JP2018147023A - Provision device, provision method and provision program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To appropriately provide service related to a disease according to a user.SOLUTION: This provision device is an information processing device for predicting a predicted disease predicted to develop in a user, based on user's environmental information or biological information, of diseases associated with the user, and has an acquisition unit and a provision unit. The acquisition unit acquires the environmental information about user's environment. The provision unit provides service related to the predicted disease which is the disease predicted to develop in the user, based on the environmental information acquired by the acquisition unit, of the diseases associated with the user.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a providing device, a providing method, and a providing program.

  2. Description of the Related Art Conventionally, techniques for providing various services such as information provision according to users have been provided. For example, based on information such as a search log, a technique for searching and guiding a route suitable for the physical characteristics of each user is provided.

JP 2006-194895 A

  However, in the above-described conventional technology, it is not always possible to appropriately provide a service related to a disease according to a user. The above-mentioned conventional technology targets the physical characteristics of users such as disabled persons such as wheelchair users and the elderly, and deals with diseases that occur irregularly in the user, such as weather diseases and weather pain. Difficult to do.

  The present application has been made in view of the above, and an object thereof is to provide a providing apparatus, a providing method, and a providing program that appropriately provide a service related to a disease according to a user.

  The providing apparatus according to the present application, when an onset occurs in the user, based on the environment information acquired by the acquisition unit, among an acquisition unit that acquires environment information related to the user's environment, and a disease associated with the user And a providing unit that provides a service related to a predicted disease that is a predicted disease.

  According to one aspect of the embodiment, there is an effect that a service related to a disease can be appropriately provided according to a user.

FIG. 1 is a diagram illustrating an example of a providing process according to the embodiment. FIG. 2 is a diagram illustrating a configuration example of the providing system according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the providing apparatus according to the embodiment. FIG. 4 is a diagram illustrating an example of a user information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of the environment information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of the biological information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of a prediction model information storage unit according to the embodiment. FIG. 8 is a diagram illustrating an example of a pharmaceutical institution information storage unit according to the embodiment. FIG. 9 is a diagram illustrating an example of the countermeasure information storage unit according to the embodiment. FIG. 10 is a flowchart illustrating an example of generation of a prediction model according to the embodiment. FIG. 11 is a flowchart illustrating an example of the providing process according to the embodiment. FIG. 12 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the providing apparatus.

  DESCRIPTION OF EMBODIMENTS Hereinafter, embodiments for providing a providing apparatus, a providing method, and a providing program according to the present application (hereinafter referred to as “embodiments”) will be described in detail with reference to the drawings. In addition, the providing apparatus, the providing method, and the providing program according to the present application are not limited by this embodiment. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description is omitted.

(Embodiment)
[1. Provision processing)
First, an example of a providing process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a providing process according to the embodiment. In the example of FIG. 1, the providing device 100 uses the information related to the environment where the user is located (hereinafter also referred to as “environment information”) and the user's biological information among the diseases associated with the user. The case of predicting a disease predicted to develop (hereinafter also referred to as “predictive disease”) is shown. Note that the environment information is not limited to information related to weather (phenomena) such as temperature and pressure acquired by the sensor shown in FIG. May be. For example, the environment information may include information on various environments such as the user's work environment and human relationships.

  In the example of FIG. 1, the disease associated with the user is assumed to be a user's disease. In the example of FIG. 1, the user himself / herself may be registered in the providing system 1, or the providing apparatus 100 may be predicted based on the user's biological information, behavior information, or the like. For example, the providing apparatus 100 may predict a user's disease based on behavior information such as a user's hospital history or a drug purchase history.

  In the example of FIG. 1, the providing apparatus 100 illustrates a case where a user's predicted disease is predicted using a plurality of prediction models corresponding to each disease. In the example of FIG. 1, the providing apparatus 100 illustrates a case where a service related to a predicted disease is provided to a user based on the predicted disease of the user predicted using a prediction model.

[Configuration of provided system]
Prior to the description of FIG. 1, the configuration of the providing system 1 will be described with reference to FIG. FIG. 2 is a diagram illustrating a configuration example of the providing system according to the embodiment. As illustrated in FIG. 2, the providing system 1 includes a terminal device 10 and a providing device 100. The terminal device 10 and the providing device 100 are connected via a predetermined network N so as to be communicable by wire or wirelessly. The providing system 1 illustrated in FIG. 2 may include a plurality of terminal devices 10 and a plurality of providing devices 100.

  The terminal device 10 is an information processing device used by a user. The terminal device 10 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. FIG. 1 shows a case where the terminal device 10 is a smartphone. The terminal device 10 has a function such as a GPS (Global Positioning System) sensor, and can detect and acquire the position of the user. The terminal device 10 has various functions such as a temperature sensor and an atmospheric pressure sensor, and can detect and acquire environmental information on which the user is placed. Further, the terminal device 10 has various functions such as a heart rate sensor and can detect and acquire the biological information of the user. For example, a user using the terminal device 10 may acquire the user's own biometric information by the terminal device 10 by wearing a wearable device that can communicate with the terminal device 10. For example, a user who uses the terminal device 10 wears a wristband type wearable device that can communicate with the terminal device 10, and the terminal device 10 acquires information on the user's own heartbeat (pulse) by the terminal device 10. It may be possible.

  In addition, the terminal device 10 transmits various information and the like to the providing device 100. For example, the terminal device 10 transmits various information such as position information, environment information, and biological information about the user to the providing device 100. Hereinafter, the terminal device 10 may be referred to as a user. That is, hereinafter, the user can be read as the terminal device 10. The terminal device 10 receives various operations by the user. In the example of FIG. 1, a case where the user who uses the terminal device 10 is a user identified by the user ID “U1” (hereinafter referred to as “user U1”) is shown.

  Further, in the example of FIG. 1, the terminal device 10 will be described as the terminal devices 10-1 and 10-2 in accordance with the change in display contents on the terminal device 10. The terminal devices 10-1 and 10-2 are the same terminal device 10. Hereinafter, the terminal devices 10-1 and 10-2 will be referred to as the terminal device 10 when they are not particularly distinguished.

  The providing apparatus 100 is an information processing apparatus that predicts a predicted disease that is predicted to develop in the user based on the user's environmental information or biological information, among diseases associated with the user (hereinafter also referred to as “disease”). is there. Further, the providing apparatus 100 predicts a user's predicted disease using a plurality of prediction models corresponding to each disease. Further, the providing apparatus 100 provides a user with a service related to a predicted disease based on the predicted disease of the user predicted using the prediction model.

  From here, the flow of a provision process is demonstrated using FIG. FIG. 1 is a diagram illustrating an example of a providing process according to the embodiment. For example, FIG. 1 is a diagram illustrating an example of a providing process for providing a service related to a predicted disease to a user using a plurality of prediction models respectively corresponding to each disease. In the example of FIG. 1, it is assumed that prediction models M1 to M3 corresponding to each disease have been generated as shown in the prediction model information storage unit 124.

  Each prediction model M1 to M3 shown in the prediction model information storage unit 124 in FIG. 1 stores a disease identified by a disease ID in association with each other. Further, each weight (value) such as “feature 1” to “feature 3” is stored in association with each of the prediction models M1 to M3 and the like shown in the prediction model information storage unit 124 in FIG.

  For example, in the example of FIG. 1, the prediction model information regarding the prediction model M1 indicates that the prediction model corresponds to the disease “headache” identified by the disease ID “IL11”. Further, for example, in the example of FIG. 1, the prediction model information regarding the prediction model M2 indicates that the prediction model corresponds to the disease “back pain” identified by the disease ID “IL12”.

