Electronic device for predicting developmental disability of newborn by using regression model-based prediction model and driving method thereof

Abstract

According to various embodiments, an electronic device for predicting a developmental disability of a newborn by using a regression model-based prediction model comprises a communication interface and a processor, wherein the processor is configured to: receive, via the communication interface, body data of parents of the newborn, environmental data of the parents, variable data of the parents, and body data of the newborn; determine a developmental disability value of the newborn by inputting, to the prediction model, the body data of the parents, the environmental data of the parents, the variable data of the parents, and the body data of the newborn; and when the developmental disability value of the newborn exceeds a threshold value, determine that a neurodevelopmental disability will occur in the newborn. The prediction model is trained on the basis of body data of a plurality of parents, environmental data of the plurality of parents, variable data of the plurality of parents, body data of a plurality of newborns born to the plurality of parents, and the developmental disability values of the plurality of newborns.

Description

Electronic device for predicting developmental disorders in newborns using a regression-based prediction model and its operating method

[0001] Various embodiments of the present invention relate to an electronic device and an operating method thereof for predicting developmental disorders in a newborn using a regression model-based prediction model, and more particularly, to an electronic device and an operating method thereof for measuring a parent's physical, environmental, and variable data and a newborn's data, inputting the measured data into a regression model to learn the data, and determining whether a newborn has a developmental disorder based on the outputted result value.

[0002]

[0003] As the proportion of newborns decreases, parents are at a point where their children require even greater protection. Therefore, early detection of developmental disabilities in newborns is crucial.

[0004] Recent advances in prenatal care have led to significant increases in newborn survival rates. However, the difficulty in predicting developmental disabilities in newborns remains a limitation. While methods for obtaining data on parents and newborns have existed, there has been a lack of effective methods for predicting developmental disabilities in newborns and addressing them.

[0005]

[0006] Accordingly, the present invention can provide a device that inputs parental physical data, parental environmental data, parental variable data, and newborn physical data, which are generally available in general hospitals easily accessible to newborns and parents, into a prediction model, and estimates the newborn's body and environment to predict whether or not the newborn has a developmental disorder.

[0007]

[0008] According to various embodiments, an electronic device for predicting developmental disorders in a newborn using a regression model-based predictive model comprises a communication interface and a processor, wherein the processor is configured to receive, through the communication interface, physical data of parents of the newborn, environmental data of the parents, variable data of the parents, and physical data of the newborn, and input the physical data of the parents, environmental data of the parents, variable data of the parents, and physical data of the newborn into the predictive model to determine a developmental disorder value of the newborn, and if the developmental disorder value of the newborn exceeds a threshold value, determine that the newborn has a neurodevelopmental disorder, and the predictive model is learned based on physical data of a plurality of parents, environmental data of the plurality of parents, variable data of the plurality of parents, and physical data of a plurality of newborns born from the plurality of parents and developmental disorder values ​​of the plurality of newborns.

[0009]

[0010] According to various embodiments of the present invention, there is an effect of being able to predict whether a newborn has a developmental disorder using only data predicted using parental physical data, parental environmental data, parental variable data, and newborn physical data.

[0011]

[0012] FIG. 1 illustrates a block diagram of an electronic device and a network according to various embodiments of the present invention.

[0013] FIG. 2 is a flowchart illustrating a method of operating an electronic device according to various embodiments.

[0014]

[0015] Hereinafter, various embodiments of the present document will be described with reference to the attached drawings. It should be understood that the embodiments and the terms used therein are not intended to limit the technology described in the present document to a specific embodiment, but rather include various modifications, equivalents, and / or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar components. The singular expression may include plural expressions unless the context clearly indicates otherwise. In this document, expressions such as "A or B" or "at least one of A and / or B" may include all possible combinations of the items listed together. Expressions such as "first," "second," "first," or "second," may modify the corresponding components regardless of order or importance, and are only used to distinguish one component from another, but do not limit the corresponding components. When it is said that a component (e.g., a first component) is “(functionally or communicatively) connected” or “connected” to another component (e.g., a second component), said component may be directly connected to said other component, or may be connected via another component (e.g., a third component).

[0016] In this document, "configured to" may be used interchangeably with, for example, "suitable for," "capable of," "modified to," "made to," "capable of," or "designed to," either in hardware or software. In some contexts, the phrase "a device configured to" may mean that the device is "capable of" doing something together with other devices or components. For example, the phrase "a processor configured to perform A, B, and C" may mean a dedicated processor (e.g., an embedded processor) for performing the operations, or a general-purpose processor (e.g., a CPU or application processor) that can perform the operations by executing one or more software programs stored in a memory device.

