50 results about "Nursing knowledge" patented technology

The invention discloses a nursing risk intervention decision-making system and method based on a knowledge graph, and relates to the technical field of medical informationization, and the method comprises the steps: building a dynamic medical nursing knowledge graph based on multi-source nursing data and a deep learning model, and carrying out the entity recognition through a bidirectional long-short term memory network and a conditional random field; capturing evolution characteristics of an entity relationship through a marginal attention mechanism and a time sequence diagram convolutional network, and optimizing a knowledge graph structure based on connectivity constraints and a relationship weight threshold; a knowledge structure is dynamically adjusted by using a strengthened learning driven graph optimization mechanism, the relationship weight between entities is optimized in real time, a multi-level risk representation model is constructed, and accurate recognition and early warning of nursing risks are realized through local risk feature extraction, global risk information aggregation and time sequence risk evolution analysis. According to the method, the complex association relationship between the medical entities and the nursing risk decision are deeply fused, and the multi-dimensional medical entity features are constructed into the association network between the entities, so that early risk identification is realized.

The invention relates to the technical field of medical care, in particular to an intelligent query method for a nursing knowledge base, which comprises the following steps of: performing multi-form standardized processing on a nursing problem input by a user, deeply analyzing a query request by utilizing a semantic understanding model, and obtaining an intelligent query result based on an inference algorithm; the method comprises the following steps: automatically selecting related literatures, clinical data and nursing experience data from a nursing knowledge base, generating a preliminary nursing scheme candidate set in combination with a nursing professional knowledge graph, and performing personalized screening and optimization on the scheme by using an intelligent recommendation model in combination with the current illness state, treatment progress and historical nursing data of a patient; the intelligent recommendation model is self-optimized through user feedback, and the nursing scheme recommendation precision is continuously improved. The nursing knowledge base automatically fuses newest medical research results and clinical nursing practice data. The problems that in the prior art, nursing scheme recommendation accuracy is low, updating is not timely, and the individuation degree is insufficient are effectively solved.

The invention discloses a knowledge graph-based nursing corpus automatic creation method, and relates to the field of artificial intelligence technology and medical information processing, and the method comprises a multi-dimensional data acquisition stage, a knowledge graph construction stage, a corpus automatic generation and updating stage, and a corpus quality control and evaluation stage. According to the automatic corpus generation method driven by the knowledge graph, dynamic generation and real-time updating of corpora are achieved by constructing an ontology structure, a corpus generation template and an updating algorithm in the nursing field; through a deep learning model and a transfer learning technology, new nursing knowledge and a diversified information structure are continuously introduced into the corpus, and the diversity of corpus content is increased.

The invention discloses a traditional Chinese medicine nursing management system and method based on a cloud follow-up visit management platform, and belongs to the technical field of intelligent nursing. The management system comprises the following modules: a dynamic data acquisition module for acquiring physiological parameters, living habits and environmental data of a patient in real time and performing primary processing; the dynamic health archive construction module is used for constructing and continuously updating an individualized health archive of the patient based on the data collected in real time; the traditional Chinese medicine knowledge map and reasoning module depends on the traditional Chinese medicine nursing knowledge map and combines an intelligent reasoning technology to realize accurate traditional Chinese medicine syndrome differentiation and treatment analysis and automatically generate a personalized nursing scheme; the multi-dimensional patient feedback module is used for constructing a feedback mechanism fusing objective data, patient subjective feeling and medical care evaluation so as to extract key nursing effect information; and the nursing scheme iterative optimization module dynamically adjusts and optimizes the traditional Chinese medicine nursing scheme based on the feedback data, and realizes personalized and self-adaptive continuous improved nursing.