  For example, in the example of FIG. 1, the prediction model information related to the prediction model M1 includes feature 1 weight “0.5”, feature 2 weight “−0.4”, and feature 3 weight “0.2”. And so on. In the example of FIG. 1, the providing apparatus 100 predicts the predictive disease of the user U1 based on the predictive models M1 to M3 and the like stored in the predictive model information storage unit 124 in this way. Each model may be generated using various conventional techniques relating to machine learning as appropriate. For example, the model generation may be performed using a technique related to machine learning of supervised learning such as SVM (Support Vector Machine). Further, for example, the model generation may be performed using a technique related to machine learning of unsupervised learning. For example, the generation of the model may be performed using a deep learning technique. For example, the model generation may be performed by appropriately using various deep learning techniques such as DNN (Deep Neural Network), RNN (Recurrent Neural Network), and CNN (Convolutional Neural Network). The description relating to the generation of the model is merely an example, and the generation of the model may be performed by a learning method appropriately selected according to information that can be acquired.

  Here, the map information MP1 shown in FIG. 1 will be briefly described. The map information MP1 shown in FIG. 1 is a diagram schematically showing the position of the user U1, the position of each medical institution, and the like. For example, the map information MP1 includes three areas: area A identified by area ID “AR11”, area B identified by area ID “AR12”, and area C identified by area ID “AR13”. Indicates that

  Further, in the area A in the map information MP1 of FIG. 1, the HA general hospital (see FIG. 8), which is a pharmaceutical institution identified by the pharmaceutical institution ID “ES11”, and the pharmaceutical institution identified by the pharmaceutical institution ID “ES22” The PB pharmacy (see FIG. 8) is located. The size of the area here may be set as appropriate according to the purpose of the processing. For example, areas of various sizes such as “prefecture”, “city”, “ward”, and “town” may be set as appropriate.

  Further, the area B in the map information MP1 of FIG. 1 includes an HB clinic (see FIG. 8) that is a pharmaceutical institution identified by the pharmaceutical institution ID “ES12” and a pharmaceutical institution identified by the pharmaceutical institution ID “ES21”. It shows that a certain PA drug store (see FIG. 8) is located.

  Further, the example of FIG. 1 shows a case where the user U1 is located at a point LC1 in the map information MP1. In the example of FIG. 1, the terminal device 10 used by the user U1 acquires the environment information of the user U1 by various sensors such as the temperature sensor TMP and the atmospheric pressure sensor ATP. In the example of FIG. 1, the terminal device 10 used by the user U1 acquires biometric information of the user U1 using various sensors such as a heart rate sensor HTR. Note that the icons of the temperature sensor TMP, the atmospheric pressure sensor ATP, and the heart rate sensor HTR shown in FIG. 1 are icons corresponding to information detected by each sensor, and do not indicate the shape of each sensor.

  In FIG. 1, the providing device 100 acquires the environment information and biological information of the user U1 from the terminal device 10-1 used by the user U1 (step S11). For example, the providing device 100 acquires information on the temperature (air temperature) at the point LC1 where the user U1 is detected by the temperature sensor TMP, from the terminal device 10-1. For example, the providing apparatus 100 acquires information on the atmospheric pressure at the point LC1 where the user U1 is detected by the atmospheric pressure sensor ATP from the terminal apparatus 10-1. In addition, for example, the providing apparatus 100 acquires information regarding the heart rate of the user U1 detected by the heart rate sensor HTR from the terminal device 10-1.

  Moreover, the provision apparatus 100 acquires the information regarding the weather of the area where the user U1 is located based on the information regarding the point LC1 where the user U1 is acquired, which is acquired from the terminal apparatus 10-1. For example, the environment information of the user U1 may be estimated by the providing apparatus 100 based on the position information acquired from the terminal apparatus 10 and the acquisition date (time), or an external information processing apparatus that provides weather information May be obtained from For example, the providing apparatus 100 may acquire information regarding the weather in the area where the user U1 is located from an external information processing apparatus that provides weather information.

  Then, the providing device 100 predicts the predictive disease of the user U1. First, the providing apparatus 100 specifies the chronic disease of the user U1 (Step S12). In the example of FIG. 1, the providing device 100 identifies the chronic disease of the user U1 based on the information stored in the user information storage unit 121.

  “User ID” shown in the user information storage unit 121 in FIG. 1 indicates identification information for identifying a user. For example, the user identified by the user ID “U1” corresponds to the user U1. Further, “age” shown in the user information storage unit 121 in FIG. 1 indicates the age of the user. “Gender” shown in the user information storage unit 121 in FIG. 1 indicates the gender of the user. Further, “disease information” shown in the user information storage unit 121 in FIG. 1 indicates information related to the user's disease.

  The “disease ID” shown in the user information storage unit 121 in FIG. 1 indicates identification information for identifying a disease. Further, “name” shown in the user information storage unit 121 in FIG. 1 indicates the name of the corresponding disease. Further, the “threshold value” shown in the user information storage unit 121 in FIG. 1 indicates a threshold value used for determining whether or not the corresponding disease has developed in the user.

  For example, in the example of FIG. 1, the user U1 indicates that he is a male in his 20s. In addition, the user U1 indicates that the disease “headache” identified by the disease ID “IL11” and the disease “back pain” identified by the disease ID “IL12” are present. In the example of FIG. 1, the threshold used for determining whether or not the disease “headache” identified by the disease ID “IL11” has developed in the user U1 is “0.5”. In the example of FIG. 1, the threshold used for determining whether or not the disease “back pain” identified by the disease ID “IL12” has developed in the user U1 is “0.7”.

  For example, in the example of FIG. 1, for the disease “back pain” identified by the disease ID “IL12”, the threshold used to determine whether or not the user U1 has developed is “0.7”, and the disease has developed in the user U2. The threshold used for determining whether or not is “0.4”. In the example of FIG. 1, the case where each user's threshold value is varied according to the symptom and severity (onset history, etc.) of each user for each disease is shown.

  In the example of FIG. 1, as shown in the user information storage unit 121, each user is associated with information related to the disease of the user, and the providing apparatus 100 is based on the association between each user and the disease. Identify the user's disease. Specifically, as shown in the user information storage unit 121, the providing apparatus 100 identifies two diseases, the disease “headache” and the disease “back pain”, associated with the user U1, as the chronic disease of the user U1. Further, as shown in the user information storage unit 121, the providing apparatus 100 has a threshold value of “0.5” for the disease “headache” and a threshold value of “0.7” for the disease “back pain” for the user U1. Identify that.

  Then, the providing apparatus 100 predicts a user's predicted illness using a prediction model and a threshold corresponding to each chronic illness. For example, the providing apparatus 100 inputs a user's environmental information and biological information to a prediction model corresponding to a certain illness, and outputs a score output by the prediction model (hereinafter also referred to as a “prediction score”) and the illness threshold of the user. Based on the above, it is predicted whether the chronic illness will develop in the user. Specifically, the providing apparatus 100 inputs the user's environmental information and biological information to a prediction model corresponding to a certain illness, and when the prediction score output by the prediction model is equal to or greater than the illness threshold of the user, Predicts that chronic disease will develop in the user.

  In the example of FIG. 1, the providing apparatus 100 predicts the predicted disease of the user using the prediction models M <b> 1 and M <b> 2 and threshold values corresponding to each chronic disease for headache and low back pain that are prevalent in the user U <b> 1. For example, the providing apparatus 100 predicts whether a headache develops in the user U1 using the prediction model M1 corresponding to the headache and the threshold value “0.5” for a headache that is illness in the user U1. Specifically, the providing apparatus 100 calculates the prediction score related to the headache of the user U1 by inputting the environment information and the biological information of the user U1 acquired in step S11 into the prediction model M1. Then, if the predicted score related to the calculated headache is equal to or greater than the threshold “0.5”, the providing apparatus 100 predicts the headache as the predicted disease of the user U1.

