241 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 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 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 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 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 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 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.

The invention relates to an intelligent hospital guide method and system, an intelligent terminal and a storage medium, and relates to the field of intelligent medical treatment, and the method comprises the steps: collecting the case information of a patient, the case information comprising medical history information and symptom description; performing data processing on the case information by using an illness state analysis model to obtain illness state characteristics of the patient; potential diseases of the patient are retrieved in a disease database according to the illness state characteristics; determining a department and a hanging suggestion corresponding to the potential disease; screening out recommended doctors from a doctor qualification database according to the department; and pushing the patient hanging suggestion and registration information corresponding to the recommended doctor to a user terminal of the patient. The method has the effect of improving the accuracy and efficiency of medical triage.

The invention discloses a patient-expert intelligent matching system and a triage method based on a diagnosis and treatment path, and accurate mapping from pathological features to expert capabilities is realized by constructing a patient personalized diagnosis and treatment path and expert capability map. According to the method, a dynamic triage mechanism is adopted, deep learning and reinforcement learning technologies are combined, and the matching process of patients and experts is optimized according to the diagnosis and treatment path requirements of the patients and the real-time states of medical resources. The problems that traditional triage is low in efficiency and inaccurate in expert matching are solved, a new technical scheme is provided for optimal configuration of medical resources, and the method can be widely applied to intelligent triage scenes of medical institutions at all levels.

The invention discloses an internet hospital hierarchical diagnosis and treatment and resource scheduling method and system, and the method comprises the following steps: responding to a diagnosis and treatment request sent by a user, and obtaining diagnosis and treatment data containing the chief complaint information of a patient; inputting the diagnosis and treatment data into an illness state evaluation model for feature extraction, and outputting a feature vector representing the illness state of the patient; the feature vector at least comprises a first dimension parameter representing an emergency degree of an illness state and a second dimension parameter representing a complexity degree of the illness state; determining a target resource node in a hierarchical medical resource database based on the feature vector; and generating a resource scheduling instruction according to the target resource node, and establishing a communication link between the user terminal and a server corresponding to the target resource node. According to the method, a two-dimensional evaluation system of urgency and complexity is introduced, and a dynamic threshold adjustment mechanism based on supply and demand prediction is combined, so that the utilization efficiency of medical resources and the accuracy of triage matching are remarkably improved.

The invention discloses an emergency rescue room nursing resource dynamic scheduling method and system based on demand prediction, and relates to the technical field of resource dynamic scheduling. The method comprises the following steps: collecting and preprocessing historical emergency room data, and constructing a multi-dimensional feature data set; respectively training an improved GRU model and a Transform model for high-precision prediction of patient inflow and triage level distribution in a future time period, and quantifying a prediction result as standard nursing time; based on real-time nurse data, a multi-target optimization model with the purposes of minimizing patient waiting time, balancing nurse loads, controlling cost and meeting preferences is built, a Pareto optimal scheduling scheme set is solved and generated through a genetic algorithm, and a manager selects a final scheme according to the actual situation; according to the invention, the conversion from passive response to active accurate scheduling is realized, and the operation efficiency, the resource utilization rate and the nursing quality of the emergency rescue room are effectively improved.

The invention discloses an intelligent outpatient service guidance and triage management method and system based on artificial intelligence. The method comprises the following steps that patient information, symptoms and outpatient service data are collected to generate triage input; constructing a triage optimization model based on the input, setting waiting time, path cost, load rate and priority satisfaction degree as targets, and setting a time window, resources and dependency constraints; an improved squirrel search algorithm is initialized, a population code is adopted to represent a distribution scheme, and a fitness function is established; updating the candidate scheme according to a fitness function in iteration; repairing and locally optimizing the updating scheme; when the operation data fluctuates, self-adaptive adjustment is triggered, and updating is continued; and when the fitness function converges or the iteration upper limit is reached, outputting an optimal triage scheme, including departments, time windows and path allocation, and executing incremental optimization when the patient arrives or the shift arrangement is changed. The triage efficiency is improved, the load is balanced, and the requirements of priority patients are met.

In an embodiment, a method of interacting with a patient via a virtual triage system and for determining a suggested care path is provided. The method includes: receiving an input communication from a patient via a user interface in communication with the virtual triage system, the virtual triage system including a stored engagement model; parsing the input communication using the stored engagement model to derive a meaning from the input communication; gathering information from the patient via the user interface, the information related to the derived meaning of the input communication; accessing health data associated with the patient; generating a suggested care path using a stored care model based on the gathered information and the health data; and outputting the suggested care path to the patient via the user interface.

