CN116312936A - Design method and device for cognitive digital drug assisting use software - Google Patents

Design method and device for cognitive digital drug assisting use software Download PDF

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CN116312936A
CN116312936A CN202310222964.XA CN202310222964A CN116312936A CN 116312936 A CN116312936 A CN 116312936A CN 202310222964 A CN202310222964 A CN 202310222964A CN 116312936 A CN116312936 A CN 116312936A
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data
patient
information
prescription
personal information
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文冬
王少昌
周艳红
万象隆
胡峰溢
李斌
刘哲源
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University of Science and Technology Beijing USTB
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Abstract

The invention relates to the technical field of information, in particular to a design method and a device of software for assisting in using a cognitive digital drug, wherein the design method of the software for assisting in using the cognitive digital drug comprises the following steps: importing doctor prescriptions and personal information of patients to obtain prescription data and personal information data; processing data information generated by the digital medicine to obtain initial data, and extracting features according to the initial data to obtain first feature data; sending a medication reminder to the patient according to the initial data and the prescription data; deep learning is carried out through a convolutional neural network according to the first characteristic data, and second characteristic data are obtained; pushing auxiliary treatment information to the patient according to the second characteristic data and the personal information data; based on the second characteristic data and the prescription information data, a patient motivation task is formulated, and the medication effect and experience of the digital medicine are improved.

Description

Design method and device for cognitive digital drug assisting use software
Technical Field
The invention relates to the technical field of information, in particular to a design method and device for cognitive digital drug adjuvant use software.
Background
Digital medicine refers to a digital product having therapeutic indications, therapeutic targets and therapeutic intended effects (or capable of improving therapeutic efficiency and effects by innovative techniques) similar to those of traditional medicines. Compared with the traditional medicine, the digital medicine has the advantages of easy and interesting compliance, timely statistical feedback of diagnosis and treatment data, immediate and automatic optimization of diagnosis and treatment scheme based on diagnosis and treatment data through artificial intelligence, no pharmacological toxic or side effect and the like. Referring to the research and development flow of traditional medicines in the cognitive impairment field, based on the existing technical foundation, a research and development model of the cognitive impairment digital medicines is designed, and can provide referenceable guidance for research and development of the digital medicines in the field in the future, so that research and development of the digital medicines in the field are accelerated.
How to properly embed the auxiliary software of the digital medicine into the digital medicine and excite the auxiliary software in the digital medicine to work normally through reasonable biological stimulation becomes the research direction of current attention. In the existing digital medicine assisting software, the defects exist in the aspects of helping patients and doctors to master the physical condition of the patients in real time, and the patients cannot be effectively assisted and rewarded in real time in the process of using the medicine, so that the medicine using effect is poor and the treatment experience is poor.
Disclosure of Invention
The embodiment of the invention provides a design method and a device for cognitive digital drug adjuvant use software. The technical scheme is as follows:
in one aspect, a method for designing software for assisting in use of cognitive digital drugs is provided, the method is implemented by electronic equipment, and the method comprises the following steps:
and importing the doctor prescription and the personal information of the patient to obtain prescription data and personal information data.
And processing data information generated by the digital medicine to obtain initial data, and extracting features according to the initial data to obtain first feature data.
And sending a medication reminder to the patient according to the initial data and the prescription data.
And performing deep learning through a convolutional neural network according to the first characteristic data to obtain second characteristic data.
And pushing auxiliary treatment information to the patient according to the second characteristic data and the personal information data.
And formulating a patient motivational task based on the second characteristic data and the prescription information data.
Optionally, the importing doctor prescription and patient personal information, obtaining prescription data and personal information data, includes:
and transmitting the personal information data and the prescription data from the hospital prescription server to a patient mobile phone end by using a wireless communication mode of a B/S architecture mode, wherein the personal information data and the prescription data are stored by SQLite.
Optionally, the processing the data information generated by using the digital medicine to obtain initial data, and performing feature extraction according to the initial data to obtain first feature data includes:
the method comprises the steps of transmitting medication condition data information of a patient in a wireless communication mode, performing filtering and information model conversion operation through a data conversion interface between a digital medicine and a platform to obtain initial data, and performing feature extraction according to the initial data by adopting a sorting condition mutual information method to obtain first feature data.
