JP2021086530A - Cpap management system, program, and cpap management method - Google Patents

Abstract

To provide a CPAP management system and a program for supporting continuation of treatment using a CPAP device.SOLUTION: In a CPAP management system 1, a storage unit stores data indicating an answer to a questionnaire made to a past user and data related to a use aspect of a CPAP device by a past user in association with each other. A terminal 5 includes: an acquisition unit 52 for acquiring data showing an answer to a questionnaire made to a new user who is a user to start treatment using the CPAP device; and an extraction unit 53 for extracting data on the use aspect of the CPAP device for the new user based on data acquired by the acquisition unit 52 and data stored in the storage unit. A question includes at least one or more of an item for checking a tendency of sleep of the user, an item for checking user motivation for treatment using the CPAP device, and an item for checking a tendency of the user relative to a change in situations.SELECTED DRAWING: Figure 2

Description

The present invention relates to CPAP management systems, programs and CPAP management methods.

In recent years, due to the increase in QOL (Quality of Life), oxygen therapy and artificial respiration therapy at home have become widespread. In addition, respiratory therapy for patients with sleep apnea syndrome (SAS, Sleep Apnea Syndrome) has become widely known.

Here, for the treatment of sleep apnea syndrome, a CPAP (Continuous Positive Airway Pressure) device is used in which a mask is fixed to the face and air is forcibly sent into the respiratory tract with a blower. The CPAP device has a structure in which a main unit with a built-in blower, control unit, etc. is placed at a position away from the human body, and the main unit and the mask fixed to the face are connected by a hose, and air is sent through the hose. It has become.

For example, Patent Document 1 discloses a CPAP device that constantly maintains a therapeutic pressure at an optimum level with respect to a patient's airway resistance.

Japanese Unexamined Patent Publication No. 2008-264181

However, there is a tendency for nearly 30% of patients to drop out of treatment with a CPAP device in the initial stage (for example, about half a year) after starting treatment with a CPAP device. It is possible that the cause of the dropout is that, in addition to the feeling of resistance to sleeping with the equipment attached, the appropriate prescription and appropriate model selection (humidification function, temperature control function of the sending air, etc.) have not been performed. .. Therefore, it is important to follow up so that the patient can continue treatment with the CPAP device.

The present invention has been made in view of the above problems, and an object of the present invention is to provide a CPAP management system, a program, and a CPAP management method that support the continuation of treatment using a CPAP device.

In order to achieve the above object, the CPAP management system according to the present invention has a server having a storage unit for storing data about a past user who has used the CPAP device in the past, and a storage unit stored in the storage unit of the server. A CPAP management system including a terminal for extracting data on a usage mode of the CPAP device for a new user who is a user who newly starts treatment using the CPAP device using the data, and the storage unit is the above-mentioned storage unit. The data indicating the answers to the questionnaires conducted to the past users and the data on the usage mode of the CPAP device by the past users are stored in association with each other, and the terminal is a user who newly starts the treatment using the CPAP device. A CPAP device for the new user based on an acquisition unit that acquires data indicating the answers to the questionnaire conducted to a new user, data acquired by the acquisition unit, and data stored in the storage unit. The questionnaire is provided with an extraction unit for extracting data on usage patterns of the user, items for confirming the user's sleep tendency, items for confirming the user's motivation for treatment using the CPAP device, and the user's tendency for changes in the situation. Includes at least one of the items to be confirmed.

Thereby, based on the difference in the tendency of the new user and the past user to answer the questionnaire, the data regarding the usage mode of the CPAP device for the new user can be extracted from the usage mode of the CPAP device of the past user. It is considered that the usage mode of the CPAP device extracted in this way is more likely to be adapted to a new user who can find commonality with the past user in view of the tendency of the response of the questionnaire. Therefore, such a treatment mode makes it easier for a new user to continue the treatment using the CPAP device for a predetermined period or longer, and can further reduce the possibility of withdrawal from the treatment. Thus, according to the embodiment, it is possible to support the continuation of treatment using the CPAP device.

In addition, the extraction unit extracts data on the usage mode of the CPAP device by a past user who has continued the treatment using the CPAP device for a predetermined period or longer.

As a result, based on the difference in the tendency of the new user and the past user to answer the questionnaire, the usage mode of the CPAP device by the past user who has continued the treatment using the CPAP device for a predetermined period or longer is changed to the usage mode of the CPAP device for the new user. Can be extracted as data about. It is considered that the usage mode of the CPAP device is more likely to be adapted to a new user who can find commonality with the past user in view of the tendency of the response of the questionnaire. Therefore, depending on the treatment mode, it becomes easy for a new user to continue the treatment using the CPAP device for a predetermined period or longer. In addition, since it is possible to propose a usage mode of the CPAP device for a new user based on the usage mode of the CPAP device of a past user who has succeeded in continuing the treatment for a predetermined period or longer, the new user has anxiety about the use of the CPAP device. For anxiety, it is possible to support the elimination of anxiety of new users by using successful examples of past users. In this way, it is possible to support the introduction of a smoother CPAP device in the life of a new user with treatment.

Further, the acquisition unit further acquires the attribute information data of the new user, and the storage unit further acquires the attribute information data of the past user as data associated with data relating to the usage mode of the CPAP device by the past user. The attribute information data includes at least information indicating the height, weight, age, and gender of the user, and the extraction unit includes the attribute information of the past user that matches or is similar to the attribute information of the new user. Data about the usage mode of the CPAP device by the associated past user is extracted.

Thereby, based on the matching or similar tendency of the attribute information of the new user and the past user, it is possible to extract the data regarding the usage mode of the CPAP device for the new user from the usage mode of the CPAP device of the past user. It is considered that the usage mode of the CPAP device is more likely to be adapted to a new user who can find commonality with the past user in view of the tendency of the response of the questionnaire. Therefore, depending on the treatment mode, it becomes easy for a new user to continue the treatment using the CPAP device for a predetermined period or longer.

Further, the server is communicably connected to a plurality of CPAP devices, and the storage unit stores performance data indicating the treatment results of each of the plurality of past users transmitted from the plurality of CPAP devices, and the storage unit stores the results. The server or the terminal further includes a learning unit that refers to the actual data, learns about a past user who has continued treatment using a CPAP device for a predetermined period or more, and generates a neural network from the learning result, and further includes the extraction unit. Uses the neural network to extract data on the usage mode of the CPAP device by a past user who has continued the treatment using the CPAP device for a predetermined period or longer.

As a result, it is possible to extract data on the usage mode of the CPAP device 2 for a new user by using machine learning performed by a learning unit that behaves like AI (artificial intelligence) by a neural network. Therefore, by continuing the learning, the accuracy of the adaptation regarding the usage mode of the CPAP device for the extracted new user can be further improved.

In order to achieve the above object, the program according to the present invention includes a step of acquiring data indicating answers to a questionnaire conducted to a new user who is a user who newly starts treatment using a CPAP device, and the above-mentioned. Data showing the answers to the questionnaire conducted to the new user, data showing the answers to the questionnaire conducted to the past users who used the CPAP device in the past, and the usage mode of the CPAP device by the past users. The information processing apparatus is made to perform a step of extracting data relating to the usage mode of the CPAP apparatus for the new user based on the data associated with the data relating to the data.

