CN117710166A - Teaching guidance detection system and method for pediatric neural nursing - Google Patents
Teaching guidance detection system and method for pediatric neural nursing Download PDFInfo
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Abstract
The application discloses a teaching guidance detecting system and method for pediatric neural nursing relates to the field of intelligent analysis, and the teaching guidance detecting system and method for pediatric neural nursing is used for generating nursing steps needing improvement by acquiring nursing records of child neural patient nursing by a student and carrying out semantic feature analysis and global semantic fusion on the nursing records of child neural patient nursing by adopting a deep learning technology. By the method, specific improvement suggestions and practical guidance can be provided for medical students, and the medical students can understand and master the knowledge and skills of child nerve nursing more deeply, so that the nursing quality and the patient safety are improved.
Description
Technical Field
The present application relates to the field of intelligent analysis, and more particularly, to a teaching guidance detection system and method for pediatric neural care.
Background
The teaching guidance detection system for the pediatric neural nursing is a system based on computer technology and education theory, and aims to help teachers or students to conduct guidance and assessment in the teaching and learning process of the pediatric neural nursing so as to promote the students to learn and master the pediatric neural nursing knowledge and skills. Through teaching guidance detection, students can understand and master knowledge and skills of child nerve nursing more deeply, help them to apply theoretical knowledge in practice, and therefore learning effect and practice ability can be detected, and patients can be better served.
But often do not provide real-time feedback due to conventional tutorial detection. Students may need to wait for assessment and feedback from a teacher or expert, and cannot know in time whether they have improper care procedures or need improved care procedures during their care, thereby affecting the patient's course of treatment.
Thus, an optimized teaching guidance testing regimen for pediatric neural care is desired.
Disclosure of Invention
The present application has been made in order to solve the above technical problems.
According to one aspect of the present application, there is provided a teaching instruction detection system for pediatric neural care, comprising:
the pediatric neural patient nursing record acquisition module is used for acquiring nursing records of a student for nursing the pediatric neural patient;
the nursing record semantic coding module is used for carrying out semantic coding on the nursing record of the nursing of the pediatric neural patient so as to obtain a nursing semantic understanding feature matrix of the pediatric neural patient;
the characteristic attention enhancement module is used for enhancing the attention of the pediatric neural patient nursing semantic understanding characteristic matrix to obtain the pediatric neural patient nursing semantic understanding enhancement characteristic matrix;
the semantic association analysis module is used for carrying out segmentation on the pediatric neural patient nursing semantic understanding reinforcement feature matrix to obtain a sequence of pediatric neural patient nursing semantic understanding reinforcement feature vectors, and then carrying out semantic association analysis processing to obtain pediatric neural patient nursing semantic understanding reinforcement global feature vectors;
The feature optimization module is used for carrying out manifold hyper-convex correlation derivative representation optimization on the pediatric neural patient care semantic understanding enhancement global feature vector so as to obtain an optimized pediatric neural patient care semantic understanding enhancement global feature vector;
and the nursing step generating module is used for generating nursing steps needing improvement based on the nursing semantic understanding reinforced global feature vector of the optimized pediatric neural patient and carrying out corresponding guidance on the nursing steps.
In the teaching instruction detection system for pediatric neural nursing, the nursing record semantic coding module comprises: the word segmentation processing unit is used for carrying out word segmentation processing on the nursing records to obtain the nursing records subjected to word segmentation processing; the semantic understanding unit is used for carrying out semantic understanding on the nursing records subjected to word segmentation processing to obtain a sequence of nursing semantic understanding feature vectors of the pediatric neural patient; and a feature arrangement unit for arranging the sequence of pediatric neural patient care semantic understanding feature vectors into the pediatric neural patient care semantic understanding feature matrix.
In the teaching instruction detection system for pediatric neural nursing, the semantic understanding unit is used for enabling the nursing records after word segmentation to pass through a nursing record semantic encoder comprising an embedded layer to obtain a sequence of nursing semantic understanding feature vectors of the pediatric neural patient.
In the teaching guidance detection system for pediatric neural care, the feature attention enhancement module is configured to: inputting the pediatric neural patient care semantic understanding feature matrix into a bidirectional attention mechanism based on a convolutional neural network to obtain the pediatric neural patient care semantic understanding reinforcement feature matrix.
In the teaching guidance detection system for pediatric neural care, the semantic association analysis module comprises a feature matrix segmentation unit, a semantic association analysis module and a semantic association analysis module, wherein the feature matrix segmentation unit is used for segmenting the pediatric neural patient care semantic understanding enhancement feature matrix to obtain a sequence of the pediatric neural patient care semantic understanding enhancement feature vector; and the semantic association feature extraction unit is used for enabling the sequence of the pediatric neural patient nursing semantic understanding enhancement feature vector to pass through a semantic association feature extractor based on a bidirectional long-short term neural network model so as to obtain the pediatric neural patient nursing semantic understanding enhancement global feature vector.