  Further, for example, the providing apparatus 100 predicts whether back pain will develop in the user U1 using the prediction model M2 corresponding to the back pain and the threshold value “0.7” for the back pain that is prevalent in the user U1. Specifically, the providing apparatus 100 calculates the prediction score related to the back pain of the user U1 by inputting the environment information and the biological information of the user U1 acquired in step S11 into the prediction model M2. Then, when the calculated prediction score related to low back pain is equal to or greater than the threshold “0.7”, the providing apparatus 100 predicts low back pain as a predictive disease of the user U1.

  In the example of FIG. 1, the prediction score calculated by inputting the environment information and biological information of the user U1 acquired in step S11 into the prediction model M1 is “0.6”, and is greater than or equal to the threshold value “0.6”. Therefore, the providing apparatus 100 predicts a headache as a predicted disease of the user U1 (step S13). In the example of FIG. 1, the prediction score calculated by inputting the environment information and biological information of the user U1 acquired in step S11 to the prediction model M2 is “0.65”, and the threshold value “0.7”. Therefore, the providing apparatus 100 predicts that the back pain is not a predicted disease of the user U1 (step S14). Therefore, in the example of FIG. 1, the providing apparatus 100 predicts a headache as a predicted disease of the user U1 among the chronic diseases of the user U1 (step S15).

  Then, the providing apparatus 100 provides a service based on the predictive disease “headache” to the user U1. In the example of FIG. 1, it is assumed that the providing apparatus 100 provides the user U1 with an information providing service such as information on a pharmaceutical institution corresponding to the predictive disease “headache” and information on self-handling. First, the providing apparatus 100 determines information regarding a pharmaceutical institution to be provided to the user U1. For example, the providing apparatus 100 determines information on the pharmaceutical institution to be provided to the user U1 based on information on the pharmaceutical institution stored in the pharmaceutical institution information storage unit 125 (see FIG. 8).

  In the example of FIG. 1, the providing device 100 provides the user U1 with information on a pharmaceutical institution that is located in the area A and can cope with headache because the user U1 is located in the area A and the predicted disease is headache. Determined as information about the institution. In the example of FIG. 1, since the disease “headache” identified by the disease ID “IL11” is included in the corresponding diseases (see FIG. 8) of the HA general hospital and the PB pharmacy located in the area A, the providing apparatus 100 has the HA Information on the general hospital and the PB pharmacy is determined as information on a pharmaceutical institution to be provided to the user U1.

  Further, the providing apparatus 100 determines information regarding self-handling provided to the user U1. For example, the providing apparatus 100 determines information on the handling information to be provided to the user U1 based on the information on the handling information stored in the handling information storage unit 126 (see FIG. 9). For example, the providing apparatus 100 determines the content stored in association with the user's predicted illness in the countermeasure information storage unit 126 illustrated in FIG. 9 as information regarding self-handling to be provided to the user U1. In the example of FIG. 1, the providing apparatus 100 determines the content CT11 (see FIG. 9) stored in association with the disease “headache” identified by the disease ID “IL11” as information regarding self-handling provided to the user U1. To do. Specifically, the providing apparatus 100 determines to provide the content CT11 including the content related to the headache prevention method to the user U1 who is likely to develop a headache. The information relating to self-handling provided to the user by the providing apparatus 100 is not limited to the above, but may be various information such as information on a plan that can be acquired on a predetermined network and information on an action that can be performed on the predetermined network. May be.

  Then, the providing device 100 provides information to the terminal device 10 used by the user U1 (step S16). In the example of FIG. 1, the providing device 100 is a terminal where the user U1 uses the information related to the HA general hospital identified by the pharmaceutical institution ID “ES11” and the PB pharmacy identified by the pharmaceutical institution ID “ES22” and the content CT11. To the device 10. For example, even if there is a HE eye clinic (not shown) in the area A in FIG. 1, the providing apparatus 100 provides the user U1 with information regarding the HE eye clinic, assuming that the hospital is not capable of handling headaches. do not do. As described above, the providing apparatus 100 can provide the user U1 with an appropriate service by providing the user U1 with information related only to the medical institution that corresponds to the predictive disease of the user U1.

  And the terminal device 10 which received information provision from the provision apparatus 100 displays the information provided from the provision apparatus 100 (step S17). In the example of FIG. 1, the terminal device 10-2 displays the provision information INF <b> 11 including information related to the predictive disease of the user U <b> 1 and the response method. For example, the terminal device 10-2 displays the prediction information PIL11 indicating that the onset of the chronic headache is predicted for the user U1. In addition, for example, in the terminal device 10-2, the area A where the user U1 is located has a HA general hospital identified by the medical institution ID “ES11” and a PB pharmacy identified by the medical institution ID “ES22”. Guidance information PES11 is displayed. For example, the terminal device 10-2 displays the self-handling information PCW11 indicating the target method that the user U1 can perform in an earthquake as a headache prevention method.

  As described above, the providing apparatus 100 predicts a chronic disease whose score output by the prediction model is equal to or more than the threshold as a predicted disease of the user, using the prediction model and the threshold corresponding to each chronic disease of the user. Then, the providing apparatus 100 appropriately provides services related to the disease according to the user by providing the user with a medical institution that can cope with the user's disease predicted as the predictive disease or correspondence information that can be processed by the user. Can be provided.

[1-1. Provided service)
In the above-described example, information provision is shown as an example of the service provided by the providing apparatus 100 to the user. However, the providing apparatus 100 can provide any service as long as it is a service related to the user's predictive disease. Good. For example, the providing apparatus 100 may provide a reservation service of a medical institution regarding a user's predictive disease. For example, the providing apparatus 100 may provide a hospital consultation reservation service related to a user's predictive disease. For example, the providing apparatus 100 displays a hospital reservation site related to the user's predictive disease on the user's terminal apparatus 10 or displays a hospital's consultation available time or the like on the user's terminal apparatus 10, thereby receiving the hospital consultation. A reservation service may be provided. In addition, for example, the providing apparatus 100 may provide a hospital consultation reservation service by calling a hospital regarding a user's predicted disease.

  In the example of FIG. 1, the providing apparatus 100 may provide an HA general hospital consultation reservation service capable of handling headaches in the area A where the user U1 is located, for the user U1 who is predicted to develop headache. . For example, the providing apparatus 100 may provide a hospital consultation reservation service to the user U1 by causing the terminal apparatus 10 of the user U1 to display the Web reservation page of the HA general hospital.

[1-2. (Prediction model)
In the example of FIG. 1, the providing apparatus 100 may generate the prediction models M1 to M3 and the like, or may acquire the prediction models M1 to M3 and the like from an external information processing apparatus. For example, the providing apparatus 100 may generate the prediction models M1 to M3 using information about the user.

  For example, for each user who can acquire environmental information and biological information, the providing apparatus 100 acquires environmental information and biological information when a disease develops in the user, and based on the information, predictive models M1 to M3 are obtained. It may be generated. For example, the providing apparatus 100 specifies a time point of onset of the disease in the user by causing the user to transmit information indicating the onset of the disease when the disease develops for the predetermined user, and the environment of the user corresponding to the time point Based on the information and the biological information, prediction models M1 to M3 and the like may be generated.

  In addition, for each user who can acquire environmental information and biological information, the providing apparatus 100 estimates the onset of the disease in the user based on the user's behavior information, and the user's corresponding to the time when the disease is estimated to have occurred is provided. Prediction models M1 to M3 and the like may be generated based on environmental information and biological information. The providing apparatus 100 may estimate the onset of the disease in the user based on the behavior information that the user has received the hospital or purchased the medicine.

  Moreover, although the case where one model corresponds to each disease is shown in the example of FIG. 1, a plurality of models may be associated with each disease. For example, the providing apparatus 100 may generate a model of each disease for each user group having similar attributes. For example, the providing apparatus 100 may generate a model of each disease for each user group classified by sex.