The invention provides a robot hospital guide method based on multi-modal data and an intelligent hospital guide robot, and relates to the technical field of robots. The intelligent hospital guide robot comprises a multi-mode sensing module, an intention understanding and decision center module and a behavior execution and guide module. The method is applied to the intelligent hospital guide robot, and comprises the following steps: collecting multi-modal data through a multi-modal sensing module; performing information fusion and intention understanding on the acquired multi-modal data through an intention understanding and decision-making center module, and generating a decision-making instruction; executing a decision instruction through a behavior execution and guidance module, so as to execute personification interaction by controlling joint movement of the intelligent hospital guide robot and / or execute visual guidance by rendering and superposing an augmented reality navigation arrow on a display screen of the intelligent hospital guide robot; the problems of unfriendly interaction, single perception, rigid planning and the like in the prior art are solved, and the triage accuracy, the user experience and the adaptability in complex medical scenes are remarkably improved.

The present disclosure relates to a multidimensional adaptive sensing system and method for event detection with improved accuracy. The system dynamically adjusts, using baseline and compound models, a triggering conditional threshold of each event based on real-time variations in environmental parameters and sensor health. The adaptive system allows for fine-tuning of sensitivity to environmental changes, seasonal variations, and sensor decay, thereby reducing false positives and negatives. Installation flexibility is achieved by selecting and deploying a subset of models tailored to available data at the sensor's location, ensuring optimal operation regardless of specific environmental conditions or sensor configurations. The adaptive sensing system is used in environmental control systems for dynamic routine management, sensor health management, a triage system for complex event analysis, and multifaceted compliance assurance solution. Further, the adaptive sensing system is used for automated configuration, model deployment, and maintenance by leveraging edge computing units and a central control unit.

The invention provides an intelligent triage method and system for outpatient and emergency treatment, and relates to the technical field of medical information. The method comprises the steps that physiological data, symptom description text data, facial image data and voice audio data of a patient are collected through a multi-mode sensor; analyzing the image and audio data by using an emotion calculation engine to generate emotion state data; processing the physiological and text data through an analysis model to generate illness state emergency degree data; fusing emotion and illness state data to form a preliminary triage result; in combination with the real-time medical resource state data, a triage decision-making scheme including department allocation, processing priority and personalized pacifying strategies is generated through a dynamic decision-making device; finally, guiding and pacifying are executed through the execution terminal and the interaction robot, automation, intelligence and humanization of the triage process are achieved, the triage efficiency and accuracy are remarkably improved, medical resource configuration is optimized, and care for psychological needs of patients is enhanced.

Systems and computer-implemented methods of automated physiological monitoring and prognosis of a plurality of subjects. A system includes a plurality of monitoring devices, each having a portion configured for deployment on a surface either opposite a concha or over a mastoid region of a subject, where real-time physiological parameter monitoring is performed. Each monitoring device also includes processor-executable program code configured to periodically generate respective values indicative of real-time physiological signs for the respective subject, and a transmitter configured to periodically and wirelessly transmit these periodically generated respective values to a mobile communication and display device. The mobile communication and display device is configured to use these periodically received respective values for the plurality of subjects from the plurality of monitoring devices to periodically generate a respective prognosis score for each subject, and to periodically generate an alert for at least two of the subjects.

The present invention discloses a system and method for healthcare diagnostics using foundation models with uncertainty triage, designed to deliver reliable, explainable, and safety-assured diagnostic outcomes across multimodal clinical data. The invention integrates a foundation model processor pretrained on diverse medical datasets with an uncertainty estimation processor configured to quantify epistemic and aleatoric uncertainties in diagnostic predictions. A triage control unit dynamically classifies cases into high, medium, and low-confidence categories based on computed uncertainty indices, ensuring that only high-confidence cases are automatically finalized, while uncertain or ambiguous cases are routed for clinician review. The system further incorporates a feedback adaptation processor that recalibrates model parameters and uncertainty thresholds based on expert feedback, maintaining alignment with clinical reliability standards over time. Implemented as a hardware-integrated diagnostic device, the invention supports real-time inference, secure data handling, and interpretability visualization through uncertainty heatmaps and attention overlays.

The invention discloses an outpatient service management method and system based on an HIS system, belongs to the technical field of computers, and achieves precise triage and personalized treatment path recommendation by integrating multi-dimensional state information of a patient and real-time department data and combining a dynamic path planning algorithm. The method has the advantages of improving triage accuracy, optimizing treatment path planning and improving outpatient service management efficiency.

