4286 results about "Medical practitioner" patented technology

The invention discloses a semantic enhancement auxiliary inquiry system and method based on a medical knowledge graph, and the system comprises a patient information receiving module, a theoretical knowledge graph storage library, an evidence-based knowledge graph storage library, a double-track diagnosis path construction module, a medical knowledge conflict judgment module, and a semantic enhancement report generation module. The double-track diagnosis path construction module inquires the received patient information in a theoretical knowledge graph storage library and an evidence-based knowledge graph storage library which are independent from each other in parallel, and a theoretical diagnosis path and an evidence-based diagnosis path are generated respectively; the medical knowledge conflict judgment module dynamically compares the two paths in real-time interaction, and performs priority judgment according to a preset medical judgment rule; finally, the semantic enhancement report generation module presents the two paths and the conflict judgment result to the user at the same time. Transparency and interpretability of the whole interrogation process are assisted, and reliable semantic enhancement decision support is provided for doctors when the doctors face complex medical knowledge conflicts.

The invention relates to the field of medical image analysis, in particular to a deep learning prediction system and method based on multi-modal thyroid cancer lymph node metastasis, and the system comprises a data collection module, a preprocessing module, a nodule segmentation module, a feature extraction module, a feature fusion module, a metastasis prediction module, an interpretability analysis module and a result display module. An ultrasonic image, an elastic imaging image, an ultra-micro blood flow image and clinical index data of a patient are integrated, an improved U-Net algorithm is used for precise segmentation of a thyroid nodule region, a multi-branch deep network is used for extracting multi-modal features, a dynamic weight fusion algorithm is used for integrating the features, and the accuracy of the thyroid nodule region is improved. According to the method, the thyroid cancer lymph node metastasis state (non-metastasis, central region metastasis or lateral neck metastasis) is predicted, meanwhile, a two-dimensional interpretability framework of Grad-CAM activation diagram and SHAP value contribution degree analysis is introduced, an intuitive prediction basis is provided for doctors, and the thyroid cancer lymph node metastasis prediction accuracy is remarkably improved.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

The invention provides a multi-mode medical record intelligent generation and interaction system and method based on a wearable terminal and an MOE architecture, and relates to the technical field of AI large models. The terminal comprises a wearable terminal used for carrying out real-time acquisition and interaction on multi-modal data in a medical scene to obtain multi-modal medical data; the communication network is used for realizing data interaction between the wearable terminal and the server-side AI large model processing system; the server side AI large model processing system is used for performing semantic analysis, structured extraction and medical record document generation based on the multi-modal medical data to obtain an editable first draft of a medical record document; and the wearable terminal is also used for receiving and presenting the first draft of the medical record document returned by the server side AI large model processing system, and editing and confirming the first draft of the medical record document to form a returned draft of the medical record document. The problem that in the prior art, a terminal lacks multi-modal real-time fusion and LLM closed loop, and doctor duplication interaction cannot be achieved is solved.

The invention relates to the technical field of medical data analysis, in particular to a brain tumor curative effect analysis system which comprises a tumor data sensing layer for collecting multi-department diagnosis and treatment data, tumor image data and patient pathology monitoring data; the tumor feature processing center extracts data features, correlates data and core features before and after treatment through an attention mechanism, and generates a tumor complete-cycle unified feature map; the therapeutic effect dynamic analysis unit evaluates the therapeutic effect in stages, and outputs a therapeutic effect index and a recurrence risk value through a self-supervised model; the dynamic adaptation decision module is used for generating personalized treatment adjustment suggestions based on the dynamic change of the blood brain barrier in combination with the curative effect index, the recurrence risk value and the multi-omics characteristics of the patient; and the AI multi-department consultation unit automatically matches similar cases with field expert suggestions, and formulates a target diagnosis and treatment scheme based on a visual platform and multi-department doctor collaborative consultation in combination with personalized treatment adjustment suggestions. Therefore, the problems of lagging effect evaluation, insufficient diagnosis and treatment suggestions and the like in the prior art are solved.

