JP2022106064A - Dementia symptom determination program - Google Patents

Abstract

To automatically and accurately determine dementia symptoms of a subject.SOLUTION: A dementia symptom determination program includes; a receiving step of receiving input of voice of a subject; an extraction step of converting the voice received in the receiving step into text data and subjecting the same to morphemic analysis, thereby extracting pronouns contained in the text data; information acquisition step of acquiring pronoun frequency information on a frequency of the pronouns in the text data extracted in the extraction step and facial expression image information capturing a facial expression of the subject; and a determination step of determining dementia symptoms of the subject based on the pronoun frequency information and the facial expression image information acquired in the information acquisition step, and with reference to three levels or more of relevance between a combination including reference pronoun frequency information on the frequency of the pronouns in the text data and reference facial expression image information capturing a past facial expression of the subject, and a determination type of dementia symptom.SELECTED DRAWING: Figure 4

Description

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

With the progress of the aging society in recent years, the number of patients with dementia is increasing. When you fall into dementia, it may appear as a sign to the outside even if you are not aware of it. If the signs of possible dementia can be detected in advance, various prescriptions can be given at that stage to avoid the risk of developing full-scale dementia. However, in the past, no system has been proposed for automatically and highly accurately discriminating such signs of dementia.

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

In order to solve the above-mentioned problems, the dementia sign discrimination program according to the present invention is a dementia sign discrimination program for discriminating the signs of dementia of the subject, and is a reception step that accepts the input of the voice of the subject. , The extraction step of extracting the synonyms contained in the text data by converting the voice received by the reception step into text data and performing morphological analysis of the data, and the extracted synonyms extracted by the extraction step. Refer to the information acquisition step for acquiring the synonymous frequency information regarding the frequency in the above text data, the reference synonym frequency information regarding the frequency of the synonyms in the text data, and the degree of association of three or more stages between the discrimination type of the sign of dementia. Then, based on the synonymous frequency information acquired by the information acquisition step, the computer is made to execute the discrimination step of discriminating the sign of dementia of the subject.

It is possible to automatically and accurately determine the signs of dementia in a subject without any special skill 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.

Hereinafter, the dementia 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 dementia sign discrimination system 1 to which a dementia sign discrimination program to which the present invention is applied is implemented. The dementia sign discrimination 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.

Database 3 stores various information necessary for discriminating signs of dementia. Information necessary for discriminating signs of dementia 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 dementia 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 synonym frequency information, reference tone information, facial image information, reference intent information, reference brain wave information, reference attribute information, and signs of dementia 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 dementia sign discrimination system 1 having the above-described configuration will be described.

In the dementia 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 dementia 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. Patients with dementia 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 dementia are displayed. This discriminant type of dementia sign indicates all types of dementia sign, respectively. The types of discrimination of signs of dementia are, for example, A is "no abnormality", B is "severe dementia", and C is "a preliminary group that is not dementia but has signs of dementia". The discrimination type of the sign of dementia may also indicate the magnitude and degree of the symptom of dementia.

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 dementia as this output solution. .. Reference pronoun frequency information and reference tone information are arranged on the left side through this degree of association, and discriminant types A, B, C, D, E, ... Of signs of dementia 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 dementia. In other words, this degree of association is an index showing what kind of dementia 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 dementia that is 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 dementia 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 dementia 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 dementia 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 dementia, past subjects who did not have dementia, and even those who did not have dementia, but were 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 dementia 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 synonym frequency information and a reference tone. If it is information, it may be determined what kind of dementia 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 dementia 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 dementia sign discrimination types A and B is linked, but from the previous case, the degree of association of w13 leading to the dementia 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 dementia, 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 dementia sign discrimination type C is w15. Discrimination of signs of dementia 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 dementia sign discrimination type B is w17, and the dementia 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 learned data, the above-mentioned learned data will be used in the actual search for proposing the discrimination type of the sign of dementia. 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 dementia. 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 discriminant type of dementia sign 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. In this node 61d, the discriminant type C of the sign of dementia is associated with w19, and the discriminant type D of the sign of dementia is associated with the degree of association w20. In such a case, the discriminant type C of the sign of dementia with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the discriminant type D of the sign of dementia 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 the combination of the above-mentioned reference pronoun frequency information and the reference facial expression image information and the discrimination type of the sign of dementia for the combination are set to three or more levels of association. There is.