[1-2-1. Threshold)
Further, in the example of FIG. 1, for each disease, the case where the threshold value of each user is changed according to the symptom and severity (onset history, etc.) of each user is shown. A common threshold may be used for users.

  For example, the providing apparatus 100 specifies a time point of onset of the disease in the user by causing the user to transmit information indicating the onset of the disease when the disease develops for the predetermined user, and the environment of the user corresponding to the time point The threshold value of the user may be determined in accordance with a prediction score output when information and biological information are input to a prediction model corresponding to a disease. For example, when the user U1 transmits information indicating the occurrence of the disease “headache”, the providing apparatus 100 uses the environmental information and the biological information of the user U1 corresponding to the onset time of the disease “headache” in the user U1 as the disease “headache”. ”Is input to the prediction model M1 corresponding to“ ”, and the prediction score“ 0.5 ”is calculated. In this case, the providing apparatus 100 may determine the threshold value corresponding to the disease “headache” of the user U1 to “0.5”. Further, for example, the providing apparatus 100 causes the user U1 to transmit the information indicating the occurrence of the disease “headache” a plurality of times, thereby obtaining the average value or the minimum value of the predicted scores calculated at each time point. The threshold value corresponding to “headache” may be determined.

  For example, the providing apparatus 100 may determine a threshold value corresponding to the disease “headache” of the user U1 based on information on a user similar to the user U1. For example, when the threshold value corresponding to the disease “headache” of the user U50 who is the attribute “male” other than the user U1 is “0.6”, the providing apparatus 100 has the disease “of the user U50 whose attribute is similar to the user U1. The threshold value “0.6” corresponding to “headache” may be determined as the threshold value corresponding to the disease “headache” of the user U1. For example, the providing apparatus 100 may determine a threshold value corresponding to each disease for each user group having similar attributes.

[1-3. Environmental information)
In the example of FIG. 1, the providing apparatus 100 has shown a case where the environment information of the user U1 is acquired from the terminal apparatus 10, but the providing apparatus 100 can receive the environment information of the user U1 from an external information processing apparatus other than the terminal apparatus 10. May be obtained. For example, the providing apparatus 100 may acquire the environment information of the user U1 from an external information processing apparatus that provides various open data related to weather. For example, the providing apparatus 100 acquires the position information of the user U1 from the terminal measure 10 used by the user U1, and responds to the environment information of the user U1 from various open data related to the weather based on the position information and the acquisition date and time. Information may be acquired.

  Further, as described above, the environment information is not limited to information related to weather (phenomena) such as temperature and pressure acquired by the sensor shown in FIG. For example, the environment information may include information on various environments such as the user's work environment and human relationships. For example, the environmental information may include information on stresses and the like that the user has in the company or the like.

  For example, the providing apparatus 100 may estimate information related to various environments such as the user's work environment and human relations based on various behavior information of the user on the Internet, and may use the estimated information as environmental information. . For example, the providing apparatus 100 estimates a user's work environment, home environment, and the like based on information posted by the user in a social networking service (hereinafter sometimes referred to as “SNS”), and uses the estimated information. It may be used as environmental information. For example, the providing apparatus 100 may be in an environment where the user is stressed when the user makes a post regarding work dissatisfaction or home dissatisfaction in an SNS service such as Twitter (registered trademark) or Facebook (registered trademark). The estimated information may be used as environment information. In addition, for example, the providing apparatus 100 estimates that the user is in an environment where the user is stressed when the user makes a post regarding dissatisfaction at the workplace or dissatisfaction with a message service such as a blog or LINE (registered trademark). The estimated information may be used as environment information. Note that the acquisition source of the above information is an example, and the providing apparatus 100 estimates information related to various environments such as the user's work environment and human relations based on various behavior information of the user on the Internet. The information obtained may be used as environment information.

[2. Configuration of providing device]
Next, the configuration of the providing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration example of the providing apparatus 100 according to the embodiment. As illustrated in FIG. 3, the providing apparatus 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The providing device 100 includes an input unit (for example, a keyboard and a mouse) that receives various operations from an administrator of the providing device 100 and a display unit (for example, a liquid crystal display) for displaying various types of information. May be.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to the network by wire or wireless, and transmits / receives information to / from the terminal device 10.

(Storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As illustrated in FIG. 3, the storage unit 120 according to the embodiment includes a user information storage unit 121, an environment information storage unit 122, a biological information storage unit 123, a prediction model information storage unit 124, and a pharmaceutical institution information storage unit. 125 and a countermeasure information storage unit 126.

(User information storage unit 121)
The user information storage unit 121 according to the embodiment stores various types of information regarding the user. For example, the user information storage unit 121 stores various types of information regarding user attributes. FIG. 4 is a diagram illustrating an example of a user information storage unit according to the embodiment. The user information storage unit 121 illustrated in FIG. 4 includes items such as “user ID”, “age”, “gender”, and “disease information”. The “disease information” includes items such as “disease ID”, “name”, and “threshold value”.

  “User ID” indicates identification information for identifying a user. For example, the user identified by the user ID “U1” corresponds to the user U1 illustrated in the example of FIG. “Age” indicates the age of the user identified by the user ID. The “age” may be a specific age of the user identified by the user ID, such as 35 years old. “Gender” indicates the gender of the user identified by the user ID.

  The “disease ID” in the “disease information” indicates identification information for identifying a disease. In addition, “name” in “disease information” indicates the name of the corresponding disease. The “threshold value” in the “disease information” indicates a threshold value used for determining whether or not the corresponding disease has occurred in the user.

  For example, in the example of FIG. 4, the age of the user (user U1) identified by the user ID “U1” is “20s”, and the gender is “male”. Further, for example, the user U1 indicates that the disease “headache” identified by the disease ID “IL11” and the disease “back pain” identified by the disease ID “IL12” are present. In the example of FIG. 4, the threshold used for determining whether or not the disease “headache” identified by the disease ID “IL11” has developed in the user U1 is “0.5”. In the example of FIG. 4, the threshold used for determining whether or not the disease “back pain” identified by the disease ID “IL12” has developed in the user U1 is “0.7”.

  For example, in the example of FIG. 4, the age of the user (user U2) identified by the user ID “U2” is “20s”, and the gender is “female”. Further, for example, the user U2 indicates that the disease “lumbar pain” identified by the disease ID “IL12” and the disease “asthma” identified by the disease ID “IL13” have the disease. In the example of FIG. 4, the threshold used for determining whether or not the disease “back pain” identified by the disease ID “IL12” has developed in the user U2 is “0.4”. In the example of FIG. 4, the threshold used for determining whether or not the disease “asthma” identified by the disease ID “IL13” has developed in the user U2 is “0.6”.

  For example, in the example of FIG. 4, for the disease “back pain” identified by the disease ID “IL12”, the threshold used to determine whether or not the user U1 has developed is “0.7”, and the disease has developed in the user U2. The threshold used for determining whether or not is “0.4”. In this way, for each disease, the threshold value of each user may be varied according to the symptom and severity (onset history, etc.) of each user.

  The user information storage unit 121 is not limited to the above, and may store various types of information according to the purpose. For example, the user information storage unit 121 may store various information such as demographic attribute information and psychographic attribute information. For example, the user information storage unit 121 may store information such as name, family structure, and income.

(Environmental information storage unit 122)
The environment information storage unit 122 according to the embodiment stores various types of information regarding environment information regarding the user's environment. FIG. 5 is a diagram illustrating an example of the environment information storage unit according to the embodiment. For example, the environment information storage unit 122 stores, as the environment information, information related to the environment where the user is located at the position of the user at a certain time (time). For example, the environment information storage unit 122 stores weather information at a user's position at a certain time (time) as environment information. The environment information storage unit 122 illustrated in FIG. 5 includes items such as “user ID” and “environment information”. The “environment information” includes items such as “time”, “position”, “temperature (° C.)”, “atmospheric pressure (hPa)”, and “weather”.

