376 results about "Triage" patented technology

The invention discloses a medical intelligent decision-making method based on Deepseek and a time sequence causal knowledge graph, and the method comprises the following steps: 1, constructing an initial static medical knowledge graph, and generating a dynamic time sequence causal knowledge graph; 2, finely adjusting and training the DeepSeek model to enable the DeepSeek model to adapt to the medical field; and step 3, receiving and analyzing the text uploaded by the patient, performing intelligent triage and disease risk prediction, and realizing accurate matching of patient symptoms and target departments and intelligent prediction and early warning of potential diseases. The method aims at providing accurate triage and disease risk prediction for patients, constructing a scientific and efficient medical intelligent decision-making mechanism and optimizing medical resource allocation.

The invention provides an intelligent pre-examination triage method based on multi-modal data and edge calculation, and relates to the technical field of multi-modal data, and the method comprises the steps: collecting the facial expression, wound part, sound and physiological signals of a patient through medical equipment; multi-modal features are extracted by using a deep learning network and dynamic weighted fusion is carried out to obtain a patient state evaluation result; performing data time sequence alignment and feature extraction based on edge nodes, dynamically adjusting calculation precision and realizing task migration; multi-source data are collected for time sequence alignment calibration and feature fusion, the emergency degree is calculated, and group damage distribution is predicted. According to the method, real-time processing and dynamic optimization of multi-modal data are realized, and the accuracy and efficiency of pre-examination triage are improved.

The invention discloses an intelligent emergency pre-examination triage system based on big data, and relates to the technical field of data analysis, the emergency pre-examination triage system comprises a data acquisition module, an evaluation index extraction module, an intelligent analysis module, a triage evaluation module, a triage decision module, a feedback interaction module and a patient information management module, all the modules are connected through electric signals. According to the intelligent emergency treatment pre-examination triage system based on the big data, the comprehensive evaluation model and the triage evaluation coefficient are integrated, and the symptoms of the patient are evaluated quickly and accurately, so that the triage level and the recommended treatment department are quickly determined, the emergency treatment efficiency is remarkably improved, the waiting time of the patient is shortened, and the patient experience is improved. According to the method, medical resources are configured more reasonably, meanwhile, more comprehensive patient portraits can be provided for medical staff according to multi-dimensional information such as medical history and examination results of patients, and making of more accurate diagnosis and treatment decisions is facilitated.

The invention discloses an intelligent dynamic triage system and method for emergency patients. The triage system comprises a data acquisition module used for acquiring basic information, physiological parameters and historical records of patients; the artificial intelligence analysis module generates a preliminary triage result; the Internet of Things connection module collects hospital resource data in real time; the central control processing unit is connected with each module to receive data; the dynamic grading decision-making module divides the patients into critical symptoms, severe symptoms, subcritical symptoms and common symptoms; the resource allocation module dynamically generates a resource allocation scheme according to the grading result; the diagnosis and treatment process management module tracks the whole process of the patient and updates state data; when the state of the patient changes, the system transmits data to the artificial intelligence analysis module again for evaluation, triggers the grading decision and resource allocation module, dynamically updates the triage priority and the resource allocation scheme, and achieves closed-loop feedback and continuous optimization.

A method and system for evaluating the survival state of a wounded person on the basis of incomplete parameters. The method comprises: acquiring visual information, audio information and thermal imaging multi-modal data of a target; inputting the multi-modal data into a feature extraction network, so as to obtain multi-modal feature data; and combining the multi-modal feature data with physiological indexes, so as to form a multi-modal data set, and on the basis of a trained survival index model, obtaining an evaluation result for the survival state of the target. The present invention provides a more comprehensive and more accurate evaluation result for the survival state of a wounded person, so as to implement rapid and accurate triage of wounded persons at a disaster site.

The present invention relates to the technical field of medical triage, and discloses an emergency patient triage system based on artificial intelligence, including: a data acquisition module: used to obtain the patient's identity and basic information by performing face recognition and fingerprint recognition on the patient, and receive health data uploaded by a wearable device, and transmit it to the patient's corresponding historical pathology library; a medical interaction module: used to provide an interactive platform for patients to remotely input pathological conditions based on 5G wireless communication technology; a multimodal data fusion module; a pathological feature matching module; a triage matching module; and a system integration and display module. Through the convolutional neural network model combined with pathological feature matching, based on the patient's question reply content, the condition and the judgment of the severity, a rapid judgment of the possibility of the disease, the generation of consultation content and the optimization of triage results are achieved, thereby improving the accuracy and efficiency of triage and reducing misdiagnosis and missed diagnosis.

