dropout so that a part of the output of the hidden node is not transmitted to the next hidden node in order to prevent overfitting.

The training epoch inputs the training biometric data to each of the one or more input nodes included in the input layer of the one or more network functions of the bone mineral density prediction model with respect to all the training data included in the training data set, and to the training biometric data. Compare the labeled learning bone density change data (ie, correct answer) with bone density change data (ie, output) of the BMD prediction model to derive an error, and use the derived error as the output layer of one or more network functions of the BMD prediction model. By propagating from one or more hidden layers to the input layer, it may be an operation of updating the weight set in each link. That is, when the calculation using the BMD prediction model and the weight update process for the BMD prediction model are performed on all the learning data included in the training data set, it may be one epoch.

In addition, when generating the bone density change prediction model, when the learning epoch for learning the bone density change prediction model is less than or equal to a predetermined epoch, the processor 110 may set a learning rate of the bone density change prediction model to a predetermined value or more. . In generating the BMD prediction model, when the learning epoch for learning the BMD prediction model is equal to or greater than a predetermined epoch, the processor 110 may set the learning rate of the BMD prediction model to be equal to or less than a predetermined value. The learning rate may mean an update degree of weights. For example, in the early stage of learning, the learning rate can be set high (ie, the weighting degree of the update is large), so that the output of the learning data can quickly access the label of the learning data. For example, later in the training, you can set the learning rate low (that is, by making the weight update smaller) to reduce the error between the output of the training data and the label of the training data (that is, to improve accuracy). can do. The above disclosure about the learning rate is only an example, and the disclosure is not limited thereto.

In addition, the processor 110 may perform the training of the BMD prediction model more than a predetermined epoch, and then determine whether to stop learning using the verification data set. The predetermined epoch may be part of the overall learning goal epoch. The processor 110 may make a part of the training data set a verification data set. The verification data is data corresponding to the learning data, and may be data for determining whether to stop learning. The processor 110 may determine whether the learning effect of the BMD prediction model is greater than or equal to a predetermined level after the training of the BMD prediction model is repeated over a predetermined epoch. For example, when the processor 110 performs a learning with a target repetition learning number of 100,000 times using 1 million learning data, the processor 110 uses 1000 verification data after performing 10000 repetitive learning, which is a predetermined epoch. 10 repetitive learning (ie, 10 epochs), and when the change of neural network output is less than or equal to a predetermined level during 10 repetitive learning, it may be determined that further learning is meaningless and the learning may be terminated. That is, the verification data may be used to determine the completion of the learning based on whether the effect of the epoch-specific learning in the repetitive learning of the neural network is above or below a certain level. The above-described learning data, the number of verification data, and the number of repetitions are only examples, and the present disclosure is not limited thereto.

In addition, the processor 110 may set at least one of the training data sets as a test data set. The test data is data corresponding to the training data, and may be data used to verify performance after the training of the model is completed. The processor 110 may use one or more biometric data and bone density change data labeled on the biometric data as a test data set. The processor 110 inputs a bone density change factor included in a test data set into a bone density change prediction model, compares the output of the bone density change prediction model with the labeled bone density change data, and compares the bone density change amount with respect to the test data set. The percentage of correct answers of the predictive model can be determined. The processor 110 inputs the learning biometric data of the user included in the test data into the bone density change prediction model, and the bone density change data (ie, output) output from the bone density change prediction model and the user included in the test data. By comparing the learning bone density change data (that is, the correct answer) of, when the error is less than a predetermined value (that is, the correct percentage is above a predetermined level), it is possible to determine the activation of the bone density change prediction model. The processor 110 compares the bone density change data (ie, output) output from the bone density change amount prediction model with the learning bone density change data of the user included in the test data (ie, the correct answer), and the error is greater than or equal to a predetermined value. In addition, the training of the bone mineral density change prediction model may be further performed by a predetermined epoch or the deactivation of the bone mineral density change prediction model. When the processor 110 deactivates the bone density change prediction model, the processor 110 may discard the bone density change prediction model. The processor 110 may determine the performance of the generated bone density change prediction model based on factors such as accuracy, precision, recall, and the like. The above performance evaluation criteria are merely examples and the present disclosure is not limited thereto. According to an embodiment of the present disclosure, the processor 110 may independently generate one or more bone density change prediction models by learning one or more network functions included in each bone density change prediction model, and evaluate the performance to obtain a certain performance or more. Only neural networks can be used for bone mineral density measurements.

According to an embodiment of the present disclosure, a bone density change prediction model may be generated based on learning biometric data and learning bone density data of a user.

In addition, the processor 110 may input a factor for changing bone density into the learned bone density change prediction model. The processor 110 may input each item included in the bone density change factor to each node included in the input layer of the bone density change prediction model. The processor 110 calculates each item by using the weight of the link connected to the input layer and delivers each item to one or more hidden nodes included in the hidden layer. The operation may include any mathematical algorithm. For example, the operation may include a product, a compound product, and the like, but the above description is only an example and the present disclosure is not limited thereto. The processor 110 may calculate biometric data including one or more items input to the input layer and propagate it to the output layer via one or more hidden nodes. The processor 110 may calculate a value of each hidden node included in the hidden layer using the weights of the links connected to the hidden node and propagate it to a hidden node included in another hidden layer or an output node included in an output layer. . The learned bone density change prediction model may be a neural network learned by a method for generating a bone density change prediction model according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the processor 110 may extract a high-correlation item having a high correlation with bone density change data among one or more items of the bone density change factor. In addition, the processor 110 may generate risk avoidance information for changing a highly related item. The processor 110 may extract an input item having a high correlation with the output of the neural network by grasping the trained neural network's connection relationship and the connection weights of the respective roads. The processor 110 may determine the item of the input data that plays a decisive role in the output value by performing a back projection from the output of the neural network based on the input bone density change factor to the input. For example, when it is predicted that the amount of change in the bone density change data is increased at the output of the neural network, the processor 110 calculates the output of the neural network inversely to determine an item of input data that has a decisive effect on the amount of change in the bone density change data. can do. For example, an item of input data that has had a decisive influence on bone density change data may be pregnancy or smoking, etc., or may be a visual impairment that is generally considered low in relation to bone density change. The foregoing items are merely examples and the present disclosure is not limited thereto. Accordingly, according to an embodiment of the present disclosure, the processor 110 may perform a calculation from an output of a neural network to an input to extract a critical item that causes a risk for a change in bone density predicted by a user. In addition to the cause, it is possible to extract other causes for which the association has been learned by the neural network.