Optionally, the performing deep learning through a convolutional neural network according to the first feature data to obtain second feature data includes:
establishing a dialogue generating model through a word vector representation method based on a neural network based on the first characteristic data; converting the input first feature data into a computer binary code through a Seq2Seq neural network model introducing an attention mechanism; and performing deep learning based on a convolutional neural network according to the computer binary code to obtain second characteristic data.
Optionally, the formulating a patient motivation task based on the second feature data includes:
and acquiring a memory state of the patient in the current test environment, and carrying out dynamic matching of the excitation task according to the mental state of the patient by a learning method of a long-period memory network based on the second characteristic data and the prescription data.
In another aspect, a device for designing software for assisting use of a cognitive digital drug is provided, the device being applied to a method for assisting use of a cognitive digital drug, the device comprising:
and the information acquisition module is used for importing the doctor prescription and the patient personal information to obtain prescription data and personal information data.
The feature extraction module is used for processing data information generated by the digital medicine to obtain initial data, and extracting features according to the initial data to obtain first feature data.
And the medicine prompt module is used for sending a medicine prompt to a patient according to the initial data and the prescription data.
And the deep learning module is used for performing deep learning through a convolutional neural network according to the first characteristic data to obtain second characteristic data.
And the virtual assistant module is used for pushing auxiliary treatment information to the patient according to the second characteristic data and the personal information data.
And the incentive system module is used for formulating a patient incentive task based on the second characteristic data and the prescription information data.
Optionally, the medicine prompt module is further configured to:
the personal information data and the prescription data are transmitted to the mobile phone end of the patient from the hospital prescription server by using a wireless communication mode of a B/S architecture mode, and are stored by SQL ite.
Optionally, the medicine prompt module is further configured to:
the method comprises the steps of transmitting medication condition data information of a patient in a wireless communication mode, performing filtering and information model conversion operation through a data conversion interface between a digital medicine and a platform to obtain initial data, and performing feature extraction according to the initial data by adopting a sorting condition mutual information method to obtain first feature data.
Optionally, the virtual assistant module is further configured to:
based on the first characteristic data, establishing a dialogue generating model through a word vector representation method based on a neural network, and solving the mapping problem of a cyclic neural network between sequences; converting the input first feature data into a computer binary code through a Seq2Seq neural network model introducing an attention mechanism; and performing deep learning based on a convolutional neural network according to the computer binary code to obtain second characteristic data.
Optionally, the excitation system module is further configured to:
and acquiring a memory state of the patient in the current test environment, and carrying out dynamic matching of the excitation task according to the mental state of the patient by a learning method of a long-period memory network based on the second characteristic data and the prescription data.
In another aspect, an electronic device is provided, the electronic device including a processor and a memory, the memory storing at least one instruction, the at least one instruction loaded and executed by the processor to implement a cognitive digital drug adjuvant use software design method as described above.
In another aspect, a computer readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement a cognitive digital drug adjunctive use software design method as described above is provided.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the invention properly embeds the auxiliary software of the digital medicine into the digital medicine, and excites the auxiliary software in the digital medicine to work normally through reasonable interaction, collects and transmits the medicine taking data of a patient and the brain electro-physiological data of a human body, and transmits the data to a server platform through a certain transmission mode, so as to analyze and process the data, and make guiding comments and prompting suggestions for the medical rehabilitation of the patient. The medical device helps patients and doctors to master the physical condition of the patients in real time, and the patients give real-time help and rewards in the process of using the medicines, so that the medicine effect and experience are improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a design method of software for assisting in using a cognitive digital drug according to an embodiment of the present invention;
FIG. 2 is a block diagram of a software design device for assisting in the use of a cognitive digital drug according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The embodiment of the invention provides a design method for software for assisting in using a cognitive digital drug, which can be realized by electronic equipment, wherein the electronic equipment can be a terminal or a server. A flow chart of a method for designing a cognitive digital drug adjuvant software, as shown in fig. 1, the process flow of the method may include the following steps:
s1, importing a doctor prescription and personal information of a patient to obtain prescription data and personal information data.
