JP2022106068A - Depression symptom determination program - Google Patents

Abstract

To automatically and accurately determine depression symptoms of a subject.SOLUTION: A depression symptom determination program for determining depression symptom of a subject causes a computer to execute an information acquisition step of acquiring brain wave information regarding a brain wave of the subject, and a determination step of determining depression symptom of the subject based on the brain wave information acquired in the information acquisition step and with reference to three or more levels of relevance between reference brain wave information regarding a brain wave of the subject in the past and a determination type of depression symptom.SELECTED DRAWING: Figure 16

Description

The present invention relates to a depression sign discrimination program for discriminating signs of depression in a subject.

In recent years, the number of patients with depression has increased. When depression occurs, it may appear as a sign to the outside even if the person is not aware of it. If the signs of possible depression can be detected in advance, various prescriptions can be given at that stage to avoid the risk of full-scale depression. However, in the past, no system has been proposed for automatically and highly accurately discriminating such signs of depression.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is depression capable of automatically and highly accurately discriminating signs of depression in a subject. The purpose is to provide a symptom discrimination program.

In order to solve the above-mentioned problems, the depression sign discrimination program according to the present invention is information for acquiring brain wave information regarding the brain wave of the subject in the depression sign discrimination program for discriminating the signs of depression of the subject. Based on the brain wave information acquired by the above information acquisition step, refer to the three or more levels of association between the acquisition step, reference brain wave information regarding the brain waves of the subject in the past, and the discrimination type of signs of depression. It is characterized by having a computer perform a discrimination step for discriminating the signs of depression of the subject.

It is possible to automatically and accurately determine the signs of depression in a subject without any special skills or experience.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the depression sign discrimination program to which the present invention is applied will be described in detail with reference to the drawings.

The first embodiment FIG. 1 is a block diagram showing an overall configuration of a depression sign discrimination system 1 to which a depression sign discrimination program to which the present invention is applied is implemented. The depression sign determination system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, and a database 3 connected to the search device 2.

The information acquisition unit 9 is a device for a person who utilizes this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the search device 2 described later. The information acquisition unit 9 outputs the detected information to the search device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. The information acquisition unit 9 also includes a temperature sensor, a humidity sensor, a flow rate sensor, and other sensors capable of identifying substances and physical properties. The information acquisition unit 9 may be configured by means for automatically fetching character strings and data posted on a site on the Internet.

The database 3 stores various information necessary for determining the signs of depression. Information necessary for discriminating signs of depression includes reference synonym frequency information regarding the frequency of synonyms in text data, reference tone information regarding voice tones, facial expression image information that captures the subject's facial expression, and actions. Data set with signs of depression determined for reference intent information regarding intents, reference brain wave information regarding past subject's brain waves, reference attribute information regarding subject attributes, etc. Is remembered.

That is, the database 3 contains such reference pronoun frequency information, reference tone information, facial image information, reference intent information, reference brain wave information, reference attribute information, and signs of depression of the subject. And are associated with each other and remembered.

The search device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be the one to be converted. The user can obtain a search solution by the search device 2.

FIG. 2 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via an operation button, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 according to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is configured by a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program will be read and executed by the control unit 24.

The operation in the depression sign discrimination system 1 having the above-described configuration will be described.

In the depression sign discrimination system 1, it is premised that a combination of the reference pronoun frequency information and the reference tone information is formed, for example, as shown in FIG.

The reference pronoun frequency information is information indicating how much the pronoun is included in the text data when the voice spoken by the subject who determines the sign of depression is converted into text data. For example, the subject says, "I will go to Osaka with Fujimoto tomorrow by 13:00 on the Shinkansen," and "I will go to Osaka tomorrow. The meaning of the former is clearer, while the meaning of the latter is unclear. Depressed patients have a higher proportion of pronouns in their spoken textual data. By extracting this in units of text data, it becomes reference pronoun frequency information.

In actually quantifying the frequency of pronouns in text data, the volume of the entire text data should be detected by counting the number of phrases, words, case components, noun phrases, and characters in the text data. You may do it. Then, the volume of the pronouns included in the volume of the entire text data is similarly counted via the number of clauses, the number of words, the number of case components, the number of noun phrases, the number of characters, and the like. Then, the ratio of the volume of the pronoun to the volume of the entire text data is detected as the frequency described above. It is premised that the units (number of phrases, number of words, number of case components, number of noun phrases, number of characters, etc.) for measuring the entire text data and the volume of pronouns are shared with each other.

In the present invention, in detecting the reference pronoun frequency information, the input of the voice of the past subject is accepted. This input may be accepted via a microphone or the like. Then, the voice of the subject is converted into text data and morphologically analyzed to extract the pronoun included in the text data. For pronouns, words such as "that", "it", and "kore" are extracted by morphological analysis. Similarly, morphological analysis is used when extracting phrase structures (case components, noun phrases, etc.).

The reference tone information is information related to the tone of the voice extracted from the subject in the past. The tone of this voice refers to, for example, the pitch of the sound (the number of vibrations per second of the sound wave, that is, the frequency), the sound itself, or the strength of the sound. The tone of the voice may be detected and analyzed by detecting the height and the strength of the voice through a general voice detector.

The reference tone information may be linked with the text data in the reference pronoun frequency information. For example, in the phrase "I'm going to Osaka tomorrow, that, that, that," the voice tone is associated with each noun phrase (case component) such as "tomorrow" and "that". It may be used as reference tone information.

As the input data, such reference pronoun frequency information and reference tone information are arranged side by side. The intermediate node 61 shown in FIG. 3 is a combination of the reference pronoun frequency information and the reference tone information as such input data. Each intermediate node 61 is further connected to an output. In this output, the discriminant types A to E of the signs of depression are displayed. This discriminating type of sign of depression indicates all types of signs of depression, respectively. The types of discrimination of signs of depression are, for example, A is "no abnormality", B is "severe depression", and C is "a preliminary group that is not depressed but has signs of depression". The discrimination type of the sign of depression may also indicate the magnitude and degree of the symptom of depression.