[0017] An electronic device according to various embodiments of the present document may include, for example, at least one of a smartphone, a tablet PC, a desktop PC, a laptop PC, a netbook computer, a workstation, and a server.

[0018] Referring to FIG. 1, an electronic device (101) within a network environment (100) according to various embodiments is described. The electronic device (101) may include a bus (110), a processor (120), a memory (130), an input / output interface (140), a display (150), and a communication interface (160). In some embodiments, the electronic device (101) may omit at least one of the components or additionally include other components. The bus (110) may include a circuit that connects the components (110-170) to each other and transmits communication (e.g., control messages or data) between the components. The processor (120) may include one or more of a central processing unit, an application processor, or a communication processor (CP). The processor (120) may, for example, execute operations or data processing related to control and / or communication of at least one other component of the electronic device (101).

[0019] The memory (130) may include volatile and / or non-volatile memory. The memory (130) may store, for example, commands or data related to at least one other component of the electronic device (101). According to one embodiment, the memory (130) may store software and / or programs (140).

[0020] The input / output interface (140) can, for example, transmit commands or data input from a user or another external device to other component(s) of the electronic device (101), or output commands or data received from other component(s) of the electronic device (101) to the user or another external device.

[0021] The display (150) may include, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a micro electro mechanical systems (MEMS) display, or an electronic paper display. The display (150) may, for example, display various contents (e.g., text, images, videos, icons, and / or symbols) to the user. The display (150) may include a touch screen and may receive, for example, a touch, gesture, proximity, or hovering input using an electronic pen or a part of the user's body. The communication interface (160) may, for example, establish communication between the electronic device (101) and an external device (e.g., a first external electronic device (102), a second external electronic device (104), or a server (108)). For example, the communication interface (160) can be connected to a network (162) via wireless communication or wired communication to communicate with an external device (e.g., a second external electronic device (104) or a server (108)).

[0022] The wireless communication may include, for example, cellular communication using at least one of LTE, LTE-A (LTE Advance), CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), or GSM (Global System for Mobile Communications). In one embodiment, the wireless communication may include, for example, at least one of WiFi (wireless fidelity), Bluetooth, Bluetooth low energy (BLE), Zigbee, near field communication (NFC), Magnetic Secure Transmission, radio frequency (RF), or body area network (BAN). In one embodiment, the wireless communication may include GNSS. The GNSS may be, for example, GPS (Global Positioning System), Glonass (Global Navigation Satellite System), Beidou Navigation Satellite System (hereinafter "Beidou"), or Galileo, the European global satellite-based navigation system. Hereinafter, in this document, "GPS" may be used interchangeably with "GNSS." Wired communication may include at least one of, for example, USB (universal serial bus), HDMI (high definition multimedia interface), RS-232 (recommended standard 232), power line communication, or POTS (plain old telephone service).The network (162) may include at least one of a telecommunications network, for example, a computer network (e.g., a LAN or WAN), the Internet, or a telephone network.

[0023] Each of the first and second external electronic devices (102, 104, 106) may be the same or a different type of device as the electronic device (101). According to various embodiments, all or part of the operations executed in the electronic device (101) may be executed in another one or more electronic devices (e.g., electronic devices (102, 104, 106), or server (108). According to one embodiment, when the electronic device (101) is to perform a certain function or service automatically or upon request, the electronic device (101) may request at least some functions related thereto from another device (e.g., electronic devices (102, 104, 106), or server (108)) instead of executing the function or service by itself or in addition. The other electronic device (e.g., electronic devices (102, 104, 106), or server (108)) may execute the requested function or additional function and transmit the result to the electronic device (101). The electronic device (101) may process the received result as is or additionally to provide the requested function or service. For this purpose, for example, cloud computing, distributed computing, or client-server computing technology may be used.

[0024]

[0025] FIG. 2 is a flowchart illustrating a method of operating an electronic device according to various embodiments.

[0026] In operation 201, the electronic device (101) (e.g., the processor (120) of FIG. 1) can receive the parental body data, parental environmental data, parental variable data, and newborn body data of the newborn through the communication interface (160) (e.g., the communication interface (160) of FIG. 1). For example, the electronic device (101) can obtain the parental body data, parental environmental data, parental variable data, and newborn body data measured from an external server of the user (e.g., a general hospital server) or obtain the parental body data, parental environmental data, parental variable data, and newborn body data measured through a sensor module (not shown) implemented within the electronic device (101).