  “User ID” indicates identification information for identifying a user. The “time” in the “environment information” indicates the time (date) when the environment information was acquired. In the example of FIG. 5, “TM11-1” or the like is illustrated, but “date and time” may include a specific date and time such as “February 13, 2017 21:38:56”. Good. Further, “position” in “environment information” indicates a position where the environment information is acquired. “Position” shows an abstract code such as “LC11-1”, but may be information indicating latitude and longitude. Further, for example, “position” may include the role of the position (house, office, etc.), area name, address, and the like.

  In addition, “temperature (° C.)” in “environment information” indicates information regarding the temperature acquired at the corresponding time and position. In addition, “atmospheric pressure (hPa)” in “environment information” indicates information regarding the atmospheric pressure acquired at the corresponding time and position. In addition, “weather” in “environment information” indicates information regarding the weather acquired at the corresponding time and position.

  For example, in the example of FIG. 5, the user (user U1) identified by the user ID “U1” is located at the position “LC11-1” on the date “TM11-1”. Further, for example, in the example of FIG. 5, the surrounding of the position LC11-1 of the user U1 indicates that the temperature is “5 (° C.)”, that is, 5 degrees Celsius on the date “TM11-1”. Further, for example, in the example of FIG. 5, the area around the position LC11-1 of the user U1 indicates that the atmospheric pressure is “1000 (hPa)”, that is, 1000 hectopascals at the date “TM11-1”. For example, in the example of FIG. 5, the area around the position LC11-1 of the user U1 indicates that the weather was “cloudy” at the date “TM11-1”.

  As shown in the example of FIG. 5, it is not necessary that all the environmental index values such as “temperature (° C.)”, “atmospheric pressure (hPa)”, “weather”, etc. are acquired simultaneously. May be acquired at different timings. For example, in the example of FIG. 5, among the environmental index values such as “temperature (° C.)”, “atmospheric pressure (hPa)”, “weather”, the area around the position LC12-2 of the user U2 is the date “TM12-2”. "Indicates that only an environmental index value indicating that the atmospheric pressure was" 1005 (hPa) "was acquired. The environment information storage unit 122 is not limited to the above, and may store various types of information according to the purpose.

(Biological information storage unit 123)
The biological information storage unit 123 according to the embodiment stores various types of information related to the user's biological information. FIG. 6 is a diagram illustrating an example of the biological information storage unit according to the embodiment. For example, the biometric information storage unit 123 stores information related to a user's heartbeat, blood pressure, and the like at a certain time (time) as biometric information. The biometric information storage unit 123 illustrated in FIG. 6 includes items such as “user ID” and “biometric information”. The “biological information” includes items such as “time”, “heartbeat (bpm)”, and “blood pressure (mmHg)”.

  “User ID” indicates identification information for identifying a user. In addition, “time” in “biological information” indicates the time (date) when the biological information is acquired. In the example of FIG. 6, “TM21-1” or the like is illustrated, but “date and time” may include a specific date and time such as “February 13, 2017 21:38:56”. Good.

  “Heartbeat (bpm)” in the “biological information” indicates information regarding the heartbeat of the user acquired at the corresponding time. Further, “blood pressure (mmHg)” in “biological information” indicates information regarding the blood pressure of the user acquired at the corresponding time.

For example, in the example of FIG. 6, the user (user U1) identified by the user ID “U1” has a heartbeat of “75 (bpm)” at the date and time “TM21-1”, that is, the beat of the user U1 for 1 minute. This indicates that the number (beats per minute) is “75”. For example, in the example of FIG. 6, the user (user U1) identified by the user ID “U1” has a blood pressure of “110/80 (mmHg)” on the date “TM21-1”, that is, above the user U1. The blood pressure (systolic blood pressure) is “110 millimeters of mercury” and the lower blood pressure (diastolic blood pressure) is “80 millimeters of mercury”.

  Note that, as shown in the example of FIG. 6, it is not necessary for all the biometric index values such as “heartbeat (bpm)” and “blood pressure (mmHg)” to be acquired at the same time. May be obtained at For example, in the example of FIG. 6, the blood pressure of the user U2 is “102/70 (mmHg)” at the date and time “TM21-1” among the biometric index values such as “heartbeat (bpm)” and “blood pressure (mmHg)”. It is shown that only the biometric index value indicating that was obtained. In addition, the biological information storage unit 123 is not limited to the above, and may store various information according to the purpose.

(Prediction model information storage unit 124)
The prediction model information storage unit 124 according to the embodiment stores information related to the prediction model. For example, the prediction model information storage unit 124 stores prediction model information generated by the generation process. FIG. 7 is a diagram illustrating an example of a prediction model information storage unit according to the embodiment. The prediction model information storage unit 124 illustrated in FIG. 7 stores a prediction model corresponding to each disease. The prediction model information storage unit 124 illustrated in FIG. 7 includes items such as “feature 1” to “feature 3” corresponding to the respective prediction models M1 to M3 as the prediction model information.

  For example, in the example of FIG. 7, the prediction model information regarding the prediction model M1 indicates that the prediction model corresponds to the disease “headache” identified by the disease ID “IL11”. Further, for example, in the example of FIG. 7, the prediction model information regarding the prediction model M2 indicates that the prediction model corresponds to the disease “back pain” identified by the disease ID “IL12”.

  For example, in the example of FIG. 7, the prediction model information regarding the prediction model M <b> 1 has a feature 1 weight of “0.5”, a feature 2 weight of “−0.4”, and a feature 3 weight of “0.2”. And so on. For example, when the feature (feature value) of the model is expressed by an m-dimensional vector, the number of features is m, and the weights of the features 1 to m are stored. For example, in the example of FIG. 7, the feature of the prediction model may be a feature corresponding to environmental information, biological information, or user information. For example, in the example of FIG. 7, the feature 1 of the prediction model may correspond to the environment information “atmospheric pressure” and the feature 2 may correspond to the biological information “heartbeat”.

  Note that the prediction model information storage unit 124 is not limited to the above, and may store various prediction model information according to the purpose.

(Pharmaceutical institution information storage unit 125)
The pharmaceutical institution information storage unit 125 according to the embodiment stores various types of information related to the pharmaceutical institution. FIG. 8 is a diagram illustrating an example of a pharmaceutical institution information storage unit according to the embodiment. For example, the medical institution information storage unit 125 stores information related to medical institutions including hospitals and pharmacies. The medical institution information storage unit 125 illustrated in FIG. 8 includes items such as “institution ID”, “type”, “name”, “area”, and “corresponding disease”.

  “Institution ID” indicates identification information for identifying a medical institution. “Type” indicates the type of the corresponding medical institution. “Name” indicates the name of the corresponding pharmaceutical institution. “Area” indicates an area where the corresponding medical institution is located. The “corresponding disease” indicates a disease that can be handled by a pharmaceutical institution.

  For example, in the example of FIG. 8, the medical institution identified by the institution ID “ES11” indicates that the type is “hospital” and the name is “HA General Hospital”. Further, for example, in the example of FIG. 8, the medical institution identified by the institution ID “ES11” is located in the area A, and the corresponding disease is a disease (headache) identified by the type ID “IL11”. Show.

  Further, for example, in the example of FIG. 8, the pharmaceutical institution identified by the institution ID “ES21” has the type “pharmacy”, the name “PA drug store”, and is located in the area C.

  The medical institution information storage unit 125 is not limited to the above, and may store various types of information according to the purpose.