The invention discloses an intelligent online reservation management system, and relates to the technical field of reservation management, the system comprises a login module, an outpatient service information display module, an online reservation module, a data analysis module and a customer service module, the online reservation module comprises an intelligent triage unit, a number source distribution unit and a reservation priority unit, the intelligent triage unit recommends corresponding departments for patients, the number source allocation unit is used for allocating number source numbers for the patients, the corresponding departments are recommended for the patients through the intelligent triage unit, the number sources are dynamically allocated through the number source allocation unit according to the capacities of the departments, the reserved numbers and the illness state importance degrees, reasonable allocation of resources is ensured, and the number source allocation efficiency is improved. The appointment priority unit determines a doctor-seeing sequence according to factors such as illness state importance and fulfillment rate of the patients, preferentially processes emergency and important patients, and promotes the patients to fulfill the appointment on time by rating the patients, so that the effect of online appointment experience of the patients is improved.

The invention belongs to the technical field of hospital triage systems, and provides an outpatient and emergency triage system, comprising: an information collection module collecting basic information and symptoms of a patient as original data, and preprocessing the data; the intelligent triage module classifies and predicts symptom data input by a patient through a medical knowledge graph and a machine learning algorithm; the triage result output module allocates departments to the patients according to the classification and prediction results of the intelligent triage module, and predicts the waiting time of the patients based on historical data; the interaction module displays and informs the patient according to the distribution information of the triage result output module and the predicted waiting time; through the intelligent triage module, the severity of the illness state of a patient can be rapidly judged, critical patients can be processed preferentially, so that distribution of medical resources is optimized, the waiting time of the patient in a hospital can be shortened through prediction and scheduling of waiting time of the patient, and the treatment efficiency is improved.

The invention relates to the technical field of intelligent triage, in particular to an outpatient service automatic triage method based on multi-modal data fusion, which comprises the following steps of: acquiring the number of reception people, queue length and registration increment through a platform, inputting K-means clustering to evaluate reception load, calculating reception efficiency per unit time, extracting a chief complaint feature vector, and inputting the chief complaint feature vector into an SVM (Support Vector Machine) matching department. A matching degree and queue length weighted sorting path is combined, a dynamic threshold value and diversity constraints are introduced, the reception pressure and the emergency level are fused to adjust the priority, and a triage result is output. According to the method, the matching degree and balance are improved and the path flexibility and fault tolerance are optimized by fusing the three-dimensional pressure level and the sliding window calculation efficiency, extracting the symptom vector to optimize the matching precision, fusing the matching degree and the queue dynamic weight, introducing the diversity factor to expand the path and constructing the multi-dimensional evaluation model dynamic response; and the waiting stability, the resource utilization efficiency and the service quality are improved.

The invention discloses an emergency patient intelligent pre-examination triage system, and particularly relates to the field of emergency patient pre-examination triage, comprising: a data processing module performs standardization processing on patient chief complaint text and sign data, and constructs a pre-examination data set; the disease prediction module inputs the pre-examination data set into a multi-label disease prediction model, and outputs a potential disease set and a prediction probability thereof; the probability adjustment module performs confidence correction on the prediction result in combination with the historical medical history of the patient; the graph construction module constructs a potential disease semantic adjacency graph according to the semantic similarity; the detection item determination module performs co-occurrence path compression on the basis of the semantic graph structure, extracts a minimum detection item set and generates an auxiliary detection item list; the triage guidance module schedules and ranks the detection items according to the real-time states of the detection resources, triage detection guidance is generated, and efficient and flexible emergency treatment primary screening planning is achieved.