Thereby, based on the difference in the tendency of the new user and the past user to answer the questionnaire, the data regarding the usage mode of the CPAP device for the new user can be extracted from the usage mode of the CPAP device of the past user. It is considered that the usage mode of the CPAP device extracted in this way is more likely to be adapted to a new user who can find commonality with the past user in view of the tendency of the response of the questionnaire. Therefore, such a treatment mode makes it easier for a new user to continue the treatment using the CPAP device for a predetermined period or longer, and can further reduce the possibility of withdrawal from the treatment. Thus, according to the embodiment, it is possible to support the continuation of treatment using the CPAP device. In addition, based on the bedroom temperature and humidity information acquisition function built into the CPAP device, the temperature and humidity data of the blast is recorded, and as data on the usage mode (for users who have continued treatment using the CPAP device for a predetermined period or longer). It may be associated with the answer of the questionnaire of the past user of the CPAP device that recorded the temperature / humidity data. That is, the temperature / humidity data may be included in the usage mode of the CPAP device of the past user.

In order to achieve the above object, the CPAP management method according to the present invention is data indicating the response of a questionnaire conducted by the information processing device to a new user who is a user who newly starts treatment using the CPAP device. Data indicating the response of the questionnaire conducted by the information processing device to the new user, and the response of the questionnaire conducted to the past user who has used the CPAP device in the past. The present invention includes a step of extracting data regarding the usage mode of the CPAP device for the new user based on the data in which the data indicating the above and the data relating to the usage mode of the CPAP device by the past user are associated with each other.

Thereby, based on the difference in the tendency of the new user and the past user to answer the questionnaire, the data regarding the usage mode of the CPAP device for the new user can be extracted from the usage mode of the CPAP device of the past user. It is considered that the usage mode of the CPAP device extracted in this way is more likely to be adapted to a new user who can find commonality with the past user in view of the tendency of the response of the questionnaire. Therefore, such a treatment mode makes it easier for a new user to continue the treatment using the CPAP device for a predetermined period or longer, and can further reduce the possibility of withdrawal from the treatment. Thus, according to the embodiment, it is possible to support the continuation of treatment using the CPAP device.

According to the present invention, it is possible to support the continuation of treatment using the CPAP device.

FIG. 1 is a diagram showing a configuration of a CPAP management system communicatively connected to a CPAP device. FIG. 2 is a diagram showing a configuration of a CPAP management system. FIG. 3 is a diagram showing an example of good treatment continuation data of past users stored in a cloud server. FIG. 4 is a diagram showing an example of data showing the initial dropout tendency of past users stored in the cloud server. FIG. 5 is a diagram showing an example of data showing a case in which treatment is discontinued due to transfer / cure / death of the person, etc., although the data is suspected of dropping out of treatment with the CPAP device. FIG. 6 is a diagram showing an example of data showing a case in which the treatment is temporarily stopped due to an overseas trip, a long-term business trip, or the like, although the data is suspected of being dropped from the treatment by the CPAP device. FIG. 7 is a diagram showing an example of usage information created for each past user by aggregating the data transmitted from each CPAP device. FIG. 8 is a diagram showing an example of the contents of the questionnaire. FIG. 9 is a diagram showing an example in which the tendency of the usage mode of each CPAP device of a plurality of past users is represented by a percentage (%). FIG. 10 is a diagram showing an example in which the tendency of the usage mode of each CPAP device of a plurality of past users is represented by a percentage (%). FIG. 11 is a flowchart showing an example of data flow regarding past users. FIG. 12 is a flowchart showing an example of a data flow related to support for a new user.

An embodiment (embodiment) for carrying out the present invention will be described in detail with reference to the drawings. The present invention is not limited to the contents described in the following embodiments. In addition, the components described below include those that can be easily assumed by those skilled in the art and those that are substantially the same. Furthermore, the components described below can be combined as appropriate.

CPAP (Continuous Positive Airway Pressure) is continuous positive airway pressure therapy. Specifically, in CPAP, air pressured by the CPAP device 2 is sent from the nose or the like to the airway to widen the airway and prevent apnea during sleep. The CPAP device 2 includes a hose that sends air at a preset pressure, a mask that touches the nose, a humidifier, a temperature / humidity measurement function, a GPS (Global Positioning System) function, a modem (MODEM) function, and the like. The humidifier also includes a configuration in which the humidifier is separate from the CPAP device 2. In FIG. 1, the GPS receiver 21 is illustrated as a configuration that bears the GPS function of the CPAP device 2.

Many studies have demonstrated the effect of CPAP on sleep apnea syndrome, including the fact that CPAP users lived longer when CPAP was performed than when CPAP was not performed. .. It is now widely used as a standard treatment for patients with sleep apnea syndrome (SAS, Sleep Apnea Syndrome).

FIG. 1 is a diagram showing a configuration of a CPAP management system 1 communicatively connected to a CPAP device 2. The CPAP management system 1 is configured by connecting a plurality of CPAP devices 2a, 2b, ..., A cloud server 3, a server 4, a terminal 5, and a mobile terminal 9 via a network N. There is. In the following, a plurality of CPAP devices 2a, 2b, ... Will be referred to as "CPAP device 2". The specific mode of the communication line constituting the network N may be wired, wireless, or a mixture thereof.

In FIG. 1 and FIG. 2 described later, the cloud server 3 and the server 4 are connected to each other so as to be able to communicate with each other via the network N (server system S). One device including the above may function as a server in the same manner as the server system S.

A user who uses the CPAP device 2 sends air from the CPAP device 2 at a pressure magnitude corresponding to a preset pressure selected in advance. Here, the setting pressure appropriate for each user tends to be different. If an inappropriate set pressure is selected for the user, the pressure of the air sent in will be too strong and it will be difficult to sleep, or conversely the pressure of the air will be too weak and the apnea will not be resolved sufficiently. Tends to occur in situations where Here, there is a situation in which it is preferable to estimate and select a set pressure that is considered appropriate for the user in advance. Further, a user who uses the CPAP device 2 receives CPAP by using one of a plurality of masks having different shapes. The plurality of users each have a differently shaped face. Therefore, the mask that fits each user's face better tends to be different for each user. If CPAP is performed using a mask that does not fit the user's face, air leaks from the gap between the face and the mask, the user feels stuffy, and the mask and hose are washed. There is a tendency that CPAP cannot be preferably carried out, such as feeling the same suffocation even when the mask fitting is changed due to maintenance such as. Here, there is a situation in which it is preferable to select a mask that fits the user better.

Further, the setting of the CPAP device 2 such as the magnitude of the pressure for sending air to the user who uses the CPAP device 2 and the time for sending air (sleep time of the user) is set by the doctor according to the medical condition of the user. In other words, the setting is not uniform and can be set for each user who uses the CPAP device 2. The time of sleep of the user and the pressure at which the air is blown to suit the user tend to differ from user to user. Here, there is a situation in which it is preferable to make a setting more suitable for the user.