(Handling information storage unit 126)
The handling information storage unit 126 according to the embodiment stores handling information provided to the user. For example, the handling information storage unit 126 stores handling information that can be implemented by the user himself / herself without going to a medical institution. FIG. 9 is a diagram illustrating an example of the countermeasure information storage unit according to the embodiment. The handling information storage unit 126 illustrated in FIG. 9 includes items such as “disease ID” and “handling information”. Further, the “handling information” includes items such as “content” and “content”.

  “Disease ID” indicates identification information for identifying a disease. “Content” in “handling information” indicates content including the handling information. FIG. 9 shows an example in which conceptual information such as “CT11” is stored in “content”. Actually, however, specific content including various information such as character information, images, and moving images, or A file path name indicating the storage location is stored. In addition, “content” in “handling information” indicates the details of the handling information included in the content.

  For example, in the example of FIG. 9, the disease identified by the disease ID “IL11” indicates that there is content CT11 and content CT12 as the handling information. For example, the disease “headache” identified by the disease ID “IL11” indicates that there is content CT11 and content CT12 as coping information that can be performed by the user himself / herself without going to a medical institution.

  The handling information storage unit 126 is not limited to the above, and may store various types of information according to the purpose.

(Control unit 130)
Returning to the description of FIG. 3, the control unit 130 is a controller and is stored in a storage device inside the providing apparatus 100 by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. Various programs (corresponding to an example of a provided program) are implemented by executing the RAM as a work area. The control unit 130 is a controller, and is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  As illustrated in FIG. 3, the control unit 130 includes an acquisition unit 131, a generation unit 132, a prediction unit 133, and a provision unit 134, and realizes or executes information processing functions and operations described below. . Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 3, and may be another configuration as long as the information processing described below is performed. Further, the connection relationship between the processing units included in the control unit 130 is not limited to the connection relationship illustrated in FIG. 3, and may be another connection relationship.

(Acquisition part 131)
The acquisition unit 131 acquires various information. The acquisition unit 131 acquires various types of information from an external device such as the terminal device 10. The acquisition unit 131 includes a user information storage unit 121, an environment information storage unit 122, a biological information storage unit 123, a prediction model information storage unit 124, a pharmaceutical institution information storage unit 125, a countermeasure information storage unit 126, and the like. Get various information from.

  For example, the acquisition unit 131 acquires environment information related to the user's environment. For example, the acquisition unit 131 acquires environment information including weather information related to the weather at a location where the user is located. For example, the acquisition unit 131 acquires the user's biological information.

  In the example of FIG. 1, the acquisition unit 131 acquires environment information and biological information of the user U1 from the terminal device 10 used by the user U1. For example, the acquisition unit 131 acquires information about the temperature (air temperature) at the point LC1 where the user U1 is detected by the temperature sensor TMP from the terminal device 10. In addition, for example, the acquisition unit 131 acquires information regarding the atmospheric pressure at the point LC1 where the user U1 is detected by the atmospheric pressure sensor ATP from the terminal device 10. For example, the acquisition unit 131 acquires information related to the heart rate of the user U1 detected by the heart rate sensor HTR from the terminal device 10.

  Moreover, the acquisition part 131 acquires the information regarding the weather of the area where the user U1 is located based on the information regarding the point LC1 where the user U1 is acquired acquired from the terminal device 10. For example, the environment information of the user U1 may be estimated by the acquisition unit 131 based on the position information acquired from the terminal device 10 and the acquisition date (time), or an external information processing apparatus that provides weather information May be obtained from For example, the acquisition unit 131 may acquire information related to the weather in the area where the user U1 is located from an external information processing apparatus that provides weather information.

(Generator 132)
The generation unit 132 generates various information. For example, the generation unit 132 generates various models such as a prediction model. For example, the prediction unit 133 generates prediction models M1 to M3 stored in the prediction model information storage unit 124 (see FIG. 7).

  For example, the generation unit 132 generates a prediction model based on a disease that has developed in a predetermined user and the biological information and environmental information of the predetermined user at the time of the onset of the disease. For example, the generation unit 132 generates a plurality of prediction models corresponding to each of a plurality of diseases based on a plurality of diseases that have occurred in a predetermined user and biometric information and environmental information of the predetermined user at the time of the onset of each disease. Is generated.

  For example, the generation unit 132 generates a prediction model corresponding to a disease based on environment information and biological information when the disease has occurred in the user and user information of the user who has developed the disease. For example, the generation unit 132 may store the environment information when a disease develops in the user stored in the environment information storage unit 122 (see FIG. 5) or the disease stored in the biometric information storage unit 123 (see FIG. 6). A predictive model corresponding to the disease is generated based on the biometric information at the time of the onset and the user information of the user who developed the disease stored in the user information storage unit 121 (see FIG. 4). For example, the generation unit 132 may store the environment information when a disease develops in the user stored in the environment information storage unit 122 (see FIG. 5) or the disease stored in the biometric information storage unit 123 (see FIG. 6). Stored in the prediction model information storage unit 124 (see FIG. 7) based on the biometric information when the symptom develops and the user information of the user who developed the disease stored in the user information storage unit 121 (see FIG. 4). Prediction models M1 to M3 are generated.

  Note that when the providing apparatus 100 acquires the prediction models M1 to M3 and the like from an external information processing apparatus, the providing apparatus 100 does not have to include the generation unit 132.

(Prediction unit 133)
The prediction unit 133 predicts various information. For example, the prediction unit 133 predicts various information using the various models generated by the generation unit 132. For example, the prediction unit 133 uses the prediction models M1 to M3 stored in the prediction model information storage unit 124 to predict a disease that develops in the user.

  For example, the predicting unit 133 uses a prediction model generated based on a disease that has developed in a predetermined user and the biological information and environmental information of the predetermined user at the time of the onset of the disease to determine a disease that will develop in the user. Predict. For example, the predicting unit 133 generates a plurality of predictions generated for each of the plurality of diseases based on the plurality of diseases that have occurred in the predetermined user and the biological information and environment information of the predetermined user at the time of the onset of each disease. The model is used to predict the disease that will develop in the user.

  In the example of FIG. 1, the prediction unit 133 predicts the predicted disease of the user U1. In the example of FIG. 1, the prediction unit 133 identifies the chronic disease of the user U1. In the example of FIG. 1, the predicting unit 133 specifies the chronic disease of the user U1 based on the information stored in the user information storage unit 121.

  In the example of FIG. 1, the prediction unit 133 predicts a user's predicted illness using a prediction model and a threshold corresponding to each illness for the user's illness. For example, the prediction unit 133 inputs the user's environmental information and biological information to a prediction model corresponding to a certain chronic disease, and based on the prediction score output by the prediction model and the threshold of the chronic disease that the user has, Predict whether it will develop. Specifically, the prediction unit 133 inputs the user's environmental information and biological information to a prediction model corresponding to a certain chronic disease, and when the prediction score output by the prediction model is equal to or greater than the threshold value of the chronic disease, Predicts that chronic disease will develop in the user.

  In the example of FIG. 1, the prediction unit 133 predicts the predicted disease of the user using the prediction models M <b> 1 and M <b> 2 and threshold values corresponding to each chronic disease for headache and low back pain that are prevalent in the user U <b> 1. In the example of FIG. 1, the prediction unit 133 predicts whether a headache develops in the user U1 using a prediction model M1 corresponding to the headache and a threshold value “0.5” for a headache that is ill with the user U1. In the example of FIG. 1, the prediction unit 133 calculates the prediction score related to the headache of the user U1 by inputting the environment information and the biological information of the user U1 acquired by the acquisition unit 131 into the prediction model M1. In the example of FIG. 1, the prediction unit 133 predicts a headache as a predicted disease of the user U1 if the calculated prediction score regarding the headache is equal to or greater than the threshold “0.5”.