The invention specifically discloses a triage training system and method based on a large language model and a knowledge graph, and relates to the technical field of medical training. The system comprises a multi-modal data sensing and processing module which is used for accessing and processing diversified training data sources; the dynamic time sequence knowledge graph construction and updating module comprises an entity relationship extraction unit, a multi-modal alignment unit and a knowledge fusion and conflict resolution unit; the multi-agent collaborative service module is composed of large language model agents which are special in function and work cooperatively, and comprises an extraction agent, a construction agent, a reasoning agent and a teaching agent; and the immersive interactive training and feedback module comprises a virtual case generation unit, a multi-modal interactive interface and a deep feedback system. According to the invention, the authenticity, accuracy and effectiveness of training are significantly improved, it is ensured that the learned knowledge and skills can efficiently adapt to a real emergency treatment scene, and the accuracy and efficiency of triage classification in emergency treatment are improved.

The invention provides a separate hospital guidance method and system for a large medical model, electronic equipment and a storage medium. The method comprises the following steps: receiving symptom description information of a natural language of a patient; performing natural language processing on the symptom description information by utilizing a large medical model, and determining symptom entity information and basic information of the patient; inputting the symptom entity information into a pre-constructed medical knowledge graph for matching, and determining candidate diseases and departments thereof corresponding to the patient; generating personalized triage suggestions by using the medical big model according to the candidate diseases and the departments thereof corresponding to the patient and in combination with the basic information of the patient; that is to say, according to the medical big model and the medical knowledge graph, symptom entity identification is performed on the symptom description of the patient, and then the corresponding candidate disease and the department thereof are determined, so that a manual separate hospital guide mode is avoided, the separate hospital guide efficiency and accuracy are improved, the waste of medical resources is reduced, and the fairness and sustainability of medical services are realized.

A system and method for clinical decision support include a processor configured to receive a clinical service request; parse the clinical service request into a set of semantic tokens, classify the clinical service request according to the type, severity, and urgency of the clinical service request; execute a preliminary triage operation with the semantic tokens to identify a relevant clinical rule and a primary workflow for responding to the clinical service request based on a classification and / or label, the relevant clinical rule and primary workflow are each unique and tailored for a specific medical service provider organization; generate a decision support recommendation based on the relevant clinical rule and primary workflow; and transfer the decision support recommendation to an organizational processing subsystem for the specific medical service provider organization based on the primary workflow.

The invention discloses an automatic medicine vending machine based on AI inquiry. The automatic medicine vending machine comprises a machine body, an intelligent interaction system, an AI inquiry system, a medicine storage bin, a control system, a payment system and a printing device. A constant-temperature and constant-humidity system and a disinfection and air purification module are arranged in the machine body. The intelligent interaction system collects symptom information in a multi-mode mode, and the AI inquiry system achieves symptom analysis, intelligent triage and personalized medication recommendation based on a medical knowledge graph and a natural language processing technology. The medicine storage bin carries a three-dimensional matrix type medicine cabinet and an intelligent inventory system, and intelligent replenishment is carried out through an LSTM algorithm. And the control system adopts a controller and triple verification to realize high-precision medicine discharging operation. The payment system supports multiple settlement modes and block chain evidence storage data. And the printing device outputs the medication guide sheet and the anti-counterfeit label. According to the invention, AI and automation technologies are deeply integrated, the service of less than or equal to 3 minutes in the whole process of inquiry-payment-medicine dispensing is realized, and the safety and convenience of medicine use are improved.

The invention is suitable for the technical field of medical robots, and particularly relates to an intelligent hospital guide method and system and an intelligent hospital guide robot, and the method comprises the steps: obtaining department attribute data and a multi-mode operation record of a target patient, and integrating dynamic information; performing data feature fusion according to the multi-modal operation record and the department attribute data, and determining a triage adaptive feature group of the target patient; calling a historical triage database according to the triage adaptation feature group of the target patient to obtain the number of abnormal first triage instances in the historical triage database; screening in a historical triage database through the triage adaptation feature group of the target patient to obtain the number of second triage instances associated with the triage adaptation feature group; and according to the first triage instance number and the second triage instance number, the triage conflict coefficient is constructed, the hospital guide information of the target patient is determined according to the triage conflict coefficient, and special precise hospital guide based on the real-time features and historical data improves the doctor seeing experience of the patient.

The invention provides a medical aid decision-making method and device, relates to the field of intelligent medical treatment, and adopts three models with different capabilities to cover different plates of a whole inquiry process, thereby effectively reducing common illusion problems of a large model. The triage route is responsible for a special medical logic reasoning model, and it is ensured that the patient is accurately guided to the needed medical service. The multi-modal model is responsible for collecting the complaint information of the patient through an efficient voice interaction technology, meanwhile, effective communication and recording are carried out, and detailed data support is provided for follow-up diagnosis. The medical expert model realizes semantic alignment on medical data through special training, can deeply understand slight differences in expression of patients, and further improves the accuracy and efficiency of diagnosis and treatment.