Each combination (intermediate node) of the reference pronoun frequency information and the reference tone information is associated with each other through three or more levels of association with the discrimination type of the sign of depression as this output solution. .. The reference pronoun frequency information and the reference tone information are arranged on the left side through this degree of association, and the discrimination types A, B, C, D, E, ... Of the signs of depression are on the right side through this degree of association. Are arranged in. The degree of association indicates the degree to which the reference pronoun frequency information arranged on the left side and the reference tone information are highly related to the discrimination type of the sign of depression. In other words, this degree of association is an index showing what kind of depressive sign discrimination type the reference pronoun frequency information and the reference tone information are likely to be associated with, and each reference pronoun. From the frequency information and the reference tone information, it shows the accuracy in selecting the discrimination type of the sign of depression most likely to be applicable to the subject. In the example of FIG. 3, w13 to w22 are shown as the degree of association. These w13 to w22 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of association of each combination as an intermediate node with the discriminant type of the sign of depression as an output. On the contrary, the closer to one point, the lower the degree of association between each combination as an intermediate node with the discriminant type of the sign of depression as an output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference pronoun frequency information, the reference tone information, and the discrimination type of the sign of depression in that case was suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 3 is created.

In the process of accumulating these data, past subjects who actually suffered from depression, past subjects who did not suffer from depression, and even those who did not have depression, were placed in the preliminary group. Reference synonym frequency information and reference tone information are detected from the past subjects who have become. At the same time, the subject actually applied the result of discrimination of the signs of depression by a specialist or a doctor to a predetermined discrimination type, digitized this, and the above-mentioned reference synonym frequency. You may want to train a dataset of information and reference tone information.

In the process of constructing this learning data, it is not limited to the case of actually extracting data from the subject, but assuming a fictitious subject, a certain reference pronoun frequency information and a reference tone. If it is information, it may be determined what kind of depressive sign discrimination type is actually applied to it, and it may be converted into data and learned.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference pronoun frequency information P01 and the reference tone information P16, the discrimination type of the sign of depression is analyzed from the past data. If there are many cases of A, the degree of association that leads to A is set higher, and if there are many cases of B and there are few cases of A, the degree of association that leads to B is increased and the degree of association that leads to A. Is set low. For example, in the example of the intermediate node 61a, the output of the depression sign discrimination types A and B is linked, but from the previous case, the degree of association of w13 leading to the depression sign discrimination type A is set to 7 points. The degree of association of w14, which leads to the discrimination type B of the sign of depression, is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 3, the node 61b is a node in which the reference tone information P14 is combined with the reference pronoun frequency information P01, and the degree of association of the depression sign discrimination type C is w15. Discrimination of signs of depression The degree of association of type E is w16. The node 61c is a node in which the reference tone information P15 and P17 are combined with the reference pronoun frequency information P02, the degree of association of the depression sign discrimination type B is w17, and the depression sign discrimination type D. The degree of association is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used in the actual search for proposing the discrimination type of signs of depression. In such a case, tone information is acquired in addition to pronoun frequency information from a new subject who newly proposes a discrimination type of signs of depression. In addition to such pronoun frequency information, the method of acquiring tone information is the same as the above-mentioned reference pronoun frequency information and reference tone information.

Based on the pronoun frequency information and tone information newly acquired in this way, the optimum type of discrimination of signs of depression is searched for. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired pronoun frequency information is the same as or similar to P02 and the tone information is the same as or similar to P17, the node 61d is associated via the degree of association. The node 61d is associated with the depressive sign discrimination type C by w19 and the depression sign discrimination type D by the degree of association w20. In such a case, the discriminant type C of the sign of depression with the highest degree of association is selected as the optimal solution. However, it is not essential to select the one with the highest degree of association as the optimal solution, and the discriminant type D of the sign of depression in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. .. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, an example of the degree of association w1 to w12 extending from the input is shown in Table 2 below.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weights in the selection of the intermediate node 61 may all be the same.

FIG. 4 shows an example in which three or more levels of association between the above-mentioned reference pronoun frequency information, the reference facial expression image information, and the discrimination type of the sign of depression for the combination are set. There is.

The reference facial expression image information is information about the image of the facial expression of the subject who acquires the reference pronoun frequency information. The facial expression image information for reference may be extracted as a characteristic part for detecting the depression by analyzing the image data obtained by capturing the facial expression of the subject with a camera. .. Assuming that a depressed patient may show a peculiar smile in his facial expression, the presence or absence of the peculiar smile may be detected by analyzing a facial image. Further, the facial expression image information for reference may be composed of not only a still image but also a moving image. In the case of a moving image, it may be linked with the text data in the reference pronoun frequency information. For example, in the phrase "I will go to Osaka tomorrow, that, that,", the content of the video is in chronological order for each noun phrase (case component) such as "tomorrow" and "that". It may be associated with the target and used as reference facial expression image information. In addition to image analysis, the method of capturing such reference facial expression image information and facial expression image information may be, if necessary, using deep learning technology, automatically discriminating based on the feature amount of the analyzed image, and converting it into data. ..

In the example of FIG. 4, it is assumed that the input data is, for example, reference pronoun frequency information P01 to P03 and reference facial expression image information P18 to 21. The intermediate node shown in FIG. 5 is a combination of the reference pronoun frequency information and the reference facial expression image information as such input data. Each intermediate node is further linked to the output. In this output, the discriminant type of the sign of depression as an output solution is displayed.

