Psychological nursing method and system based on hepatobiliary surgery patients

Abstract

The application provides a psychological nursing method and system based on a hepatobiliary surgery patient, and the method comprises the following steps: collecting data of a target object; determining a character image feature of the target object by analyzing and processing the data of the target object; constructing a psychological nursing intervention knowledge base within the cognitive range of the target object by matching psychological nursing knowledge based on the character image of the target object; generating a psychological nursing intervention scheme conforming to the character image of the target object based on the psychological nursing intervention knowledge base and the language style of the target object by matching the language style of the target object; and triggering the psychological nursing intervention scheme when the probability of the negative psychological state of the target object exceeds a preset threshold by monitoring the psychological state of the target object in real time. The application generates a psychological nursing scheme for a hepatobiliary surgery patient through deep user image and intelligent language matching, improves the psychological nursing effect of the hepatobiliary surgery patient, and promotes psychological health management.

Description

Technical Field

[0001] This application relates to the field of psychological nursing monitoring technology, specifically a psychological nursing method and system for hepatobiliary surgery patients. Background Technology

[0002] Currently, while the concept of personalized care has been applied in providing psychological nursing care to hepatobiliary surgery patients, its implementation still heavily relies on patients' health data. This involves analyzing this data to identify patient groups with similar conditions, disease progression, and psychological states, and then generating targeted nursing plans based on these group characteristics. While this model appears to achieve "personalization," it is actually a limited adjustment based on the commonalities of the group. In practice, the significant differences in cognitive levels among hepatobiliary surgery patients often lead to significantly reduced nursing effectiveness, or even ineffectiveness.

[0003] Hepatobiliary surgical diseases are often complex, involve long treatment periods, and carry high surgical risks, making patients inherently more sensitive and vulnerable psychologically when facing their illness. Furthermore, patients' cognitive levels are influenced by various factors such as age, education background, living environment, and understanding of the disease, exhibiting significant individual differences. For example, for patients with higher levels of education and some medical knowledge, nursing plans based on similar cases, which include professional explanations of disease pathology, treatment principles, and rehabilitation mechanisms, along with psychological guidance, are likely to be more easily understood and accepted, thus playing a positive role.

[0004] However, the situation is quite different for patients with lower cognitive levels and a lack of medical knowledge. They may have difficulty understanding the overly technical terminology and logic in the nursing care plan, and may also misunderstand or resist the psychological adjustment methods conveyed by nursing staff.

[0005] Patients with different cognitive levels have different ways of receiving and needing nursing information. Patients with high cognitive levels may prefer detailed and in-depth information, including various possibilities and coping strategies during treatment; while patients with lower cognitive levels may need simple, intuitive, and easy-to-understand guidance. Too much technical information may confuse and stress them. If nursing plans fail to fully consider these differences and are only developed based on the similarity of health data, they are likely to fail to accurately address the psychological needs of patients and be difficult to achieve practical results.

[0006] Therefore, how to break through the limitations of this approach based on the similarity of health data and truly achieve psychological care that can be adapted to patients with different cognitive levels has become a key issue in improving the effectiveness of psychological care for hepatobiliary surgery patients. Summary of the Invention

[0007] To overcome the aforementioned problems in the existing technology, this application provides a psychological nursing method and system for hepatobiliary surgery patients, which adopts the following technical solution:

[0008] Firstly, this application provides a psychological nursing method for hepatobiliary surgery patients, including:

[0009] Collect data from the target object;

[0010] Determine the character profile features of the target object based on the target object's data;

[0011] Based on the characteristics of the target object's profile, psychological nursing knowledge is matched to construct a psychological nursing intervention knowledge base within the target object's cognitive range;

[0012] Language style matching is performed on the target subjects, and a psychological nursing intervention plan that matches the target subjects' language style is generated based on the psychological nursing intervention knowledge base and the target subjects' language style.

[0013] The psychological state of the target object is monitored in real time. When the probability of the target object's negative psychological state exceeds a preset threshold, the psychological nursing intervention plan is triggered.