The reference facial expression image information is information related to the facial expression image of the subject for which the reference pronoun frequency information is acquired. The facial expression image information for reference may be extracted as a characteristic part for detecting the dementia by analyzing the image data obtained by capturing the facial expression of the subject with a camera. .. Assuming that a patient with dementia 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 dementia 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 dementia 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 discriminant types of signs of dementia are arranged on the right side through this degree of association. The degree of association indicates whether or not the reference pronoun frequency information and the reference facial expression image information arranged on the left side are highly related to the discrimination type of the sign of dementia. In other words, this degree of association is an index showing what kind of dementia sign discrimination type is likely to be associated with each reference pronoun frequency information and reference facial image image information, and is a reference pronoun frequency information. It shows the accuracy in selecting the most probable dementia sign discrimination type 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 discriminant type of signs of dementia.

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 dementia 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 dementia is analyzed from the past data. For example, in the example of the intermediate node 61a, the output of the dementia sign discrimination types A and B is linked, but from the previous case, the degree of association of w13 leading to the dementia 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 dementia, 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 dementia sign discrimination type C is w15. , Dementia sign discrimination type E has a degree of association of 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 dementia sign discrimination type B is w17, and the dementia 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 examined for the discrimination type of the sign of dementia 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 dementia sign discrimination type C by w19 and the dementia sign discrimination type D by the degree of association w20. In such a case, the discriminant type C of the sign of dementia with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the discriminant type D of the sign of dementia 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 facial expression image information for reference, and the discrimination type of the sign of dementia 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 dementia 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 dementia 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 dementia 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 dementia 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 dementia 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 dementia, the degree of association shown in FIG. 6 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 brain wave information is the reference brain wave information. If it can be identified as W, the combination is associated with node 61c, in which node 61c has a degree of association w17 for dementia sign discrimination type B and dementia sign discrimination type D. It is associated with the degree of association w18. As a result of such a degree of association, based on w17 and w18, the discrimination type of the sign of dementia is actually newly obtained from the pronoun frequency information, the tone information, and the electroencephalogram information.

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 dementia 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 include age, gender, occupation, current social activities, information on behaviors and behaviors related to dementia from the past to the present, various diseases other than dementia, 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 this dementia, 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 dementia.

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 dementia 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 a judgment result with an extremely low degree of association is connected as a slight sign, and 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 dementia 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 dementia actually discriminated in the past is used.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the above-mentioned learned data will be used when actually determining a new sign of dementia 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 dementia is discriminated. In such a case, the degree of association shown in FIG. 10 acquired in advance is referred to. Since the method for estimating the specific type of discriminating signs of dementia is the same as that in the first embodiment, the description below will be omitted.

Further, in the second embodiment, the discrimination type of the sign of dementia 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 dementia actually discriminated in the past is used.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the above-mentioned learned data will be used when actually determining a new sign of dementia 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 dementia is discriminated. In such a case, the degree of association shown in FIG. 11 acquired in advance is referred to. Since the method for estimating the specific type of discriminating signs of dementia is the same as that in the first embodiment, the description below will be omitted.

Further, in the second embodiment, the discrimination type of the sign of dementia 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 dementia actually discriminated in the past is used.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the above-mentioned learned data will be used when actually determining a new sign of dementia 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 dementia is discriminated. In such a case, the degree of association shown in FIG. 12 acquired in advance is referred to. Since the method for estimating the specific type of discriminating signs of dementia is the same as that in 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 dementia. 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 dementia 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 dementia 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 dementia is processed to increase the weight, in other words, the sign of dementia. 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 more discriminant type of dementia sign, and the reference information F is a search as a more dementia sign discriminating type. 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 dementia 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 dementia is performed. That is, the degree of association itself leading to the discrimination type of the sign of dementia may be controlled based on the reference information F to H. Alternatively, after determining the discrimination type of the sign of dementia 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 discriminant type of the sign of dementia as a search solution based on the reference information F to H should reflect what was 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 dementia 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 dementia 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, the discrimination type of the sign of dementia can be estimated by performing the solution search in the same manner. At this time, as shown in FIG. 13 described above, the signs of dementia are discriminated through further other reference information (reference information F, G, H, etc.) with respect to the search solution obtained through the degree of association. 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 dementia may be searched as a search solution as an alternative to the discriminant type of the sign of dementia. In such a case, the search solution can be similarly obtained by preparing data associated with the prescription of dementia 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 Dementia 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 (13)