Each combination (intermediate node) of the reference pronoun frequency information and the reference facial expression image information is associated with each other through three or more levels of association with the discrimination type of the sign of depression as this output solution. .. The reference pronoun frequency information and the reference facial expression image information are arranged on the left side through this degree of association, and the discrimination type of the sign of depression is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the discriminant type of the sign of depression with respect to the reference pronoun frequency information and the reference facial expression image information arranged on the left side. In other words, this degree of association is an index showing what kind of reference pronoun frequency information and reference facial image image information are likely to be associated with the discrimination type of signs of depression, and is a reference pronoun frequency information. It shows the accuracy in selecting the most probable sign of depression from the reference facial image information. In addition to the pronoun frequency information, the search solution changes depending on the state of the actual facial expression image. Therefore, by combining these reference pronoun frequency information and reference facial expression image information, it is possible to search for the optimum type of discrimination of signs of depression.

In the example of FIG. 4, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 steps, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, to 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 has past data on which of the reference pronoun frequency information, the reference facial expression image information, and the discrimination type of the sign of depression in that case is suitable for discriminating the actual search solution. By accumulating and analyzing and analyzing these, the degree of association shown in FIG. 4 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference pronoun frequency information P01 and the reference facial expression image information P20, the discrimination type of the sign of depression is analyzed from the past data. For example, in the example of the intermediate node 61a, the output of the depression sign discrimination types A and B is linked, but from the previous case, the degree of association of w13 leading to the depression sign discrimination type A is set to 7 points. The degree of association of w14, which leads to the discrimination type B of the sign of depression, is set to 2 points.

In the example of the degree of association shown in FIG. 4, the node 61b is a node in which the reference facial expression image information P18 is combined with the reference pronoun frequency information P01, and the degree of association of the depressive sign discrimination type C is w15. , The degree of association of the type E for discriminating the signs of depression is w16. The node 61c is a node in which the reference facial expression image information P19 and P21 are combined with the reference pronoun frequency information P02, the degree of association of the depression sign discrimination type B is w17, and the depression sign discrimination type. The degree of association of D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned pronoun frequency information, facial expression image information regarding the subject to be newly inspected for the discrimination type of the sign of depression is acquired. The facial expression image information corresponds to the reference facial expression image information.

The credit rating is searched based on the pronoun frequency information and facial expression image information newly acquired in this way. In such a case, the degree of association shown in FIG. 4 (Table 1) acquired in advance is referred to. For example, when the newly acquired pronoun frequency information is the same as or similar to P02 and the facial expression image information is P21, the node 61d is associated via the degree of association. The node 61d is associated with the depressive sign discrimination type C by w19 and the depression sign discrimination type D by the degree of association w20. In such a case, the discriminant type C of the sign of depression with the highest degree of association is selected as the optimal solution. However, it is not essential to select the one with the highest degree of association as the optimal solution, and the discriminant type D of the sign of depression in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. .. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

As an alternative to the reference pronoun frequency information, the reference intent information described below may be used. This reference intent information is information managed in each processing operation unit included in the text data, and defines an action name.

An intent usually defines an action that identifies a business process (processing operation). For example, all actions such as "throw in the trash", "eat rice", "watch TV", "go shopping", "ride the train", "listen to music" are defined as intents.

Morphological analysis is performed on the text data, and intents are assigned to each of them. This intent allocation refers to the intent table created and saved in advance.

In the intent table, which intent contains the wording obtained by morphological analysis is defined. For example, in the case of the intent "go shopping", the morphologically analyzed wording included in this is "go shopping", "go shopping", "go shopping", and "procure" in addition to "go shopping". Various things such as "come" are included. Similarly, in the case of the intent "get on the train", there are various morphologically analyzed words included in this, such as "go on the Yamanote line", "get on the Chuo line", "use the train", and "use the train". Things are included.

In the intent table, various words that have been morphologically analyzed are associated and recorded for each such intent, and by reading this, it is possible to assign intents to each of the words that have been morphologically analyzed. Will be.

As shown in FIG. 5, the combination of the reference intent information consisting of such intents and the reference facial expression image information, and the discrimination type of the sign of depression as the output data are mutually intermediate nodes 61. Let them learn by being associated with each other through the degree of association.

Then, intent information and facial expression image information are newly extracted from the subject, and the signs of depression as a search solution are analyzed via the corresponding reference intent information.

It should be noted that the combination of the reference intent information and the reference tone information and the discrimination type of the sign of depression as the output data are related to each other through the degree of association of the intermediate node 61 and learned. , When the intent information and the tone information are newly input from the subject, it is possible to search the search solution in the same manner.

In FIG. 6, in addition to the above-mentioned reference pronoun frequency information and reference tone information, there are three or more levels of association between the combination of the reference electroencephalogram information and the discrimination type of the sign of depression for the combination. The example that is set is shown.

The reference EEG information is information about the EEG of the subject. The electroencephalogram of the subject can be measured from a commercially available electroencephalograph. Since it may be possible to grasp the signs of depression by making a judgment by combining such reference EEG information, this is added as an explanatory variable. This reference electroencephalogram information may be composed of information that captures changes over time. In such a case, it may be linked with the text data in the reference pronoun frequency information. For example, in the phrase "I will go to Osaka tomorrow, that, that,", the changes in brain waves over time for each noun phrase (case component) such as "tomorrow" and "that". May be associated with and used as reference pronoun frequency information.

In such a case, as shown in FIG. 6, the degree of association is such that the set of combinations of the reference pronoun frequency information, the reference tone information, and the reference electroencephalogram information is the node 61a to 61e of the intermediate node as described above. It will be expressed.

For example, in FIG. 6, in FIG. 6, the reference pronoun frequency information P02 is associated with the association degree w3, the reference tone information P15 is associated with the association degree w7, and the reference electroencephalogram information W is associated with the association degree w11. Similarly, in the node 61e, the reference pronoun frequency information P03 is associated with the association degree w5, the reference tone information P15 is associated with the association degree w8, and the reference brain wave information V is associated with the association degree w10.