In this way, preparing for the user to receive CPAP under better conditions, such as selecting a mask to fit and setting the CPAP device 2, is one of the factors for continuing CPAP. The CPAP management system 1 has a function of performing information processing regarding support for such preparations.

The CPAP device 2 transmits to the cloud server 3 at a predetermined timing via the network N including information that can grasp the usage state of the device such as information on the number of days of use of the device.

The cloud server 3 receives and stores the data transmitted from the CPAP device 2. That is, the cloud server 3 stores data of a plurality of users transmitted from the plurality of CPAP devices 2a, 2b, .... The user data is treated as data of a user (past user) who has used the CPAP device in the past.

The server 4 analyzes the information contained in the data transmitted from the CPAP device 2 stored in the cloud server 3. The server 4 analyzes data for each CPAP device 2a, 2b, .... Specifically, the server 4 individually analyzes the continuation status of the treatment using the CPAP device 2 by each user of the CPAP devices 2a, 2b, .... Hereinafter, the specific configuration and operation of the server 4 will be described.

FIG. 2 is a diagram showing the configuration of the CPAP management system 1. The server 4 includes a communication unit 40 and a learning unit 41.

The communication unit 40 communicates with the CPAP device 2, the cloud server 3, and the terminal 5. Specifically, the communication unit 40 has a communication circuit or the like that functions as a NIC (Network interface controller), and communicates with another information processing device or the like. The communication is performed, for example, via the network N, but may be directly connected to the cloud server 3 and the server 4 and the server 4 and the terminal 5 shown in FIGS. 1 and 2. The terminal 5 also includes a communication unit 51 having the same configuration as the communication unit 40. The communication unit 51 shown in FIG. 2 is configured to communicate with the server 4 by a direct connection between the server 4 and the terminal 5, but the communication unit 51 can communicate with a plurality of configurations including the server 4 via the network N. It may be provided.

The learning unit 41 processes the user data transmitted from the CPAP device 2 via the communication unit 40. Specifically, the learning unit 41 includes, for example, an arithmetic circuit such as a CPU (Central Processing Unit), a memory module that functions as a RAM (Random access memory), a software program and data (hereinafter, hereinafter,) that the arithmetic circuit reads and executes. The configuration includes a large-capacity storage device for storing programs, etc.). Examples of the large-capacity storage device include a hard disk drive, a solid state drive, a flash memory, and the like.

In the embodiment, the learning unit 41 is configured to read the data of the past user stored in the cloud server 3 via the communication unit 40 and process the read data of the past user, but the configuration is limited to this. Instead, the user data transmitted from the CPAP device 2 via the communication unit 40 may be directly received and processed.

For example, the learning unit 41 performs preprocessing on the data of the past user. The pre-processing refers to a process of extracting necessary data from the data of a past user, a process of processing data into a format suitable for processing by the learning unit 41, and the like.

Here, actual data showing the status of treatment by the CPAP device 2 will be described with reference to FIGS. 3 to 6. In the embodiment, it is assumed that the usage time of the CPAP device 2 is managed in chronological order for each user.

FIG. 3 shows an example of the data of the past user stored in the cloud server 3, and shows an example of the data of the past user who has continued the treatment by the CPAP device 2 for a predetermined period or more. A in FIG. 3A indicates a fixed time (4 hours). FIG. 3A shows an example of the daily usage time of the CPAP device 2 in one month. FIG. 3B shows a detailed example of the time zone in which the CPAP device 2 is used. The user data as shown in FIGS. 3 (a) and 3 (b) is treated as data of past users who have undergone treatment using the CPAP device 2 for a predetermined period or longer.

The predetermined period is, for example, one month, but is not limited to one month, and may be three months, six months, or the like, and can be arbitrarily set. However, during the predetermined period, the user (new user) who will newly start the treatment using the CPAP device 2 can already experience the treatment using the CPAP device 2 and can continue for the "predetermined period" or longer. It is desirable to set it in consideration of the extent to which it can be presented as a model for other users.

When the CPAP device 2 is used properly, it can be seen that it is used for a certain period of time or more in a relatively regular time zone. The fixed time is 4 hours in the example shown in FIGS. 3A and 3B, but is not limited to 4 hours.

FIG. 4 shows an example of past user data stored in the cloud server 3 and is an example of past user data in which the CPAP device 2 is not properly used. FIG. 4A shows an example of the daily usage time of the CPAP device 2 in one month. A in FIG. 4A indicates a fixed time (4 hours). FIG. 4B shows a detailed example of the time zone in which the CPAP device 2 is used.

It can be seen that when the CPAP device 2 is not used properly, irregular use is conspicuous and use for a certain period of time or longer is small. Such user data is not treated as data of past users who have undergone treatment using the CPAP device 2 for a predetermined period or longer.

FIG. 5 is a diagram showing an example of data that is suspected of dropping out of treatment with the CPAP device 2, but is not treated as data that is dropped out due to a justifiable reason. FIG. 5A shows an example of the daily usage time of the CPAP device 2 in one month. FIG. 5B shows a detailed example of the time zone in which the CPAP device 2 is used.

FIG. 5 shows when the user has stopped using the CPAP device 2, the CPAP device 2 has not been assigned, the user has been transferred, the user has healed, the user has died, or the device has been installed. Shows data such as when a failure occurs. These data are those in which the CPAP device 2 is no longer used for a valid reason, but are not treated as data of past users who have been treated with the CPAP device 2 for a predetermined period or longer.

FIG. 6 is a diagram showing an example of data suspected of dropping out of treatment with CPAP device 2. FIG. 6A shows an example of the daily usage time of the CPAP device 2 in one month. FIG. 6B shows a detailed example of the time zone in which the CPAP device 2 is used.

FIG. 6 is data when the CPAP device 2 is not used for a certain period of time or longer. In the example shown in FIG. 6, the fixed period is 14 consecutive days, but is not limited to 14 consecutive days.

If the CPAP device 2 cannot be used due to overseas travel or a failure of the CPAP device 2, but then the use of the CPAP device 2 is resumed, what is the withdrawal from the treatment by the CPAP device 2? Although it is not determined, it is not treated as data of past users who have been treated using the CPAP device 2 for a predetermined period or longer. Further, in the case of the initial use of the CPAP device 2, the data often has a pattern as shown in FIG. The initial use means, for example, use for less than half a year, but is not limited to less than half a year. For the determination of long-term overseas travel or business trip, the GPS device provided in the CPAP device 2 and the GPS information of the user's mobile terminal are managed by the server system S, and due to the positional relationship between the two, whether the trip is a dropout from treatment or not. It may be configured to determine whether or not it is not used due to a business trip. Further, the determination may be made based on the positional relationship between the GPS information of the mobile terminal and the home information in which the CPAP device 2 is installed.