Optionally, the personal information data and the prescription data are transmitted from the hospital prescription server to the patient mobile phone end by using a wireless communication mode of a B/S architecture mode for data transmission, and are stored by SQL ite.
In a possible implementation manner, the mobile phone side submits a request to acquire data of the hospital prescription server side through the URLConnect. Firstly, calling a URL object openConnect i on method to create a URLConnect object; then setting a URLConnect parameter and a POST request attribute; secondly, obtaining an output stream corresponding to the URLConnect instance to send request parameters; and finally, reading the data of the hospital prescription server.
S2, processing data information generated by the digital medicine to obtain initial data, and extracting features according to the initial data to obtain first feature data.
Optionally, the processing the data information generated by using the digital medicine to obtain initial data, and performing feature extraction according to the initial data to obtain first feature data includes:
the medication condition data information of the patient is transmitted in a wireless communication mode, filtering and information model conversion operation are carried out through a data conversion interface between the digital medicine and the platform, initial data are obtained, and feature extraction is carried out according to the initial data by adopting a sorting condition mutual information method, so that first feature data are obtained.
In a feasible implementation mode, medication condition data information of a patient is transmitted in a Bluetooth or Wi f i mode and the like, a data conversion interface connected between the digital medicine and the platform is designed, medication condition related data of the patient is converted into a proper data model, and then the data model is processed and analyzed again. The method comprises the steps of extracting features of electroencephalogram signals of a patient by adopting a sorting condition mutual information method, and extracting features by adopting the sorting condition mutual information method.
S3, sending a medication reminder to the patient according to the initial data and the prescription data.
In one possible embodiment, referring to a doctor prescribing a digital medication, viewing the details of the medication information on the day, adding and updating reminders, and viewing a history. Providing related video, picture and text information, community service communication and electronic calendars, medication compliance and pharmaceutical statistics services, and long-term health education. The system can be compatible with an IOS and android id dual platform, a user can log in an account through a mobile phone number, can log in through WeChat and QQ third party software authorization, can change account information after logging in, and can provide new requirements and problem communication through a user feedback interface. The cloud terminal framework is built, multidimensional sharing of data and doctors is realized, the medication efficiency is improved, and the medication effect is enhanced.
And S4, performing deep learning through a convolutional neural network according to the first characteristic data to obtain second characteristic data.
Optionally, deep learning is performed through a convolutional neural network according to the first feature data, so as to obtain second feature data, including:
based on the first characteristic data, establishing a dialogue generating model through a word vector representation method based on a neural network; converting the input first feature data into a computer binary code through a Seq2Seq neural network model introducing an attention mechanism; and performing deep learning based on the convolutional neural network according to the computer binary code to obtain second characteristic data.
In one possible implementation, deep learning is to learn the internal rules and representation layers of sample data, so that the interpretation of information obtained in the learning process is greatly facilitated, and deep learning can learn details which cannot be observed by people in the data and can help us extract features in the data. The auxiliary software can be used for serving the treatment process of the rehabilitation patient by adopting an intelligent customized algorithm according to the patient information and the medication condition.
The training process of convolutional neural networks includes two phases, forward propagation and backward propagation. The forward propagation process mainly includes: convolution feature extraction, pooling and error calculation. The back propagation process mainly includes: error feedback and weight updating. Initializing the weight by adopting a random assignment mode; the information is sequentially transmitted to a convolution layer, a pooling layer and a full-connection layer, wherein the convolution layer and the pooling layer can extract the most obvious characteristic of the observed data through one filter, and can also extract richer characteristic information of a patient by stacking a plurality of convolution layers and pooling layers; transforming and calculating information of a plurality of hidden layers in the full-connection layer, and transmitting the information to an output layer; and finally, comparing the actual output result with the expected output result, and directly outputting the result if the error function meets the precision requirement. If the first characteristic data is not satisfied, the deviation and the weight are reversely transmitted back to update the weight until the weight tends to be stable, and the second characteristic data is obtained.
S5, pushing auxiliary treatment information to the patient according to the second characteristic data and the personal information data.