The invention provides a thyroid cancer auxiliary diagnosis and metastasis risk prediction method based on deep learning, and relates to the technical field of artificial intelligence auxiliary medical treatment, and the method comprises the steps: extracting ultrasonic image multi-scale features through a self-adaptive neural architecture search network, combining clinical examination data, fusing diagnosis and treatment knowledge through a neural symbol inference device, and carrying out the prediction of the metastasis risk. Generating a knowledge enhancement feature map; constructing a feature propagation field by using a dynamic neural field network, solving a dynamic evolution equation, and generating a spatial-temporal feature field representing the dynamic change of focus features; constructing a tumor diffusion kinetic model by using an implicit neural representation network and a nerve ordinary differential equation network, calculating a transition probability based on an optimal transmission algorithm, solving an optimal control equation, and outputting a metastasis risk prediction result of each organ; the thyroid cancer diagnosis accuracy and metastasis risk prediction reliability can be effectively improved, and doctors can be assisted in accurate diagnosis and treatment.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an artificial intelligence medical diagnosis system based on multi-dimensional information fusion. Comprising the steps that a self-adaptive diagnosis path planning module judges whether a user request belongs to a preset non-diagnosis and treatment category or not, if yes, a quick response path is activated, and a standardized answer is retrieved and returned to a user; the preliminary diagnosis module generates a candidate disease hypothesis list, verifies the candidate disease hypothesis list and outputs a verified disease hypothesis list; the dynamic knowledge enhancement module generates missing knowledge according to the disease knowledge graph and the query verification disease hypothesis list and supplements the missing knowledge into the medical knowledge graph; a composite confidence evaluation module performs confidence evaluation on each hypothesis disease in the verification disease hypothesis list, and outputs a final disease confidence; the result integration module sorts the final disease confidence in a descending order and integrates the multi-dimensional information of each hypothetical disease to generate a structured differential diagnosis report; the system and the method can assist doctors in realizing high-accuracy, high-reliability and explainable intelligent medical diagnosis.

The invention discloses a medical insurance intelligent auditing and path recommending method and system based on a large language model, and particularly relates to the technical field of medical insurance auditing, and the method comprises the steps: S1, analyzing massive medical insurance policy documents through a first LLM, and generating a comprehensive confidence coefficient in combination with triple confidence coefficients of semantics, context support degree and timeliness, after grading processing, constructing a dynamically updated medical insurance rule knowledge base; s2, converting free text medical advices of doctors into structured data by means of a second LLM, quantifying the integrity of the medical advices through field weights, and dynamically asking and complementing information in combination with constraint urgency; s3, associating the diagnosis and treatment scheme with the medical insurance information of the patient, matching rules in real time, and generating a color identifier for early warning violation; s4, recommending compliance alternative schemes in combination with the medical knowledge graph, and sorting according to multi-dimensional scores such as medical insurance economy and the like; and S5, automatically extracting multi-system data, and generating a standardized medical insurance declaration report. The medical insurance management efficiency is improved, the burden of medical care and medical insurance departments is relieved, and reasonable use of medical insurance funds is guaranteed.

The invention relates to the technical field of information security management, and discloses an information security management method and system based on digital medical treatment. The method comprises the following steps: verifying doctor professional qualification, patient authorization range, data sensitivity level and time permission validity in a multi-institution medical data access request, and generating a permission verification result; analyzing ontology semantic attributes and sensitivity characteristics of medical search keywords input by a user, and determining a corresponding hierarchical encryption strategy based on the permission verification result; performing homomorphic encryption transformation on the medical term synonym set according to the hierarchical encryption strategy, and constructing a medical ciphertext database; and receiving a medical query request, executing symptom disease association matching and ciphertext state logical reasoning operation in the medical ciphertext database, and outputting an encrypted state search result. According to the method, the privacy leakage risk caused by search behavior trajectory analysis is effectively prevented, and the security and availability of medical data are improved.

The invention relates to the technical field of auxiliary training, and discloses an AR auxiliary training system for improving the diagnosis and treatment capability of general practitioners on undifferentiated diseases, and an immersive training environment is constructed by adopting an augmented reality technology. The system comprises an environment sensing module, a space positioning module, a case data module, a virtual rendering module, an interactive acquisition module, an intelligent response module, a state control module and an evaluation calculation module. The environment sensing module adopts an RGB-D camera and an inertial measurement unit to collect environment and head movement data of a user; the spatial positioning module determines a virtual patient placement position based on an SLAM algorithm; the virtual rendering module renders a three-dimensional virtual patient in a real environment; the interaction acquisition module supports voice and gesture multi-mode interaction; the intelligent response module generates an intelligent patient answer; the evaluation calculation module carries out comprehensive evaluation from three dimensions. The invention provides a highly immersive and intelligent training environment, and has the advantages of low cost, reusability and high standardization degree.