[0027] In one embodiment, parental physical data may include gender, age, and gestational age of the newborn. For example, parental physical data may further include maternal age, pregnancy history, parity, and amniotic fluid volume during pregnancy, in addition to the above. Furthermore, parental physical data may include gestational age of less than 32 weeks, pregnancy course, diabetes, hypertension, use of prenatal steroids, and histological chorioamnionitis.

[0028] In one embodiment, parental environmental data may include the parent's highest level of education, the parent's occupation, and the parent's marital status. For example, parental environmental data may further include the parent's country of origin, parental cognitive tests, parental language tests, and parental social skills tests.

[0029] In one embodiment, the parental variable data may include the parent's IARVPPD, where IARVPPD may be the total duration of time spent on an invasive ventilator.

[0030] In one embodiment, the newborn's physical data may include the newborn's head circumference, Apgar score, and birth weight. Here, the Apgar score may be a scoring system designed to quickly assess the newborn's health status immediately after birth.

[0031] In another embodiment, the newborn's head circumference and birth weight may be included. For example, the brain:body weight ratio (BBR) value between the newborn's head circumference and birth weight may be calculated.

[0032]

[0033] Here, BBR can represent weight ratio, head can represent head circumference, and weight can represent birth weight.

[0034] In operation 203, the electronic device (101) (e.g., the processor (120) of FIG. 1) may input parental physical data, parental environmental data, parental variable data, and newborn physical data into a prediction model to determine a developmental disability value of the newborn. According to one embodiment, the prediction model may be learned based on a plurality of parental physical data, a plurality of parental environmental data, a plurality of parental variable data, and a plurality of newborns born from a plurality of parents and a plurality of newborns' developmental disability values.

[0035] In one embodiment, the regression model may utilize any type of algorithm, such as linear regression, logistic regression, regression tree, support vector regression, or kernel regression. These are examples only and are not limiting.

[0036] Although not shown, the electronic device (101) may include an evaluation step for evaluating the performance of the prediction model during the learning process of the prediction model. In the evaluation step, the prediction model may be evaluated using an evaluation data set. The evaluation of the prediction model may be a step for evaluating the prediction model learned through the learning step and making predictions for new data using the prediction model. Specifically, the evaluation step may be a step for measuring whether the learned prediction model is capable of generalization to new data. For example, the electronic device (101) may measure the root mean square error (RMSE) based on the estimated developmental disability value of the newborn and the developmental disability value of the newborn derived from the liver magnetic resonance elastography to evaluate the accuracy of the prediction method.

[0037] According to one embodiment, the electronic device (101) (e.g., the processor (120) of FIG. 1) may be set to determine that there is a neurodevelopmental disorder if the value obtained from the following [Mathematical Formula 1] obtained as a result of learning the prediction model is greater than a preset value.

[0038] [Mathematical Formula 1]

[0039] (1)

[0040] (2)Estimated probability:

[0041] Here, A, B, C, D, E, F, G, and H may represent weight values ​​derived from the learning results of the prediction model. In addition, the data values ​​in [Mathematical Formula 1] may vary depending on the data values ​​extracted from multiple users. In addition, sex may represent gender, parity may represent fertility, amnionic fluid may represent the amount of amniotic fluid during pregnancy, marriage may represent whether the parents are married, diabetes may represent diabetes, gestational age may represent the gestational age of the newborn, and BBR may represent the weight ratio between the newborn's head and birth weight.

[0042] In operation 205, the electronic device (101) (e.g., the processor (120) of FIG. 1) may determine that the newborn has a neurodevelopmental disorder if the developmental disorder value of the newborn exceeds a threshold value. According to one embodiment, the electronic device (101) may set the preset value to 0.252, and may extract from [Mathematical Formula 1] described above. If the value is confirmed to be greater than the preset value of 0.276, the newborn may be judged to have a neurodevelopmental disorder. According to another embodiment, the extracted [Mathematical Formula 1] described above If the value is confirmed to be less than the preset value of 0.252, it can be determined that the newborn is likely not to have a neurodevelopmental disorder.

[0043] In one embodiment, a regression-based predictive model can determine whether a newborn has a neurodevelopmental disorder based on whether the developmental disorder value determined by the model exceeds a specific threshold. For example, the predictive model can determine that the newborn has a neurodevelopmental disorder if the determined developmental disorder value exceeds the threshold, and can determine that the newborn does not have a neurodevelopmental disorder if the determined developmental disorder value is below the threshold.