[0014] Furthermore, the step of analyzing and processing the data of the target object includes: extracting features from the data of the target object through feature engineering to obtain various feature vectors.

[0015] Furthermore, determining the portrait features of the target object includes:

[0016] The various feature vectors are input into the corresponding feature encoder in the attention mechanism, and the encoder outputs a latent semantic vector. The latent semantic vector is dynamically calculated using a multi-head self-attention function to calculate the weight distribution of various features in the overall portrait representation, and outputs a weighted fusion high-dimensional representation. A weight gate is generated by the sigmoid activation function in the gating mechanism to adjust the weights of each dimension of the weighted fusion high-dimensional representation, dynamically adjusting the contribution of the feature combination, and outputting the portrait features of the target object.

[0017] Furthermore, determining the portrait features of the target object also includes: dynamically selecting the weights of the real-time and historical portrait features of the target object through a sliding window, comprehensively considering the state change trend of the target object, and achieving smooth updates through weighted fusion to ensure that the portrait reflects the current state of the target object.

[0018] Furthermore, the step of matching psychological nursing knowledge with the profile characteristics of the target object to construct a psychological nursing intervention knowledge base within the target object's cognitive range includes:

[0019] The similarity measure between the profile features of the target object and the feature vector of the preset structured psychological nursing knowledge base is obtained; psychological nursing knowledge items with similarity exceeding a preset threshold are used as a set of candidate intervention schemes; the set of candidate intervention schemes is weighted and sorted based on the key feature weights and clinical priority information of the target object to obtain a comprehensive score; the intervention schemes with the highest scores are used as the psychological nursing intervention knowledge base for the target object.

[0020] Furthermore, the step of generating a psychological nursing intervention plan that matches the target object's language style based on the psychological nursing intervention knowledge base and the target object's language style includes:

[0021] Natural language expression information of the target object is collected and converted into text data. The text data is analyzed based on feature engineering to construct the language style feature vector of the target object. Combining the language style feature vector and the psychological nursing intervention knowledge base, a psychological nursing intervention plan that conforms to the language style of the target object is generated based on text generation technology.

[0022] Secondly, this application also provides a psychological care system for hepatobiliary surgery patients, comprising:

[0023] The data acquisition module is used to collect data from the target object;

[0024] The module for determining the profile features of the target object is used to determine the profile features of the target object by analyzing and processing the data of the target object.

[0025] The psychological nursing intervention knowledge base acquisition module is used to construct a psychological nursing intervention knowledge base within the cognitive range of the target object by matching psychological nursing knowledge based on the target object's profile.

[0026] The psychological nursing intervention plan generation module is used to generate a psychological nursing intervention plan that matches the target object's language style based on the psychological nursing intervention knowledge base and the target object's language style.

[0027] The psychological nursing intervention program triggering module is used to monitor the psychological state of the target object in real time. When the probability of the target object's negative psychological state exceeds a preset threshold, the psychological nursing intervention program is triggered.

[0028] Thirdly, this application provides an electronic device, comprising:

[0029] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the method as described in the first aspect.

[0030] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the method described in the first aspect.

[0031] Fifthly, this application provides a computer program that, when executed by a computer, performs the method described in the first aspect.

[0032] In one possible design, the program in the fifth aspect can be stored wholly or partially on a storage medium packaged with the processor, or it can be stored wholly or partially on a memory not packaged with the processor.

[0033] This application has the following beneficial effects:

[0034] 1. This application involves collecting data on target individuals; analyzing and processing the data to determine the characteristics of the target individuals' personal profiles; and constructing a psychological nursing intervention knowledge base within the target individuals' cognitive scope by matching psychological nursing knowledge with their personal profiles. By constructing a psychological nursing intervention knowledge base within the cognitive scope of hepatobiliary surgery patients, this application enables psychological nursing based on the patients' cognition, thereby accurately reaching the patients' psychological needs and producing practical psychological nursing effects.