When using home information, the above-mentioned patient attribute information D1 or device information D11 may include information indicating the address of the position (home) where the CPAP device 2 is installed. As a means for identifying the location (latitude and longitude) of the CPAP device 2 from the address, the "service for deriving the latitude and longitude from the address" provided by another information processing device connected via the network N is used. Alternatively, the server system S may have the same function as this service. Further, the calculation of the distance between the location (latitude and longitude) of the CPAP device 2 and the location (latitude and longitude) of the user's mobile terminal is also "different" provided by another information processing device connected via the network N. A service that calculates the distance between two points from the latitude and longitude of each of the two points may be used, or the server system S may have the same function as this service. Further, it is arbitrary how far the CPAP device 2 and the user's mobile terminal are to determine a long-term overseas trip or business trip, but a threshold value for making this determination is predetermined and stored in the storage unit. ing.

When making a determination using GPS information, the mobile terminal of the user who uses the CPAP device 2 and the CPAP device 2 are associated in advance. FIG. 1 illustrates a mobile terminal 9 provided with a GPS receiver 91 as a mobile terminal of the user. It is assumed that the user who uses the CPAP device 2 wears the mobile terminal 9 when moving. The cloud server 3 receives, in addition to the data transmitted from the CPAP device 2, data indicating the latitude and longitude output by the GPS receiver 91 provided in the user's mobile terminal 9, and stores the data in association with each other. Here, it is desirable that the timing at which data is periodically transmitted from the CPAP device 2 and the timing at which data indicating the latitude and longitude output by the GPS receiver 91 are transmitted are synchronized. Perfect synchronization is not an essential requirement, but it is desirable that the time difference in reception timing is within the time difference that is considered appropriate as the correspondence between the usage status of the CPAP device 2 and the location of the user. The data indicating the latitude and longitude output by the GPS receiver 21 of the CPAP device 2 may be sent to the cloud server 3 together with the data shown in FIGS. 3 to 6 each time, or the CPAP device 2 may be used. It may be sent to the cloud server 3 only at the time of startup. A configuration connected to the network N such as the CPAP device 2 and the mobile terminal 9 is connected to the server system S via the network N by a function realized by its own hardware and software (application, etc.) to perform data. Can be exchanged.

When the data as shown in FIG. 6 is obtained, the learning unit 41 is provided in the location (latitude and longitude) of the CPAP device 2 that sent the data and the mobile terminal of the user associated with the data. Based on the latitude and longitude output by the GPS receiver, the distance between the CPAP device 2 and the user (when the data was transmitted) is calculated. The learning unit 41 compares the calculated distance with the above-mentioned threshold value, and determines whether or not the data at that time corresponds to the pattern of "determined as a long-term overseas trip or a business trip" determined by the threshold value. The data for the period when it is determined that the user's mobile terminal is located far away from the CPAP device 2 so as to correspond to the "long-term overseas travel or business trip" pattern is not "dropout from treatment" but "travel or" It is judged that it is not used due to a business trip. Of the data, the portion determined to be "not used due to travel or business trip" is not used for determining whether or not the treatment with the CPAP device 2 is continued for a predetermined period or longer. That is, the portion of the data that is determined to be "not used due to travel or business trip" is treated as indicating "failure to continue treatment using the CPAP device 2 for a predetermined period or longer". Absent.

As described above, the learning unit 41 uses the latitude and longitude indicated by the GPS receiver 21 provided in the CPAP device 2 or the latitude and longitude corresponding to the location of the pre-registered CPAP device 2 and the CPAP device 2. Based on the distance between the latitude and longitude indicated by the GPS receiver 91 provided in the mobile terminal 9 of the user, it is determined whether or not the user is moving so that the CPAP device 2 cannot be used. The learning unit 41 ignores the past data of the period during which the user is moving in determining the duration of use of the CPAP device 2. This makes it possible to distinguish between "when there is a valid reason for suspending the use of the CPAP device 2 installed at the user's home" such as when traveling or on a business trip, and when the user stops using the CPAP device 2. become. Therefore, the accuracy of "determination of the continuation status of use of the CPAP device 2 by the user" performed based on the data transmitted from the CPAP device 2 can be further improved.

The learning unit 41 extracts the data of the past user who has continued the treatment by the CPAP device 2 for a predetermined period or more as shown in FIG. 3 from the data of the past user stored in the cloud server 3.

The data described with reference to FIGS. 3 to 6 is a part or all of the data obtained in the process of each user performing treatment using the CPAP device 2. For example, the data provided by a certain user may be a part of the data as shown in FIG. 3 and a part of the data as shown in FIG. Further, the data provided by a certain other user may be a part of the data as shown in FIG. 3 and a part of the data as shown in FIG. Here, if the data as shown in FIG. 3 is included, the data may be treated as "the treatment using the CPAP device 2 is continued for a predetermined period or more". This is because the data shown in FIG. 3 is "the treatment using the CPAP device 2 is continued for a predetermined period or longer". Moreover, the data shown in FIGS. 5 and 6 do not definitively indicate that it was difficult to get used to the treatment using the CPAP device 2.

In the same way, data including both the data shown in FIG. 4 and the data shown in FIG. 3 may be treated in the same manner. That is, if the data as shown in FIG. 3 is included in a part, the data may be treated as "the treatment using the CPAP device 2 is continued for a predetermined period or more". However, when the data shown in FIG. 4 is included, unlike the case where the data shown in FIGS. 5 and 6 is included, "it is difficult to continue the treatment using the CPAP device 2 for a predetermined period or longer. It may be the data of a user who has "have been". This is because the data shown in FIG. 4 shows the tendency of users who "irregular use is conspicuous and use for a certain period of time or more is small". The inclusion of even a part of such data may be accompanied by difficulty in getting used to the treatment using the CPAP device 2 as compared with the case where the data as shown in FIG. 3 is simply obtained. Therefore, the user's data including the data shown in FIG. 4 may be excluded from the data that "the treatment using the CPAP device 2 continues for a predetermined period or longer". What kind of data is handled and how it is handled can be arbitrarily set by, for example, including a parameter (reference data) indicating how to handle the data in a program or the like to be executed and processed by the learning unit 41.

Further, the learning unit 41 may have a function of aggregating the data of past users stored in the cloud server 3 and generating data including usage information for each past user.

FIG. 7 is a diagram showing an example of usage information D created for each past user by aggregating the data transmitted from each CPAP device 2. By browsing the usage information D or the printed matter of the usage information D, it becomes easier to grasp the usage status of the CPAP device 2 by the past user.

The usage information D is the patient attribute information D1 described later, the prescription information D2 which is the setting information of the CPAP device 2, the usage days information D3 which is the information about the number of days of use of the CPAP device 2, and the information about the usage time of the CPAP device 2. Time information D4, apnea and hypopnea information D5, pressure information used by CPAP device 2, working pressure information D6, information about leakage of CPAP device 2. Based on leak information D7, for example, graph D8 showing the daily usage time of the CPAP device 2 for one month, graph D9 showing the details of the time zone in which the CPAP device 2 is used, and apnea hypopnea information D5. Includes graph D10 showing temporal changes in OAI, CAI, HI, device information D11 and the like, which will be described later. The elements constituting the usage information D may be other than those described above.