In the teaching guidance detection system for pediatric neural care, the feature optimization module is configured to: performing manifold hyper-convex correlation derivative representation optimization on the pediatric neural patient care semantic understanding enhancement global feature vector by using the following optimization formula to obtain the optimized pediatric neural patient care semantic understanding enhancement global feature vector; wherein, the optimization formula is:
；
Wherein,representing the pediatric neural patient care semantic understanding enhanced global feature vector, < >>A +.f. representing the pediatric neurological patient care semantic understanding enhanced global feature vector>Characteristic value of individual position->A +.f. representing the pediatric neurological patient care semantic understanding enhanced global feature vector>Characteristic value of individual position->And->Representing a weight superparameter->A +.f. representing the optimized pediatric neurological patient care semantic understanding enhanced global feature vector>Characteristic values of the individual positions.
In the teaching guidance detection system for pediatric neural care, the care step generation module is configured to: generating the nursing step needing improvement through a generator by using the optimized pediatric neural patient nursing semantic understanding reinforced global feature vector, and carrying out corresponding guidance on the nursing step.
According to another aspect of the present application, there is provided a teaching instruction detection method for pediatric neural care, comprising:
acquiring a nursing record of a student for nursing the pediatric neural patient;
carrying out semantic coding on the nursing records of the nursing of the pediatric neural patient to obtain a nursing semantic understanding feature matrix of the pediatric neural patient;
the pediatric neural patient nursing semantic understanding feature matrix is subjected to attention enhancement to obtain a pediatric neural patient nursing semantic understanding enhancement feature matrix;
The pediatric neural patient nursing semantic understanding enhancement feature matrix is segmented to obtain a sequence of pediatric neural patient nursing semantic understanding enhancement feature vectors, and then semantic association analysis processing is carried out to obtain pediatric neural patient nursing semantic understanding enhancement global feature vectors;
performing manifold hyper-convex correlation derivative representation optimization on the pediatric neural patient care semantic understanding enhancement global feature vector to obtain an optimized pediatric neural patient care semantic understanding enhancement global feature vector;
and generating a nursing step needing improvement based on the optimized pediatric neural patient nursing semantic understanding reinforced global feature vector, and carrying out corresponding guidance on the nursing step.
In the above teaching instruction detection method for pediatric neural care, performing semantic coding on a care record of pediatric neural patient care to obtain a pediatric neural patient care semantic understanding feature matrix, including: performing word segmentation on the nursing records to obtain segmented nursing records; carrying out semantic understanding on the nursing records subjected to word segmentation processing to obtain a sequence of nursing semantic understanding feature vectors of the pediatric neural patient; and arranging the sequence of pediatric neural patient care semantic understanding feature vectors into the pediatric neural patient care semantic understanding feature matrix.
In the teaching instruction detection method for pediatric neural nursing, the semantic understanding is performed on the nursing record after word segmentation processing to obtain a sequence of pediatric neural patient nursing semantic understanding feature vectors, including: and (3) passing the nursing records subjected to word segmentation through a nursing record semantic encoder comprising an embedded layer to obtain a sequence of nursing semantic understanding feature vectors of the pediatric neural patient.
Compared with the prior art, the method adopts an artificial intelligence technology based on a deep neural network model, performs semantic feature analysis and global semantic fusion on the nursing record of the child neural patient nursing by acquiring the nursing record of the child neural patient nursing by a student and adopts a deep learning technology, so that nursing steps needing improvement are generated. By the method, specific improvement suggestions and practical guidance can be provided for medical students, and the medical students can understand and master the knowledge and skills of child nerve nursing more deeply, so that the nursing quality and the patient safety are improved.
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The foregoing and other objects, features and advantages of the present application will become more apparent from the following more particular description of embodiments of the present application, as illustrated in the accompanying drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the application and not constitute a limitation to the application. In the drawings, like reference numerals generally refer to like parts or steps.
Fig. 1 is a block diagram of a teaching instruction detection system for pediatric neural care, according to an embodiment of the application.
Fig. 2 is a schematic architecture diagram of a teaching instruction detection system for pediatric neural care, according to an embodiment of the application.
Fig. 3 is a block diagram of a care record semantic coding module in a teaching guidance detection system for pediatric neural care according to an embodiment of the application.
Fig. 4 is a block diagram of a semantic association analysis module in a teaching guidance detection system for pediatric neural care according to an embodiment of the application.
Fig. 5 is a flowchart of a teaching instruction detection method for pediatric neural care, according to an embodiment of the application.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
It should be noted that, the term "first\second\third" in the embodiments of the present application merely distinguishes similar objects, and does not represent a specific order for the objects, and it is understood that "first\second\third" may interchange a specific order or sequence where allowed. It is to be understood that the "first\second\third" distinguishing objects may be interchanged where appropriate such that the embodiments of the present application described herein may be implemented in sequences other than those illustrated or described herein.