[0035] 2. This application generates a psychological nursing intervention plan that matches the target object's language style based on a psychological nursing intervention knowledge base and the target object's language style. By monitoring the target object's psychological state in real time, the intervention plan is triggered when the probability of a negative psychological state exceeds a preset threshold. This application generates psychological nursing plans that match the language style of hepatobiliary surgery patients. Through deep user profiling and intelligent language matching, this application improves the effectiveness of psychological nursing care for hepatobiliary surgery patients and promotes mental health management. Attached Figure Description

[0036] Figure 1 This application provides an embodiment of a psychological care system for hepatobiliary surgery patients.

[0037] Figure 2This is a flowchart illustrating a psychological nursing method for hepatobiliary surgery patients according to an embodiment of this application.

[0038] Figure 3 Flowchart for determining the portrait of a person in this application embodiment;

[0039] Figure 4 This is a flowchart illustrating the process of determining hierarchical tags for a person's portrait in an embodiment of this application.

[0040] Figure 5 This is a system flowchart of an embodiment of this application;

[0041] Figure 6 This is a schematic diagram of a computer device according to an embodiment of this application. Detailed Implementation

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0043] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0044] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0045] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0046] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0047] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0048] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0049] It should be noted that the psychological nursing method for hepatobiliary surgery patients provided in this application is generally executed by a server / terminal device, and correspondingly, the psychological nursing system for hepatobiliary surgery patients is generally set up in the server / terminal device.

[0050] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0051] In this embodiment of the application, the target object refers to a hepatobiliary surgery patient.

[0052] Example 1

[0053] Reference Figure 2 , Figure 2 This is an optional flowchart of a psychological nursing method for hepatobiliary surgery patients provided in an embodiment of the present disclosure. The psychological nursing method for hepatobiliary surgery patients can be executed by a terminal, by a server, or by a combination of both. The psychological nursing method for hepatobiliary surgery patients includes, but is not limited to, the following steps 201 to 205.

[0054] Step 201: Collect data from the target object.

[0055] In this embodiment of the application, the data acquisition module is used for the target object's basic data, disease and treatment-related data, psychological state data, behavioral data, cognitive indicators, and environmental and social data.

[0056] In this embodiment, the basic data of the target subjects includes age, education level, occupation, and language preference. Disease and treatment-related data includes the disease type, complications, and medication records of hepatobiliary surgery patients. Psychological state data includes test results obtained through standard scales and the target subjects' subjective symptom descriptions. Behavioral data includes the target subjects' daily expression style and text complexity. Cognitive indicators include the target subjects' educational background and level of medical knowledge. Environmental and social data includes the target subjects' family support and community participation.

[0057] Step 202: By analyzing and processing the data of the target object, the portrait characteristics of the target object are determined.

[0058] In one possible implementation, the analysis and processing of the target object's data includes:

[0059] Feature engineering is used to extract features from the data of the target object, obtaining various feature vectors. In this embodiment, feature engineering is used to extract features from the data of the target object, obtaining various feature vectors. For example, demographic attributes in the basic numerical values ​​of the target object are encoded into categorical feature vectors; disease grouping labels and treatment plan features of the target object are constructed using knowledge from the field of hepatobiliary surgery, and combined with the disease stage and medication intensity indicators of the target object, a set of disease-related features of the target object is formed; scale scores are quantified, and central tendency and fluctuation features in the scale are extracted through statistical analysis to form the psychological state features of the target object; temporal features are extracted from behavioral data such as sleep duration and activity intensity of the target object to obtain behavioral data features of the target object; the results of the cognitive test scale of the target object are converted into fixed-dimensional vectors to obtain the authentication index features of the target object; and the comprehensive living environment quality index and social support of the target object are combined to construct the social environment support feature vector of the target object.

[0060] In one possible implementation, the characteristics of the target object's portrait are determined; please refer to [reference needed]. Figure 3 The specific content includes:

[0061] Step 31: Input the various feature vectors into the corresponding feature encoder in the attention mechanism. The encoder outputs a latent semantic vector. Assuming the target object contains N different categories of feature vectors, which can be denoted as x... iWhere i is the category index, i ranges from 1 to N, and each x i After passing through the corresponding feature encoder E i Mapped to a latent semantic vector H i H i =E i (x i ).