Further, the usage days information D3, the usage time information D4, the apnea hypopnea information D5, the usage pressure information D6, and the leak information D7 are information for grasping the usage status of the CPAP device 2 of the past user. is there. Further, the item included in the apnea hypopnea information D5 shown in FIG. 7 is an example, and other items and ratios are added by improving the function of the CPAP device 2.

In particular, the patient attribute information D1 and the device information D11 will be described below.

The patient attribute information D1 includes information such as the user's name, height, weight, date of birth, age, and gender. In FIG. 7, the name is illustrated by "Chest Ichiro". In addition, the height is schematically shown in the column of "height: aaa.a". A numerical value of the height of the user is actually set in "aaa.a". In addition, the body weight is schematically shown in the column of "body weight: bb.b". A numerical value of the user's weight is actually set in "bb.b". In addition, the date of birth is schematically shown in the column of "yyyy / mm / dd". In "yyyy / mm / dd", the user's year of birth (for example, the Christian era), the date of birth, and the date of birth are actually set separated by a slash (/). In addition, the age is schematically shown in the column of "age: cc". In "cc", a numerical value of the user's age (for example, full age) is actually set. Gender is illustrated in the "Gender: Male" column. The "gender" dealt with in the embodiment is the gender of the living body, not the gender that the individual mentally recognizes.

The patient attribute information D1 further includes data showing the results of some or all of various tests related to the user's respiratory disease performed in connection with the use of the CPAP device 2, other medical test data, the user's medical history, and the user's. It may include drug history and other user information related to the use of the CPAP device 2. Further, the information may be information that is managed separately from the patient attribute information D1 and included in the usage information D.

The items included in the device information D11 shown in FIG. 7 are a serial number (S / N), a model number, and a model name. The S / N is a unique identification number (for example, a plurality of numbers or a combination of one or more alphabets and numbers) given to each of a plurality of CPAP devices 2a, 2b, ..., And is used. It is an identification number given to the CPAP device 2 used by the user corresponding to the information included in the information D. The model No. is an identification number (for example, a plurality of numbers or a combination of one or more alphabets and numbers) given to the model of the CPAP device 2 used by the user corresponding to the information included in the usage information D. The model name is a name given to the model of the CPAP device 2 used by the user corresponding to the information included in the usage information D.

The device information D11 may include information other than the model. For example, with respect to the mask provided for the use of the CPAP device 2, the device information D11 may include the same information as the S / N of the CPAP device 2, the model number, and a part or all of the model name. .. Further, the information about the mask may be information that is managed separately from the device information D11 and included in the usage information D.

Further, regarding the bedding (pillows, futons, beds, etc.) used by the user who uses the CPAP device 2, the bedding is specified as in the information about the model of the CPAP device 2 as shown in the device information D11 and the above-mentioned mask. As possible information, it may be included in the device information D11 or the usage information D as information separate from the device information D11.

The patient attribute information D1 and the device information D11 may be registered in the CPAP device 2 in advance before the CPAP device 2 is provided to the user, or may be manually input to the CPAP device 2 before and after the CPAP device 2 is provided to the user. May be done.

The patient attribute information D1 may be information included in the data acquired by the questionnaire described later together with the answers to the questionnaire. Further, the patient attribute information D1 manages the information obtained from each user of the CPAP device 2 by a dedicated test (acquisition of an answer to a question prepared in advance) conducted separately from the questionnaire described later. The information may be manually registered by a person.

Further, as data related to past users, data indicating answers to questionnaires is stored in the cloud server 3. The contents of the questionnaire and the choices of answers are set for the purpose of supporting the grasp of the tendency of the user. The user's tendency is related to the success or failure of the continuation of the treatment using the CPAP device 2. The data indicating the answers to the questionnaire and the data transmitted from the CPAP device 2 to the cloud server 3 via the network N are stored in association with each other for each user. It should be noted that the data indicating the response to the questionnaire may be associated with the data such as the usage information D described with reference to FIG. 7.

FIG. 8 is a diagram showing an example of the contents of the questionnaire. The questionnaires shown in FIG. 8 are "(1) sleep habits", "(2) sleep time", "(3) willingness to treat" and "(4) acceptance of changes", "(5) bedding". This is a questionnaire for asking users questions such as "(6) sleeping position" and obtaining answers to each of these questions.

Each question has multiple answer options. For example, "(1) Sleeping habits" includes "I often sleep by 10 pm", "I often sleep after midnight", and "I often sleep in the morning or during the day". Answer options such as "make a nap better" are set. For other questions, as shown in FIG. 8, a plurality of answer options are set.

The terminal 5 is provided so as to be able to acquire the selection result of the answer to the questionnaire as described with reference to FIG. Specifically, the terminal 5 includes an acquisition unit 52 and an extraction unit 53 in addition to the communication unit 51 described above. The acquisition unit 52 has, for example, a display unit 5M capable of displaying questionnaire questions and answer options as shown in FIG. 8, and an input unit capable of selecting and inputting question options. The display unit 5M is configured to include a display device such as a liquid crystal display, an OLED (Organic Light Emitting Diode) display, or a CRT (Cathode Ray Tube). The input unit is configured to include an input device such as a keyboard and a mouse. The acquisition unit 52 may have a configuration in which the display unit 5M and the input unit are integrally provided like a touch panel.

The data indicating the answers of the past users to the questionnaire is acquired by using a terminal such as the terminal 5 as the answers to the questionnaire presented before the start of the treatment using the CPAP device 2, for example, and the cloud server. It is stored in 3. At this time, the terminal may be provided so as to be able to communicate directly with the cloud server 3 via the network N, or may transmit data to the cloud server 3 via the server 4 and the network N. The timing of acquiring data indicating the responses of past users to the questionnaire is, for example, before the past user starts treatment using the CPAP device 2, but is not limited to this, and is not limited to this, and is after the start of treatment (during treatment or completion of treatment). Later) may be. Further, the method of obtaining the answer is not limited to the one using the terminal 5. For example, the administrator of data on past users who can access the cloud server 3 may manually input the answers to the questionnaires interviewed by the past users, or a dedicated questionnaire prepared separately from the terminal 5. An answer input device may be used.

The acquisition of the questionnaire response by the acquisition unit 52 is not limited to the user (past user) whose data has already been obtained as explained with reference to FIGS. 3 to 6, for example, and the treatment using the CPAP device 2 will be performed from now on. It is also done for the user who is going to start (new user).

The learning unit 41 communicates with the cloud server 3 via the communication unit 40. The learning unit 41 learns about the past user who has continued the treatment using the CPAP device 2 for a predetermined period or more based on the data of the past user stored in the cloud server 3, and uses the CPAP device 2 from the learning result. Generate a neural network NN regarding the tendency of past users to continue the treatment. That is, such learning is so-called machine learning.