In the dementia sign discrimination program that discriminates the signs of dementia of the subject
A reception step that accepts the voice input of the subject,
An extraction step for extracting pronouns contained in the text data by converting the voice received by the reception step into text data and performing morphological analysis of the voice data.
An information acquisition step for acquiring pronoun frequency information regarding the frequency of the extracted pronouns extracted by the extraction step in the text data, and
The above-mentioned program is based on the pronoun frequency information acquired by the above-mentioned information acquisition step by referring to the degree of association between the reference pronoun frequency information regarding the frequency of the pronoun in the text data and the degree of association between the discrimination type of the sign of dementia in three or more stages. A dementia sign discrimination program characterized by having a computer perform a discriminant step to discriminate signs of dementia of an examiner. In the dementia sign discrimination program that discriminates the signs of dementia of the subject
A reception step that accepts the voice input of the subject,
An information acquisition step of acquiring intent information related to an intent that defines an action for specifying a processing operation included in the text data by converting the voice received by the reception step into text data and performing morphological analysis. When,
Refer to the three or more levels of association between the reference intent information regarding the intent that defines the action that specifies the processing operation and the discrimination type of the sign of dementia, and use the intent information acquired by the above information acquisition step. Based on this, a dementia sign discrimination program characterized by causing a computer to perform a discrimination step for discriminating the signs of dementia of the subject. In the dementia sign discrimination program that discriminates the signs of dementia of the subject
A reception step that accepts the voice input of the subject,
An information acquisition step for acquiring tone information regarding the tone of the voice received by the above reception step, and
Based on the tone information acquired by the above information acquisition step, the degree of association between the reference tone information regarding the tone of the voice and the discrimination type of the sign of dementia is referred to, and the dementia of the subject is described. A dementia sign discrimination program characterized by having a computer perform a discriminant step to discriminate a sign. In the dementia sign discrimination program that discriminates the signs of dementia of the subject
A reception step that accepts the voice input of the subject,
An extraction step for extracting pronouns contained in the text data by converting the voice received by the reception step into text data and performing morphological analysis of the voice data.
An information acquisition step for acquiring pronoun frequency information regarding the frequency of the extracted pronouns extracted by the extraction step in the text data and tone information regarding the tone of the voice received by the reception step.
Reference for the frequency of synonyms in the text data Refer to the degree of association between the three or more levels of association between the dementia frequency information and the dementia sign discrimination type, and refer to the synonym frequency information acquired by the above information acquisition step. A discrimination step for discriminating the signs of dementia of the subject based on the acquired tone information while giving priority to those having a higher degree of association between the synonymous frequency information and the discrimination type of the signs of dementia. A dementia sign discrimination program characterized by having a computer execute and. In the dementia sign discrimination program that discriminates the signs of dementia of the subject
A reception step that accepts the voice input of the subject,
By converting the voice received by the reception step into text data and performing morphological analysis of this, the intent information regarding the intent that defines the action that specifies the processing operation included in the text data, and the reception step The information acquisition step to acquire the tone information about the received voice tone, and
Refer to the three or more levels of association between the reference intent information regarding the intent that defines the action that specifies the processing operation and the discrimination type of the sign of dementia, and use the intent information acquired by the above information acquisition step. Prioritize those with a higher degree of association between the intent information for reference and the type of discrimination of signs of dementia according to the above, and based on the acquired tone information, the signs of dementia of the subject are given. A dementia sign discrimination program characterized by having a computer perform a discrimination step. In the dementia sign discrimination program that discriminates the signs of dementia of the subject
A reception step that accepts the voice input of the subject,
An extraction step for extracting pronouns contained in the text data by converting the voice received by the reception step into text data and performing morphological analysis of the voice data.
An information acquisition step for acquiring pronoun frequency information regarding the frequency of the extracted pronouns extracted by the extraction step in the text data and tone information regarding the tone of the voice received by the reception step.