[0062] Step 32: Dynamically calculate the weight distribution of various features in the overall image representation using a multi-head self-attention function on the latent semantic vectors, and output a weighted fusion high-dimensional representation. For all latent semantic vectors H... i The weight distribution of various features in the overall profile representation is dynamically calculated using a multi-head self-attention function. Assuming the multi-head self-attention function is MHA(·), the high-dimensional representation Z of the weighted fusion is: Z = MHA(H1, H2, ..., H N The multi-head self-attention mechanism calculates the attention weights for each head h, as shown below: Q h ,K h V h Let d represent the query, key, and value vector corresponding to the h-th header, respectively. k This represents the scaling factor for the key vector dimension. The outputs of all heads are concatenated and linearly transformed to obtain the multi-head output: MHA(H) = Concat(head1, head2, ..., head...). h )*W O W O This is the linear transformation weight matrix.

[0063] Step 33: A weight gate is generated using the sigmoid activation function in the gating mechanism. This gate weights and adjusts the dimensions of the high-dimensional representation of the weighted fusion, dynamically adjusting the contribution of the feature combination and outputting the portrait features of the target object. The gating mechanism filters and controls the information flow. The gating G can be calculated using the multi-head output Z: G = σ(W g *Z+b g ), where σ is the sigmoid activation function, W g b g These are the parameters of the gating layer. The final portrait feature P of the target object can be obtained by calculating the element-wise product of the multi-head attention output Z and the gating layer G: This represents element-wise product.

[0064] In one possible implementation, determining the portrait features of the target object further includes:

[0065] Step 41: Aggregate the portrait features of the target object based on hierarchical clustering.

[0066] Step 42: Based on the hierarchical clustering results, the clusters at each level are explained and named. The top-level label represents the macro-classification, the middle-level labels are subdivided into specific psychological state and behavioral categories, and the bottom-level labels are further refined into specific indicators.

[0067] Step 43: Associate the labels in the hierarchical clustering with the portrait feature mapping, and determine the contribution of each label to the portrait features through feature evaluation.

[0068] In this embodiment, hierarchical clustering is used to aggregate the portrait features of the target object. Based on the hierarchical clustering results, the process includes: initializing each feature as an independent cluster, calculating the similarity matrix between clusters, evaluating the distance between clusters using the average link method, updating the distance matrix between clusters, and repeatedly evaluating and updating the distance matrix between clusters until a preset stopping condition is met or the required number of clusters is satisfied. For example, the top-level label can be classified as good or sub-healthy mental health; the bottom-level labels can be anxiety level, cognitive dysfunction type, etc.

[0069] This application achieves multi-level structuring of the target object through hierarchical clustering. By recursively merging features based on their similarity, a hierarchical tree structure of labels is constructed, effectively realizing the hierarchical structured label expression of the target object based on the person's profile.

[0070] In one possible implementation, determining the portrait features of the target object further includes: dynamically selecting the weights of the real-time and historical portrait features of the target object through a sliding window, comprehensively considering the state change trend of the target object, and achieving smooth updates through weighted fusion to ensure that the portrait reflects the current state of the target object.

[0071] Step 203: Based on the profile characteristics of the target object, match psychological nursing knowledge and construct a psychological nursing intervention knowledge base within the cognitive scope of the target object.

[0072] In this embodiment of the application, the step of matching psychological nursing knowledge with the portrait features of the target object to construct a psychological nursing intervention knowledge base within the cognitive range of the target object includes:

[0073] The similarity metric between the target object's profile features and the feature vector of a pre-defined structured psychological nursing knowledge base is obtained. This application can use cosine similarity or Euclidean distance to obtain the similarity metric. Psychological nursing knowledge items with similarity exceeding a preset threshold are selected as a set of candidate intervention programs. Based on the key feature weights and clinical priority information of the target object, the set of candidate intervention programs is weighted and ranked to obtain a comprehensive score. The intervention programs with the highest scores are then used as the psychological nursing intervention knowledge base for the target object.