In a possible embodiment, a scale measuring tool is used in the dialog in order to formulate targeted interventions, on the other hand knowledge of the user preferences is required in order to provide alternatives thereto. In order to acquire the activity condition and emotion state of the old, technologies such as camera monitoring, emotion recognition and the like can be used for recognition and feedback. For the home patients, the most convenient service of the voice assistant is to obtain answers to some medical questions, and the knowledge base of the voice assistant is carefully created by obtaining enough authoritative health knowledge from a professional channel, so that the aged cognitive impairment patients can obtain accurate answers. In order to deepen the memory of the elderly users on the information, the system can start short message notification besides fixed-point pushing and send text contents to the mobile phone of the elderly patients. The records of the health conditions are transmitted to the user in a periodical reporting mode, so that the user can clearly determine the health conditions of the user, and the assistant can warn the found problems and provide a corresponding adjustment scheme. When a patient has a more urgent problem, it is necessary to ensure that the patient smoothly contacts the family and other medical help hotlines. In addition, after the planning is carried out, the compliance of the patient is difficult to be ensured by voice supervision, reminding and encouragement of a voice assistant, so that the progress of the patient is required to be sent to the family members of the patient regularly, the family members are encouraged to establish a health plan together, the affective contact between the patient and the family members is enhanced, the self-effectiveness feeling of the patient is further improved, and finally the aim of optimizing the behavior habit is fulfilled.
And S6, formulating a patient motivation task based on the second characteristic data and the prescription information data.
Optionally, formulating the patient motivational task based on the second characteristic data includes:
and acquiring the memory state of the patient in the current test environment, and dynamically matching the excitation tasks according to the mental state of the patient by a learning method of a long-term and short-term memory network based on the second characteristic data and the prescription data.
In one possible embodiment, the long and short term memory network (Long Short Term Memory networks, LSTM) is composed of repeating units, each unit being internally controlled by mainly forgetting, input and output gates, which receive input from the previous unit and prescription data input x prescribed by the doctor at the current time step t t . Each LSTM cell contains a cell state c t And hidden state h t They are modulated by 4 neural network layers that control the flow of information into and out of the cell memory.
LSTM first passes through forget gate to input current x t And the upper section hidden layer output h t-1 The calculation is performed, and the formula for controlling the LSTM forgetting gate is as follows (23):
f t ＝σ(W f x t +U f h t-1 +b f )……(23)
wherein W is f A weight matrix representing mapping of hidden layer inputs to forgetting gates, a weight matrix representing connecting previous output states to forgetting gates, b f Representing the bias vector, σ represents the activation function, in this case s i gmod. Forgetting door t f Control of a previous memory cell c by means of an s igmod activation function t-1 For the current memory cell c t And discard redundant information, the forgetting gate is determined by the gate reading out a value between 0 and 1, 1 indicates "complete hold", 0 indicates "complete holdAll abandons.
Adding new information to single cells, input gate control input x t And h t-1 The influence degree of the input layer s igmod on the current storage unit determines the information to be updated, and the tanh layer obtains an updated content c t And pair c t-1 And updating.
Finally, the output gate can control the current cell c t For hidden state unit h t By means of an output gate o t And the filtering unit state is used for updating the hidden state, obtaining the final output of the LSTM unit, and dynamically matching the excitation task of the patient according to the final output.
The part focuses on a task rewarding system of auxiliary software, receives data processed by a virtual assistant for the second time, adopts automation to formulate tasks for the medication condition of each stage of a patient, and generates tasks suitable for the patient. In the daily training task process, the game difficulty, the game completion time and the game ranking score rule are calculated to obtain relevant scores, and when a patient completes the task or logs in a training system to perform daily sign-in, the motivation score of the patient can be increased. At the same time, an interactive interface of the point mall is designed and completed, and the incentive points acquired by the patient can be used for exchanging vouchers in the point mall or unlocking part of functions of the virtual assistant. After exiting the training system, the incentive system automatically updates the training content and the card punching calendar.