As used in this disclosure and in the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
Various exemplary embodiments, features and aspects of the disclosure will be described in detail below with reference to the drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
Pediatric neural care is a field of care directed specifically to neurological diseases or neurological dysfunction in children (neonates to adolescents). It relates to the prevention, diagnosis, treatment and rehabilitation care of the nervous system diseases of children. The scope of pediatric neurological care is broad and includes, but is not limited to, the following: 1. development of the nervous system: pediatric neuroprotection concerns the normal developmental processes of the pediatric nervous system, including the formation and functional development of brain, spinal cord, nerve and neuromuscular junctions. 2. Neurological diseases: pediatric neural care relates to the care of various childhood neurological disorders, such as brain tumors, hydrocephalus, brain injury, cerebral spinal fluid malformations, epilepsy, encephalitis, cerebral palsy, and the like. 3. Neurological dysfunction: pediatric neurological care also includes care for neurological disorders such as autism, hyperactivity, learning disorders, movement disorders, and the like, intended to provide support and promote quality of life and functional development in infants. 4. Nursing intervention: pediatric neural care involves care intervention for childhood neurological diseases or disorders, including medication management, pain management, care assessment, rehabilitation care, home education, and the like. The pediatric neural care requires specialized knowledge and skills, and caregivers need to know physiological and pathological characteristics of the pediatric nervous system, grasp basic principles and skills of the pediatric care, and the ability to effectively communicate and cooperate with children and their families. The goal of pediatric neural care is to provide safe, effective and comprehensive care, promote rehabilitation and functional development of neurological diseases in children, and at the same time focus on psychological and social needs of children and households.
The teaching instruction detection system for the child nerve nursing is a system combining computer technology and education theory, and aims to provide instruction and assessment for the child nerve nursing teaching and learning process. The main objective of the system is to help the teacher or student to better understand and master the knowledge and skills of pediatric neural care and to apply them in practice. However, one limitation of conventional teaching guidance detection systems is that they cannot provide real-time feedback, and students may need to wait for assessment and feedback from a teacher or expert. This may result in the student not knowing in time whether he or she is in an improper or need for an improved care step during the care procedure, which may affect the patient's course of treatment. Accordingly, an optimized teaching guidance detection method for pediatric neural care is desired.
Fig. 1 is a block diagram of a teaching instruction detection system for pediatric neural care, according to an embodiment of the application. Fig. 2 is a schematic architecture diagram of a teaching instruction detection system for pediatric neural care, according to an embodiment of the application. As shown in fig. 1 and 2, a teaching instruction detection system 100 for pediatric neural care according to an embodiment of the present application includes: a pediatric neural patient care record acquisition module 110 for acquiring a care record of pediatric neural patient care by a learner; a nursing record semantic coding module 120, configured to perform semantic coding on a nursing record of nursing of the pediatric neural patient to obtain a pediatric neural patient nursing semantic understanding feature matrix; the feature attention enhancement module 130 is configured to perform attention enhancement on the pediatric neural patient care semantic understanding feature matrix to obtain a pediatric neural patient care semantic understanding enhancement feature matrix; the semantic association analysis module 140 is configured to segment the pediatric neural patient care semantic understanding enhancement feature matrix to obtain a sequence of pediatric neural patient care semantic understanding enhancement feature vectors, and then perform semantic association analysis processing to obtain a pediatric neural patient care semantic understanding enhancement global feature vector; the feature optimization module 150 is configured to perform manifold hyper-convex correlation derivative representation optimization on the pediatric neural patient care semantic understanding enhancement global feature vector to obtain an optimized pediatric neural patient care semantic understanding enhancement global feature vector; and a nursing step generating module 160, configured to generate a nursing step that needs to be improved based on the optimized pediatric neural patient nursing semantic understanding reinforcement global feature vector, and perform corresponding guidance for the nursing step.
In the embodiment of the present application, the pediatric neural patient care record obtaining module 110 is configured to obtain a care record of the pediatric neural patient care by a learner. It should be understood that the care record for pediatric neurological patient care includes patient basic information, physical examination of the patient, care observation and evaluation, and care measures. In particular, care measures performed by the trainee are recorded, including information on the administration condition (drug name, dose, route), care operation (e.g., diaper change, cleaning care, cannula care), rehabilitation care (e.g., physical therapy, language therapy), and the like, and care effects and results. The effects and results of the student after the care, including improvement of symptoms of the patient, changes in vital signs, improvement of functions, etc., are recorded. Based on the above, in the technical scheme of the application, the nursing record of the child neurological patient nursing by the student is obtained, and the performance of the student in the actual nursing process can be supervised and evaluated. This helps determine the strength and improvement points of the learner in terms of care skills, knowledge and judgment to provide targeted training and instruction. Likewise, the student's care records can be used for teaching and learning evaluation purposes, and the student's understanding and application ability of the child neurological patient care can be evaluated according to the student's care records, so that basis is provided for subsequent teaching plans and personalized guidance.