[0074] In this embodiment of the application, the similarity metric between the obtained portrait features of the target object and the feature vector of the preset structured psychological nursing knowledge base can be expressed as: Where p i k represents the feature vector of the target object's portrait. i Let i represent the i-th knowledge base feature vector.

[0075] In this embodiment of the application, the comprehensive score result of the candidate intervention plan can be represented as Score. i =SIM i ×w i +P i , where P i Indicates clinical priority, w i This indicates the weight of features in the person's profile. Successful implementation of this application will allow for the priority selection of the most clinically valuable psychological nursing interventions.

[0076] This application achieves the construction of a personalized psychological nursing intervention knowledge base for the target object by vector matching and weighted sorting of the target object and psychological nursing knowledge.

[0077] Step 204: By matching the language style of the target object, a psychological nursing intervention plan that matches the target object's personality profile is generated based on the psychological nursing intervention knowledge base and the target object's language style.

[0078] In this embodiment of the application, the step of generating a psychological nursing intervention plan that matches the target object's language style based on the psychological nursing intervention knowledge base and the target object's language style includes:

[0079] The natural language expression information of the target object is collected and converted into text data. The text data is analyzed based on feature engineering to construct the language style feature vector of the target object. The natural language expression information of the target object includes speech data collected based on speech recognition and written language data collected based on text processing. The speech data of the target object is then converted into text data.

[0080] In this embodiment, the text data of the target object is first preprocessed, including text cleaning to remove noise and irrelevant information, and word segmentation and part-of-speech tagging to obtain basic language units. Subsequently, deep semantic representation technology is used to convert the processed text into a continuous vector representation, fully capturing the contextual dependencies and semantic connections between words. Simultaneously, statistical analysis is used to calculate the high-frequency vocabulary, sentence structure, and intonation features of the target object, quantifying its language rhythm and expression style. These multi-dimensional features are then fused to finally construct a complete language style feature vector.

[0081] By combining the language style feature vector and the psychological nursing intervention knowledge base, a psychological nursing intervention plan that conforms to the language style of the target object is generated based on text generation technology.

[0082] In this embodiment, a vector angle-based metric is used to calculate the similarity between the target object's language style feature vector and the psychological nursing intervention plan vector, reflecting their stylistic consistency. Based on the calculated similarity score, the psychological nursing intervention plans are ranked, and plans that match the target object's language style are selected as a candidate set. This ensures that the selected content performs well in terms of semantic relevance and style adaptability.

[0083] In this embodiment, based on conditional text generation technology, the language style feature vector of the target object is used as a conditional input. Combined with selected psychological nursing intervention knowledge content, an attention mechanism is used to assign different weights to the language style features and the intervention knowledge content to generate a psychological nursing intervention plan. This achieves dynamic integration and synergistic optimization of language style features and intervention knowledge. The generated psychological nursing intervention plan ensures both the professionalism of psychological nursing and the consistency of intervention goals, while fully reflecting the target object's expression habits and emotional characteristics, thereby enhancing the personalized targeting and patient acceptance of the plan.

[0084] Step 205: By monitoring the psychological state of the target object in real time, when the probability of the target object's negative psychological state exceeds a preset threshold, the psychological nursing intervention plan is triggered.

[0085] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0086] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0087] Example 2

[0088] Continued reference Figure 5 As a response to the above Figure 2 The implementation of the method shown in this application provides an embodiment of a psychological nursing method for hepatobiliary surgery patients. This system embodiment is similar to... Figure 2 Corresponding to the illustrated method embodiment, this system can be specifically applied to various electronic devices, including: a data acquisition module 501, a module 502 for determining the portrait features of the target object, a psychological nursing intervention knowledge base acquisition module 503, a psychological nursing intervention plan generation module 504, and a psychological nursing intervention plan triggering module 505. Wherein:

[0089] Data acquisition module 501 is used to acquire data from the target object;

[0090] The character profile feature determination module 502 is used to determine the character profile features of the target object by analyzing and processing the data of the target object.