  Further, in the example of FIG. 1, the prediction unit 133 predicts whether back pain will develop in the user U1 using the prediction model M2 corresponding to the back pain and the threshold value “0.7” for the back pain that is afflicted with the user U1. To do. In the example of FIG. 1, the prediction unit 133 calculates the prediction score related to the back pain of the user U1 by inputting the environment information and the biological information of the user U1 acquired by the acquisition unit 131 into the prediction model M2. In the example of FIG. 1, the prediction unit 133 predicts low back pain as a predicted disease of the user U1 if the calculated prediction score related to low back pain is equal to or greater than the threshold “0.7”.

  In the example of FIG. 1, the prediction unit 133 predicts a headache as a predicted disease of the user U1. Further, in the example of FIG. 1, the prediction unit 133 predicts that the back pain is not a predicted disease of the user U1. Therefore, in the example of FIG. 1, the predicting unit 133 predicts a headache as a predicted disease of the user U1 among the diseases of the user U1.

  In the example of FIG. 1, the prediction unit 133 determines a service to be provided to the user U1 based on the predicted disease “headache”. In the example of FIG. 1, it is assumed that the prediction unit 133 provides the user U1 with an information providing service such as information on a pharmaceutical institution corresponding to the predictive disease “headache” and information on self-handling. In the example of FIG. 1, the prediction unit 133 determines information related to a pharmaceutical institution provided to the user U1. In the example of FIG. 1, the prediction unit 133 determines information on the pharmaceutical institution to be provided to the user U1 based on information on the pharmaceutical institution stored in the pharmaceutical institution information storage unit 125 (see FIG. 8).

  In the example of FIG. 1, since the user U1 is located in the area A and the predicted disease is a headache, the prediction unit 133 is a medicine that provides the user U1 with information on a pharmaceutical institution that is located in the area A and can handle headaches. Determined as information about the institution. In the example of FIG. 1, the prediction unit 133 determines information regarding the HA general hospital and the PB pharmacy as information regarding a pharmaceutical institution that is provided to the user U1.

  In the example of FIG. 1, the prediction unit 133 determines information regarding self-handling provided to the user U1. In the example of FIG. 1, the prediction unit 133 determines information about the handling information to be provided to the user U1 based on the information about the handling information stored in the handling information storage unit 126 (see FIG. 9). In the example of FIG. 1, the predicting unit 133 determines the content stored in association with the user's predicted disease in the handling information storage unit 126 illustrated in FIG. 9 as information regarding self-handling to be provided to the user U1. In the example of FIG. 1, the prediction unit 133 determines the content CT11 (see FIG. 9) stored in association with the disease “headache” identified by the disease ID “IL11” as information regarding self-handling provided to the user U1. To do. In the example of FIG. 1, the predicting unit 133 determines to provide the content CT11 including the content related to the headache prevention method to the user U1 who is likely to develop a headache.

(Providing unit 134)
The providing unit 134 provides various types of information to an external device such as the terminal device 10. For example, the providing unit 134 may provide various types of information generated by the generating unit 132 to an external device. Further, the providing unit 134 may provide various types of prediction information predicted by the prediction unit 133 to an external device.

  For example, the providing unit 134 provides a service related to a predictive disease that is a disease predicted to develop in the user based on the environmental information acquired by the acquiring unit 131 among the diseases associated with the user. For example, the providing unit 134 provides a service related to a predictive disease predicted to develop in the user based on the user's biological information. For example, the providing unit 134 provides a service related to a predictive disease predicted by the predicting unit 133 to occur in the user.

  For example, the providing unit 134 provides coping method information that is information relating to coping with a predictive disease. For example, the providing unit 134 provides information regarding a medical institution that is within a predetermined range from the user's position and that corresponds to the predicted disease, as the countermeasure information. For example, the providing unit 134 provides information regarding a coping method for a predictive disease acquired in a predetermined network as the coping method information. For example, the providing unit 134 provides a reservation service for a medical institution corresponding to a predictive disease.

  In the example of FIG. 1, the providing unit 134 provides information to the terminal device 10 used by the user U1. In the example of FIG. 1, the providing unit 134 is a terminal where the user U1 uses information related to the HA general hospital identified by the pharmaceutical institution ID “ES11” and the PB pharmacy identified by the pharmaceutical institution ID “ES22” and the content CT11. To the device 10.

[3. (Prediction model generation process flow)
Next, the procedure of the providing process by the providing system 1 according to the embodiment will be described with reference to FIG. FIG. 10 is a flowchart illustrating an example of generation of a prediction model according to the embodiment. Specifically, FIG. 10 is a flowchart illustrating an example of generation of a prediction model related to generation of a prediction model.

  As shown in FIG. 10, the providing apparatus 100 acquires environmental information and biological information when a disease develops in the user (step S101). For example, the providing apparatus 100 acquires, for each user who can acquire environmental information and biological information, environmental information and biological information when a disease develops in the user.

  The providing apparatus 100 acquires user information of a user who has developed a disease (step S102). For example, the providing apparatus 100 acquires user information of a user who has developed a disease from the user information storage unit 121 (see FIG. 4).

  After that, the providing apparatus 100 generates a prediction model corresponding to the disease based on the environment information and biological information when the disease develops in the user and the user information of the user who developed the disease (step S103). For example, the providing device 100 may provide the environment information when a disease develops in the user stored in the environment information storage unit 122 (see FIG. 5) or the disease stored in the biometric information storage unit 123 (see FIG. 6). A predictive model corresponding to the disease is generated based on the biometric information at the time of the onset and the user information of the user who developed the disease stored in the user information storage unit 121 (see FIG. 4).

[4. (Providing process flow)
Next, the procedure of the providing process by the providing system 1 according to the embodiment will be described with reference to FIG. FIG. 11 is a flowchart illustrating an example of the providing process according to the embodiment. Specifically, FIG. 11 is a flowchart illustrating an example of providing processing using a prediction model.

  As illustrated in FIG. 11, when the providing apparatus 100 acquires the user's environment information and biological information (step S201: Yes), the providing apparatus 100 determines whether there is a disease whose predicted score is equal to or greater than a threshold among the user's chronic diseases. (Step S202). In the example of FIG. 1, the providing device 100 acquires the environment information and biological information of the user U1 from the terminal device 10 used by the user U1. And the providing apparatus 100 is a prediction model corresponding to the chronic disease of the user U1 memorize | stored in the user information storage part 121 (refer FIG. 4) among the prediction models memorize | stored in the prediction model information storage part 124 (refer FIG. 7). Is used to calculate the predictive score of the disease of the user U1. Then, the providing apparatus 100 determines whether there is a disease whose predicted score is equal to or greater than a threshold among the chronic diseases of the user U1.

  Then, when there is a disease whose predicted score is equal to or greater than the threshold among the user's chronic diseases (step S202: Yes), the providing apparatus 100 provides a service related to a disease whose predicted score is equal to or greater than the threshold (step S203).

  The providing apparatus 100 does not acquire the user's environmental information and biometric information (step S201: No), or when there is no disease whose predicted score is greater than or equal to the threshold among the user's chronic diseases (step S202: No). It returns to step S201 and repeats a process.

[5. effect〕
As described above, the providing apparatus 100 according to the embodiment includes the acquisition unit 131 and the providing unit 134. The acquisition unit 131 acquires environment information regarding the user's environment. The providing unit 134 provides a service related to a predicted disease that is a disease predicted to develop in the user based on the environmental information acquired by the acquiring unit 131 among the diseases associated with the user.

  Accordingly, the providing apparatus 100 according to the embodiment provides a service related to a predictive disease that is a disease predicted to develop in the user based on the user's environmental information among the diseases associated with the user. It is possible to appropriately provide a service related to a disease according to a user.