Similarly, when such a degree of association is set, the sign of depression is discriminated based on the pronoun frequency information newly acquired from the subject to be inspected, the tone information, and the electroencephalogram information.

In discriminating the discrimination type of the sign of depression, the degree of association shown in FIG. 6 acquired in advance is referred to. For example, it can be specified that the synonymous frequency information is the same as or similar to the reference synonym frequency information P02, the acquired tone information is the same as or similar to the reference tone information P15, and the acquired brain wave information is the reference brain wave information. If it can be identified as W, the combination is associated with node 61c, which has a degree of association w17 for the depressive sign discriminating type B and a depressive sign discriminating type D for the node 61c. It is associated with the degree of association w18. As a result of such a degree of association, based on w17 and w18, the pronoun frequency information, the tone information, and the electroencephalogram information are used to actually obtain the discrimination type of the sign of depression.

When the reference intent information is used as an alternative to the reference pronoun frequency information, the buy search can be performed in the same manner.

In FIG. 7, in addition to the above-mentioned reference pronoun frequency information and reference tone information, there are three or more levels of association between the combination of the reference attribute information and the discrimination type of the sign of depression for the combination. The example that is set is shown.

The reference attribute information is information indicating the attribute of the subject. The attributes of the subject are the subject's age, gender, occupation, current social activities, information on behaviors and behaviors related to depression from the past to the present, various diseases other than depression, etc. Information about is also included.

In such a case, as shown in FIG. 7, the degree of association is such that the set of combinations of the reference pronoun frequency information, the reference tone information, and the reference attribute information is set as the nodes 61a to 61e of the intermediate node as described above. It will be expressed.

For example, in FIG. 7, in the node 61c, the reference pronoun frequency information P02 is associated with the association degree w3, the reference tone information P15 is associated with the association degree w7, and the reference attribute information W is associated with the association degree w11. Similarly, in the node 61e, the reference pronoun frequency information P03 is associated with the association degree w5, the reference tone information P15 is associated with the association degree w8, and the reference attribute information V is associated with the association degree w10.

Similarly, when such a degree of association is set, discrimination is made based on the pronoun frequency information newly acquired from the subject, the tone information, and the attribute information.

In discriminating the discrimination type of depression, the degree of association shown in FIG. 7 acquired in advance is referred to. For example, it can be specified that the pronoun frequency information is the same as or similar to the reference pronoun frequency information P02, the acquired tone information is the same as or similar to the reference tone information P15, and the acquired attribute information is the reference attribute information. When it can be identified that it is W, the combination is associated with the node 61c, and in this node 61c, the discrimination type B is associated with the association degree w17, and the discrimination type D is associated with the association degree w18. As a result of such a degree of association, based on w17 and w18, a new discrimination type of the subject is actually obtained based on the pronoun frequency information, the tone information, and the attribute information. ..

Further, as shown in FIG. 8, the reference information U is composed of reference synonym frequency information, reference tone information, facial image information, reference intent information, reference brain wave information, and reference attribute information, and is referred to. As the reference information V, reference synonym frequency information, reference tone information, facial image information, reference intent information, reference brain wave information, and reference attribute information that form the degree of association in combination with the reference information U are used. The determination may be made based on the degree of association of the combinations of. The output solution corresponds to the above-mentioned discrimination type of each sign of depression.

At this time, the output obtained for the reference information U may be used as the input data as it is, and may be associated with the output via the intermediate node 61 in combination with the reference information V. For example, for the reference information U, after the output solution is output, this may be used as an input as it is, and the output may be searched by using the degree of association with other reference information V.

The present invention is not limited to the case of searching for a discrimination type as a search solution, and as shown in FIG. 9, the prescription can be used as a search solution by learning the prescription according to the discrimination type in advance. Can be output. After verifying in advance what prescription is effective for which discriminant type, an effective prescription is associated with each discriminant type. Then, the discriminant type may be searched in the same manner as described above, and the prescription associated with the searched discriminant type may be output together with the discriminant type or as a substitute for the discriminant type. Further, as an alternative to the discrimination type, the prescription itself may be trained as a data set with the above-mentioned reference information. As a result, when the input data acquired from the subject is input, a more effective prescription will be output straight.

FIG. 9 shows an example in which the degree of association is formed by combining the reference synonym frequency information and the reference tone information, but the present invention is not limited to this, and all the above-mentioned reference information can be used. As for the degree of association as input data, it is of course possible to train the prescription itself as a data set with the above-mentioned reference information.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for a depressive discrimination type without any special skill or experience. Further, according to the present invention, it is possible to make a judgment of this search solution with higher accuracy than that made by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since there are many cases where the above-mentioned input data and output data are not completely the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A dataset may be created between the data and the output data and trained.

Further, according to the present invention, there is a feature that the optimum solution search is performed through the degree of association set to three or more stages. The degree of association can be described by a numerical value from 0 to 100%, for example, in addition to the above-mentioned 10 levels, but is not limited to this, and any stage can be described as long as it can be described by a numerical value of 3 or more levels. It may be configured.

In a situation where there are multiple possible candidates for a search solution by determining the creditworthiness with higher creditworthiness and lower misunderstanding based on the degree of association expressed by such numerical values of three or more levels. It is also possible to search and display in descending order of the degree of association.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even if the judgment result has a very low degree of association, it is connected as a slight sign, and it may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable to appear once in tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one should be emphasized based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided, for example, via a public communication network such as the Internet. Further, when knowledge, information, and data regarding synonymous frequency information, tone information, facial expression image information, intent information, brain wave information, and attribute information are acquired, the degree of association is increased or decreased according to these.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is done by the system side or the user side based on the contents of research data, treatises, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from the public communication network. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating a trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and learned, and the degree of association related to the output data is self-formed from there. You may let it.