The invention discloses a medical intelligent question and answer method and system based on multi-modal data, a medium and a product, and relates to the field of medical intelligent question and answer. Performing knowledge retrieval in a preset vector database according to the query content to obtain a knowledge retrieval result; performing query intention recognition on the query content to obtain a query intention recognition result; generating a query answer according to the knowledge retrieval result and the query intention recognition result; wherein the query answer comprises one or more of text, image annotation, voice synthesis and video interpretation. The system supports various output forms such as text, image annotation, voice synthesis and video interpretation, explains and analyzes the illness state or medical diagnosis and treatment results of the patient from multiple dimensions, provides rich and visual diagnosis and treatment reports and interactive interfaces for doctors and patients, and effectively improves the user experience and decision-making efficiency.

The invention discloses an interpretability analysis and decision sharing verification method and system for a rectal cancer prognosis model, and relates to the field of medical artificial intelligence interpretability. The method comprises the following steps: carrying out gradient weighting class activation mapping analysis on a prognosis model to generate an image thermodynamic diagram; calculating the contribution degree of the multi-modal features by using an SHAP interpreter; an integrated visual interface is constructed, and patient data, model prediction and the explanation result are presented to a doctor together; the doctor performs independent risk assessment based on the interface information; finally, decisions of doctors and the model are compared, and model auxiliary efficiency is evaluated. Through a doctor-model decision sharing verification mechanism which is explained and innovated in a multi-level mode, the transparency and clinical credibility of the complex AI prognosis model are remarkably improved, the value of time sequence data in dynamic risk assessment can be verified, and clinical landing application of the AI model is powerfully promoted.

Disclosed are approaches that may provide image-guidance to interventionalists by providing true 3D visualization and quantitative feedback in real-time. A guidance system may allow a physician to manipulate a medical device and see a 3D rendering with quantitative feedback floating in mixed reality, next to standard monitors. Image tracking may detect and co-register the medical device's 3D position using, for example, bi-plane C-arm X-ray fluoroscopy and provide a 3D trajectory as quantitative feedback. Patterns in a fluoroscopic image may be used to accurately determine an object's z-position from a single angle projection.

The invention provides an intelligent virtuality and reality combined psychological health service device based on digital elements, and aims to improve the accuracy and individuation level of psychological health management. The device firstly obtains the physiological indexes, behavior data, environmental factor data and social economic data of an individual, and carries out multi-modal fusion to form comprehensive feature data. Based on this, a psychological health environment factor model is constructed to quantify the influence of the external environment on the individual psychological state, and the calculation weight and prediction logic of the negative emotion large model are optimized. And the optimized negative emotion large model is used for identifying an individual emotion state, analyzing factors such as social environment, economic pressure and life events in combination with the mental health environment factor model, and generating an individual mental health assessment result. And according to an evaluation result, the virtual digital doctor provides intelligent pre-inquiry and other services. Through data-driven intelligent analysis and virtual-real combined intervention means, the accessibility, accuracy and intervention effect of psychological health services are improved.

The invention relates to a smart hospital collaborative diagnosis and treatment management method and system, and the method comprises the steps: receiving the appointment registration information of a patient, building a data mapping relation with a hospital information system based on the appointment registration information, and generating a corresponding treatment identifier; after the patient arrives at the hospital, information of the sign-in patient is collected, a sign-in sequence is established, and according to appointment registration information and diagnosis and treatment service data parameters corresponding to the sign-in patient, a treatment serial number is dynamically distributed and a waiting prompt is pushed; after the doctor receives the diagnosis, generating an examination item list based on the preliminary diagnosis information input by the doctor, obtaining the real-time state and queuing data of equipment required by each examination in the examination item list, constructing an execution time window of examination items, generating an optimal examination sequence, and pushing an examination guide path to the patient terminal; and after the patient finishes seeing a doctor, generating a personalized propaganda and education task list according to the diagnosis and treatment information of each stage, and pushing the personalized propaganda and education task list to the patient terminal. The method has the effect of improving the hospital operation efficiency.