Accordingly, the method for predicting bone density change data according to an embodiment of the present disclosure may provide insight into other causes and the amount of bone density change in addition to the factors known to the clinician generally.

In addition, the processor 110 may generate risk avoidance information for changing a highly related item. For example, when the processor 110 determines that the pregnancy of the corresponding user is a highly related item when the bone density change amount of the bone density change data is large (for example, osteopenia) using the learned neural network, the processor 110. In order to avoid such high-related items, the user may generate and provide lifestyle, eating habits, medical treatment and medicine information to increase bone density during pregnancy, which may be provided to the user, and provide the same to the user. The above-mentioned high association item is merely an example, and the present disclosure is not limited thereto.

In one embodiment of the present disclosure, the user may check the prediction information according to the change in bone density according to the change in life when the change in life is scheduled (for example, medication, childbirth, etc.). Therefore, the user can confirm his / her bone density prediction value prior to the change in life, and thus can make a decision about the change in his life.

According to an embodiment of the present disclosure, the processor 110 may generate bone density prediction data based on the bone density change data. More specifically, the processor 110 may generate bone density prediction data including information on future bone density corresponding to the bone density change rate calculated from the bone density change data. In this case, the bone density prediction data may be generated by matching a time point corresponding to each of the future prediction bone density data. For example, as shown in (a) of FIG. 6, when the rate of change of the monthly bone density (that is, each monthly slope on the graph of FIG. 6 (b)) is 0.000067, 0.000033, 0.000533, 0.0026, and 0.001, respectively. Based on the average value of the monthly bone density change rate of 0.000847, the bone density prediction data may be generated to become the bone density change rate between the following September and October, and the bone density change rate from June to July when the bone density data started to fall. The average value may be reflected to generate bone density prediction data. That is, the processor 110 may generate future bone density change data of the user through the average value of the bone density change rate calculated through the user's bone density change data or the recent bone density change rate. Accordingly, each of the plurality of users may be provided with information on future bone density data through his / her bone mineral density change history, and may systematically perform health care according to his / her future bone density through more accurate bone density prediction data. . Specific numerical values for the above-described bone density change rate are exemplary only, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the network unit 130 may include a transmitter and a receiver. The network unit 130 may include a wired / wireless internet module for network connection. Wireless Internet technologies may include Wireless LAN (Wi-Fi), Wireless Broadband (Wibro), World Interoperability for Microwave Access (Wimax), High Speed Downlink Packet Access (HSDPA), and the like. Wired Internet technologies may include Digital Subscriber Line (XDSL), Fibers to the home (FTTH), Power Line Communication (PLC), and the like.

In addition, the network unit 130 may include a short range communication module, and may transmit and receive data to and from an electronic device including a short range communication module, which is located at a relatively short distance with the service processing apparatus. As a short range communication technology, Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, and the like may be used. In one embodiment of the present disclosure, the network unit 130 may detect the connection state of the network and the transmission and reception speed of the network. The data received through the network unit 130 may be stored through the memory 120 or transmitted to other electronic devices in a short distance through a short range communication module.

In addition, the network unit 130 may transmit and receive data to another computing device, a server, and the like for outputting the bone density change data according to an embodiment of the present disclosure. The network unit 130 may transmit / receive data necessary for an embodiment of the present disclosure, such as health examination data and bone density data of a user, to another computing device, a server, or the like. In addition, the network unit 130 may enable communication between a plurality of computing devices so that learning of the BMD prediction model may be distributed in each of the plurality of computing devices. The network unit 130 may enable communication between a plurality of computing devices to perform distributed processing of bone density data calculation using a bone density change prediction model.

The memory 120 may store a computer program for outputting bone density change data according to an embodiment of the present disclosure, and the stored computer program may be read and driven by the processor 110.

The memory 120 according to the embodiments of the present disclosure may store a program for the operation of the processor 110 and temporarily or permanently store input / output data (eg, bone density data, medical examination data, etc.). You can also save. The memory 120 may store data regarding a display and sound. The memory 120 may include a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD or XD memory), RAM Random Access Memory (RAM), Static Random Access Memory (SRAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Programmable Read-Only Memory (PROM), Magnetic Memory, Magnetic At least one type of storage medium may be included. The above description of the memory is only an example, and the present disclosure is not limited thereto.

3 is a schematic diagram illustrating a network function associated with one embodiment of the present disclosure.

Throughout this specification, computational models, neural networks, network functions, neural networks may be used in the same sense. A neural network may consist of a set of interconnected computing units, which may generally be referred to as "nodes." Such "nodes" may be referred to as "neurons". The neural network comprises at least one node. The nodes (or neurons) that make up the neural networks may be interconnected by one or more "links".

Within a neural network, one or more nodes connected via a link may form a relationship of input node and output node relatively. The concept of an input node and an output node is relative; any node in an output node relationship for one node may be in an input node relationship in relation to another node, and vice versa. As mentioned above, the input node to output node relationship can be created around the link. More than one output node can be connected to a single input node via a link, and vice versa.