Specifically, the learning unit 41 relates to the device information D11 of the past user who has continued the treatment using the CPAP device 2 for a predetermined period or more among the data of the plurality of users obtained from the plurality of CPAP devices 2, for example. Generate a neural network NN capable of performing a weighted evaluation of its relevance to other information about the user. The other information is data showing the answers to the questionnaire as explained with reference to FIG. 8, for example, and is data showing the answers to the questionnaires of the past users. Further, the other information may be a part or all of the patient attribute information D1 of the past user excluding the name. In particular, the height, weight, age, and gender of the past user can be used as other information.

The neural network NN intervenes between the input and the output and functions as a weighting layer for deriving the output from the input. The neural network NN enables weighting-based output based on the relationship between the input data and the data about past users as described with reference to FIGS. 3-7.

The data input to the neural network NN is, for example, data showing the answers to the questionnaire as described with reference to FIG. 8, and is data showing the answers to the questionnaires of new users. When such data is input, the neural network NN is a past user who has continued the treatment using the CPAP device 2 for a predetermined period or more for each of the various answers to the plurality of questions included in the questionnaire as shown in FIG. Among them, it plays a function of weighting to give a higher evaluation to past users who gave the same answer. Therefore, the learning unit 41 that generated the neural network NN has a larger number of questions that answered the same answer as the new user to the questionnaire among the past users who continued the treatment using the CPAP device 2 for a predetermined period or longer. Give users a higher rating. The output of the neural network NN is an output corresponding to a past user with a higher evaluation.

The learning unit 41 that generated the neural network NN performs weighting performed based on the input and outputs data regarding the usage mode of the CPAP device 2 for the new user. The data regarding the usage mode of the CPAP device 2 for the new user is, for example, data regarding the usage mode of the CPAP device 2 by a past user who has continued the treatment using the CPAP device 2 for a predetermined period or longer.

More specifically, for example, a part or all of the information included in the device information D11 of the user corresponding to the "past user who has continued the treatment using the CPAP device 2 for a predetermined period or more" described with reference to FIG. , Is output as data regarding the usage mode of the CPAP device 2 for the new user. That is, the model and model No. of the CPAP device 2 used by the past user, the setting of the CPAP device 2, the model and model No. of the mask used, and the like relate to the usage mode of the CPAP device 2 for the new user. It is output as data.

As a result, it is possible to obtain information for providing the new user with the same usage mode of the CPAP device 2 as the past user who has a stronger tendency to give the same answer to the questionnaire as the new user. It is considered that the course of treatment using the CPAP device 2 tends to be closer for new users and past users who have a stronger tendency to respond to the questionnaire. Therefore, by providing the same usage mode of the CPAP device 2 as the past user to the new user, it is possible to support the continuation of the treatment using the CPAP device 2 by the new user.

Further, the data input to the neural network NN may further include the attribute information of the new user. The attribute information of the new user includes at least information indicating the height, weight, age, and gender of the new user. The attribute information of the new user may further include additional information as described with reference to device information D11.

When data indicating the response of the new user's questionnaire and the attribute information of the new user is input, the neural network NN has a function of performing weighting based on the attribute information in addition to the weighting related to the above-mentioned questionnaire response. Play. The weighting based on the attribute information gives a higher evaluation to the past user who has the attribute information closer to the input new user's attribute information. Therefore, the learning unit 41 that generated the neural network NN gives a higher evaluation to the past users who are closer to the new user in terms of the answers to the questionnaire and the attribute information among the past users who have continued the treatment using the CPAP device 2 for a predetermined period or more. The output of the neural network NN is an output corresponding to a past user with a higher evaluation.

The degree of error range of the height difference, weight difference, and age difference between the new user and the past user is set in advance based on the tendency of the user's physique to be categorized in the treatment using the CPAP device 2. Is desirable.

When the answer to the questionnaire of the new user and the data indicating the attribute information of the new user are input, the acquisition unit 52 receives the answer of the questionnaire as described with reference to FIG. 8 as the information about the new user, and the new user. Acquire attribute information (height, weight, age, gender, etc.).

The output format of the neural network NN may indicate, for example, data regarding the usage mode of the CPAP device 2 of the past user who received the highest evaluation by weighting, or the usage mode of the CPAP device 2 of a plurality of past users. It may indicate data about. As data showing the usage mode of the CPAP device 2 of a plurality of past users, for example, when a plurality of past users whose evaluation is equal to or higher than a predetermined value are derived by weighting, the use of each CPAP device 2 of the plurality of past users is shown. The tendency of the aspect is shown as a percentage (%).

FIG. 10 is a diagram showing an example in which the tendency of the usage mode of each CPAP device 2 of a plurality of past users is represented by a percentage (%). In FIG. 9, masks MsD1 and MsD2, which are more frequently used among a plurality of past users whose evaluation is more than a predetermined value by weighting, are sequentially displayed on the display unit 5M as a goodness of fit as a percentage (%). In FIG. 10, the goodness of fit of each of the plurality of plans (plans 1 and 2) regarding the set pressure of the CPAP device 2 is displayed on the display unit 5M as a percentage (%).

The extraction unit 53 is based on the data acquired by the acquisition unit 52 and the learning based on the data stored in the cloud server 3 by the learning unit 41 of the server 4, and the data regarding the usage mode of the CPAP device 2 for a new user. Is extracted. Specifically, the extraction unit 53, like the learning unit 41, constitutes a large-capacity storage device that stores a calculation circuit such as a CPU, a memory module that functions as a RAM, a program that the calculation circuit reads and executes, and the like. Included in. When the acquisition unit 52 acquires data indicating the answer to the questionnaire as the information about the new user, the extraction unit 53 transmits the information about the new user to the server 4 via the acquisition unit 52. The learning unit 41 of the server 4 inputs the information about the new user to the neural network NN, and outputs the data regarding the usage mode of the CPAP device 2 for the new user as the output of the neural network NN. The server 4 returns data regarding the usage mode of the CPAP device 2 for the new user as a response to the information about the new user transmitted from the extraction unit 53. The extraction unit 53 uses the returned data regarding the usage mode of the CPAP device 2 for the new user as the extracted data, and outputs the data to the display unit 5M.

Hereinafter, the main data flow in the CPAP management system 1 will be described with reference to the flowcharts of FIGS. 11 and 12.

FIG. 11 is a flowchart showing an example of data flow regarding past users. First, the data of the user who uses the CPAP device 2 is accumulated (step S1). Specifically, for example, the data transmitted from each of the plurality of CPAP devices 2 connected to the cloud server 3 via the network N to the cloud server 3 is stored in the cloud server 3. The cloud server 3 functions as a storage unit for storing data related to past users who have used the CPAP device 2 in the past. The cloud server 3 substantially functions as a storage unit of the server 4.

The learning unit 41 extracts data of past users who have continued treatment using the CPAP device 2 for a predetermined period or longer (step S2). Specifically, the learning unit 41, for example, as described with reference to FIGS. 3 to 6 described above, partially or wholly as shown in FIG. 3, “treatment using the CPAP device 2 continues for a predetermined period or longer. "Is" data extracted. Here, as described above, the learning unit 41 may exclude the data "it was difficult to continue the treatment using the CPAP device 2 for a predetermined period or more" from the extraction target.