The invention relates to the technical field related to anesthesia nursing data processing, in particular to an anesthesiology department clinical nursing data management method and system, and the method comprises the steps: collecting anesthesiology department clinical nursing data, configuring a first data mapping table and a second data mapping table through static features and dynamic features according to a platform end and an application end, and generating a nursing knowledge graph, adjacent event features are obtained, and multi-scale risk assessment is combined with dynamic threshold reminding. The technical problems that time sequence correlation and cooperative influence of all events in the anesthesia process are not fully considered, risk assessment standards cannot be dynamically adjusted for different anesthesia stages, and risk misjudgment is likely to be caused are solved, and by configuring a first data mapping table and a second data mapping table and combining time entropies and correlation rule support degrees corresponding to time intervals of adjacent events, the risk assessment standard can be dynamically adjusted according to the time entropies and the correlation rule support degrees. Layered management and accurate mapping of static and dynamic characteristics in clinical nursing data of the anesthesiology department are realized, the risk threshold is dynamically adjusted according to time sequence characteristics of different anesthesia stages, and the accuracy and timeliness of risk assessment are remarkably improved.

The embodiment of the invention provides a trauma nursing scheme generation method and system based on artificial intelligence, and the method comprises the steps: obtaining first basic medical data of a target object to be subjected to trauma nursing, and the first basic medical data comprises individual feature data, gene data, trauma text data and medical history text data; and performing data preprocessing and reconstruction operation on the first basic medical data to generate second basic medical data of the target object. And inputting the second basic medical data into a pre-constructed nursing analysis model to obtain at least one subsequent nursing stage label of the target object. And based on the subsequent nursing stage label, a nursing knowledge base and historical nursing data of a plurality of reference objects, determining a wound nursing recommendation scheme of the target object, thereby improving the accuracy of wound nursing scheme generation.

The invention discloses an intelligent question and answer system based on a nursing knowledge graph. The system comprises a nursing knowledge graph construction module, an AI intelligent question and answer module and a display module. The nursing knowledge graph construction module is used for collecting nursing data, performing document structuring and knowledge extraction on the collected nursing data and constructing a nursing knowledge graph; the AI intelligent question and answer module is used for receiving and analyzing various nursing knowledge questions proposed by a user, then generating query prompts, obtaining confidence coefficients of candidate answers by calling a large language model and combining the nursing knowledge graph, and searching answer options for the questions; and the display module is used for displaying nursing questions, nursing measures and nursing evaluation according to the answers, providing knowledge association information for the answers in a knowledge graph form, and helping a user to understand a question scene and a context.

The invention discloses a pediatric nursing knowledge question answering method and system based on multi-modal large model enhancement, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining multi-modal pediatric nursing related data, extracting knowledge entities, obtaining description text data of each knowledge entity, and forming text representation of the entities; establishing triple information according to the knowledge entities to generate a pediatric nursing knowledge basic database, and extracting triads to construct a knowledge graph; extracting multi-order graph data of each entity, and learning to generate knowledge graph representation of the entity; the text representation and the knowledge graph representation are fused, comprehensive knowledge information representation of the entity is obtained, the comprehensive knowledge information representation is used for finely adjusting a preset multi-modal large model, and pediatric nursing knowledge questions and answers are conducted through the finely-adjusted multi-modal large model. According to the method, more accurate, more reliable and more intelligent knowledge questions and answers are realized in a pediatric nursing scene through deep fusion of the structured constraint of the knowledge graph and the generalization ability of the multi-modal large model.

The invention relates to a liver transplantation post-operation nursing recommendation method and system based on an NNN link system, and the method comprises the steps: carrying out the construction of a knowledge graph based on the related data of a liver transplantation recipient post-operation NNN link, and constructing a corresponding liver transplantation post-operation nursing knowledge graph; and putting the liver transplantation postoperative nursing knowledge graph into clinical application, predicting an NNN nursing decision scheme matched with liver transplantation symptom information according to the liver transplantation symptom information of a clinical liver transplantation recipient, and executing the NNN nursing decision scheme on a patient. According to the method, an auxiliary decision-making system can be generated, medical staff are helped to reasonably set an outcome target for a liver transplantation recipient and formulate targeted nursing measures, the nursing efficiency is improved, and time and energy consumed by medical care decision making are saved. According to the invention, nurses can be guided to accurately identify and determine existing and potential health problems of liver transplantation recipients. Meanwhile, by introducing an NNN link system, systematized, standardized and intelligent management of postoperative care of liver transplantation recipients is realized.