A personalised artificial-intelligence (AI) case-manager platform provides real-time, policy-constrained clinical decision support. It ingests heterogeneous data from electronic-health records (EHRs), connected medical devices, and clinician inputs; fuses them with a traceable, multilingual reasoning engine; and screens every candidate action through a multi-tier policy-constraint layer that respects patient-consent artefacts, safety grammars, and jurisdictional rules. Dual-factor credential verification and dynamic, role-based access control secure all protected-health-information (PHI) transactions, while a cryptographically chained audit trail—keyed by a global trace identifier—records inputs, rules, overrides, and triggered workflows. Authorised feedback is adjudicated and fed to adaptive learning modules that tune patient-specific and population-level behaviour. A conflict-detection service flags discordant data streams, and a governance-validated workflow engine can issue proactive alerts, automated record updates, or human-in-the-loop escalations. The platform thus delivers explainable, equitable, and continuously learning automation without compromising privacy or clinical accountability.

The invention discloses a tumor image segmentation method and system based on multi-modal image fusion, and the method comprises the steps: firstly extracting the dimension features of CT, MRI and PET, expanding the dimension features into a four-dimensional model, and achieving the initial segmentation of a feature region; performing registration fusion, group collaborative optimization and blind spot compensation to obtain a fused image with high consistency and complete boundary; then texture morphological features are extracted, and suspicious types are intelligently discriminated; dynamically evaluating the reliability of each modal and weighting to generate initial segmentation; and finally, carrying out inclination angle correction three-dimensional reconstruction, carrying out high-sensitivity detection on an invisible boundary, and outputting an accurate target segmentation result. According to the method, the precision and reliability of tumor suspicious region segmentation are remarkably improved, and efficient technical support is provided for subsequent accurate diagnosis and treatment planning of doctors.

The present invention relates to a cognitive computing-assisted clinical decision support system designed to enable personalized neurological rehabilitation. The system acquires structured user data across clinical, anatomical, radiological, etiological, pathological, and rehabilitation domains to create individualized profiles. These profiles are mapped against a repository of historical cases using analog matching and similarity scoring to generate stratified, evidence-based rehabilitation recommendations. Real-time monitoring of rehabilitation progress is performed using global recovery and function outcome indicators, allowing for dynamic adjustment of treatment plans. Clinician intervention modules ensure safety, interpretability, and context-aware customization. The system incorporates a continuous feedback mechanism to refine future predictions and recommendations, making it increasingly adaptive over time. The invention improves rehabilitation outcome prediction accuracy, reduces recovery variability, and optimizes functional outcomes by transforming static rehabilitation models into intelligent, responsive, and personalized care pathways.

The invention discloses a sugar chain marker combination for predicting prognosis of liver cancer transarterial chemoembolization and application of the sugar chain marker combination. The invention discovers and verifies that the combination composed of NA3Fb, NA4Fb and NA4F2b can be used as a novel marker for evaluating TACE treatment reaction and prognosis by analyzing a serum sugar chain map of a liver cancer patient for the first time. Based on the marker combination, a TACE-GT prognosis scoring model and system are established, and the progression risk of a patient within 12 months after a TACE operation can be effectively distinguished by noninvasively detecting the expression levels of three carbohydrate chains in serum of the patient and calculating a specific score. The detection method provided by the invention is based on a blood sample, and has the advantages of noninvasiveness and convenience, and a risk assessment system can provide individualized curative effect prediction and decision support for clinicians before and after TACE treatment, and has important values for realizing precise treatment of liver cancer and improving prognosis of patients.

The invention discloses an operation index determination method and device for an endoscopic surgery, equipment and a storage medium, and can be applied to the technical field of surgical operation data processing. The method comprises the following steps: firstly, acquiring a surgical operation data set of a target operation object for an endoscopic surgery; the surgical operation data set comprises a plurality of pieces of surgical operation data sorted according to a time sequence. Each piece of surgical operation data comprises five-dimensional data, namely endoscope action data, endoscope type data, instrument action data, instrument type data and surgical object data. And constructing a hidden Markov model by using the surgical operation data set. In the hidden Markov model, the surgical operation described by the surgical operation data is the observed quantity, and the surgical state is the state quantity, and based on the hidden Markov model, the statistical principle is utilized to carry out quantitative evaluation on the surgical operation indexes, so that the accurate evaluation of the surgical operation when a doctor carries out the endoscopic surgery is realized, and the reference effectiveness of the operation indexes is improved.