  Moreover, in the providing apparatus 100 according to the embodiment, the acquisition unit 131 acquires environment information including weather information regarding the weather at the point where the user is located.

  Thereby, the providing apparatus 100 according to the embodiment provides a service related to a predicted disease, which is a disease predicted to develop in the user, based on environmental information including weather information related to the weather at a location where the user is located. It is possible to appropriately provide a service related to a disease according to a user.

  Moreover, in the providing apparatus 100 according to the embodiment, the acquisition unit 131 acquires the biological information of the user. The providing unit 134 provides a service related to a predictive disease predicted to develop in the user based on the user's biological information.

  Thereby, the provision apparatus 100 which concerns on embodiment provides the service regarding the disease appropriately according to a user by providing the service regarding the prediction disease estimated to develop to the said user based on a user's biometric information. be able to.

  In addition, the providing apparatus 100 according to the embodiment includes a prediction unit 133. The prediction unit 133 predicts a disease that develops in the user by using a prediction model that is generated based on a disease that has developed in the predetermined user and the biological information and environmental information of the predetermined user at the time of the onset of the disease. . The providing unit 134 provides a service related to a predictive disease predicted to develop in the user by the predicting unit 133.

  Thereby, the providing apparatus 100 according to the embodiment uses the prediction model generated based on the disease that has developed in the predetermined user and the biological information and environmental information of the predetermined user at the time of the onset of the disease. By providing a service related to a predicted disease predicted to develop to a user, a service related to a disease can be appropriately provided according to the user.

  Moreover, in the providing apparatus 100 according to the embodiment, the prediction unit 133 includes a plurality of diseases based on a plurality of diseases that have developed in a predetermined user and the biological information and environmental information of the predetermined user at the time of the onset of each disease. The disease which develops to a user is predicted using the some prediction model produced | generated about each of these.

  Thereby, the providing apparatus 100 according to the embodiment generates each of the plurality of diseases based on the plurality of diseases that have occurred in the predetermined user and the biological information and environmental information of the predetermined user at the time of the onset of each disease. By predicting a disease that develops in the user using a plurality of prediction models, a service related to the disease can be appropriately provided according to the user.

  In the providing apparatus 100 according to the embodiment, the providing unit 134 provides coping method information that is information regarding coping with a predictive disease.

  Thereby, the provision apparatus 100 which concerns on embodiment can provide the service regarding a disease appropriately according to a user by providing the countermeasure information which is the information regarding the countermeasure with respect to a prediction disease.

  Further, in the providing apparatus 100 according to the embodiment, the providing unit 134 provides information on a medical institution that is a medical institution within a predetermined range from the user's position and that corresponds to the predicted disease, as the countermeasure information.

  Thereby, the providing apparatus 100 according to the embodiment provides the information about the medical institution that is a medical institution within a predetermined range from the user's position as the countermeasure information and corresponds to the predictive disease, according to the user. Services related to diseases can be provided appropriately.

  Further, in the providing apparatus 100 according to the embodiment, the providing unit 134 provides information on a coping method for a predictive disease acquired in a predetermined network as the coping method information.

  Thereby, the providing apparatus 100 according to the embodiment appropriately provides a service related to a disease according to a user by providing information regarding a method for dealing with a predicted disease acquired in a predetermined network as the countermeasure information. be able to.

  Moreover, in the providing apparatus 100 according to the embodiment, the providing unit 134 provides a reservation service for a medical institution corresponding to a predictive disease.

  Thereby, the provision apparatus 100 which concerns on embodiment can provide the service regarding a disease appropriately according to a user by providing the reservation service of the medical institution corresponding to a prediction disease.

[6. Hardware configuration)
The providing apparatus 100 according to the above-described embodiment is realized by a computer 1000 configured as shown in FIG. 12, for example. FIG. 12 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the providing apparatus. The computer 1000 includes a CPU 1100, RAM 1200, ROM 1300, HDD (Hard Disk Drive) 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. The communication interface 1500 receives data from other devices via the network N and sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the network N.

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. In addition, the CPU 1100 outputs the generated data to the output device via the input / output interface 1600.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the providing apparatus 100 according to the embodiment, the CPU 1100 of the computer 1000 implements the function of the control unit 130 by executing a program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them. However, as another example, these programs may be acquired from other devices via the network N.

  As described above, some of the embodiments and modifications of the present application have been described in detail with reference to the drawings. It is possible to carry out the present invention in other forms that have been modified and improved.

[7. Others]
In addition, among the processes described in the above-described embodiments and modifications, all or a part of the processes described as being automatically performed can be manually performed, or are described as being performed manually. All or part of the processing can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily changed unless otherwise specified. For example, the various types of information illustrated in each drawing is not limited to the illustrated information.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  In addition, the above-described embodiments and modifications can be combined as appropriate within a range that does not contradict processing contents.

  In addition, the “section (module, unit)” described above can be read as “means” or “circuit”. For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

DESCRIPTION OF SYMBOLS 1 Provision system 100 Provision apparatus 121 User information storage part 122 Environmental information storage part 123 Biometric information storage part 124 Predictive model information storage part 125 Pharmaceutical organization information storage part 126 Coping information storage part 130 Control part 131 Acquisition part 132 Generation part 133 Prediction part 134 Providing unit 10 Terminal device N Network

Claims (13)

An acquisition unit for acquiring environmental information about the user's environment;
A providing unit that provides a service related to a predicted disease that is a disease predicted to develop in the user based on the environmental information acquired by the acquiring unit among the diseases associated with the user;
A providing device comprising: The acquisition unit
The providing apparatus according to claim 1, wherein the environment information including meteorological information on the weather at the point where the user is located is acquired. The acquisition unit
Obtaining biometric information of the user;
The providing unit includes:
The providing apparatus according to claim 1, wherein a service related to the predicted disease predicted to develop in the user is provided based on the biometric information of the user. A prediction unit that predicts a disease that develops in the user using a prediction model that is generated based on a disease that has developed in the predetermined user and the biological information and environmental information of the predetermined user at the time of the onset of the disease;
Further comprising
The providing unit includes:
The providing apparatus according to claim 3, wherein the prediction unit provides a service related to the predictive disease predicted to develop in the user. The prediction unit
Using a plurality of prediction models generated for each of the plurality of diseases based on a plurality of diseases that have developed in a predetermined user and the biological information and environmental information of the predetermined user at the time of the onset of each disease, The providing apparatus according to claim 4, wherein a disease occurring in the user is predicted. The providing unit includes:
The providing apparatus according to claim 1, wherein coping method information that is information on coping with the predictive disease is provided. The providing unit includes:
The providing apparatus according to claim 6, wherein the user is provided with information related to the predictive disease. The providing unit includes:
The providing apparatus according to claim 7, wherein coping method information that is information relating to coping with the predictive disease is provided. The providing unit includes:
The providing apparatus according to claim 8, wherein information relating to a method for dealing with the predictive disease acquired in a predetermined network is provided as the countermeasure information. The providing unit includes:
The providing apparatus according to claim 8, wherein the coping method information is information on a pharmaceutical institution within a predetermined range from the position of the user and corresponding to the predictive disease. The providing unit includes:
The provision apparatus according to any one of claims 1 to 10, wherein a reservation service of a medical institution corresponding to the predictive disease is provided. A providing method executed by a computer,
An acquisition process for acquiring environmental information about the user's environment;
A providing step of providing a service relating to a predicted disease that is a disease predicted to develop in the user based on the environmental information acquired by the acquiring step among the diseases associated with the user;
The providing method characterized by including. An acquisition procedure for acquiring environmental information about the user's environment;
A providing procedure for providing a service related to a predicted disease that is a disease predicted to develop in the user based on the environmental information acquired by the acquisition procedure among the diseases associated with the user;
A program for causing a computer to execute.

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