In this embodiment of the present application, the care record semantic encoding module 120 is configured to semantically encode the care record of the pediatric neural patient care to obtain a pediatric neural patient care semantic understanding feature matrix. Fig. 3 is a block diagram of a care record semantic coding module in a teaching guidance detection system for pediatric neural care according to an embodiment of the application. Specifically, in the embodiment of the present application, as shown in fig. 3, the care record semantic coding module 120 includes: a word segmentation processing unit 121, configured to perform word segmentation processing on the care record to obtain a care record after word segmentation processing; the semantic understanding unit 122 is configured to perform semantic understanding on the care record after the word segmentation processing to obtain a sequence of pediatric neural patient care semantic understanding feature vectors; and a feature arrangement unit 123 for arranging the sequence of pediatric neural patient care semantic understanding feature vectors into the pediatric neural patient care semantic understanding feature matrix.
Specifically, the word segmentation processing unit 121 is configured to perform word segmentation processing on the care record to obtain a care record after word segmentation processing. It should be appreciated that considering that a lot of text information is included in the care record, if the processing is directly performed, the processing time is longer and the efficiency is lower. Based on the above, in the technical scheme of the application, the nursing record is subjected to word segmentation processing to obtain the nursing record after word segmentation processing, so that the nursing record can help to filter out some meaningless characters or punctuation marks, thereby purifying data and improving the effect of subsequent processing. In addition, the word segmentation processing of the nursing records can segment the recorded text according to the units of words, divide the continuous character sequence into meaningful words, and each word represents different nursing information, so that the meaning and the context relation of each word can be understood, and the text processing efficiency and accuracy are improved.
Specifically, the semantic understanding unit 122 is configured to perform semantic understanding on the care record after the word segmentation processing to obtain a sequence of semantic understanding feature vectors for pediatric neural patient care. Specifically, in the embodiment of the application, the semantic understanding unit is used for enabling the nursing records after word segmentation to pass through a nursing record semantic encoder comprising an embedded layer to obtain a sequence of nursing semantic understanding feature vectors of the pediatric neural patient. Accordingly, it is considered that each piece of care information of the care record after the word segmentation process has semantic information between contexts. Therefore, in order to better understand text semantic information in the nursing record after word segmentation, in the technical scheme of the application, the nursing record after word segmentation is processed through a nursing record semantic encoder comprising an embedded layer to obtain a sequence of nursing semantic understanding feature vectors of the pediatric neural patient, and semantic association and information among each piece of nursing information can be extracted from the sequence.
It is worth mentioning that the embedding layer can map each nursing record word into a low-dimensional continuous vector space, and the distance between the words with similar semantics in the vector space is closer, so that the semantic information of the nursing record words can be effectively represented, and semantic association of the words in the context can be considered. Further, the meaning and the context of the words can be better understood by learning the context information of the words. This is particularly important for words in care records, as the same word may have different meanings in different contexts. Therefore, the processing procedure can better utilize text information in the nursing records, and improve semantic understanding and application capability of the nursing records.
Specifically, the feature arrangement unit 123 is configured to arrange the sequence of the pediatric neural patient care semantic understanding feature vectors into the pediatric neural patient care semantic understanding feature matrix. It should be appreciated that each pediatric neuro-patient care semantic understanding feature vector in the sequence of pediatric neuro-patient care semantic understanding feature vectors is considered to represent different care information. Therefore, in the technical scheme of the application, the sequence of the pediatric neural patient care semantic understanding feature vectors is arranged as the pediatric neural patient care semantic understanding feature matrix, the pediatric neural patient care semantic understanding feature vectors can be arranged according to a certain structure and sequence, and the obtained pediatric neural patient care semantic understanding feature matrix can capture the relevance among the pediatric neural patient care semantic understanding features. That is, adjacent feature vectors are arranged adjacently in a matrix, and the similarity or correlation between them can be more easily observed.
In the embodiment of the present application, the feature attention enhancing module 130 is configured to perform attention enhancement on the pediatric neural patient care semantic understanding feature matrix to obtain a pediatric neural patient care semantic understanding enhancement feature matrix. Specifically, in an embodiment of the present application, the feature attention enhancing module is configured to: inputting the pediatric neural patient care semantic understanding feature matrix into a bidirectional attention mechanism based on a convolutional neural network to obtain the pediatric neural patient care semantic understanding reinforcement feature matrix. Accordingly, it is considered that Convolutional Neural Networks (CNNs) are excellent in processing text data in natural language processing tasks. Therefore, in the technical scheme of the application, the pediatric neural patient nursing semantic understanding feature matrix is input into a bidirectional attention mechanism based on a convolutional neural network to obtain the pediatric neural patient nursing semantic understanding reinforcement feature matrix, and key pediatric neural patient nursing local features can be extracted from the pediatric neural patient nursing semantic understanding reinforcement feature matrix, so that important semantic information in a nursing record is captured. It is worth mentioning that the bi-directional attention mechanism is capable of performing the attention calculations in both directions of the feature matrix, i.e. forward and backward. It should be appreciated that by calculating the attention weight, features can be weighted, important features are emphasized and non-important features are suppressed so that context information and association information in the feature matrix can be simultaneously focused to better capture the dependency and semantic association between features.