2nd Embodiment In the 2nd embodiment, the discrimination type of the sign of depression is discriminated only from the reference pronoun frequency information. For example, as shown in FIG. 10, the degree of association between the reference pronoun frequency information acquired in the past and the discrimination type of the sign of depression actually discriminated in the past is used.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new sign of depression from now on. In such a case, the pronoun frequency information is newly acquired. The acquisition method is the same as that of the first embodiment.

Based on the pronoun frequency information newly acquired in this way, the discrimination type of the sign of depression is discriminated. In such a case, the degree of association shown in FIG. 10 acquired in advance is referred to. Since the specific method for estimating the type of discrimination of signs of depression is the same as that of the first embodiment, the description below will be omitted.

Further, in the second embodiment, the discrimination type of the sign of depression may be discriminated only from the reference intent information. In such a case, for example, as shown in FIG. 11, the degree of association between the reference intent information acquired in the past and the discrimination type of the sign of depression actually discriminated in the past is used.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new sign of depression from now on. In such a case, intent information is newly acquired. The acquisition method is the same as that of the first embodiment.

Based on the newly acquired intent information in this way, the discrimination type of the sign of depression is discriminated. In such a case, the degree of association shown in FIG. 11 acquired in advance is referred to. Since the specific method for estimating the type of discrimination of signs of depression is the same as that of the first embodiment, the description below will be omitted.

Further, in the second embodiment, the discrimination type of the sign of depression may be discriminated only from the reference tone information. In such a case, for example, as shown in FIG. 12, the degree of association between the reference tone information acquired in the past and the discrimination type of the sign of depression actually discriminated in the past is used.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new sign of depression from now on. In such a case, tone information is newly acquired. The acquisition method is the same as that of the first embodiment.

Based on the tone information newly acquired in this way, the discrimination type of the sign of depression is discriminated. In such a case, the degree of association shown in FIG. 12 acquired in advance is referred to. Since the specific method for estimating the type of discrimination of signs of depression is the same as that of the first embodiment, the description below will be omitted.

Similarly, in the second embodiment, the search solution is not limited to the case of searching for the discrimination type, and the prescription is output as the search solution by learning the prescription according to the discrimination type in advance. Of course, you may do so.

Further, both the first embodiment and the second embodiment are not limited to the above-described embodiments, and as shown in FIG. 13, for example, the reference information as the keynote and the discrimination type of the sign of depression. You may use the degree of association of 3 or more levels. In such a case, the solution search will be performed based on the degree of association with the discrimination type of the sign of depression according to the newly acquired information in three or more stages. The underlying reference information is, for example, reference synonym frequency information, etc., but is not limited thereto, and any reference information (reference synonym frequency information, reference) in the first embodiment and the second embodiment. Tone information for reference, facial image information, intent information for reference, brain wave information for reference, attribute information for reference, etc.) can also be applied.

Similarly, in these cases, when the information corresponding to the reference information used as the learning data is input, the solution search is performed based on the above-mentioned method.

The search solution obtained through the degree of association may be further modified based on other reference information, or the weighting may be changed.

The other reference information referred to here corresponds to any reference information other than the reference information which is the keynote when any of the above-mentioned reference information is used as the keynote reference information.

For example, as one of the other reference information, it is assumed that B is previously discriminated as a discriminant type of a sign of depression in a certain reference tone information P14. When the tone information corresponding to the reference tone information P14 is newly acquired, the search solution B as the discrimination type of the sign of depression is processed to increase the weight, in other words, the sign of depression. It is set in advance to perform a process that leads to the search solution B as the discrimination type of.

For example, the other reference information G is an analysis result suggesting a search solution C as a discriminant type of a sign of more depression, and the reference information F is a search as a discriminant type of a sign of more depression. It is assumed that the analysis result suggests the solution D. When the actually acquired information is the same as or similar to the reference information G after the setting with the reference information in this way, the weighting of the search solution C as a discrimination type of the sign of depression is increased. Perform processing. On the other hand, when the actually acquired information is the same as or similar to the reference information F, the process of increasing the weighting of the search solution D as the discrimination type of the sign of depression is performed. That is, the degree of association itself leading to the discrimination type of the sign of depression may be controlled based on the reference information F to H. Alternatively, after determining the discrimination type of the sign of depression only by the above-mentioned degree of association, the obtained search solution may be modified based on the reference information F to H. In the latter case, how to modify the discrimination type of the sign of depression as a search solution based on the reference information F to H should reflect the one designed on the system side each time. Become.

Further, the reference information is not limited to the case where it is composed of any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such a case, the more the case leads to the discrimination type with the sign of depression suggested by the reference information, the higher the modification of the discrimination type as the search solution obtained through the degree of association may be made. ..

Similarly, as shown in FIG. 14, it also becomes a keynote when forming a degree of association with a discriminant type of a sign of depression for a combination having a keynote reference information and another reference information. The reference information is any reference information (reference synonym frequency information, reference tone information, facial expression image information, reference intent information, reference brain wave information, reference attribute information) in the first embodiment and the second embodiment. Etc.) are also applicable. Other reference information includes any reference information in the first and second embodiments other than the underlying reference information.

At this time, if the reference information that is the keynote is the reference pronoun frequency information, the other reference information includes any reference information in the other one embodiment and the second embodiment.

In such a case as well, by searching for a solution in the same manner, it is possible to estimate the discrimination type of the sign of depression. At this time, as shown in FIG. 13 described above, the signs of depression are discriminated from the search solution obtained through the degree of association through further reference information (reference information F, G, H, etc.). You may try to modify the type.

Also in the second embodiment, the degree of association may be learned by combining not only one but also two or more other reference information.