In an input node and output node relationship connected via one link, the output node may be determined based on data input to the input node. Here, the node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, if more than one input node is interconnected by each link to one output node, the output node is set to values input to the input nodes associated with the output node and to the links corresponding to the respective input nodes. The output node value may be determined based on the weight.

As described above, a neural network is formed by interconnecting one or more nodes through one or more links to form an input node and an output node relationship within the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the relationship between the nodes and the links, and the value of the weights assigned to the links. For example, if there are the same number of nodes and links, and there are two neural networks with different weight values between the links, the two neural networks may be recognized as different from each other.

The neural network may comprise one or more nodes. Some of the nodes that make up the neural network may construct one layer based on distances from the original input node, for example, a set of nodes with a distance n from the original input node, You can configure n layers. The distance from the original input node may be defined by the minimum number of links that must pass to reach the node from the original input node. However, the definition of such a layer is arbitrary for explanation, and the order of the layer in the neural network may be defined in a manner different from that described above. For example, a layer of nodes may be defined by distance from the final output node.

The initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among the nodes in the neural network. Alternatively, in a neural network network, in a relationship between nodes based on a link, it may mean nodes having no other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes of the nodes in the neural network. Also, the hidden node may refer to nodes constituting a neural network other than the first input node and the last output node. The neural network according to an embodiment of the present disclosure may have the same number of nodes in the input layer as the number of nodes in the output layer, and the number of nodes decreases and then increases again as the number of nodes goes from the input layer to the hidden layer. Can be. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer is smaller than the number of nodes in the output layer, and the number of nodes decreases as the node progresses from the input layer to the hidden layer. have. In addition, the neural network according to another embodiment of the present disclosure may have a larger number of nodes in the input layer than the number of nodes in the output layer, and the number of nodes increases as the number of nodes increases from the input layer to the hidden layer. Can be. The neural network according to another embodiment of the present disclosure may be a neural network of a combination of the neural networks described above.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can be used to identify latent structures of data. In other words, you can identify the potential structures of photos, texts, videos, voices, and music (e.g., what objects are in the photos, what the content and emotions of the text are, and what the content and emotions of the voice are). . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, Generic Adversarial Networks (GAN), restricted boltzmann (RBM) machine), deep belief network (DBN), Q network, U network, Siamese network and the like. The description of the deep neural network described above is merely an example and the present disclosure is not limited thereto.

The neural network may be trained in at least one of supervised learning, unsupervised learning, and semi supervised learning. Training of neural networks is intended to minimize errors in the output. In the neural network learning, the neural network error is inputted from the output layer of the neural network in order to repeatedly input the training data into the neural network, calculate the neural network output and target errors for the training data, and reduce the errors. The process of updating the weight of each node of the neural network by backpropagation in the direction. In the case of teacher learning, the learning data using the correct answer is labeled in each learning data (ie, the labeled learning data), and in the case of the comparative learning, the correct answer may not be labeled in each learning data. That is, for example, the learning data in the case of teacher learning regarding data classification may be data in which a category is labeled in each of the learning data. The labeled training data is input to the neural network, and an error can be calculated by comparing the label of the training data with the output (category) of the neural network. As another example, in the case of non-comparative learning on data classification, an error may be calculated by comparing learning data as an input with a neural network output. The calculated error is propagated backward in the neural network (ie, the direction from the output layer to the input layer), and the connection weight of each node of each layer of the neural network may be updated according to the reverse propagation. The connection weight of each node to be updated may be changed according to a learning rate. The computation of the neural network for the input data and the backpropagation of the errors can constitute a learning cycle. The learning rate may be applied differently according to the number of repetitions of the learning cycle of the neural network. For example, in the early stages of neural network learning, high learning rates can be used to allow the neural network to quickly achieve a certain level of performance, increasing efficiency, and lower learning rates for later learning.

In the learning of neural networks, in general, the training data may be a subset of the actual data (i.e., the data to be processed using the trained neural network), thus reducing the error for the training data but not the error for the actual data. There may be an increasing learning cycle. Overfitting is a phenomenon in which an error about the actual data is increased by excessively learning the training data. For example, a neural network that learns a cat by showing a yellow cat may not recognize that the cat is a cat other than a yellow cat. Overfitting can act as a cause of increased errors in machine learning algorithms. Various optimization methods can be used to prevent such overfitting. In order to prevent overfitting, a method of increasing learning data, regulating, or dropping out of a node of a network in the course of learning may be applied.

4 is an exemplary view showing a model for predicting bone density change according to an embodiment of the present disclosure.

A learning method in a prediction model of bone mineral density change according to an embodiment of the present disclosure will be described. In this example, one or more hidden layers may be included between the hidden layer 1 440 and the hidden layer 2 450, and the one or more hidden layers may be omitted.

The computing device 100 may generate a bone density variation prediction model including one or more network functions 400. The network function 400 may include one input layer 430, one or more hidden layers, and one output layer 460.

The computing device 100 may calculate each of the item values input to the input node included in the input layer 430 of the network function 400 with the weights set in the link connected to the input node and propagate them to the hidden layer. . For example, the first hidden node 420 included in the hidden layer 1 440 is a value obtained by calculating a value transmitted to the first input node 401 and a first weight 411, and a second input node 402. ), The value passed to the third input node 403, the value passed to the third input node 403, the value passed to the fourth input node 404, and the fourth weighted value. The calculated value, the value transferred to the fifth input node 405, and the value calculated by the fifth weight may be received. For example, the first hidden node 420 included in the hidden layer 1 440 is a value obtained by multiplying the value transmitted to the first input node 401 by the first weight 411, and the second input node 402. A value multiplied by a second weighted value, a value multiplied by a value passed to the third input node 403 and a third weight value, a value multiplied by a value passed to the fourth input node 404 and a fourth weight value, The sum of the value multiplied by the fifth weighted value and the value transmitted to the fifth input node 405 may be received. The description of the above operation is merely an example, and the present disclosure is not limited thereto.