More specifically, in the embodiment of the application, the feature attention enhancement module comprises a pediatric neural patient care semantic understanding feature pooling unit, a feature attention enhancement module and a feature attention enhancement module, wherein the pediatric neural patient care semantic understanding feature pooling unit is used for pooling the pediatric neural patient care semantic understanding feature matrix along the horizontal direction and the vertical direction respectively to obtain a first pediatric neural patient care semantic understanding pooling vector and a second pediatric neural patient care semantic understanding pooling vector; the pediatric neural patient care semantic understanding feature association coding unit is used for carrying out association coding on the first pediatric neural patient care semantic understanding pooling vector and the second pediatric neural patient care semantic understanding pooling vector to obtain a pediatric neural patient care semantic understanding bidirectional association matrix; the activating unit is used for inputting the pediatric neural patient care semantic understanding bidirectional correlation matrix into a Sigmoid activating function to obtain a pediatric neural patient care semantic understanding bidirectional correlation weight matrix; and the weight applying unit is used for calculating the point-by-point multiplication between the pediatric neural patient care semantic understanding bidirectional association weight matrix and the pediatric neural patient care semantic understanding feature matrix to obtain the pediatric neural patient care semantic understanding reinforcement feature matrix.
In this embodiment of the present application, the semantic association analysis module 140 is configured to segment the pediatric neural patient care semantic understanding enhancement feature matrix to obtain a sequence of pediatric neural patient care semantic understanding enhancement feature vectors, and then perform semantic association analysis processing to obtain a pediatric neural patient care semantic understanding enhancement global feature vector. Fig. 4 is a block diagram of a semantic association analysis module in a teaching guidance detection system for pediatric neural care according to an embodiment of the application. More specifically, in the embodiment of the present application, as shown in fig. 4, the semantic association analysis module 140 includes a feature matrix segmentation unit 141, configured to segment the pediatric neural patient care semantic understanding enhancement feature matrix to obtain a sequence of the pediatric neural patient care semantic understanding enhancement feature vectors; and a semantic association feature extraction unit 142, configured to pass the sequence of pediatric neural patient care semantic understanding enhancement feature vectors through a semantic association feature extractor based on a bidirectional long-short term neural network model to obtain the pediatric neural patient care semantic understanding enhancement global feature vector.
Specifically, in the embodiment of the present application, the feature matrix segmentation unit 141 is configured to segment the pediatric neural patient care semantic understanding enhancement feature matrix to obtain a sequence of the pediatric neural patient care semantic understanding enhancement feature vectors. It should be appreciated that there is a correlation between semantic and contextual relevance between the enhanced feature vectors in view of neighboring pediatric neurological patient care semantics. Therefore, in order to better observe and analyze the correlation between the real-time pediatric neural patient care semantic understanding enhancement features, in the technical scheme of the application, the pediatric neural patient care semantic understanding enhancement feature matrix is segmented to obtain a sequence of pediatric neural patient care semantic understanding enhancement feature vectors. That is, adjacent feature vectors are arranged adjacently in the sequence, and the similarity or correlation between them can be more easily observed, providing more reliable and accurate input for the following.
Specifically, in the embodiment of the present application, the semantic association feature extraction unit 142 is configured to pass the sequence of the pediatric neural patient care semantic understanding enhancement feature vectors through a semantic association feature extractor based on a bidirectional long-short term neural network model to obtain the pediatric neural patient care semantic understanding enhancement global feature vector. Accordingly, consider a long and short term memory neural network (LSTM) as a model of a recurrent neural network suitable for processing sequence data. By using LSTM, contextual information in a sequence can be modeled to better understand semantics and dependencies in a sequence. That is, the bi-directional LSTM can consider both forward and backward contexts in a sequence to further enhance the understanding of the sequence. Based on the above, in the technical scheme of the application, the sequence of the pediatric neural patient care semantic understanding enhancement feature vector is extracted by the semantic association feature extractor based on the bidirectional long-short term neural network model to obtain the pediatric neural patient care semantic understanding enhancement global feature vector, so that the relevance between the features in the sequence can be captured. It is worth mentioning that the LSTM model is able to memorize and update the state information in the sequence and adjust the weights of the features according to the context. This helps to extract semantic associations between features so that feature vectors can more accurately represent semantic information in care records. The obtained real-time pediatric neural patient nursing semantic understanding reinforced global feature vector synthesizes the information of the whole sequence, and the global feature vector can be regarded as semantic understanding and representation of the whole nursing record and has higher expression capacity.