The invention relates to the technical field of digital data processing, in particular to an intelligent triage and guide analysis method, device and system based on a large language model.The method comprises the steps that symptom information of a patient is collected in a text input or voice input mode, and the symptom information and patient identity information are associated; cleaning the collected symptom information and extracting a symptom keyword group; matching the extracted symptom keywords with data in a knowledge graph to determine a to-be-selected disease group; generating a corresponding recommended department table according to the to-be-selected disease group, and arranging the departments according to the priority; and correcting the arrangement sequence of the departments based on the historical treatment records of the patients. Through the steps, the symptom information of the patient can be efficiently collected and processed, and accurate and personalized medical suggestions can be provided. According to the method, the efficiency and the quality of medical services are greatly improved, and meanwhile, the medical experience of the patient is also improved.

The invention discloses an intelligent emergency pre-examination triage method and system based on big data, and relates to the technical field of pre-examination triage. According to the intelligent emergency pre-examination triage method based on the big data, the pre-examination data of the patient including the real-time physiological monitoring data and the main information is obtained, the pre-triage department and the disease deterioration probability are determined, then the treatment capacity coefficient of the pre-triage department is evaluated, and therefore the final triage strategy is determined, accurate matching of the departments can be achieved based on the big data, and the accuracy of the triage strategy is improved. The condition deterioration probability is dynamically evaluated, and the department treatment ability is comprehensively considered, so that a scientific triage strategy is formulated, the emergency triage efficiency and accuracy are improved, and a more timely and effective treatment opportunity is won for patients; the problems that in traditional emergency pre-examination triage, department matching is not accurate, disease deterioration risk assessment is not timely, and hospital disposal capacity is not sufficiently considered can be solved.

The invention discloses a hospital emergency triage method in combination with medical history data, and belongs to the technical field of medical triage, and the method specifically comprises the steps: obtaining the actual illness state and historical treatment data of a current patient to form a pre-examination data set, inputting an illness state prediction model to obtain a pathological entity and the confidence probability thereof, and preferably evaluating a dominant illness in combination with a danger coefficient; obtaining a target department corresponding to the dominant disease, calculating the coverage overlap ratio of the non-dominant disease and the treatment capacity of the target department, if the overlap ratio is smaller than or equal to a preset threshold value, obtaining the department corresponding to the non-dominant disease, marking the department as a candidate department, and extracting the treatment data of the department to calculate a waiting pressure coefficient; calling cross-department cooperation data to calculate candidate department response efficiency coefficients; the comprehensive turnover efficiency scores corresponding to the candidate departments are calculated, and the candidate department with the highest score is selected as the first diagnosis department, so that the patient turnover link minimization and the medical resource utilization optimization are realized on the premise that the dominant threat is preferentially treated, and the emergency treatment efficiency is improved.

The invention relates to the technical field of data processing, and discloses an emergency department patient information processing method and system based on big data, and the method comprises the steps: carrying out the standardization processing of an unstructured text and heterogeneous data through natural language processing and medical ontology mapping; the method comprises the following steps: dynamically monitoring vital sign data of a patient, extracting trend change and fluctuation amplitude, and mining a frequent pattern of illness state evolution from historical medical record data based on an FP-Growth algorithm; through a Transform-GNN-XGBoost model, the disease deterioration risk of the patient is predicted, and in combination with historical medical record data and the current state of the patient, the hospitalization duration and the treatment cost of the patient are predicted; dynamically generating an optimal triage strategy and a resource scheduling scheme through a genetic algorithm; the prediction result, the optimal triage strategy and the resource scheduling scheme are visually displayed to medical staff; the diagnosis and treatment efficiency and the resource utilization rate of the emergency department are remarkably improved.