The invention discloses a refined health care management and control method and management and control platform for pregnancy preparation people, and relates to the technical field of health care, and the method comprises the steps: building a full-period pregnancy preparation health care system, and obtaining a plurality of health treatment and nursing nodes; constructing a pregnancy preparation health nursing patient education knowledge base, and obtaining a patient education nursing node partition knowledge base; obtaining a target health care risk parameter; and obtaining health care consultation knowledge, and performing refined health care management and control on the target user. The problems that existing pregnancy preparation health care lacks refined and personalized adjustment and risk assessment for individual health conditions, living habits and specific requirements, accurate, objective and comprehensive health care knowledge is difficult to obtain, consequently, tension and anxiety of a patient cannot be effectively relieved, the overall medical experience is poor, and the patient experience is poor are solved. The technical problems are solved, and the technical effects of improving the overall working efficiency, the medical experience and satisfaction of the patient and the treatment effect are achieved.

The invention discloses a nursing scene data enhancement and generation method for intention recognition model training, and belongs to the technical field of artificial intelligence and smart old-age care. The method aims at solving the problems that in the prior art, high-quality nursing scene training data is deficient, and the obtaining cost is high. According to the core technical scheme, the method comprises the steps that firstly, a staring sequence and other context information (such as time, place and physiological signals) of a user are processed through a multi-modal information fusion model, and a structured initial situation vector is generated; secondly, inputting the vector into a scene generation model combined with a nursing knowledge base, and automatically generating a batch of basic nursing scene data with intention labels; key points are that a data enhancement module is introduced, and a plurality of innovative strategies such as situation element disturbance, virtual physiological data synthesis, gaze path variation and virtual scene deduction are adopted to deeply process basic data, so that the diversity and complexity of a data set are greatly enriched; and finally, combining the basic data with the enhanced data to construct a final comprehensive training data set. According to the method, large-scale and high-fidelity training data can be generated in a low-cost and high-efficiency manner, and the accuracy and robustness of the intention recognition model in a real nursing environment are remarkably improved.

The invention relates to a generative AI-based nursing case intelligent teaching question and answer robot and application thereof.A current teaching task is executed through an RAG enhanced retrieval module, question and answer knowledge is retrieved from a nursing knowledge base based on keywords in user question and answer information, and LLM is used for performing enhanced retrieval on the question and answer knowledge; the learning dynamic evaluation module performs learning performance evaluation on answer information in user question and answer information by using LLM, and the workflow management module pushes teaching tasks with corresponding learning strength to the RAG enhanced retrieval module in a matching manner to perform personalized learning path and branch scheduling. Clinical nursing cases can be created based on the generative AI, the nursing key points are answered and answered in a guiding mode, the clinical nursing thinking of students is improved in repeated practice, and the nursing teaching effect is improved. And nursing cases of different specialities are generated according to a rule enhancement formula, so that students are gradually guided to strengthen learning, and the autonomous learning ability and clinical thinking of the students are improved.

The invention relates to the technical field of medical data processing, in particular to a construction method and system for a cardiology nursing knowledge base. The system is used for performing mapping relation analysis of heart states and nursing knowledge according to heart pathology data and corresponding heart nursing data, and generating a heart state nursing relation feature matrix; performing pathological mode identification processing of heart state nursing relation characteristics on the heart state nursing relation characteristic matrix to generate a pathological mode heart state nursing relation characteristic matrix; based on the pathological mode heart state nursing relation feature matrix, an optimized prediction model of heart pathological mode and nursing information derivation is designed, and an optimized heart nursing derivation prediction model is generated; and performing intelligent feedback engine design processing of the cardiology nursing knowledge base based on the optimized heart nursing derivation prediction model so as to construct an intelligent cardiology nursing knowledge base engine. According to the invention, efficient and intelligent cardiac nursing knowledge base establishment is realized.