In the embodiment of the present application, the feature optimization module 150 is configured to perform manifold hyper-convex correlation derivative representation optimization on the pediatric neural patient care semantic understanding enhancement global feature vector to obtain an optimized pediatric neural patient care semantic understanding enhancement global feature vector. Particularly, in the technical scheme of the application, firstly, the pediatric neural patient nursing record is subjected to word segmentation processing, and the sequence of the pediatric neural patient nursing semantic understanding feature vectors is obtained through a nursing record semantic encoder comprising an embedded layer. Then, the feature vector sequences are arranged as feature matrices, and the pediatric neural patient care semantic understanding reinforcement feature matrices are obtained through a bidirectional attention mechanism based on a convolutional neural network. And then, segmenting the reinforced feature matrix into a feature vector sequence, and obtaining the child nerve patient nursing semantic understanding reinforced global feature vector through a semantic association feature extractor based on a bidirectional long-short period neural network model. Finally, a care step requiring improvement is generated by a generator, and the care step is guided. However, since the feature extraction process in the technical scheme is performed step by step, from word segmentation processing to a semantic encoder and then to a convolutional neural network and a two-way long-short-term memory network model, each step introduces a certain information loss. These stepwise processes may lead to a complex data manifold (i.e., the shape of the data distribution) of the features in the high-dimensional feature space, discretizing the confidence distribution of the feature values in the target class probability tag domain. In particular, some of the original information may be lost when the features go through the word segmentation process and the semantic encoder, as the word segmentation process may not accurately capture the meaning of all the words, and the semantic encoder may not fully retain the original semantic information. In addition, the use of convolutional neural networks and two-way long-short term memory network models may introduce some smoothing and abstraction effects, further reducing the details and discrimination of features. Due to these information loss and smoothing effects, the data manifold of the feature values in the high-dimensional feature space may become complex, and the confidence distribution of the feature values in the target class probability tag domain may also become discretized. This means that in the feature space, the confidence of the feature values of different positions to the target class probability labels may have a large difference, and the information cannot be well aggregated. This may lead to inconsistent utilization of the characteristic values at different locations during subsequent generation and instruction, thereby affecting overall generation and instruction quality. Based on the above, in the technical scheme of the application, the manifold hyper-convex correlation derivative expression optimization is performed on the pediatric neural patient care semantic understanding reinforcement global feature vector.
Specifically, in an embodiment of the present application, the feature optimization module is configured to: performing manifold hyper-convex correlation derivative representation optimization on the pediatric neural patient care semantic understanding enhancement global feature vector by using the following optimization formula to obtain the optimized pediatric neural patient care semantic understanding enhancement global feature vector; wherein, the optimization formula is:
；
wherein,representing the pediatric neural patient care semantic understanding enhanced global feature vector, < >>A +.f. representing the pediatric neurological patient care semantic understanding enhanced global feature vector>Characteristic value of individual position->A +.f. representing the pediatric neurological patient care semantic understanding enhanced global feature vector>Characteristic value of individual position->And->Representing a weight superparameter->Representing the optimized childNervous patient care semantic understanding enhanced global feature vector +.>Characteristic values of the individual positions.
In the technical scheme of the application, manifold superconvex correlation derivative expression optimization is carried out on the pediatric neural patient care semantic understanding reinforced global feature vector, and the feature value of each position in the feature vector can keep superconvex correlation in the sub-dimension by constructing a normalization function based on the robustness of information minimization loss, so that the information clustering of the feature vector is realized. Therefore, information redundancy and noise in the feature vector are effectively suppressed, the information aggregation degree of the feature vector is enhanced, and the confidence degree of the feature vector is improved.
In this embodiment, the care step generating module 160 is configured to generate a care step that needs to be improved based on the enhanced global feature vector for optimizing the semantic understanding of the pediatric neural patient care, and perform corresponding guidance for the care step. Accordingly, considering that the nursing of the pediatric neural patient involves a plurality of links and steps, the information of the aspects of the illness state, the treatment plan, the doctor's advice and the like of the patient needs to be comprehensively considered. Based on the above, in the technical solution of the present application, by strengthening the global feature vector based on the pediatric neural patient care semantic understanding, the information can be integrated to form a global care feature representation. On the basis of this global feature, problems and improvement points in the care process can be analyzed more comprehensively. In addition, the analysis of global feature vectors can be enhanced through semantic understanding of pediatric neurological patient care, and patterns and rules can be found in the analysis. These patterns may include recurring problems, potential risk factors, unreasonable care steps, and the like. By finding out these modes, improvement measures can be taken in time, improving the nursing quality and patient safety. Furthermore, the care process is a dynamic process and requires continuous improvement and optimization. Implementation of continuous improvement may be facilitated by enhancing global feature vector generation based on pediatric neurological patient care semantic understanding requiring improved care steps. Based on the generated improvement suggestions, a corresponding improvement plan can be formulated and verified and adjusted in practice to continuously improve the quality of care.