The factor of learning bone density change of the network function 400 may be propagated from the input layer 430 to the output layer 460 through the hidden layer 1 440 and the hidden layer 2 450. Adjust the weight of the network function 400 based on the error of the bone density change data (ie, the output) and the learning bone density change data (ie, the correct answer), which are output values at the output node 462 included in the output layer 460. Can be. Processor 110 passes from output layer 460 of network function 400 to input layer 430 via one or more hidden layers (e.g., in order of hidden layer2 450, then hidden layer1 440). While propagating the error, the weights set for each link may be updated (for example, after adjusting the weight of W2 (1,1) 431), the weight of W1 (1,1) 411 may be adjusted.

The computing device 100 may input each item value of items included in a user's bone density change factor to each input node included in the input layer 430 of the network function 400. For example, the computing device 100 may input a value corresponding to 'Yes', which is an item value for smoking or not, among the factors for changing the bone density into the first input node 401 of the input layer 430. The processor 110 weights the value input to the input layer 430 of the network function 400 (in this example, the first weight W1 (1,1) 411) and the second weight W2 (1,1 Output node 462 included in output layer 460 via one or more hidden nodes (in this example, including hidden layer 1 440 and hidden layer 2 450). Can propagate. The computing device 100 may send bone density data, which is an output value of the output node 462, through the network unit 130, store in the memory 120, or transmit the bone density data to display means (eg, a display). The above description of the method for generating bone density change data is only an example, and the present disclosure is not limited thereto. That is, bone density change data can be generated by using the bone density change factor as an input of the learned bone density change amount prediction model.

FIG. 5 is an exemplary diagram for describing singularities of bone density data included in a bone density data set related to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the computing device 100 may determine the singularity of one or more bone density data included in the bone density data set. In this case, the singularity of the bone density data determined by the computing device 100 may be a case where each of the one or more bone density data included in the bone density data set is outside the threshold 510 of the normal bone density value. In detail, the threshold 510 of the normal bone density value may be predetermined based on one or more items included in the user's biometric information extracted from the user's health examination data. May be information. In this case, the biometric information of the user may include gender, age, height, and weight. That is, the threshold value 510 of the normal bone density value may be a predetermined value based on at least one of the sex, age, height, and weight of the user, and may have a threshold value 510 of the normal bone density value different for each user. For example, when the user is in their 40s, the maximum value of the threshold 510 of the normal BMD may be lower than the maximum of the threshold 510 of the normal BMD when the user is 20's. For another example, when the gender of the user is male, the highest point of the threshold 510 of the normal BMD may be higher than when the gender of the user is female. The foregoing detailed description of the threshold of normal bone density values is illustrative only, and the present disclosure is not limited thereto.

FIG. 5 is a graph showing the measured bone density data of any user monthly, and as shown in FIG. 5, the threshold 510 of the normal bone density value of any user is between 1.103 and 1.115 (g / cm 2). It can have a range. In this case, it may be confirmed that the bone density data 520 measured in September of the arbitrary user deviates from the threshold 510 of the normal bone density value. That is, the computing device 100 may determine that the bone density data of the user deviates from the threshold 510 of the normal bone density value as the singularity of the bone density data. In addition, the computing device 100 may identify the time point of the user's bone density data 520 outside the threshold 510 of the normal bone density value. For example, for any of the users of FIG. 5, the computing device 100 may identify as September when the user's bone density data is outside the threshold 510 of the normal bone density value. Accordingly, the computing device 100 may determine a singularity of the bone density data of the user by identifying a threshold value 510 of the normal bone density value corresponding to the individual characteristics of the user, and identify a time point at which the singularity is determined. Specific numerical values for the threshold of normal bone density values of any of the users described above are exemplary only, and the present disclosure may include thresholds of various normal bone density values according to the user's personal characteristics (ie, biometric information).

FIG. 6 is an exemplary diagram for explaining a rate of change in bone density and its specificity based on bone density data included in a bone density data set related to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the computing device 100 may calculate a bone density change rate. Hereinafter, a method of calculating the bone density change rate will be described with reference to FIG. 6.

The computing device 100 may obtain time interval information corresponding to a change amount of one or more bone density data and view information included in each of the one or more bone density data in order to calculate a rate of change in bone density.

The computing device 100 may calculate the amount of change in bone density by comparing each of the one or more bone density data included in the bone density data set. For example, as shown in FIG. 6A, when the bone density data measured in April and May of the user are 1.111 (g / cm 2) and 1.113 (g / cm 2), respectively, the computing device ( 100) may calculate a change in bone density between April and May of the user as 0.002 (g / cm 2). In another example, if the bone density data measured in a user's July and August are 1.098 (g / cm2) and 1.020 (g / cm2), respectively, then the computing device 100 may be configured to be located between the user's July and August. The amount of change in bone density can be calculated as 0.078 (g / cm 2).