[0044]

[0045] According to various embodiments, an electronic device for predicting developmental disorders in a newborn using a regression model-based predictive model comprises a communication interface and a processor, wherein the processor is configured to receive, through the communication interface, physical data of parents of the newborn, environmental data of the parents, variable data of the parents, and physical data of the newborn, and input the physical data of the parents, environmental data of the parents, variable data of the parents, and physical data of the newborn into the predictive model to determine a developmental disorder value of the newborn, and if the developmental disorder value of the newborn exceeds a threshold value, determine that the newborn has a neurodevelopmental disorder, and the predictive model is learned based on physical data of a plurality of parents, environmental data of the plurality of parents, variable data of the plurality of parents, and physical data of a plurality of newborns born from the plurality of parents and developmental disorder values ​​of the plurality of newborns.

[0046] According to various embodiments, the parental physical data includes the gender, age, and gestational age of the newborn, the parental environmental data includes the parent's highest level of education, the parent's occupational status, and the parent's marital status, the parental variable data includes the parent's IARVPPD and Apgar score, and the newborn's physical data includes the newborn's head circumference and birth weight.

[0047] According to various embodiments, the processor is configured to determine that there is a neurodevelopmental disorder if the value obtained from the following [Mathematical Formula 1] obtained as a result of learning the prediction model is greater than a preset value.

[0048] [Mathematical Formula 1]

[0049] (1)

[0050] (2)Estimated probability:

[0051] Here, A, B, C, D, E, F, G, and H represent weight values ​​derived from the learning results of the prediction model.

[0052]

[0053] The term "module" or "part" used in this document includes a unit composed of hardware, software, or firmware, and can be used interchangeably with terms such as logic, logic block, component, or circuit, for example. The "module" or "part" can be an integrally configured component or a minimum unit or a part thereof that performs one or more functions. The "module" or "part" can be implemented mechanically or electronically, and can include, for example, an ASIC (application-specific integrated circuit) chip, FPGAs (field-programmable gate arrays), or a programmable logic device, known or to be developed in the future, that performs certain operations, and can be executed by the processor (120). At least a part of the device (e.g., modules or functions thereof) or method (e.g., operations) according to various embodiments can be implemented as instructions stored in a computer-readable storage medium (e.g., memory (130)) in the form of a program module. When the above command is executed by a processor (e.g., processor (120)), the processor can perform a function corresponding to the command. The computer-readable recording medium may include a hard disk, a floppy disk, a magnetic medium (e.g., a magnetic tape), an optical recording medium (e.g., a CD-ROM, a DVD, a magneto-optical medium (e.g., a floptical disk), a built-in memory, etc. The command may include a code generated by a compiler or a code executable by an interpreter. A module or program module according to various embodiments may include at least one or more of the above-described components, some of which may be omitted, or other components may be further included. Operations performed by a module, a program module, or other components according to various embodiments may be executed sequentially, in parallel, iteratively, or heuristically, or at least some operations may be executed in a different order, omitted, or other operations may be added.

[0054] The embodiments disclosed in this document are presented for the purpose of explaining and understanding the disclosed technical content, and do not limit the scope of the present disclosure. Therefore, the scope of the present disclosure should be interpreted to include all modifications or various other embodiments based on the technical concepts of the present disclosure.

Claims

1. In an electronic device for predicting developmental disorders in newborns using a prediction model based on a regression model, communication interface, Contains a processor, The above processor, Through the above communication interface, the physical data of the parents of the newborn, the environmental data of the parents, the variable data of the parents, and the physical data of the newborn are received, By inputting the physical data of the parents, the environmental data of the parents, the variable data of the parents, and the physical data of the newborn into the prediction model, the developmental disability value of the newborn is determined, and If the developmental disability value of the newborn exceeds the threshold value, it is determined that the newborn has a neurodevelopmental disability, The above prediction model is learned based on the physical data of a plurality of parents, the environmental data of the plurality of parents, the variable data of the plurality of parents, and the physical data of a plurality of newborns born from the plurality of parents and the developmental disability values ​​of the plurality of newborns. Electronic devices.

2. In paragraph 1, The above parental physical data includes the gender, age, and gestational age of the newborn, The above parental environmental data includes the parent's highest level of education, the parent's occupational status, and the parent's marital status. The above parent's variable data includes the parent's IARVPPD, The physical data of the newborn is characterized in that it includes the newborn's apgar score, head circumference, and birth weight. Electronic devices.

3. In paragraph 2, The above processor, Based on the results of learning the above prediction model, if the value obtained from [Mathematical Formula 1] below is greater than a preset value, it is determined that there is a neurodevelopmental disorder. Electronic devices. [Mathematical Formula 1] (1) (2)Estimated probability: Here, A, B, C, D, E, F, G, and H represent weight values ​​derived from the learning results of the prediction model.

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