The reference tone information regarding the tone of the voice and the reference tone information according to the tone information acquired by the above information acquisition step and the dementia are referred to by referring to the degree of association between the reference tone information and the discrimination type of the sign of dementia in three or more stages. Prioritize those with a higher degree of association with the symptom discrimination type at least 3 levels, and let the computer execute the discrimination step for discriminating the symptom of dementia of the subject based on the acquired synonym frequency information. A dementia sign discrimination program characterized by this. In the dementia sign discrimination program that discriminates the signs of dementia of the subject
A reception step that accepts the voice input of the subject,
By converting the voice received by the reception step into text data and performing morphological analysis of this, the intent information regarding the intent that defines the action that specifies the processing operation included in the text data, and the reception step The information acquisition step to acquire the tone information about the received voice tone, and
The reference tone information regarding the tone of the voice and the reference tone information according to the tone information acquired by the above information acquisition step and the dementia are referred to by referring to the degree of association between the reference tone information and the discrimination type of the sign of dementia in three or more stages. Prioritize those with a higher degree of association with the symptom discrimination type at least 3 levels, and let the computer execute the discrimination step for discriminating the symptom of dementia of the subject based on the acquired intent information. A dementia sign discrimination program characterized by this. In the above information acquisition step, further electroencephalogram information regarding the subject's electroencephalogram is acquired.
In the above-mentioned discrimination step, the degree of association between the combination having the above-mentioned reference synonym frequency information and the reference EEG information regarding the brain waves of the past subject and the discrimination type of the sign of dementia is referred to, and the above is described. The dementia sign discrimination program according to claim 1 or 4, wherein the sign of dementia of the subject is discriminated based on the synonymous frequency information and the electroencephalogram information acquired by the information acquisition step. In the above information acquisition step, attribute information regarding the attributes of the subject is further acquired.
In the above-mentioned discrimination step, the degree of association between the combination having the above-mentioned reference pronoun frequency information and the reference attribute information regarding the attributes of the subject in the past and the discrimination type of the sign of dementia is referred to, and the above-mentioned degree of association is referred to. The dementia sign discrimination program according to claim 1 or 4, wherein the pronoun frequency information and the attribute information acquired by the information acquisition step are used to discriminate the signs of dementia of the subject. The dementia 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. .. In a dementia prescription search program that searches for each prescription for a subject's signs of dementia
A reception step that accepts the voice input of the subject,
An extraction step for extracting pronouns contained in the text data by converting the voice received by the reception step into text data and performing morphological analysis of the voice data.
An information acquisition step for acquiring pronoun frequency information regarding the frequency of the extracted pronouns extracted by the extraction step in the text data, and
Based on the pronoun frequency information acquired by the information acquisition step, the subject's A dementia prescription search program characterized by having a computer perform search steps to search for dementia prescriptions. In a dementia prescription search program that searches for each prescription for a subject's signs of dementia
A reception step that accepts the voice input of the subject,
An information acquisition step of acquiring intent information related to an intent that defines an action for specifying a processing operation included in the text data by converting the voice received by the reception step into text data and performing morphological analysis. When,
The above is based on the intent information acquired by the above information acquisition step, referring to the reference intent information regarding the intent that defines the action that specifies the processing operation and the degree of association with the prescription of dementia in three or more stages. A dementia prescription search program characterized by having a computer perform search steps to search for a subject's dementia prescription. In a dementia prescription search program that searches for each prescription for a subject's signs of dementia
A reception step that accepts the voice input of the subject,
An information acquisition step for acquiring tone information regarding the tone of the voice received by the above reception step, and
Based on the intent information acquired by the above information acquisition step, the dementia prescription of the subject is determined by referring to the reference tone information regarding the voice tone and the degree of association between the dementia prescription and the three or more stages. A dementia prescription search program characterized by having a computer perform search steps to search.

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