The invention provides a multi-dimensional real-time doctor-patient communication evaluation analysis method and system, and the method comprises the steps: collecting the real-time dialogue content between a doctor and an AI simulation patient, and carrying out the processing of the real-time dialogue content, and forming a structured dialogue event sequence; dividing the dialogue event sequence into a plurality of semantic event units, inputting the semantic event units into an intention recognition model, and extracting a semantic tag sequence; constructing a semantic intention trajectory based on the semantic tag sequence, matching the semantic intention trajectory with a predefined dimension scoring model, and generating a three-dimensional preliminary scoring result of treatment universality, polite normalization and humanistic care normalization; obtaining a dynamic weight distribution parameter, carrying out weighted fusion on the dynamic weight distribution parameter and the preliminary scoring result to generate a comprehensive scoring result, and judging whether the comprehensive scoring result is lower than a preset threshold value or not; and if yes, generating a dynamic feedback signal. According to the method, the comprehensive accuracy and situation adaptability of doctor-patient communication evaluation can be improved, and the effect of doctor-patient communication training is improved.

The invention relates to the technical field of intelligent medical information, in particular to a urinary surgery risk AI intelligent assessment method and system. The method comprises the steps that intraoperative data in the urinary surgery operation process are collected in real time; inputting the intraoperative data into a risk feature fusion AI model, performing context association analysis of different stages of the operation, and fusing each intraoperative multi-source modal feature to generate a risk assessment comprehensive feature set; analyzing the risk assessment comprehensive feature set by using a risk assessment prediction AI model, dynamically adjusting the priority and prediction weight of each risk index in combination with a doctor judgment priority weight adjustment mechanism, and predicting to obtain an intra-operative real-time comprehensive risk score; and visually displaying the intraoperative real-time comprehensive risk score and the risk prompt information in a terminal in real time. According to the AI intelligent assessment method, the causal relationship between the risks is constructed by associating the data of each stage of the operation with doctor judgment, and the AI intelligent assessment method capable of dynamically predicting the key risks in the operation process is realized.

The invention discloses a traditional Chinese medicine intelligent diagnosis and treatment system and method. The diagnosis and treatment system comprises a medical thinking layer used for clinical information reasoning decision; the expert mixed framework layer is used for refined data processing, analysis and reasoning; the clinical tool layer is used for collecting and analyzing clinical information; the interaction layer is used for providing digital human, voice, image-text and other interaction modes for patients and doctors, and the medical thinking layer and the expert mixed framework layer are in communication interaction with the interaction layer and the clinical tool layer. The diagnosis and treatment system has the advantages that expert models in different fields are combined through the expert mixed framework layer to achieve refined and intelligent support and optimization of the traditional Chinese medicine diagnosis and treatment process, the traditional Chinese medicine clinical real diagnosis and treatment process is simulated, the medical thinking layer is applied to conduct clinical information reasoning and decision making, and the diagnosis and treatment efficiency is improved. And the reliability and interpretability of the traditional Chinese medicine intelligent diagnosis and treatment paradigm are improved, so that a grassroots traditional Chinese medicine doctor can be assisted to improve the differentiation accuracy, and the popularity of traditional Chinese medicine diagnosis and treatment services is promoted.

The embodiment of the invention discloses a medical data processing method and related equipment based on multi-source medical data cross validation, and the method identifies abnormal record data and generates medical record modification suggestion information through automatic cross validation matching, can automatically integrate medical record data provided by multiple sources, and improves the efficiency of medical record data processing. The medical record data belonging to the abnormal record data are identified through mutual cross validation, so that the processing efficiency of the medical record data from multiple sources is improved, omission and delay caused by manual comparison of medical record archive files from multiple sources are avoided, the advantage of large data scale of the medical record data from multiple sources is utilized, and the medical record data processing efficiency is improved. The method provides a clear direction for subsequent accurate correction, is beneficial to finding and correcting error medical record information, guarantees the accuracy of the medical record information, provides direct reference for doctors to check, reduces the communication cost between patients and doctors, and is beneficial to guaranteeing the health and safety of the patients. The method is widely applied to the technical field of medical data processing.

The present disclosure relates to a remote surgical robot system and a method for device interconnection therein. The method comprises: receiving second device information, which is sent by a server, of a pairable second device; acquiring target second device information, which is determined on the basis of the second device information, of a target second device that is expected to be paired, and sending a first pairing request to the server; receiving device network information, which is sent by the server when detecting that the first pairing request matches a second pairing request sent by the target second device, of the target second device; and establishing pairing with the target second device on the basis of the device network information, thereby implementing direct communication with the target second device. When the server detects that a pairing request initiated by a remote surgeon console matches a pairing request initiated by a patient surgery platform, device network information of a peer device is sent to at least one of the two for establishing the pairing, thereby avoiding incorrect pairing, ensuring the reliability of establishing the pairing, and improving the safety of remote surgery.