As the above-mentioned search solution, the prescription for depression may be searched as a search solution as an alternative to the discriminant type of the sign of depression. In such a case, the search solution can be similarly obtained by preparing data associated with the prescription of depression for the combination of the above-mentioned basic reference information and other reference information through three or more levels of association. Can be searched.

Third Embodiment As shown in FIGS. 15 and 16, for reference when using three or more levels of association between the reference information as the keynote and the discrimination type of the sign of depression. An example of various variations of the above will be explained.

As an example of the reference information that serves as the keynote, there is reference magnetoencephalogram information obtained by performing a magnetoencephalogram examination on a past subject. This reference magnetoencephalogram information may be composed of image data obtained by MEG (magnetoencephalography examination), or may be composed of MRI data scanned for the brain. The reference magnetoencephalogram information may be further composed of data obtained by extracting characteristic portions by using a deep learning technique known for these image data. Since the degree of depression and these magnetoencephalographic information are considered to be correlated, the above-mentioned degree of association is constructed in advance by learning the data sets in which they are associated with each other.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new sign of depression from now on. In such a case, the magnetoencephalogram information is newly acquired.

The newly acquired magnetoencephalogram information is acquired from a subject who newly determines a sign of depression, and the type and acquisition method of the information are the same as the above-mentioned reference magnetoencephalogram information. Since the specific solution search method is the same as that of the first and second embodiments, the description below will be omitted.

As an example of the reference information as the keynote, there is reference meal content information regarding the meal contents of the subject in the past. This reference meal content information is the content of the meal actually ingested by the subject in the past (meal, etc.), the amount of the meal, the time of the meal, the time interval for taking three meals, and the number of nutrients and calories ingested. This reference meal content may be obtained by dividing it into periods such as one day, one week, and one month.

Since the degree of depression and the information on these dietary contents are considered to be correlated, the above-mentioned degree of association is constructed in advance by learning the data sets in which they are associated with each other.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new sign of depression from now on. In such a case, new meal content information is acquired.

The newly acquired meal content information is acquired from a subject who newly determines a sign of depression, and the type and acquisition method of the information are the same as those for reference meal content information described above. Since the specific solution search method is the same as that of the first and second embodiments, the description below will be omitted.

An example of the underlying reference information is reference sleep information regarding the sleep state of the subject in the past. This reference sleep information is all information about sleep actually performed by the subject in the past, for example, sleep time, sleep depth, number of times of waking up during sleep and time zone, body movement during sleep. Contains all the data about. Since patients with depression have data such as sleep division and abundant awakenings, the above-mentioned degree of association is constructed in advance by learning a data set in which these are associated with each other.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new sign of depression from now on. In such a case, sleep information is newly acquired.

The newly acquired sleep information is acquired from a subject who newly determines a sign of depression, and the type and acquisition method of the information are the same as the above-mentioned reference meal content information. Since the specific solution search method is the same as that of the first and second embodiments, the description below will be omitted.

As an example of the reference information as the keynote, there is reference exercise information regarding the exercise state of the subject in the past. This reference exercise information is all information related to the exercise actually performed by the subject in the past, and is composed of, for example, the number of steps per unit time, the time of going out, the number of times of going out, the distance walked, the distance traveled, and the like. May be good. Further, the reference exercise information may be composed of information obtained by measuring the swing of the arm, the movement of the foot, the walking speed, etc. through the acceleration sensor actually attached to the subject. Further, the reference exercise information may indicate the heart rate detected through a heart rate monitor worn on the body, and may indicate the actual activity range via the position information detected through GPS or the like.

Since patients with depression are considered to correlate with the amount of exercise, the above-mentioned degree of association is pre-constructed by training the datasets in which they are associated with each other.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new sign of depression from now on. In such a case, exercise information is newly acquired.

The newly acquired exercise information is acquired from a subject who newly determines a sign of depression, and the type and acquisition method of the information are the same as those for reference exercise information described above. Since the specific solution search method is the same as that of the first and second embodiments, the description below will be omitted.

As an example of the reference information as the keynote, there is reference life time information regarding the life time zone of the subject in the past. This reference life time information is any information about the life time actually performed by the subject in the past, and for example, the time zone of the toilet may be acquired through the electricity usage time of the toilet. The time actually awake and the sleep time may be detected through the time when the electricity in the room is used. Further, it may be possible to detect which room and how many hours have been stayed through the door open / close sensor. The electricity usage time may be detected via an electricity usage meter set in a house, or may be detected via an electricity meter installed in a so-called smart house or the like.

Since patients with depression are considered to correlate with life time, the above-mentioned degree of association is pre-constructed by training the datasets in which they are associated with each other.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new sign of depression from now on. In such a case, new life time information is acquired.

The newly acquired life time information is acquired from a subject who newly determines a sign of depression, and the type and acquisition method of the information are the same as those for reference life time information described above. Since the specific solution search method is the same as that of the first and second embodiments, the description below will be omitted.

As an example of the reference information as the keynote, there is reference operation information regarding various operations in the house of the subject in the past. This reference operation information includes electrical appliances (TV, air conditioner, PC, video, vacuum cleaner, refrigerator, washing machine), lighting switches, bath gas switches, and system kitchens that past subjects actually have in their homes. Anything that any resident of a house could operate, such as the operation buttons and levers of the house, the operation of windows that open and close with a push button, is targeted. These reference operation information may be detected through IoT sensors installed on push buttons such as electric appliances, lighting switches in homes, gas switches in baths, operation buttons and levers in system kitchens, and the like. It may be detected via an electric meter installed in a so-called smart house or the like.

Since it is considered that patients with depression have a correlation with the frequency of pressing mistakes, pressing time, pressing strength, etc. when operating buttons, etc., by learning the data set in which these are related to each other, the above-mentioned Build the degree of association in advance.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new sign of depression from now on. In such a case, the operation information is newly acquired.