The computing device 100 may obtain time interval information corresponding to the viewpoint information included in each of the one or more bone density data. For example, as shown in FIG. 6A, the bone density data of the user may be measured monthly, and in this case, the time interval information corresponding to the viewpoint information included in each of the bone density data may be one month ( That is, 30 days). That is, the computing device 100 may calculate a change in bone density through one or more bone density data included in a user's bone density data set, and obtain time interval information according to each change in bone density. In addition, the computing device 100 may calculate a bone density change rate based on the bone density change amount and the time interval information. In detail, the computing device 100 may calculate the rate of change in the bone density through the change in the bone density of the user (ie, the amount of change in the bone density) and the period in which the bone density has changed (that is, the time interval information). For example, as shown in (a) of FIG. 6, it may be determined that the bone density change amount is 0.001 (g / cm2) based on the bone density data of April and May, and the time interval information is 30 days. can do. That is, as shown in (b) of FIG. 6, the slope between the monthly data on the graph may be a rate of change in bone density. In addition, the singularity of the bone density data determined by the computing device 100 may be a case where the rate of change in bone density calculated based on the bone density data of the user exceeds a predetermined threshold bone density change rate. In detail, when the rate of change of the bone density of the user calculated based on the user's bone density data is faster than the predetermined threshold bone density change rate, that is, when the slope on the graph is large, the computing device 100 may generate a singularity of the user's bone density data. Judging by In this case, the predetermined threshold bone density change rate may be for detecting that the user's bone density data rapidly increases or decreases. For example, referring to FIG. 6B, the computing device 100 may determine a section in which the slope sharply increases (that is, a section in which the BMD change is large, 610) as the section in which the bone density singularity of the user occurs. Can be. The above-described detailed description of the amount of bone density change, time interval information, and the rate of change in bone density is only an example to help understanding of the present disclosure, but is not limited thereto.

FIG. 7 is a flowchart for generating a bone density change prediction model that outputs bone density change data associated with an embodiment of the present disclosure.

According to one embodiment of the present disclosure, computing device 100 may receive a user's bone density data set (710). More specifically, the computing device 100 may receive the bone density data set from at least one of an electronic health record, an electronic medical record, and a medical examination DB of at least one of a hospital server and a government server, or from a bone density measuring device. have.

According to an embodiment of the present disclosure, the computing device 100 may determine a singularity of one or more bone density data included in the bone density data set and identify a time point at which the singularity exists. In this case, when the computing device 100 determines the bone density data as a singularity in the user's bone density data set, each of the one or more bone density data included in the bone density data set deviates from the threshold 510 of the normal bone density value and changes in bone density It may be at least one of the cases where the rate exceeds the critical bone density change rate.

According to an embodiment of the present disclosure, the computing device 100 may receive first health check data of the user and second health check data of a time different from the first health check data corresponding to a time point when a singularity exists. May be 730. For example, when the time point of the bone density data from which the singularity is determined among one or more bone density data of the user is May 2017, the difference between the time points of the health examination data of the user measured before and after May 2017 of the user may be The smallest checkup data (ie, first checkup data) can be received. In addition, the processor 110 may receive second medical examination data at a different time point than the first medical examination data. More specifically, the second health examination data may be health examination data of the user measured at a different time point than the first health examination data for which the singularity of the bone density data is determined. In this case, the first health check data and the second health check data received by the processor 110 may be an electronic health record, an electronic medical record, and a health check of at least one of an external server 200, that is, a hospital server and a government server. It can be received from at least one of the DB.

According to an embodiment of the present disclosure, the computing device 100 may determine a factor of bone density change by comparing the first medical examination data and the second medical examination data (740). In detail, it is possible to determine a factor that causes a change in bone density through comparison between the first medical examination data and the second medical examination data, which is the examination record at a different time point. For example, the first medical examination data corresponding to the time point when the singularity is determined in the bone density data is measured in January 2018, and the second medical examination data at a time different from the first medical examination data is determined in May 2017. When measured, the medical records of the users of January 2018 and May 2017 may be compared to identify different items among the plurality of items included in the records. For example, when an item related to “pregnancy”, which is an item recorded in the first health examination data, is not included in the second health examination data, the processor 110 may determine a factor that changed the bone density data of the user. Can be judged. In another example, if the "hyperthyroidism" recorded in the first medical examination data is not included in the second medical examination data, i.e., the hyperthyroidism found in the January 2018 medical examination results is If not found in the month (eg, before onset), the processor 110 may determine that the factor causing the change in bone density data is "hyperthyroidism". In addition, factors that may change the bone mineral density data may include smoking, drinking, childbirth, various diseases and taking medications for various diseases. The foregoing description of the factors for changing the bone density data is only an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the computing device 100 extracts the bone density data of the user from each of the first health examination data and the second health examination data, and calculates the bone density change data based on the extracted bone density data. May be generated (750). Specifically, the computing device 100 extracts bone density data of the user included in each of the first and second health examination data of the user, and compares the extracted bone density data to calculate a change amount to calculate the bone density change data. Can be generated. For example, when the bone density data of the user extracted from each of the first health examination data and the second health examination data is 1.1 (g / cm 2) and 1.3 (g / cm 2), the computing device 100 may adjust the amount of bone density change by 0.2. (g / cm2) can be calculated. The detailed description of the numerical value for calculating the amount of bone density change described above is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the computing device 100 may generate labeled training data by matching the bone density change factor with the bone density change data (760). The computing device 100 may generate learning data using the bone density change factor of the user as input of the training data and the bone density change data as the label of the training data. For example, user B's learning bone density change factor (pregnancy, hyperthyroidism, etc.) is input to learning data, and user A's learning bone density change data (0.2 (g / cm2) in this example) Can be used to create training data. The description about the generation of the above-described learning data is only an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the computing device 100 may generate a bone density change prediction model for learning one or more neural networks using labeled learning data to output the bone density change data based on the bone density change factor. 770. The computing device 100 inputs each item value of the item included in the learning bone density change factor of the training data into one or more input nodes included in the input layer of the bone density change prediction model, to generate the bone density change prediction model, and the bone density change amount The error may be calculated by comparing the bone density change data (ie, output) calculated in the output layer of the prediction model with the training bone density change data (ie, correct answer). The computing device 100 may adjust the weight of the bone density change prediction model based on the error. The computing device 100 may update the weight set for each link by propagating from the output layer included in the one or more network functions of the bone density change prediction model to the input layer based on the error, through the one or more hidden layers.

8 illustrates an embodiment of the present disclosure and a means for providing bone density change data of a user.