The invention properly embeds the auxiliary software of the digital medicine into the digital medicine, and excites the auxiliary software in the digital medicine to work normally through reasonable interaction, collects and transmits the medicine taking data of a patient and the brain electro-physiological data of a human body, and transmits the data to a server platform through a certain transmission mode, so as to analyze and process the data, and make guiding comments and prompting suggestions for the medical rehabilitation of the patient. The medical device helps patients and doctors to master the physical condition of the patients in real time, and the patients give real-time help and rewards in the process of using the medicines, so that the medicine effect and experience are improved.
Fig. 2 is a block diagram of a cognitive digital drug coaching software design device that is used to implement a cognitive digital drug coaching software design method based on a time-varying graph, according to an exemplary embodiment. Referring to fig. 2, the apparatus includes:
and the information acquisition module is used for importing the doctor prescription and the patient personal information to obtain prescription data and personal information data.
The feature extraction module is used for processing data information generated by the digital medicine to obtain initial data, and extracting features according to the initial data to obtain first feature data.
And the medicine prompt module is used for sending medicine prompt to the patient according to the first characteristic data and the prescription data.
And the deep learning module is used for performing deep learning through a convolutional neural network according to the first characteristic data to obtain second characteristic data.
And the virtual assistant module is used for pushing auxiliary treatment information to the patient according to the second characteristic data and the personal information data.
And the incentive system module is used for formulating a patient incentive task based on the second characteristic data and the prescription information data.
Optionally, the information acquisition module is further configured to:
and transmitting the personal information data and the prescription data from the hospital prescription server to a patient mobile phone end by using a wireless communication mode of a B/S architecture mode, wherein the personal information data and the prescription data are stored by SQLite.
Optionally, the feature extraction module is further configured to:
the medication condition data information of the patient is transmitted in a wireless communication mode, filtering and information model conversion operation are carried out through a data conversion interface between the digital medicine and the platform, initial data are obtained, and feature extraction is carried out according to the initial data by adopting a sorting condition mutual information method, so that first feature data are obtained.
Optionally, the deep learning module is further configured to:
based on the first characteristic data, establishing a dialogue generating model through a word vector representation method based on a neural network, and solving the mapping problem of a cyclic neural network between sequences; converting the input first feature data into a computer binary code through a Seq2Seq neural network model introducing an attention mechanism; and performing deep learning based on the convolutional neural network according to the computer binary code to obtain second characteristic data.
Optionally, the excitation system module is further configured to:
and acquiring the memory state of the patient in the current test environment, and dynamically matching the excitation tasks according to the mental state of the patient by a learning method of a long-term and short-term memory network based on the second characteristic data and the prescription data.
The invention properly embeds the auxiliary software of the digital medicine into the digital medicine, and excites the auxiliary software in the digital medicine to work normally through reasonable interaction, collects and transmits the medicine taking data of a patient and the brain electro-physiological data of a human body, and transmits the data to a server platform through a certain transmission mode, so as to analyze and process the data, and make guiding comments and prompting suggestions for the medical rehabilitation of the patient. The medical device helps patients and doctors to master the physical condition of the patients in real time, and the patients give real-time help and rewards in the process of using the medicines, so that the medicine effect and experience are improved.
Fig. 3 is a schematic structural diagram of an electronic device 300 according to an embodiment of the present invention, where the electronic device 600 may have a relatively large difference due to different configurations or performances, and may include one or more processors (centra l process i ng un its, CPU) 301 and one or more memories 302, where at least one instruction is stored in the memories 302, and the at least one instruction is loaded and executed by the processors 301 to implement the steps of the above-mentioned design method for assisting with using a cognitive digital drug.
In an exemplary embodiment, a computer readable storage medium, such as a memory including instructions executable by a processor in a terminal to perform a cognitive digital drug adjunctive use software design method as described above is also provided. For example, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.

Claims (10)

1. A method for designing a cognitive digital drug adjunctive use software, the method comprising:
importing doctor prescriptions and personal information of patients to obtain prescription data and personal information data;
processing data information generated by the digital medicine to obtain initial data, and extracting features according to the initial data to obtain first feature data;
sending a medication reminder to the patient according to the initial data and the prescription data;
deep learning is carried out through a convolutional neural network according to the first characteristic data, and second characteristic data are obtained;
pushing auxiliary treatment information to the patient according to the second characteristic data and the personal information data;
and formulating a patient motivational task based on the second characteristic data and the prescription information data.