Specifically, in the embodiment of the present application, the care step generating module is configured to: generating the nursing step needing improvement through a generator by using the optimized pediatric neural patient nursing semantic understanding reinforced global feature vector, and carrying out corresponding guidance on the nursing step. That is, classification processing is performed based on optimizing the pediatric neurological patient care semantic understanding enhancement global features, so that care steps requiring improvement are determined to be generated, and corresponding guidance is performed on the care steps. In this way, specific improvement suggestions and practice guidelines can be provided to the trainee, improving the quality of care and patient safety. It should be appreciated that the care steps generated by the generator may provide guidance in real time, helping the learner apply the improvement in practice. The generator may generate specific guidance suggestions, including specific operational steps, notes, and assessment indicators, from the pediatric neurological patient care semantic understanding enhancement global feature vectors. Such real-time guidance helps to ensure proper implementation of the improvement measures, and timely adjustments and feedback.
To sum up, a teaching instruction detection system 100 for pediatric neural care according to an embodiment of the present application is illustrated that generates care steps that require improvement by acquiring a care record of pediatric neural patient care by a learner and performing semantic feature analysis and global semantic fusion on the care record of pediatric neural patient care using a deep learning technique. By the method, specific improvement suggestions and practical guidance can be provided for medical students, and the medical students can understand and master the knowledge and skills of child nerve nursing more deeply, so that the nursing quality and the patient safety are improved.
Fig. 5 is a flowchart of a teaching instruction detection method for pediatric neural care, according to an embodiment of the application. As shown in fig. 5, a teaching instruction detection method for pediatric neural care according to an embodiment of the present application includes: s110, acquiring a nursing record of nursing of a student on the pediatric neural patient; s120, carrying out semantic coding on the nursing records of the nursing of the pediatric neural patient to obtain a nursing semantic understanding feature matrix of the pediatric neural patient; s130, carrying out attention enhancement on the pediatric neural patient nursing semantic understanding feature matrix to obtain a pediatric neural patient nursing semantic understanding enhancement feature matrix; s140, segmenting the pediatric neural patient care semantic understanding enhancement feature matrix to obtain a sequence of pediatric neural patient care semantic understanding enhancement feature vectors, and then performing semantic association analysis processing to obtain pediatric neural patient care semantic understanding enhancement global feature vectors; s150, carrying out manifold hyper-convex correlation derivative expression optimization on the pediatric neural patient care semantic understanding enhancement global feature vector to obtain an optimized pediatric neural patient care semantic understanding enhancement global feature vector; and S160, generating a nursing step needing improvement based on the optimized pediatric neural patient nursing semantic understanding reinforced global feature vector, and correspondingly guiding the nursing step.
Here, it will be understood by those skilled in the art that the specific operations of the respective steps in the above-described teaching guidance detection method for pediatric neural care have been described in detail in the above description of the teaching guidance detection system for pediatric neural care with reference to fig. 1 to 4, and thus, repetitive descriptions thereof will be omitted.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A teaching instruction detection system for pediatric neural care, comprising:
the pediatric neural patient nursing record acquisition module is used for acquiring nursing records of a student for nursing the pediatric neural patient;
the nursing record semantic coding module is used for carrying out semantic coding on the nursing record of the nursing of the pediatric neural patient so as to obtain a nursing semantic understanding feature matrix of the pediatric neural patient;
The characteristic attention enhancement module is used for enhancing the attention of the pediatric neural patient nursing semantic understanding characteristic matrix to obtain the pediatric neural patient nursing semantic understanding enhancement characteristic matrix;
the semantic association analysis module is used for carrying out segmentation on the pediatric neural patient nursing semantic understanding reinforcement feature matrix to obtain a sequence of pediatric neural patient nursing semantic understanding reinforcement feature vectors, and then carrying out semantic association analysis processing to obtain pediatric neural patient nursing semantic understanding reinforcement global feature vectors;
the feature optimization module is used for carrying out manifold hyper-convex correlation derivative representation optimization on the pediatric neural patient care semantic understanding enhancement global feature vector so as to obtain an optimized pediatric neural patient care semantic understanding enhancement global feature vector;
and the nursing step generating module is used for generating nursing steps needing improvement based on the nursing semantic understanding reinforced global feature vector of the optimized pediatric neural patient and carrying out corresponding guidance on the nursing steps.