According to an embodiment of the present disclosure, the computing device 100 may further include means for receiving a user's bone density data set 810 to provide bone density change data of the user, one or more bone density data included in the bone density data set. Means (820) for determining a singularity of and identifying a time point at which the singularity is present; first health check data of the user corresponding to the time point and second health check data at a time different from the first health check data Means for receiving (830), means for determining a factor of change in bone density by comparing the first medical examination data and the second medical examination data (840), the first medical examination data and the second medical examination data Extract bone density data of the user from each, and generate bone density change data based on the extracted bone density data Means for 850, means for matching the bone density change factor with the bone density change data 860 for generating labeled learning data, and learning one or more neural networks using the labeled learning data for the bone density change factor And means for generating a bone density change prediction model for outputting the bone density change data based on.

Alternatively, the bone density data set, the first medical examination data and the second medical examination data may be an electronic health record (EHR), an electronic medical record (EMR) of at least one of a hospital server and a government server. : may be received from at least one of an electronic medical record and a health examination DB or from a bone density measuring apparatus.

Alternatively, the singularity of the bone density data may include at least one of when each of the one or more bone density data included in the bone density data set is outside the threshold of normal bone density values and when the rate of bone density change exceeds a threshold bone density change rate. May include cases.

Alternatively, the threshold value of the normal bone density value is predetermined based on one or more items included in the user's biometric information extracted from at least one of the user's first and second health examination data. And may be information based on the one or more bone density data of the user.

Alternatively, the bone density change rate is based on the time interval information corresponding to the amount of bone density change calculated by comparing each of the one or more bone density data included in the bone density data set and the viewpoint information included in each of the one or more bone density data. Can be generated.

Alternatively, the bone density change prediction model is generated through machine learning, and includes the one or more neural networks, each of the one or more neural networks comprises at least one of an input layer, one or more hidden layers, and an output layer. Wherein the input layer comprises one or more input nodes, each of the one or more hidden layers comprises one or more hidden layers, and the output layer comprises one or more output nodes, and each of the layers included in each layer Nodes are each connected via links with one or more nodes of different layers, each of which may be weighted.

Alternatively, the method may further include extracting a highly related item highly related to the bone density change data among one or more items of the bone density change factor, and generating risk avoidance information for changing the highly related item. .

Alternatively, extracting a highly related item among the one or more items of the bone density change factor, which is highly related to the bone density change data, is output by outputting the output of the bone density change prediction model based on the bone density change factor. and extracting the highly related items based on the data output through the reverse projection.

Alternatively, the method may further include generating bone density prediction data based on the bone density change data, and the bone density prediction data includes information for predicting future bone density in response to the bone density change rate calculated from the bone density change data. As such, a time point corresponding to each of the future predicted bone densities may be matched and generated.

9 is a diagram illustrating a module for providing bone density change data of a user associated with one embodiment of the present disclosure.

According to an embodiment of the present disclosure, the computing device 100 may include a module 910 for receiving a user's bone density data set, one or more bone density data included in the bone density data set, for providing the user's bone density change data. A module 920 for determining a singularity of the at least one singular point and identifying a time point at which the singularity exists; first health check data of the user corresponding to the time point and second health check data at a time different from the first health check data Module 930 for receiving the data, the first health check data and the second health check data, and a module 940 for determining a change factor of bone density, the first health check data and the second health check data Extract bone density data of the user from each, and generate bone density change data based on the extracted bone density data A module 950 for matching the module, the module for generating the labeled learning data by matching the bone density change factor with the bone density change data, and the one or more neural networks using the labeled learning data to train the bone density change factor. And a module 970 for generating a bone density change prediction model for outputting the bone density change data.

FIG. 10 is a diagram illustrating logic for providing bone density change data of a user associated with one embodiment of the present disclosure.

According to an embodiment of the present disclosure, the computing device 100 may include logic 1010 for receiving a user's bone density data set, one or more bone density data included in the bone density data set, to provide the user's bone density change data. Logic 1020 for discriminating a singularity of the singularity and identifying a point in time at which the singularity exists, the first health checkup data of the user corresponding to the time point, and the second health checkup data at a time different from the first health checkup data Logic 1030 for receiving the data, logic 1040 for determining a change factor of bone density by comparing the first medical examination data and the second medical examination data, the first medical examination data and the second medical examination data Extract bone density data of the user from each, and generate bone density change data based on the extracted bone density data. Logic 1050 to match the bone density change factor with the bone density change data and the logic 1060 for generating labeled learning data and the one or more neural networks using the labeled learning data to learn the bone density change factor And logic 1070 for generating a bone density change prediction model for outputting the bone density change data based on.

FIG. 11 illustrates a circuit for providing bone density change data of a user associated with an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the computing device 100 may include a circuit 1110 for receiving a user's bone density data set, one or more bone density data included in the bone density data set, to provide the user's bone density change data. A circuit 1120 for determining a singularity of the at least one specific point and identifying a time point at which the singularity exists; first health check data of the user corresponding to the time point and second health check data at a time different from the first health check data A circuit 1130 for receiving the data, a circuit 1140 for determining a factor of change in bone density by comparing the first medical examination data and the second medical examination data, the first medical examination data and the second medical examination data Extract bone density data of the user from each, and generate bone density change data based on the extracted bone density data. A circuit 1150 to match the bone density change factor and the bone density change data, and a circuit 1160 for generating labeled learning data and one or more neural networks using the labeled learning data to train the bone density change factor. And a circuit 1170 for generating a bone density change prediction model for outputting the bone density change data.

Those skilled in the art will further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics and algorithm steps described in connection with the embodiments disclosed herein may be electronic hardware, computer software, or combinations of both. It should be recognized that this may be implemented. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

12 shows a brief general schematic of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the present invention has been described above generally with respect to computer executable instructions that may be executed on one or more computers, those skilled in the art will appreciate that the present invention may be implemented in combination with other program modules and / or as a combination of hardware and software. will be.