The invention relates to an intelligent method and system for rapid screening and triage of aortic dissection. The method comprises the following steps: collecting text information data sets indicating chief complaint symptoms, first symptoms, past medical history and high-risk signs; based on the text information data set, risk factor identification is carried out by using a feature extraction and classification algorithm combining natural language processing with machine learning; through a correlation analysis technology, correlation between the risk factor and the chief complaint symptom and between the risk factor and the first symptom is calculated, and whether the patient belongs to an AD high-risk group or not is preliminarily evaluated by setting a threshold value according to correlation strength; starting a first-aid green channel for the target patient which is preliminarily assessed as AD high risk, measuring blood pressure of four limbs, and recording generated blood pressure data; comparing the blood pressure data of the target patient with a preset threshold value, and determining the possibility that the target patient has suspected AD through logical judgment; and performing priority triage based on the suspected AD possibility evaluation result, the severity of the clinical symptoms and the availability of the medical resources.

The invention provides a medical triage method and system based on a multi-modal model, and relates to the field of intelligent medicine.The medical triage method and system based on the multi-modal model are based on a large language model base, the RAG technology is adopted to retrieve a local knowledge base, access of different large models such as DeepSeek or local off-line deployment of the large language model can be supported, the safety and confidentiality of data are guaranteed, and the method and system are suitable for large-scale popularization and application. And the knowledge base can be updated and expanded according to the types of the cases and the update of the doctor knowledge. Multi-modal model analysis is combined with priori knowledge of a hospital, a cue word system and a data document are flexibly adjusted, and the accuracy of triage and hospital guidance is improved.

The present invention provides a multilingual, AI-powered healthcare system integrating a personalized medical assistant, real-time clinical decision support, and closed-loop device control. The system ingests multimodal patient-generated and institutional data, applies advanced language-model-driven reasoning for clinical insights and triage, and supports regulated, auditable actuation of medical devices. Key features include multilingual overlays, role- and jurisdiction-specific visualization, co-signature enforcement, audit traceability, and seamless integration with healthcare infrastructure. A “Multilingual Overlay” is a dynamically generated display layer that the system composites in real-time from clinical text, icons, and color-coded indicators, automatically adapting its language, reading direction, terminology, visual density, and role-based data visibility to the preferences, locale, and device form-factor of each authenticated viewer. The architecture supports provider- and patient-facing use cases, agentic AI for autonomous yet regulated decision-making, and robust safety and compliance features.

The invention discloses an intelligent oral disease triage method and system based on oral images and a terminal, and the method comprises the following steps: S1, building a two-dimensional convolutional neural network model based on a dynamic skeleton, which comprises a plurality of deep learning skeletons and a dynamic feature extractor for extracting corresponding features according to different deep learning skeletons, the deep learning skeleton is a neural network which takes a patient oral medical image as an input and takes a disease feature corresponding to the patient oral medical image as an output; s2, acquiring oral medical images of a patient; and S3, inputting the oral medical image picture of the patient into the two-dimensional convolutional neural network model based on the dynamic skeleton, and outputting a recommended department matched with the corresponding disease feature. The two-dimensional convolutional neural network model based on the dynamic skeleton considers the difference of different deep learning picture processing frameworks for pictures with different characteristics, and can help to more accurately carry out oral disease triage.

The invention specifically discloses an intelligent triage classification AR glasses system based on multi-modal data, and relates to the technical field of medical risk management. The system comprises an AR glasses hardware terminal module, a multi-modal data acquisition module, an intelligent analysis processing module, an AR man-machine interaction module, a data storage and transmission module and a peripheral linkage module. Wherein the AR glasses hardware terminal is in lightweight design, the multi-modal data acquisition module acquires and optimizes video, voice, physiology and other data of the wounded, the intelligent analysis processing module outputs red, yellow, green and black triage results through space-time alignment, feature fusion and AI analysis, the AR man-machine interaction module is used for optimizing interaction, and the data storage and transmission module guarantees data safety. And the peripheral linkage module realizes label printing and NFC writing. The system is short in single-time triage time, triage accuracy and environmental adaptability can be improved, information tracing is achieved, two hands of medical care are liberated, and rescue efficiency and the survival rate of wounded personnel are effectively improved.