The invention provides a rare disease risk screening model training system and method, a screening system and a medium, and belongs to the technical field of medical information. According to the invention, an innovative three-stage architecture design is adopted, unified identification of all rare disease types is realized, and the blank in the prior art is filled. When only basic clinical information exists, a rare disease high-risk patient can be accurately identified, the diagnosis time is remarkably shortened, and a treatment precedent is won for the patient. The system adopts a small parameter quantity model integration strategy, reduces hardware requirements and maintenance cost, can be widely deployed in basic medical institutions, and promotes balanced distribution of medical resources. Through the technical measures of nested cross validation, algorithm comparison, threshold optimization and the like, the system has high accuracy and reliability, and the credibility of doctors to the system is enhanced.

The invention relates to the technical field of natural language processing, and discloses a diagnosis and treatment quality evaluation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring multi-source information data of a target patient in a treatment process; wherein the multi-source information data comprises target dialogue data and associated diagnosis and treatment data; performing reconstruction based on the multi-source information data to obtain an actual diagnosis and treatment thinking chain; wherein the actual diagnosis and treatment thinking chain is used for representing a diagnosis and treatment reasoning process when a doctor inquires a target patient; performing comparative analysis based on the actual diagnosis and treatment thinking chain to generate a diagnosis and treatment quality evaluation result for the target patient; wherein the diagnosis and treatment quality evaluation result is used for performing comprehensive quality control on the reasonability of the medical treatment process. By acquiring the multi-source information data containing the target dialogue data and the associated diagnosis and treatment data, the defect of single source of traditional quality control data is made up, and finally reasonable and comprehensive quality control can be performed on the whole diagnosis and treatment process.

The invention relates to the technical field of auxiliary diagnosis and treatment systems, in particular to an intelligent auxiliary diagnosis and treatment system for doctors. Comprising a system initialization and role service registration module, a user input analysis and intention recognition module, an intelligent routing and professional tool set selection module, an SSE streaming response initialization and workflow starting module, a knowledge graph retrieval and reasoning module, a diagnosis and treatment logic reasoning and scheme generation module and a streaming result output and node state updating module. A dynamic knowledge update triggering and verifying module; according to the invention, on the system integration level, efficient integration of multi-professional medical resources is realized based on role-as-a-service architecture, and complete coverage of multi-professional knowledge modules of internal medicine, surgery, oncology and the like is ensured through a role service registration validity evaluation mechanism.

The invention discloses a left auricle plugging device provided with a real-time atrial pressure monitoring chip, and particularly relates to the technical field of left auricle plugging, the left auricle plugging device comprises a plugging device body and a connector, the top end of the connector is provided with a signal supply module, and the signal supply module is located in the plugging device body; according to the left atrial appendage plugging device, after the inductor in the nut generates a voltage signal, the signal can be transmitted to the low-noise operational amplifier and amplified, meanwhile, the electromagnetic interference between the noise of a circuit and the outside world is restrained, and the plugging device can be used for plugging the left atrial appendage of the left atrial appendage of the left atrial appendage. The physical deformation is converted into corresponding electric signals, the electric signals are transmitted to an in-vitro receiving terminal through an antenna, and a doctor can indirectly judge the recovery condition of the atrial contraction function of the patient according to the pressure signal trend.

The invention relates to the technical field of artificial intelligence, and discloses a medical intelligent assistance method, device and equipment and a storage medium, and the method comprises the steps: collecting the dialogue data of a patient and a doctor, obtaining the medical record text data of the patient, obtaining a structured medical document, and carrying out the entity recognition, so as to obtain entity information and semantic information; querying in a preset knowledge graph based on the entity information and the semantic information to obtain a first query result; and inputting the first query result into a preset medical big model to generate a first text verbal skill set. The system has the advantages that the burden of doctors is relieved in an intelligent mode, the communication efficiency is improved, and it is ensured that patients have clear and accurate information when receiving medical services. The leveled text verbal skill not only can improve the doctor-patient relationship, but also can improve the doctor-seeing experience of the patient and the overall quality of the medical service, and provides better support for doctors.