The invention discloses a rehabilitation department nursing health big data sharing platform, which comprises data acquisition, storage, analysis, user authority management and other modules. The data acquisition module is connected with multiple terminals such as a hospital information system, automatically acquires various data of patients, sorts and classifies the data, and comprises a cleaning unit to ensure data quality; a distributed architecture is adopted for data storage, and structured and unstructured data storage and backup redundancy are considered; the data analysis module comprises a plurality of sub-modules, can be used for mode mining, analytical statistics and illness state prediction, and is further provided with a personalized analysis unit. In addition, a data sharing interface module, a real-time monitoring and early warning module, a rehabilitation nursing knowledge pushing module, a platform interaction module and the like are arranged, cross-mechanism cooperation is supported, and user communication interaction is guaranteed. The platform integrates multi-source heterogeneous data, has the advantages of establishing data standards, achieving comprehensive deep analysis, improving data availability, promoting subject development, guaranteeing data safety and the like, and can improve rehabilitation nursing services and benefit patients and the society.

The invention belongs to the technical field of reasoning decision, and particularly relates to a mobile nursing decision support method and device of an integrated nursing knowledge base. The mobile nursing decision support method integrated with the nursing knowledge base comprises the following steps: firstly, constructing the nursing knowledge base, wherein the ontology relationship of the nursing knowledge base is represented by a tetrad containing confidence and a time decay function; acquiring patient data, mapping the patient data into an instance graph, and carrying out concurrent search in the knowledge base to generate a plurality of candidate reasoning paths; calculating a comprehensive semantic vector and a comprehensive confidence coefficient of each path; performing density clustering on the paths based on semantic vector similarity to form path beams, screening the path beams of which the aggregation confidence is higher than a threshold value and the beam endpoint dispersion is lower than the threshold value, and determining the central endpoints of the path beams as high-priority nursing decisions; and analyzing the decision, combining semantic information of the path bundle, generating a customized nursing instruction from the knowledge base, and issuing the customized nursing instruction to the mobile terminal.

The invention relates to the field of artificial intelligence, in particular to an AI-based nursing knowledge interesting courseware generation method and system. The method comprises the following steps: firstly, constructing an AI model library, and packaging input parameters and output parameters of each AI model in the model library; the nursing knowledge is uploaded to the system; creating a corresponding abstract task for nursing knowledge, and operating an AI model to obtain high-quality knowledge key points; supplementing cue words for the knowledge key points, and providing the cue words to an AI large model to generate materials; performing structured arrangement on courseware themes, chapters and contents by applying a mind mapping method, packaging the courseware themes, chapters and contents into AI model input parameters, and performing automatic layout through the AI model to generate a PPT file; and adjusting the PPT file, outputting the PPT file as courseware, and editing and processing courseware content. The AI model technology is utilized, key knowledge content can be quickly extracted, rich teaching materials can be generated, the courseware making method is optimized, the input of teaching group course making is effectively reduced, the courseware quality is improved, and the professional level of nursing personnel can be effectively promoted to be improved.

The invention relates to the technical field of intelligent medical care, and discloses a drainage port nursing recommendation method and system for a patient with bile leakage. The system comprises a data acquisition module used for acquiring bile drainage data and state data of skin around a drainage port in real time; the cloud decision module is internally provided with a nursing scheme recommendation model associated with a bile leakage nursing knowledge graph, and is used for carrying out fusion analysis on the multi-modal data and generating a personalized nursing plan; the local execution terminal is used for receiving and displaying the nursing plan and guiding nursing personnel to execute and record actual nursing operation data; and the closed-loop feedback module is used for returning the actual execution data and the post-nursing state data so as to optimize the nursing scheme recommendation model and the bile leakage nursing knowledge graph. The technical problems that in traditional nursing, evaluation is subjective, decision-making depends on experience, execution is not standard, and knowledge is difficult to accumulate are solved, and standardization, individuation and continuous self-optimization of bile leakage drainage opening nursing are achieved.