The newly acquired operation information is acquired from a subject who newly determines a sign of depression, and the type and acquisition method of the information are the same as those for reference operation information described above. Since the specific solution search method is the same as that of the first and second embodiments, the description below will be omitted.

As an example of the reference information as the keynote, there is reference line-of-sight image information regarding the movement of the line-of-sight of the subject in the past. This reference line-of-sight image information is an image of the line-of-sight of the subject in the past for the question. In performing the image analysis of the line of sight, a deep learning technique may be used to automatically determine the image based on the feature amount of the analyzed image and convert it into data. Further, an infrared camera may be used to detect the movement of the subject's line of sight at a high level.

Questions to be asked include, for example, displaying figures of various shapes at the same time, counting how many of the same things exist, analyzing the line of sight when moving the mark displayed on the screen, or It may be a problem that tests spatial cognitive ability, judgment, etc., such as a problem that causes a so-called mistaken search.

Depressed patients are thought to have characteristic gaze movements for such problems, so by training the datasets in which they are associated with each other, the above-mentioned degree of association is pre-constructed. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new sign of depression from now on. In such a case, the line-of-sight image information is newly acquired.

The newly acquired operation information is an image of the movement of the line of sight with respect to the problem given to the subject who newly determines the sign of depression, and the type and acquisition method of the information are the above-mentioned reference operations. Similar to information. Since the specific solution search method is the same as that of the first and second embodiments, the description below will be omitted.

As an example of the reference information as the keynote, there is reference conversation amount information regarding the conversation amount per unit time. The unit time may be any time unit such as 1 minute, 10 minutes, 60 minutes, 3 hours, and one day. The conversation volume may be simply composed of the time during which the voice is emitted, that is, the time during which the voice exceeding a predetermined volume is detected divided by the unit time. In addition, the amount of conversation is the amount of each part of speech (verb, adjective, object, pronoun, adverb, etc.) in the text data when the subject's voice is converted into text data. It may be configured with. Further, as for the conversation amount, the volume of the entire text data may be detected by converting the voice into text data and counting the number of phrases, the number of words, the number of case components, the number of noun phrases, and the number of characters. Then, the amount of the volume of the entire text data per unit time may be used as the conversation amount. It is premised that the units (number of phrases, number of words, number of case components, number of noun phrases, number of characters, etc.) for measuring the entire text data and the volume of pronouns are shared with each other.

Since the degree of symptoms of a depressed patient may be related to such conversational volume, the above-mentioned degree of association is pre-constructed by learning a data set in which they are associated with each other.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new sign of depression from now on. In such a case, the conversation amount information is newly acquired.

For the newly acquired conversation amount information, the voice of the subject who newly determines the sign of depression is converted into text data, and the conversation amount per unit time is obtained in the same manner as described above. Since the specific solution search method is the same as that of the first and second embodiments, the description below will be omitted.

Further, both the first embodiment to the third embodiment are not limited to the above-described embodiments, and as shown in FIG. 13, for example, reference information as a keynote and a type for discriminating signs of depression. You may use the degree of association of 3 or more levels. In such a case, the solution search will be performed based on the degree of association with the discrimination type of the sign of depression according to the newly acquired information in three or more stages. The underlying reference information is, for example, reference synonym frequency information and the like, but is not limited thereto, and any reference information (reference) in the first embodiment, the second embodiment, and the third embodiment. Synonymous frequency information, reference tone information, facial image information, reference intent information, reference brain wave information, reference attribute information, reference brain magnetic diagram information, reference meal content information, reference sleep information, reference driving information , Reference operation information, reference line-of-sight image information, reference conversation volume information, reference life time information, etc.) are also applicable.

Similarly, in these cases, when the information corresponding to the reference information used as the learning data is input, the solution search is performed based on the above-mentioned method.

The search solution obtained through the degree of association may be further modified based on other reference information, or the weighting may be changed.

The other reference information referred to here corresponds to any reference information other than the reference information which is the keynote when any of the above-mentioned reference information is used as the keynote reference information.

For example, as one of the other reference information, it is assumed that B has been previously discriminated as a discriminant type of a sign of depression in a certain reference life time information P14. When the tone information corresponding to the reference life time information P14 is newly acquired, the search solution B as the discrimination type of the sign of depression is processed to increase the weight, in other words, the depression. It is set in advance to perform a process that leads to the search solution B as a symptom discrimination type.

For example, the other reference information G is an analysis result suggesting a search solution C as a discriminant type of a sign of more depression, and the reference information F is a search as a discriminant type of a sign of more depression. It is assumed that the analysis result suggests the solution D. When the actually acquired information is the same as or similar to the reference information G after the setting with the reference information in this way, the weighting of the search solution C as a discrimination type of the sign of depression is increased. Perform processing. On the other hand, when the actually acquired information is the same as or similar to the reference information F, the process of increasing the weighting of the search solution D as the discrimination type of the sign of depression is performed. That is, the degree of association itself leading to the discrimination type of the sign of depression may be controlled based on the reference information F to H. Alternatively, after determining the discrimination type of the sign of depression only by the above-mentioned degree of association, the obtained search solution may be modified based on the reference information F to H. In the latter case, how to modify the discrimination type of the sign of depression as a search solution based on the reference information F to H should reflect the one designed on the system side each time. Become.

Further, the reference information is not limited to the case where it is composed of any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such a case, the more the case leads to the discrimination type with the sign of depression suggested by the reference information, the higher the modification of the discrimination type as the search solution obtained through the degree of association may be made. ..