[0091] The psychological nursing intervention knowledge base acquisition module 503 is used to construct a psychological nursing intervention knowledge base within the cognitive range of the target object by matching psychological nursing knowledge based on the person profile of the target object.

[0092] The psychological nursing intervention program generation module 504 is used to generate a psychological nursing intervention program that matches the target object's language style based on the psychological nursing intervention knowledge base and the target object's language style.

[0093] The psychological nursing intervention program triggering module 505 is used to monitor the psychological state of the target object in real time. When the probability of the target object's negative psychological state exceeds a preset threshold, the psychological nursing intervention program is triggered.

[0094] This application generates psychological nursing intervention plans for hepatobiliary surgery patients by collecting data on target individuals; analyzing and processing the data to determine the target individuals' profile characteristics; matching psychological nursing knowledge with the target individuals' profiles to construct a psychological nursing intervention knowledge base within the target individuals' cognitive scope; matching the target individuals' language styles with the psychological nursing intervention knowledge base and the target individuals' language styles to generate psychological nursing intervention plans that match the target individuals' profiles; and monitoring the target individuals' psychological states in real time, triggering the psychological nursing intervention plan when the probability of the target individuals' negative psychological states exceeds a preset threshold. This application generates psychological nursing plans for hepatobiliary surgery patients through deep user profiling and intelligent language matching, improving the effectiveness of psychological nursing for hepatobiliary surgery patients and promoting mental health management.

[0095] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0096] The computer device 6 includes a memory 6a, a processor 6b, and a network interface 6c that are interconnected via a system bus. It should be noted that only the computer device 6 with components 6a-6c is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0097] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0098] The memory 6a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 6a may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 6a may also be an external storage device of the computer device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 6. Of course, the memory 6a may include both the internal storage unit and its external storage device of the computer device 6. In this embodiment, the memory 6a is typically used to store the operating system and various application software installed on the computer device 6, such as the program code of a big data-based medical service price monitoring and management method. In addition, the memory 6a can also be used to temporarily store various types of data that have been output or will be output.

[0099] In some embodiments, the processor 6b may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 6b is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 6b is used to run program code stored in the memory 6a or process data, for example, to run the program code of the big data-based medical service price monitoring and management method described in section 1.

[0100] The network interface 6c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 6 and other electronic devices.

[0101] This application also provides another embodiment, namely, a non-volatile computer-readable storage medium storing a program of a big data-based medical service price monitoring and management method, which can be executed by at least one processor to perform the steps of the big data-based medical service price monitoring and management method as described above.

[0102] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0103] Obviously, the embodiments described above are only some embodiments of this application, and not...

[0104] All embodiments are illustrated in the accompanying drawings, which show preferred embodiments of this application, but do not limit the scope of this application.

[0105] The scope of the patent application is requested. This application may be implemented in many different forms; conversely, it provides...

[0106] The purpose of these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application.

[0107] The present application has been described in detail with reference to the foregoing embodiments, and will be of great benefit to those skilled in the art.

[0108] For the staff, they can still make modifications to the technical solutions described in the aforementioned specific embodiments.

[0109] Modifications may be made, or equivalent substitutions may be made to some of the technical features. All uses of the descriptions in this application are subject to modification.

[0110] The equivalent structure of the content in the book and its illustrations is directly or indirectly applied to other related technical fields.

[0111] The same principle applies to all domains, which are within the scope of protection of this patent application.

Claims

1. A psychological nursing method for hepatobiliary surgery patients, characterized in that, include: Collect data from the target object; By analyzing and processing the data of the target object, the characteristics of the target object's persona are determined; By matching psychological nursing knowledge with the persona of the target object, a psychological nursing intervention knowledge base within the cognitive scope of the target object is constructed. By matching the language style of the target object, a psychological nursing intervention plan that matches the target object's personality profile is generated based on the psychological nursing intervention knowledge base and the target object's language style. By monitoring the psychological state of the target object in real time, the psychological nursing intervention plan is triggered when the probability of the target object's negative psychological state exceeds a preset threshold.