The learning unit 41 associates the data extracted in step S2 with the data indicating the response to the questionnaire for each user (step S3). The data indicating the response to the questionnaire is acquired in advance via the acquisition unit 52, for example, as described above. The user-specific association of the data indicating the response to the questionnaire may be performed before the execution of step S2 with respect to the data transmitted from the CPAP device 2 and stored in the cloud server 3, for example.

The learning unit 41 performs learning based on the relationship between the data of the past user who has continued the treatment using the CPAP device 2 for a predetermined period or more and the questionnaire response data of the past user (step S4). Specifically, the learning unit 41 generates the above-mentioned neural network NN.

FIG. 12 is a flowchart showing an example of a data flow related to support for a new user. First, the questionnaire response of the new user is acquired (step S11). Specifically, for example, as described above, data indicating an answer to a questionnaire of a new user is acquired via the acquisition unit 52.

Next, the output of the neural network NN with the data indicating the questionnaire response of the new user acquired in step S1 as the input is acquired (step S12). Specifically, for example, the extraction unit 53 transmits data including information about the new user to the server 4 via the acquisition unit 52, and a neural network NN input with the information about the new user is used for the new user. Data regarding the usage mode of the CPAP device 2 of the above is output. When the output is transmitted from the server 4 to the terminal 5, the extraction unit 53 acquires the output of the neural network NN.

The extraction unit 53 outputs the usage mode of the CPAP device 2 indicated by the output of the neural network NN acquired in step S12 as the usage mode of the CPAP device 2 for a new user (step S13). Specifically, for example, the display of the mask as described with reference to FIG. 9 and the display of the set pressure of the CPAP device 2 as described with reference to FIG. 10 are performed via the display unit 5M.

As described above, according to the embodiment, the CPAP management system 1, the server (for example, the server system S including the cloud server 3), and the terminal (for example, the terminal 5) are provided. The server has a storage unit that stores data about a past user who has used the CPAP device 2 in the past. The terminal extracts data on the usage mode of the CPAP device 2 for a new user who is a user who newly starts the treatment using the CPAP device 2 by using the data stored in the storage unit of the server. The storage unit stores the data indicating the answers to the questionnaires conducted to the past users in association with the data regarding the usage mode of the CPAP device 2 by the past users. The terminal includes an acquisition unit 52 and an extraction unit 53. The acquisition unit 52 acquires data indicating the answers to the questionnaires given to the new users who are the users who newly start the treatment using the CPAP device 2. The extraction unit 53 extracts data regarding the usage mode of the CPAP device 2 for a new user based on the data acquired by the acquisition unit 52 and the data stored in the storage unit. The question to the user by the questionnaire is at least one of the items for confirming the user's sleep tendency, the item for confirming the user's motivation for treatment using the CPAP device 2, and the item for confirming the user's tendency for changes in the situation. Including the above. Here, the items for confirming the sleep tendency of the user are, for example, "(1) sleep habit", "(2) sleep time", "(5) bedding", and "(6)" shown in FIG. "Sleeping position". The item for confirming the user's motivation for treatment using the CPAP device 2 is, for example, "(3) Treatment motivation" shown in FIG. Further, the item for confirming the user's tendency to change the situation is, for example, "(4) Acceptance to change" shown in FIG. The questions are not limited to these, and the specific content of the questions can be changed as appropriate without departing from the definition.

Thereby, based on the difference in the tendency of the new user and the past user to answer the questionnaire, the data regarding the usage mode of the CPAP device 2 for the new user can be extracted from the usage mode of the CPAP device 2 of the past user. It is considered that the usage mode of the CPAP device 2 extracted in this way is more likely to be adapted to a new user who can find commonality with the past user in view of the tendency of the response of the questionnaire. For example, for patients who are positive about treatment and have a high demand for change, take an aggressive approach such as setting a stronger pressure as shown in Plan 1 of Fig. 10, or conversely, patients who have resistance or anxiety to treatment. As shown in Plan 2 of FIG. 10, there is a tendency that measures such as weakening or setting the pressure to gradually increase are more compatible. Therefore, depending on the treatment mode, it becomes easier for a new user to continue the treatment using the CPAP device 2 for a predetermined period or longer, and the possibility of withdrawal from the treatment can be further reduced. Thus, according to the embodiment, it is possible to support the continuation of treatment using the CPAP device.

In addition, the extraction unit 53 extracts data on the usage mode of the CPAP device 2 by a past user who has continued the treatment using the CPAP device 2 for a predetermined period or longer.

As a result, based on the difference in the tendency of the new user and the past user to answer the questionnaire, the CPAP device 2 for the new user can be used by the past user who has continued the treatment using the CPAP device 2 for a predetermined period or longer. Can be extracted as data on the usage mode of. It is considered that the usage mode of the CPAP device 2 is more likely to be adapted to a new user who can find commonality with the past user in view of the tendency of answering the questionnaire. Therefore, depending on the treatment mode, it becomes easy for a new user to continue the treatment using the CPAP device 2 for a predetermined period or longer. Further, since it is possible to propose a usage mode of the CPAP device 2 for a new user based on a usage mode of the CPAP device 2 of a past user who has succeeded in continuing the treatment for a predetermined period or longer, the new user is referred to as using the CPAP device 2. For the anxiety about the unknown, it is possible to support the elimination of the anxiety of the new user by using the successful examples of the past users. In this way, it is possible to support the introduction of a smoother CPAP device 2 in the life of a new user with treatment.

In addition, the acquisition unit 52 further acquires the attribute information data of the new user. The storage unit further stores the attribute information data of the past user as data associated with the data regarding the usage mode of the CPAP device 2 by the past user. The attribute information data includes at least information indicating the height, weight, age, and gender of the user. The extraction unit 53 extracts data on the usage mode of the CPAP device 2 by the past user associated with the attribute information of the past user that matches or is similar to the attribute information of the new user.

Thereby, based on the matching or similar tendency of the attribute information of the new user and the past user, it is possible to extract the data regarding the usage mode of the CPAP device 2 for the new user from the usage mode of the CPAP device 2 of the past user. It is considered that the usage mode of the CPAP device 2 is more likely to be adapted to a new user who can find commonality with the past user in view of the tendency of answering the questionnaire. Therefore, depending on the treatment mode, it becomes easy for a new user to continue the treatment using the CPAP device 2 for a predetermined period or longer.

Further, the server (for example, the server system S including the cloud server 3) is communicably connected to a plurality of CPAP devices 2. The storage unit stores performance data indicating the treatment performance of each of the plurality of past users transmitted from the plurality of CPAP devices 2. The server or terminal (for example, server 4) further includes a learning unit 41. The learning unit 41 refers to the actual data, learns about a past user who has continued the treatment using the CPAP device 2 for a predetermined period or more, and generates a neural network NN from the learned result. The extraction unit 53 uses the neural network NN to extract data on the usage mode of the CPAP device 2 by a past user who has continued the treatment using the CPAP device 2 for a predetermined period or longer.