Similarly, as shown in FIG. 14, it also becomes a keynote when forming a degree of association with a discriminant type of a sign of depression for a combination having a keynote reference information and another reference information. The reference information is any reference information (reference synonym frequency information, reference tone information, facial expression image information, reference intent information, reference brain wave information, reference attribute information) in the first to third embodiments. , Reference encephalogram information, reference meal content information, reference sleep information, reference driving information, reference operation information, reference line-of-sight image information, reference conversation volume information, reference life time information, etc.) are also applicable. Is. Other reference information includes any reference information in the first and second embodiments other than the underlying reference information.

At this time, if the reference information as the keynote is the reference magnetoencephalogram information, the other reference information includes any reference information in the other 1st to 3rd embodiments.

In such a case as well, by searching for a solution in the same manner, it is possible to estimate the discrimination type of the sign of depression. At this time, as shown in FIG. 13 described above, the signs of depression are discriminated from the search solution obtained through the degree of association through further reference information (reference information F, G, H, etc.). You may try to modify the type.

Also in the third embodiment, the degree of association may be learned by combining not only one but also two or more other reference information.

As the above-mentioned search solution, the prescription for depression may be searched as a search solution as an alternative to the discriminant type of the sign of depression. In such a case, the search solution can be similarly obtained by preparing data associated with the prescription of depression for the combination of the above-mentioned basic reference information and other reference information through three or more levels of association. Can be searched.

1 Depression sign discrimination system 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 Node

Claims (11)

In a depression symptom determination program that determines the signs of depression in a subject
Information acquisition step to acquire brain wave information about the subject's brain wave,
Based on the brain wave information acquired by the above information acquisition step, the subject is referred to the degree of association between the reference brain wave information regarding the brain waves of the past subject and the discrimination type of the sign of depression at three or more stages. A depression sign discrimination program characterized by having a computer perform a discriminant step to discriminate signs of depression. In a depression symptom determination program that determines the signs of depression in a subject
The information acquisition step to acquire the magnetoencephalogram information of the subject who performed the magnetoencephalography examination,
Refer to the three or more levels of association between the reference magnetoencephalogram information obtained by performing a magnetoencephalogram examination on past subjects and the discrimination type of signs of depression, and use the magnetoencephalogram examination acquired by the above information acquisition step. Based on the above, a depression sign determination program characterized by causing a computer to perform a determination step for determining a sign of depression in a subject. In a depression symptom determination program that determines the signs of depression in a subject
Information acquisition step to acquire meal content information about the subject's meal content,
Based on the dietary content information acquired by the above information acquisition step, the above-mentioned A depression symptom determination program characterized by having a computer perform a determination step to determine a subject's signs of depression. In a depression symptom determination program that determines the signs of depression in a subject
Information acquisition step to acquire sleep information about the subject's sleep state,
Based on the sleep information acquired by the information acquisition step, the subject is examined by referring to the degree of association between the reference sleep information regarding the sleep state of the subject in the past and the discrimination type of the sign of depression at three or more levels. A depression sign discrimination program characterized by having a computer perform a discrimination step for discriminating a person's signs of depression. In a depression symptom determination program that determines the signs of depression in a subject
Information acquisition step to acquire exercise information about the subject's exercise state,
Based on the exercise information acquired by the information acquisition step, the subject is examined by referring to the degree of association between the reference exercise information regarding the exercise state of the subject in the past and the discrimination type of the sign of depression at three or more stages. A depression sign discrimination program characterized by having a computer perform a discrimination step for discriminating a person's signs of depression. In a depression symptom determination program that determines the signs of depression in a subject
The information acquisition step to acquire the living time information about the living time zone of the subject, and
Based on the life time information acquired by the above information acquisition step, refer to the three or more levels of association between the reference life time information regarding the past life time zone of the subject and the type of discrimination of signs of depression. A depression sign discrimination program characterized by having a computer perform a discrimination step for discriminating the signs of depression of the subject. In a depression symptom determination program that determines the signs of depression in a subject
An information acquisition step to acquire operation information related to various operations in the house by the subject, and
Based on the operation information acquired by the above information acquisition step, refer to the three or more levels of association between the reference operation information related to various operations in the subject's house in the past and the type of discrimination of signs of depression. A depression sign discrimination program characterized by having a computer perform a discrimination step for discriminating the signs of depression of the subject. In a depression symptom determination program that determines the signs of depression in a subject
The information acquisition step to acquire the line-of-sight image information that captures the line-of-sight of the subject for the question that was asked,
The line-of-sight acquired by the above information acquisition step by referring to the degree of association between the reference line-of-sight image information obtained by capturing the line-of-sight of the subject in the past for the question and the discrimination type of the sign of depression in three or more stages. A depression sign discrimination program characterized by causing a computer to perform a discrimination step for discriminating a sign of depression of a subject based on image information. In a depression symptom determination program that determines the signs of depression in a subject
A reception step that accepts the voice input of the subject,
The information acquisition step of converting the voice received by the above reception step into text data and acquiring the conversation amount information obtained by calculating the conversation amount per unit time, and the information acquisition step.
Based on the conversation volume information acquired by the information acquisition step, the test is performed by referring to the degree of association between the reference conversation volume information regarding the conversation volume per unit time and the discrimination type of the sign of depression at three or more levels. A depression sign discrimination program characterized by having a computer perform a discrimination step for discriminating a person's signs of depression. In a depression symptom determination program that determines the signs of depression in a subject
An information acquisition step for acquiring life time information regarding the subject's life time zone and operation information regarding various operations in the house by the subject, and
3 A combination of reference life time information regarding the past life time zone of the subject, reference operation information regarding various operations in the past subject's house, and a discrimination type of signs of depression. It is characterized by having a computer execute a discrimination step for discriminating the sign of depression of the subject based on the life time information and the operation information acquired by the information acquisition step with reference to the degree of association of the stage or higher. Depression sign discrimination program. The depression sign discrimination program according to any one of claims 1 to 9, wherein in the discrimination step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used. ..

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