The invention relates to the technical field of knowledge management, in particular to a cardiovascular special nursing knowledge management and intelligent auxiliary method and system. The method comprises the following steps: acquiring and storing cardiovascular specialized nursing original knowledge data, executing text analysis and semantic processing, and generating a structured cardiovascular specialized nursing knowledge entry set; performing parameter analysis and weighted matching in combination with nursing demand information or patient characteristic parameters, and screening target nursing knowledge entries; and completing content combination and slot filling based on a preset nursing template, generating individualized cardiovascular specialized nursing guidance information, recording a nursing knowledge calling process and a generation result, and forming nursing behavior record data. According to the method, the accuracy and the individuation degree of calling the cardiovascular special nursing knowledge and the scientificity, the normalization and the intelligent level of nursing decision are improved.

The application relates to the technical field of teaching, and particularly discloses a nursing intelligent teaching auxiliary system and method, which comprises a cloud server, a plurality of teacher terminals and a plurality of student terminals; the cloud server comprises a knowledge base construction module, which is used for acquiring teaching documents of adult nursing science, and establishing structured course knowledge graph data based on the teaching documents; a model construction module, which is used for acquiring a general large language model, and performing field adaptability training on the general model by using the course knowledge graph data, and outputting a special model fused with adult nursing knowledge; a teaching assistant module, which is used for accepting a lesson preparation request of the teacher terminal, inputting the special model, and generating lesson preparation information; and a student assistant module, which is used for receiving an auxiliary request of the student terminal, calling the special model according to the content input by the student, and generating auxiliary teaching content. The technical scheme of the application can meet the learning needs of different students and effectively improve the teaching quality.

The present invention relates to the technical field of database construction, and in particular to a method and system for constructing a knowledge base for brain disease nursing. The method comprises the following steps: acquiring multi-source brain disease data; performing structured data screening on the multi-source brain disease data to generate structured brain disease data and unstructured brain disease data; performing multi-modal data structured stratification on the unstructured brain disease data to generate multi-modal structured stratified data; performing data preprocessing on the multi-modal structured stratified data and the structured brain disease data to generate a standard structured brain disease data set; performing context analysis on the standard structured brain disease data set to generate a semantic vector set of brain disease information; performing redundant relationship pruning processing on the semantic vector set of brain disease information to generate spatial semantic optimized data. The present invention improves the construction quality and practicality of the brain disease nursing knowledge base by integrating multi-source data, structured and unstructured information, real-time dynamic mapping of nursing needs and system feedback evaluation.

The invention relates to the technical field of natural language processing, and provides a knowledge base management method and device, equipment and a storage medium, and the method comprises the steps that a to-be-processed original document is acquired, and the original document comprises text content; based on a semantic slicing algorithm, splitting the text content of the original document to obtain one or more semantic segments; clustering the semantic segments corresponding to the plurality of original documents to obtain a plurality of knowledge units; clustering the plurality of knowledge units to obtain a plurality of knowledge topics; according to a corresponding relationship among the knowledge theme, the knowledge unit and the semantic fragment, storing the semantic fragment into a knowledge base; and managing the knowledge themes and / or knowledge units in the knowledge base. Taking the field of medical health as an example, a document of nursing knowledge is split into semantic segments, and then the semantic segments are stored in a medical knowledge base according to corresponding relations with knowledge themes and knowledge units. Taking the financial field as an example, a medical insurance document is split into semantic segments, and the semantic segments are stored in an insurance knowledge base.