2. The instructional guidance detection system for pediatric neural care according to claim 1, wherein said care record semantic coding module comprises:
The word segmentation processing unit is used for carrying out word segmentation processing on the nursing records to obtain the nursing records subjected to word segmentation processing;
the semantic understanding unit is used for carrying out semantic understanding on the nursing records subjected to word segmentation processing to obtain a sequence of nursing semantic understanding feature vectors of the pediatric neural patient;
and the feature arrangement unit is used for arranging the sequence of the pediatric neural patient nursing semantic understanding feature vectors into the pediatric neural patient nursing semantic understanding feature matrix.
3. The system for teaching instruction detection for pediatric neural care of claim 2, wherein the semantic understanding unit is configured to pass the segmented care records through a care record semantic encoder comprising an embedded layer to obtain the sequence of pediatric neural patient care semantic understanding feature vectors.
4. The instructional guidance detection system for pediatric neural care according to claim 3, characterized in that said characteristic attention enhancement module is for: inputting the pediatric neural patient care semantic understanding feature matrix into a bidirectional attention mechanism based on a convolutional neural network to obtain the pediatric neural patient care semantic understanding reinforcement feature matrix.
5. The instructional testing system for pediatric neural care according to claim 4, wherein said semantic association analysis module comprises:
the feature matrix segmentation unit is used for segmenting the pediatric neural patient nursing semantic understanding reinforcement feature matrix to obtain a sequence of pediatric neural patient nursing semantic understanding reinforcement feature vectors;
the semantic association feature extraction unit is used for enabling the sequence of the pediatric neural patient care semantic understanding enhancement feature vector to pass through a semantic association feature extractor based on a bidirectional long-short-term neural network model to obtain the pediatric neural patient care semantic understanding enhancement global feature vector.
6. The instructional guidance detection system for pediatric neural care according to claim 5, wherein said feature optimization module is configured to: performing manifold hyper-convex correlation derivative representation optimization on the pediatric neural patient care semantic understanding enhancement global feature vector by using the following optimization formula to obtain the optimized pediatric neural patient care semantic understanding enhancement global feature vector;
wherein, the optimization formula is:
；
wherein,representing the pediatric neural patient care semantic understanding enhanced global feature vector, < > >A +.f. representing the pediatric neurological patient care semantic understanding enhanced global feature vector>Characteristic value of individual position->A +.f. representing the pediatric neurological patient care semantic understanding enhanced global feature vector>Characteristic value of individual position->And->Representing a weight superparameter->A +.f. representing the optimized pediatric neurological patient care semantic understanding enhanced global feature vector>Characteristic values of the individual positions.
7. The tutorial instruction detection system for pediatric neural care of claim 6, wherein the care step generation module is configured to: generating the nursing step needing improvement through a generator by using the optimized pediatric neural patient nursing semantic understanding reinforced global feature vector, and carrying out corresponding guidance on the nursing step.
8. A teaching instruction detection method for pediatric neural care, comprising:
acquiring a nursing record of a student for nursing the pediatric neural patient;
carrying out semantic coding on the nursing records of the nursing of the pediatric neural patient to obtain a nursing semantic understanding feature matrix of the pediatric neural patient;
the pediatric neural patient nursing semantic understanding feature matrix is subjected to attention enhancement to obtain a pediatric neural patient nursing semantic understanding enhancement feature matrix;
The pediatric neural patient nursing semantic understanding enhancement feature matrix is segmented to obtain a sequence of pediatric neural patient nursing semantic understanding enhancement feature vectors, and then semantic association analysis processing is carried out to obtain pediatric neural patient nursing semantic understanding enhancement global feature vectors;
performing manifold hyper-convex correlation derivative representation optimization on the pediatric neural patient care semantic understanding enhancement global feature vector to obtain an optimized pediatric neural patient care semantic understanding enhancement global feature vector;
and generating a nursing step needing improvement based on the optimized pediatric neural patient nursing semantic understanding reinforced global feature vector, and carrying out corresponding guidance on the nursing step.
9. The method of claim 8, wherein semantically encoding the care records of pediatric neurological patient care to obtain a pediatric neurological patient care semantic understanding feature matrix comprises:
performing word segmentation on the nursing records to obtain segmented nursing records;
carrying out semantic understanding on the nursing records subjected to word segmentation processing to obtain a sequence of nursing semantic understanding feature vectors of the pediatric neural patient;
And arranging the sequence of the pediatric neural patient care semantic understanding feature vectors into the pediatric neural patient care semantic understanding feature matrix.
10. The method for teaching instruction detection for pediatric neural care of claim 9, wherein semantically interpreting the word-segmented care records to obtain a sequence of pediatric neural patient care semantic understanding feature vectors, comprising: and (3) passing the nursing records subjected to word segmentation through a nursing record semantic encoder comprising an embedded layer to obtain a sequence of nursing semantic understanding feature vectors of the pediatric neural patient.
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