In general, modules herein include routines, procedures, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Those skilled in the art will also appreciate that the methods of the present invention may be used in uniprocessor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like (each of which And may be implemented in other computer system configurations, including one or more associated devices.

The described embodiments of the invention can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer readable media. Any medium that can be accessed by a computer can be a computer readable medium, which can be volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. Removable media. By way of example, and not limitation, computer readable media may comprise computer readable storage media and computer readable transmission media.

Computer-readable storage media are volatile and nonvolatile media, temporary and non-transitory media, removable and non-removable implemented in any method or technology for storing information such as computer readable instructions, data structures, program modules or other data. Media. Computer-readable storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device or other magnetic storage. It includes, but is not limited to, a device or any other medium that can be accessed by a computer and used to store desired information.

Computer-readable transmission media typically embody computer readable instructions, data structures, program modules or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and the like. Includes all information delivery media. The term modulated data signal refers to a signal that has one or more of its characteristics set or changed to encode information in the signal. By way of example, and not limitation, computer readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, or other wireless media. Combinations of any of the above should also be included within the scope of computer readable transmission media.

An exemplary environment 1500 is illustrated that implements various aspects of the present invention, including a computer 1502, which includes a processing unit 1504, a system memory 1506, and a system bus 1508. do. The system bus 1508 connects system components, including but not limited to system memory 1506, to the processing unit 1504. Processing unit 1504 may be any of a variety of commercial processors. Dual processor and other multiprocessor architectures may also be used as the processing unit 1504.

The system bus 1508 can be any of several types of bus structures that can be further interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1506 includes read only memory (ROM) 1510 and random access memory (RAM) 1512. The basic input / output system (BIOS) is stored in nonvolatile memory 1510, such as ROM, EPROM, EEPROM, etc., which is a basic BIOS that assists in transferring information between components in the computer 1502, such as during startup. Contains routines. RAM 1512 may also include fast RAM, such as static RAM, for caching data.

Computer 1502 also includes an internal hard disk drive (HDD) 1514 (e.g., EIDE, SATA) —this internal hard disk drive 1514 may also be configured for external use within a suitable chassis (not shown). Yes, magnetic floppy disk drive (FDD) 1516 (eg, for reading from or writing to removable diskette 1518), and optical disk drive 1520 (eg, CD-ROM Disc 1522, for reading from or writing to other high capacity optical media such as DVD). The hard disk drive 1514, the magnetic disk drive 1516, and the optical disk drive 1520 are respectively connected to the system bus 1508 by the hard disk drive interface 1524, the magnetic disk drive interface 1526, and the optical drive interface 1528. ) Can be connected. The interface 1524 for external drive implementation includes, for example, at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide nonvolatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 1502, drives and media correspond to storing any data in a suitable digital format. While the above description of computer readable storage media refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will appreciate zip drives, magnetic cassettes, flash memory cards, cartridges, It will be appreciated that other types of computer readable media may also be used in the exemplary operating environment and that any such media may include computer executable instructions for performing the methods of the present invention. .

Multiple program modules may be stored in the drive and RAM 1512, including operating system 1530, one or more application programs 1532, other program modules 1534, and program data 1536. All or a portion of the operating system, applications, modules and / or data may also be cached in RAM 1512. It will be appreciated that the present invention may be implemented in various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1502 through one or more wired / wireless input devices, such as a keyboard 1538 and a mouse 1540. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. While these and other input devices are often connected to the processing unit 1504 via an input device interface 1542 that is connected to the system bus 1508, the parallel port, IEEE 1394 serial port, game port, USB port, IR interface, Etc. can be connected by other interfaces.

A monitor 1544 or other type of display device is also connected to the system bus 1508 via an interface such as a video adapter 1546. In addition to the monitor 1544, the computer generally includes other peripheral output devices (not shown) such as speakers, printers, and the like.

Computer 1502 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer (s) 1548, via wired and / or wireless communications. Remote computer (s) 1548 may be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and generally for computer 1502. Although many or all of the described components are included, for simplicity, only memory storage 1550 is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) 1552 and / or a larger network, such as a telecommunications network (WAN) 1554. Such LAN and WAN networking environments are commonplace in offices and businesses, facilitating enterprise-wide computer networks such as intranets, all of which can be connected to worldwide computer networks, such as the Internet.

When used in a LAN networking environment, the computer 1502 is connected to the local network 1552 via a wired and / or wireless communication network interface or adapter 1556. The adapter 1556 can facilitate wired or wireless communication to the LAN 1552, which also includes a wireless access point installed therein for communicating with the wireless adapter 1556. When used in a WAN networking environment, the computer 1502 may include a modem 1558, may be connected to a communication server on the WAN 1554, or other means of establishing communications over the WAN 1554, such as over the Internet. Has The modem 1558, which may be an internal or external and wired or wireless device, is connected to the system bus 1508 via the serial port interface 1542. In a networked environment, program modules or portions thereof described with respect to computer 1502 may be stored in remote memory / storage device 1550. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used.

Computer 1502 is associated with any wireless device or entity disposed and operating in wireless communication, such as a printer, scanner, desktop and / or portable computer, portable data assistant, communications satellite, wireless detectable tag. Communicate with any equipment or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technology. Thus, the communication can be a predefined structure as in a conventional network or simply an ad hoc communication between at least two devices.

Wireless Fidelity (Wi-Fi) allows you to connect to the Internet without wires. Wi-Fi is a wireless technology such as a cell phone that allows a device, for example, a computer, to transmit and receive data indoors and outdoors, ie anywhere within the coverage area of a base station. Wi-Fi networks use a wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, high-speed wireless connections. Wi-Fi may be used to connect computers to each other, to the Internet, and to a wired network (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz wireless bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band). have.