The invention discloses a pressure sore early warning and nursing intervention method based on multi-mode AI image recognition, and the method comprises the steps: synchronously obtaining skin surface texture, a temperature field and deep moisture distribution at a time through a visible light, infrared thermal image and subcutaneous moisture three-mode integrated handheld terminal; a dual-channel space-time fusion network DTSNet is adopted to deeply fuse local texture features and continuous time sequence features, and precise early warning of 0-period and deep tissue damage in advance for 72 hours is achieved; a patient-focus-nursing knowledge graph is automatically constructed in combination with an interpretable graph neural network, and an evidence-based nursing path is matched in real time and pushed to a mobile terminal; the system supports federated learning and block chain evidence storage, and continuously optimizes the model while protecting privacy; through a closed loop of collection-identification-decision-intervention-feedback, an early warning result is directly converted into specific measures such as a turning angle, a pressure reduction pad model and dressing selection, the pressure sore occurrence rate and the nursing labor cost are remarkably reduced, and the system is suitable for multiple scenes such as hospitals, communities and homes.

This invention relates to the technical field of anesthesia nursing data processing, specifically including a method and system for managing clinical nursing data in anesthesiology. The method includes: collecting clinical nursing data from anesthesiology; configuring first and second data mapping tables based on static and dynamic features according to the platform and application ends; generating a nursing knowledge graph; acquiring features of adjacent events; and conducting multi-scale risk assessment combined with dynamic threshold alerts. This invention solves the technical problem of insufficient consideration of the temporal correlation and synergistic effects of events during anesthesia, making it impossible to dynamically adjust risk assessment standards for different anesthesia stages and easily leading to misjudgments. By configuring first and second data mapping tables and combining the time entropy corresponding to the time interval of adjacent events and the support degree of association rules, it achieves hierarchical management and precise mapping of static and dynamic features in anesthesiology clinical nursing data. It dynamically adjusts risk thresholds based on the temporal features of different anesthesia stages, significantly improving the accuracy and timeliness of risk assessment.

The present invention discloses a nursing risk intervention decision system and method based on knowledge graph, which relates to the field of medical information technology, including: constructing a dynamic medical nursing knowledge graph based on multi-source nursing data and deep learning models, using a bidirectional long short-term memory network and a conditional random field for entity recognition; capturing the evolutionary characteristics of entity relationships through a marginal attention mechanism and a temporal graph convolutional network, optimizing the knowledge graph structure based on connectivity constraints and relationship weight thresholds; using a graph optimization mechanism driven by reinforcement learning to dynamically adjust the knowledge structure, optimize the relationship weights between entities in real time, and construct a multi-level risk characterization model, and realize accurate identification and early warning of nursing risks through local risk feature extraction, global risk information aggregation, and temporal risk evolution analysis. The present invention deeply integrates the complex association relationship between medical entities with nursing risk decisions, constructs multi-dimensional medical entity features into an entity association network, and realizes early risk identification.

The application discloses a traditional Chinese medicine nursing scheme intelligent generation method and system based on relationship constraint gating and curative effect feedback adaptive updating. The method constructs a traditional Chinese medicine nursing knowledge graph containing multiple types of relationships; constructs a nursing demand subgraph according to patient information and generates a patient state vector; through a relationship constraint gating graph neural network, different relationships are subjected to differentiated propagation strategies for node representation learning, including positive enhancement propagation for suitable / enhanced relationships, attenuation propagation for restricted relationships, negative punishment propagation for conflict relationships and hard shielding propagation for taboo relationships; based on the learned representation, candidate measures are recalled and a combined scheme is generated, and the optimal scheme is selected by using a scoring function that fuses matching points, cooperation points, penalty points and risk points; and relationship weights, gating parameters and scoring parameters are adaptively updated according to curative effect feedback. The application can generate an individualized scheme that conforms to traditional Chinese medicine nursing constraints and is safe and interpretable, and has closed-loop optimization capability.