The present invention relates to the field of intelligent medical technology, and in particular to an intelligent outpatient triage method and system based on a large model. An intelligent outpatient triage system based on a large model comprises: a data acquisition module and an outpatient triage module. The present invention determines the triage priority corresponding to the patient by sending the patient's structured data into the outpatient triage model for processing, and performs triage operations on the patient based on the patient's corresponding triage priority, without the need for medical staff to make judgments based on experience; four different judgment criteria are incorporated into the outpatient triage model, and the feature information expression effect and feature information fusion effect under the perspectives of the four different judgment criteria are improved through a multi-standard feature extraction layer, a feature transmission layer, and a difference complementary enhancement layer, so that the outpatient triage model can be adapted to more clinical scenarios and has higher adaptability to complex patient conditions.

The invention discloses an active diagnosis and treatment cooperation system and method based on artificial intelligence full-process dominance, and belongs to the technical field of medical artificial intelligence, and the system comprises a pre-diagnosis management module which actively carries out the medical care team distribution of a registered patient and completes chronic disease signing, performing intelligent triage after judging the disease progress and the onset risk of the signed patient, generating a pre-diagnosis examination list for the triage patient, and completing the diagnosis and treatment process of the pre-diagnosis stage; the in-diagnosis management module judges the health state of the patient and actively pushes a standardized diagnosis and treatment path to form a chronic disease prescription to be provided for a doctor, and the chronic disease prescription is pushed to the patient after being signed by the system and the doctor; and the post-diagnosis management module is used for automatically generating a dynamic tracking plan, establishing a grading follow-up visit mechanism, generating a follow-up visit scheme and automatically returning prognosis data of the patient to guide system optimization training. The whole process of chronic disease prevention and treatment is dominated through AI, the chronic disease management quality can be improved, the chronic disease prevention and treatment homogenization level is improved, and whole-process high-quality public service supply is achieved.

The invention belongs to the technical field of online medical treatment, and particularly relates to an intelligent pre-examination triage method and system based on a digital person, and the method comprises the steps: obtaining the information of a patient seeing a doctor based on the digital person; processing the obtained doctor-seeing patient information to obtain doctor-seeing patient key information; carrying out deep classification analysis on the obtained key information of the doctor-seeing patient, and judging the disease of the doctor-seeing patient; predicting the disease of the doctor-seeing patient in real time according to the obtained judgment result of the disease of the doctor-seeing patient; and based on the real-time prediction result of the disease of the patient seeing the doctor, performing recommendation of a doctor-seeing department, and completing intelligent pre-examination triage. According to the invention, based on a digital human technology in which a virtual medical assistant and a machine learning algorithm are arranged, while rapid and accurate pre-examination triage is realized through human-computer interaction, humanized and emotional support is provided for a doctor-seeing patient, questions are answered and puzzled for the doctor-seeing patient, and the experience feeling of the doctor-seeing patient is improved.

The invention belongs to the technical field of medical health management, and particularly relates to an AI personalized health education system for elderly patients with head and neck cancer, which comprises a content generation and management module, a personalized push engine, an interaction and artificial triage module, an elderly friendly interaction module and a data monitoring and safety module, by adopting an AI + manual hierarchical response mechanism and combining elderly friendly interactive design and operation training support, the participation obstacles that elderly patients are inconvenient to move and poor in informatization ability are broken through, and the intervention compliance and sustainability are improved; according to the intervention method, strict double-group random control test design is adopted, multi-dimensional and multi-time-point effect evaluation is combined with a scientific statistical analysis method, the improvement effect of AI-assisted health education on cancer recurrence, fear, psychological state and life quality of a patient can be accurately verified, and a solid evidence-based basis is provided for clinical popularization.

According to the county-level medical care dispatching system and method suitable for the elderly patients, an intelligent terminal supports multiple interaction modes, and real-time health data and request data of the elderly patients are transmitted to an Ai dispatching center module in an encrypted mode through a 5G / Internet of Things protocol; dynamically adjusting an execution strategy, generating a corresponding service scheme, and transmitting the service scheme to an execution module for medical care distribution; the county medical cloud platform optimization decision model realizes update of medical rules, promotes evolution of the Ai scheduling center module, re-deploys Ai triage priority weights, maintains terminal security and sets personalized parameters, and remotely manages intelligent terminals; and the execution module records the whole medical service process and feeds back the whole medical service process to the county medical cloud platform through block chain evidence storage to complete a closed loop. According to the scheme, resources of hospitals, communities and third-party institutions are integrated, a complete closed loop is formed from data acquisition to service evaluation in the aging-suitable design, the system performs automatic iteration according to the execution effect, and the service quality is continuously improved.