Those skilled in the art will appreciate that the various exemplary logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, It will be appreciated that for purposes of the present invention, various forms of program or design code, or combinations thereof, may be implemented. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in various ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The various embodiments presented herein may be embodied in a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term "article of manufacture" includes a computer program, carrier, or media accessible from any computer-readable device. For example, computer-readable storage media may include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flashes. Memory devices (eg, EEPROM, cards, sticks, key drives, etc.), but are not limited to these. The term “machine-readable medium” includes, but is not limited to, a wireless channel and various other media capable of storing, retaining, and / or delivering instruction (s) and / or data.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of exemplary approaches. Based on design priorities, it is understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present invention. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the invention. Thus, the present invention should not be limited to the embodiments set forth herein but should be construed in the broadest scope consistent with the principles and novel features set forth herein.

Claims (11)

A computer program stored in a computer readable storage medium containing encoded instructions, wherein the computer program, when executed by one or more processors of a computer system, causes the one or more processors to perform the following operations: The actions are:
Receiving a bone density data set of a user;
Determining a singularity of one or more bone density data included in the bone density data set and identifying a time point at which the singularity exists;
Receiving first health examination data of the user corresponding to the time point and second health examination data at a time different from the first health examination data;
Comparing the first medical examination data and the second medical examination data to determine a factor of change in bone density;
Extracting bone density data of the user from each of the first health examination data and the second health examination data and generating bone density change data based on the extracted bone density data;
Generating labeled learning data by matching the bone density change factor with the bone density change data; And
Generating a bone density change prediction model for learning one or more neural networks using the labeled learning data to output the bone density change data based on the bone density change factor;
Including,
Computer program stored on a computer readable storage medium.
The method of claim 1,
The bone density data set, the first medical examination data and the second medical examination data may include an electronic health record (EHR) and an electronic medical record (EMR) of at least one of a hospital server and a government server. And received from at least one of the health examination DB or from the bone density measuring apparatus,
Computer program stored on a computer readable storage medium.
The method of claim 1,
Singularity of the bone density data,
At least one of each of the one or more bone density data included in the bone density data set falls outside of a threshold of normal bone mineral density values and when the rate of bone density change exceeds a threshold bone density change rate,
Computer program stored on a computer readable storage medium.
The method of claim 3, wherein
The threshold value of the normal bone density value,
A reference is determined based on the one or more bone density data of the user based on one or more items included in the biometric information of the user extracted from at least one of the user's first health examination data and the second health examination data. Information,
Computer program stored on a computer readable storage medium.
The method of claim 3, wherein
The bone density change rate is,
Is generated based on the bone density change amount calculated by comparing each of the one or more bone density data included in the bone density data set and time interval information corresponding to the viewpoint information included in each of the one or more bone density data,
Computer program stored on a computer readable storage medium.
The method of claim 1,
The bone density change amount prediction model,
Generated through machine learning, the one or more neural networks, each of the one or more neural networks including at least one of an input layer, one or more hidden layers, and an output layer, the input layer being one or more input nodes Wherein each of the one or more hidden layers includes one or more hidden layers, the output layer includes one or more output nodes, and each node included in each layer links with one or more nodes of another layer. Are each connected through, wherein each link is weighted,
Computer program stored on a computer readable storage medium.
The method of claim 1,
Extracting a highly related item highly related to the bone density change data from one or more items of the bone density change factor; And
Generating risk avoidance information for changing the high associated item;
Including more;
Computer program stored on a computer readable storage medium.
The method of claim 7, wherein
Extracting a highly related item highly related to the bone mineral density change data from one or more items of the bone mineral density change factor,
Back projecting an output of the bone mineral density prediction model based on the bone mineral density change factor as an input; And
Extracting the highly related items based on the data output through the reverse projection;
Including,
Computer program stored on a computer readable storage medium.
The method of claim 1,
Generating bone density prediction data based on the bone density change data;
More, and
The bone density prediction data,
Information for predicting future bone density in response to the bone density change rate calculated from the bone density change data, the time corresponding to each of the future prediction bone density is generated to match,
Computer program stored on a computer readable storage medium.
In the method for providing bone density change data,
Receiving a bone density data set of a user;
Determining a singularity of bone density data included in the bone density data set and identifying a time point at which the singularity exists;
Receiving first medical examination data of the user corresponding to the time point and second medical examination data at a time different from the first medical examination data;
Comparing the first medical examination data and the second medical examination data to determine a factor of change in bone density;
Extracting bone density data of the user from each of the first health examination data and the second health examination data, and generating bone density change data based on the extracted bone density data;
Generating labeled learning data by matching the bone density change factor and the bone density change data; And
Generating a bone density change prediction model for learning one or more neural networks using the labeled learning data to output the bone density change data based on the bone density change factor;
Method for providing bone density change data.
A computing device that provides bone density change data,
A processor including one or more cores;
A memory storing program codes executable in the processor; And
A network unit for transmitting and receiving data with an external server;
Including, and
The processor,
Receiving a bone density data set of a user, determining a singularity of bone density data included in the bone density data set, identifying a time point at which the singularity exists, and the first health examination data and the first medical examination data of the user corresponding to the time point Receiving second medical examination data at a different time point than the first medical examination data, comparing the first medical examination data and the second medical examination data, and determining a factor of bone mineral density change, the first medical examination data and the second Extracting the bone density data of the user from each of the health examination data, generating the bone density change data based on the extracted bone density data, generating the labeled learning data by matching the bone density change factor with the bone density change data, and One or more nerves using the labeled learning data To study the change in bone mineral density to generate a prediction model to output the bone density data based on the bone density factors,
A computing device that provides bone density change data.

Images