This invention relates to the field of smart healthcare nursing technology, and discloses a method and system for recommending care for the drainage site of patients with bile leakage. The system includes: a data acquisition module for real-time acquisition of bile drainage data and skin condition data around the drainage site; a cloud-based decision-making module with a built-in nursing plan recommendation model linked to a bile leakage nursing knowledge graph, used for fusion analysis of multimodal data and generation of personalized nursing plans; a local execution terminal for receiving and displaying the nursing plan, guiding nursing staff to execute it, and recording actual nursing operation data; and a closed-loop feedback module for transmitting actual execution data and post-nursing status data back to optimize the nursing plan recommendation model and the bile leakage nursing knowledge graph. This invention solves the technical problems of subjective assessment, experience-dependent decision-making, non-standard execution, and difficulty in accumulating knowledge in traditional nursing, achieving standardization, personalization, and continuous self-optimization of bile leakage drainage site care.

The invention provides a nursing knowledge management system based on RAGflow, and belongs to the technical field of nursing information systems. The system comprises a data access and analysis module which is used for carrying out structured processing on a nursing document and outputting an analysis result in a uniform format; the knowledge slicing and governance module is used for carrying out semantic slicing on the analysis result, generating metadata and eliminating repeated contents; the indexing and retrieval module is used for constructing and maintaining a hybrid index comprising a dense vector index and a sparse inverted index; the query understanding and task decomposition module is used for identifying a query problem, extracting entity data and decomposing the query problem into execution subtasks; the RAG generation and evidence alignment module is used for generating a question and answer result with an evidence snapshot; the evidence snapshot comprises a source document and a document content position; and the agent arrangement module is used for calling a preset task template and tool according to the nursing role and executing a preset target task. According to the method, the interpretability and the traceability of the question and answer result can be realized.

The invention relates to a stoma abnormal state on-line identification and diagnosis evaluation system and method, a patient photographs a stoma part through a patient end APP, a stoma picture is collected and uploaded to a background server, the background server performs stoma abnormal intelligent evaluation on the stoma picture, and the stoma abnormal state on-line identification and diagnosis evaluation is performed on the stoma abnormal state. Whether the stoma is abnormal or not is judged, emergency propaganda and education measures corresponding to the abnormal stoma are issued, and the patient can conduct emergency treatment on the abnormal stoma in time according to the emergency propaganda and education measures. By identifying and diagnosing the scanned stoma data of the patient, judging whether the stoma data is abnormal or not and judging the type of the abnormal stoma data, and intelligently giving the corresponding stoma processing suggestions, the automatic monitoring of the stoma abnormality at home of the patient is realized, the abnormal condition is propagandized, taught and nursed in time, and the risk of the patient caused by corresponding complications is avoided. And the patient can learn online propaganda and education, so that the patient can timely supplement stoma nursing knowledge, and the risk control degree is improved.

The invention relates to the technical field of nursing knowledge base construction, in particular to a construction method of a chemotherapy-related oral mucositis nursing knowledge base. The method comprises the steps of firstly obtaining a knowledge base and access data; further obtaining an initial index weight according to the recent access trend and the total access frequency of each keyword in combination with the distribution of the keywords in the clinical nursing data; further obtaining nursing pertinence according to the difference between clinical nursing data related to each keyword in combination with a recovery effect; and finally, fusing the initial index weight and nursing pertinence to obtain a final index weight of each keyword. According to the scheme, the keyword index weight is dynamically optimized by fusing the universality, the timeliness and the nursing pertinence of the keywords, wide requirements and individual curative effects are considered, hot low-efficiency word deficiency is avoided, and the retrieval accuracy and the application value of the database are improved.

1. The name of the design product: nursing knowledge search machine. 2. The use of the design product: the design product is used for nursing knowledge search machine. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.