2. The method for designing a software for assisting use of a cognitive digital drug according to claim 1, wherein the step of importing doctor's prescription and patient's personal information to obtain prescription data and personal information data comprises:
and transmitting the personal information data and the prescription data from the hospital prescription server to a patient mobile phone end by using a wireless communication mode of a B/S architecture mode, wherein the personal information data and the prescription data are stored by SQLite.
3. The method for designing software for assisting use of a cognitive digital drug according to claim 1, wherein the processing the data information generated by using the digital drug to obtain initial data, performing feature extraction according to the initial data to obtain first feature data comprises:
the method comprises the steps of transmitting medication condition data information of a patient in a wireless communication mode, performing filtering and information model conversion operation through a data conversion interface between a digital medicine and a platform to obtain initial data, and performing feature extraction according to the initial data by adopting a sorting condition mutual information method to obtain first feature data.
4. The method for designing a software for assisting in using a cognitive digital drug according to claim 1, wherein the deep learning is performed through a convolutional neural network according to the first feature data to obtain second feature data, comprising:
establishing a dialogue generating model through a word vector representation method based on a neural network based on the first characteristic data; converting the input first feature data into a computer binary code through a Seq2Seq neural network model introducing an attention mechanism; and performing deep learning based on a convolutional neural network according to the computer binary code to obtain second characteristic data.
5. A method of designing a cognitive digital drug adjuvant software according to claim 1, wherein formulating a patient motivation task based on the second characteristic data comprises:
and acquiring a memory state of the patient in the current test environment, and carrying out dynamic matching of the excitation task according to the mental state of the patient by a learning method of a long-period memory network based on the second characteristic data and the prescription data.
6. A cognitive digital drug coaching software design device for implementing a cognitive digital drug coaching software design method, the device comprising:
the information acquisition module is used for importing doctor prescriptions and personal information of patients to obtain prescription data and personal information data;
the feature extraction module is used for processing data information generated by the digital medicine to obtain initial data, and extracting features according to the initial data to obtain first feature data;
the medicine prompt module is used for sending a medicine prompt to a patient according to the initial data and the prescription data;
the deep learning module is used for performing deep learning through a convolutional neural network according to the first characteristic data to obtain second characteristic data;
the virtual assistant module is used for pushing auxiliary treatment information to the patient according to the second characteristic data and the personal information data;
and the incentive system module is used for formulating a patient incentive task based on the second characteristic data and the prescription information data.
7. The cognitive digital drug coaching software design device according to claim 6, wherein the drug prompt module is further configured to:
and transmitting the personal information data and the prescription data from the hospital prescription server to a patient mobile phone end by using a wireless communication mode of a B/S architecture mode, wherein the personal information data and the prescription data are stored by SQLite.
8. The cognitive digital drug coaching software design device according to claim 6, wherein the drug prompt module is further configured to:
the method comprises the steps of transmitting medication condition data information of a patient in a wireless communication mode, performing filtering and information model conversion operation through a data conversion interface between a digital medicine and a platform to obtain initial data, and performing feature extraction according to the initial data by adopting a sorting condition mutual information method to obtain first feature data.
9. The cognitive digital drug coaching software design device according to claim 6, wherein the virtual helper module is further configured to:
based on the first characteristic data, establishing a dialogue generating model through a word vector representation method based on a neural network, and solving the mapping problem of a cyclic neural network between sequences; converting the input first feature data into a computer binary code through a Seq2Seq neural network model introducing an attention mechanism; and performing deep learning based on a convolutional neural network according to the computer binary code to obtain second characteristic data.
10. The cognitive digital drug coaching software design device according to claim 6, wherein the motivation system module is further configured to:
and acquiring a memory state of the patient in the current test environment, and carrying out dynamic matching of the excitation task according to the mental state of the patient by a learning method of a long-period memory network based on the second characteristic data and the prescription data.
CN202310222964.XA 2023-03-09 2023-03-09 Design method and device for cognitive digital drug assisting use software Pending CN116312936A (en)

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