2. The psychological nursing method for hepatobiliary surgery patients according to claim 1, characterized in that, The process of analyzing and processing the data of the target object includes: Feature engineering is used to extract features from the data of the target object to obtain various feature vectors.

3. The psychological nursing method for hepatobiliary surgery patients according to claim 2, characterized in that, The process of determining the portrait features of the target object includes: The various feature vectors are input into the corresponding feature encoder in the attention mechanism, and the encoder outputs a latent semantic vector; The implicit semantic vector is dynamically calculated using a multi-head self-attention function to determine the weight distribution of various features in the overall image representation, and a weighted fusion high-dimensional representation is output. The sigmoid activation function in the gating mechanism generates weight gates, which are then used to adjust the weights of each dimension of the weighted fusion high-dimensional representation. This dynamically adjusts the contribution of the feature combination and outputs the portrait features of the target object.

4. The psychological nursing method for hepatobiliary surgery patients according to claim 1, characterized in that, The process of determining the portrait features of the target object further includes: The portrait features of the target object are aggregated based on hierarchical clustering. Based on the hierarchical clustering results, each level of cluster is explained and named. The top-level label represents the macro-classification, the middle-level labels are subdivided into specific psychological state and behavioral categories, and the bottom-level labels are further refined into specific indicators. The labels in the hierarchical clustering are associated with the portrait feature mapping, and the contribution of each label to the portrait feature is determined through feature evaluation.

5. The psychological nursing method for hepatobiliary surgery patients according to claim 1, characterized in that, The determination of the target object's profile features further includes: dynamically selecting the weights of the target object's real-time and historical profile features through a sliding window, comprehensively considering the target object's state change trend, and achieving smooth updates through weighted fusion to ensure that the profile reflects the target object's current state.

6. The psychological nursing method for hepatobiliary surgery patients according to claim 1, characterized in that, The process of matching psychological nursing knowledge with the target object's profile features to construct a psychological nursing intervention knowledge base within the target object's cognitive range includes: Obtain a similarity metric between the character profile features of the target object and the feature vector of a preset structured psychological nursing knowledge base; Psychological nursing knowledge items with similarity exceeding a preset threshold are used as a set of candidate intervention programs; The candidate intervention scheme set is weighted and sorted based on the key feature weights and clinical priority information of the target object to obtain a comprehensive score result; The highest-scoring intervention programs will be used as a psychological nursing intervention knowledge base for the target group.

7. The psychological nursing method for hepatobiliary surgery patients according to claim 1, characterized in that, The process involves matching the target object's language style with the target object's language style, generating a psychological nursing intervention plan that matches the target object's profile based on a psychological nursing intervention knowledge base, including: Natural language expression information of the target object is collected and converted into text data. The text data is analyzed based on feature engineering to construct the language style feature vector of the target object. Combining the language style feature vector and the psychological nursing intervention knowledge base, a psychological nursing intervention plan that conforms to the language style of the target object is generated based on text generation technology.

8. A psychological nursing system for hepatobiliary surgery patients, used to implement the psychological nursing methods for hepatobiliary surgery patients as described in claims 1-7, characterized in that, include: The data acquisition module is used to collect data from the target object; The module for determining the profile features of the target object is used to determine the profile features of the target object by analyzing and processing the data of the target object. The psychological nursing intervention knowledge base acquisition module is used to construct a psychological nursing intervention knowledge base within the cognitive range of the target object by matching psychological nursing knowledge based on the target object's profile. The psychological nursing intervention plan generation module is used to generate a psychological nursing intervention plan that matches the target object's language style based on the psychological nursing intervention knowledge base and the target object's language style. The psychological nursing intervention program triggering module is used to monitor the psychological state of the target object in real time. When the probability of the target object's negative psychological state exceeds a preset threshold, the psychological nursing intervention program is triggered.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-7.

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