As a result, it is possible to extract data on the usage mode of the CPAP device 2 for a new user by using machine learning performed by the learning unit 41 that behaves like AI by the neural network NN. Therefore, by continuing the learning, the accuracy of the adaptation regarding the usage mode of the CPAP device 2 for the new user to be extracted can be further improved.

Further, not only the CPAP device 2 provided by a specific company, but also a multi-vendor who arbitrarily selects from the CPAP device 2 provided by a plurality of companies and provides the CPAP device 2 to a new user uses the CPAP management system 1. This gives new users more choices. Also, the information about the usage of the CPAP device 2 for new users extracted based on the more options provided in this way will contribute to the improvement of the CPAP device 2 in the future.

It should be noted that the terminals 5 used to obtain the answers to the questionnaire from each of the plurality of users do not have to be physically the same. For example, the questionnaire may be transmitted and the questionnaire response may be received via the transmission / reception of e-mail. In addition, the questionnaire may be sent and the answers to the questionnaire may be received by text chat, instant message, etc. using a social networking service (SNS), instant messenger, or other service via the network. .. In addition, by distributing an application software program for making a mobile terminal such as a smartphone function as a terminal 5 and having each user using the CPAP device 2 use it, the mobile terminal owned by each user can be used as the terminal 5. Can work. In this case, the application software program causes the information processing device to perform a step of acquiring data indicating the answers to the questionnaires given to the new users who are the users who newly start the treatment using the CPAP device 2. Functions as a software program.

Further, the application software program may further cause the mobile terminal to function as the learning unit 41 and the extraction unit 53. In this case, the mobile terminal communicates with the network N and can refer to the data of the cloud server 3. In this case, the application software program further includes data indicating the answers to the questionnaire conducted to the new user and the responses to the questionnaire conducted to the past users who have used the CPAP device 2 in the past. Based on the data in which the data indicating the above and the data related to the usage mode of the CPAP device 2 by the past user are associated with each other, the information processing device is subjected to the step of extracting the data related to the usage mode of the CPAP device 2 for the new user. Acts as a software program to let you.

The above-mentioned application software program may be stored in a server provided so as to be able to distribute the application software program, or may be stored in a storage medium such as a flash memory or a CD-ROM and provided.

Further, in the example shown in FIG. 2, the server 4 has the learning unit 41, but as described in the above-mentioned example of the application software program, the function of the learning unit 41 may be provided in the terminal 5. .. In this case, the server 4 can be omitted. That is, it is sufficient that the cloud server 3 and the terminal 5 are connected.

Further, the extraction of data regarding the usage mode of the CPAP device 2 for a new user based on the input of the data indicating the answer to the questionnaire is not limited to the input / output of the neural network NN. For example, referring to the data group in which the data of the plurality of past users transmitted from the plurality of CPAP devices 2 is associated with other data (questionnaire answers, attribute information, etc.) related to the plurality of past users. Pattern matching (matching or similarity determination) with data related to new users (questionnaire responses, attribute information, etc.) is performed, and among the data of past users that match or are similar to a predetermined degree or more, as shown in FIG. The data regarding the usage mode of the CPAP device 2 by the past user associated with the data that "the treatment using the above is continued for a predetermined period or more" may be extracted.

In addition, the attribute information of the user is not limited to the height etc. exemplified above, but also the test information such as brain waves obtained by the sleep apnea test (PSG: Polysomnography), the information on the medical record (interview), and the description contents of the screening test. It may be included.

Further, in the above-described embodiment, the learning unit 41 performs machine learning, but the CPAP management system 1 does not have to include a configuration in which machine learning is performed. The external information processing device may be configured so that the result of machine learning can be used by communicating with the CPAP management system 1. In this case, the CPAP management system 1 can choose whether to make the machine learning output available via communication. Further, a configuration that utilizes the result of machine learning that has already been performed, that is, a configuration that stores reference data indicating an input / output correspondence relationship based on machine learning in a storage unit may be adopted.

1 CPAP management system 2 CPAP device 3 Cloud server 4 Server 41 Learning unit 5 Terminal 52 Acquisition unit 53 Extraction unit 5M Display unit N Communication line NN Neural network S Server system

Claims (6)

A server having a storage unit that stores data about a past user who has used the CPAP device in the past, and a user who newly starts treatment using the CPAP device using the data stored in the storage unit of the server. A CPAP management system including a terminal for extracting data on the usage mode of the CPAP device for a new user.
The storage unit stores the data indicating the answers to the questionnaires conducted to the past users in association with the data regarding the usage mode of the CPAP device by the past users.
The terminal
An acquisition unit that acquires data indicating the answers to the questionnaire conducted to a new user who is a user who newly starts treatment using a CPAP device, and an acquisition unit.
It is provided with an extraction unit that extracts data regarding a usage mode of the CPAP device for the new user based on the data acquired by the acquisition unit and the data stored in the storage unit.
The questionnaire includes at least one of items for confirming the user's sleep tendency, items for confirming the user's motivation for treatment using a CPAP device, and items for confirming the user's tendency for changes in the situation. system. The CPAP management system according to claim 1, wherein the extraction unit extracts data on a usage mode of the CPAP device by a past user who has continued treatment using the CPAP device for a predetermined period or longer. The acquisition unit further acquires the attribute information data of the new user, and obtains the attribute information data.
The storage unit further stores the attribute information data of the past user as data associated with the data relating to the usage mode of the CPAP device by the past user.
The attribute information data includes at least information indicating the height, weight, age, and gender of the user.
The CPAP management according to claim 1 or 2, wherein the extraction unit extracts data on a usage mode of the CPAP device by a past user associated with the attribute information of the past user that matches or is similar to the attribute information of the new user. system. The server is communicably connected to a plurality of CPAP devices and is connected.
The storage unit stores performance data indicating the treatment performance of each of the plurality of past users transmitted from the plurality of CPAP devices.
The server or the terminal
Further provided with a learning unit that refers to the above-mentioned actual data, learns about a past user who has continued treatment using a CPAP device for a predetermined period or more, and generates a neural network from the learned result.
The extraction unit is described in any one of claims 1 to 3 for extracting data on a usage mode of the CPAP device by a past user who has continued treatment using the CPAP device for a predetermined period or longer by using the neural network. CPAP management system. A step to acquire data showing the answers to a questionnaire conducted to a new user who is a user who newly starts treatment using a CPAP device, and
Data showing the answers to the questionnaire conducted to the new user, data showing the answers to the questionnaire conducted to the past users who have used the CPAP device in the past, and the use of the CPAP device by the past users. A program that causes an information processing device to perform a step of extracting data related to a usage mode of the CPAP device for a new user based on the data associated with the data related to the mode. A step in which the information processing device acquires data indicating the answers to a questionnaire conducted to a new user who is a user who newly starts treatment using the CPAP device.
Data indicating the answers to the questionnaire conducted by the information processing device to the new user, data indicating the answers to the questionnaire conducted to the past users who have used the CPAP device in the past, and the past. A CPAP management method including a step of extracting data on a usage mode of a CPAP device for a new user based on data associated with data on a usage mode of the CPAP device by a user.

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