The invention discloses an intelligent triage dynamic priority judgment method for triage, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: respectively collecting the visual sign characteristics, voice complaint information characteristics and voice vital sign characteristics of a patient based on a machine vision module and a voice interaction module; performing fusion analysis on the multi-modal data through a deep learning model to generate a patient injury condition feature vector; then, in combination with the real-time resource occupation condition of the medical institution, the priority scores of the patients are dynamically adjusted, the final triage level is generated, and the patient list sequence is updated in real time; according to the method, the triage efficiency can be effectively improved, the waste of medical resources is reduced, particularly in an emergency resource allocation mode, priority allocation is further optimized through the resource efficiency coefficient, and it is ensured that the most needed patient is treated preferentially when resources are insufficient.

The invention discloses a psychiatry pre-diagnosis triage knowledge reasoning method based on a knowledge graph. The method comprises the steps that 1, pre-diagnosis original data are collected and preprocessed; 2, constructing a pre-diagnosis knowledge graph of the psychiatry department; 3, obtaining a standardized chief complaint text, and carrying out semantic coding through an improved Biofer model to generate a chief complaint semantic vector; 4, performing semantic matching on the chief complaint semantic vector and a pre-diagnosis knowledge graph of the psychiatry department, and screening and reasoning an initial node set; 5, executing rule path reasoning on the pre-diagnosis knowledge graph of the psychiatry department by adopting an improved AnyBURL algorithm; step 6, constructing a recommended triage suggestion set; 7, pushing the recommended triage suggestion set to a doctor terminal interface, and generating a triage result set; and step 8, acquiring feedback information after diagnosis, and performing incremental updating on the pre-diagnosis knowledge graph of the psychiatry department and the improved Bioformer model. According to the method, the semantic understanding ability and the reasoning decision level of the pre-diagnosis triage of the psychiatry department are improved.

The invention relates to the technical field of medical treatment, in particular to a pre-hospital and in-hospital first-aid time axis dynamic tracking and analysis method, which comprises the following steps of: acquiring patient vital sign data and a chief complaint voice stream transmitted by a pre-hospital first-aid terminal in real time, inputting a special voice recognition engine, and converting to generate structured text data; extracting a patient identity label and an illness state characteristic field and writing into a time axis database; and in-hospital medical equipment operation interface signals, geo-fence trigger events and triage system events are captured in real time, and diagnosis and treatment node data with timestamps are generated and added to the time axis database. According to the method, historical node sequences in a time axis database are extracted to construct a disease feature vector set, incremental training samples are obtained after feature sorting, the progress deviation value of real-time node data is combined to match historical similar case time axis fragments to execute weighted deduction, and a prediction time sequence of a key link is generated. And a reliable basis can be provided for medical personnel to plan resource scheduling of equipment, personnel and the like in advance.

In some aspects, a triage artificial intelligence receives a portion of a conversation between a doctor and a patient, determines, based on the conversation, one or more symptoms associated with the patient, determines that the conversation includes a set of trigger words associated with a particular symptom of the one or more symptoms, selects based on the set of trigger words associated with the particular symptom, a first artificial intelligence specialist in a first medical specialty from a plurality of artificial intelligence specialists, and provides the first artificial intelligence specialist with access to the conversation between the doctor and the patient. The first artificial intelligence specialist provides one or more decision support insights that are sent to a computing device associated with the doctor.

The invention discloses an automatic triage and accurate diagnosis method fusing multi-modal medical data, and relates to the technical field of automatic triage, and the method comprises multi-modal medical data acquisition, preprocessing and representation vector construction, and realizes department recommendation, priority determination and disease probability analysis through a triage decision network and a diagnosis inference engine. Generating an interpretable path; according to the method, multi-modal data such as texts, numerical values, images, time sequences and voices are fused, technologies such as a cross-modal attention fusion network and a medical knowledge graph are adopted, accurate triage and diagnosis are achieved, meanwhile, a structured report is generated through a confidence degree evaluation and grading processing flow, data feedback and model updating are supported, image-text report reminding is provided for a patient, and the accuracy of triage and diagnosis is improved. The medical efficiency and the diagnosis accuracy are improved.

The present disclosure relates to a multidimensional adaptive sensing system and method for event detection with improved accuracy. The system dynamically adjusts, using baseline and compound models, a triggering conditional threshold of each event based on real-time variations in environmental parameters and sensor health. The adaptive system allows for fine-tuning of sensitivity to environmental changes, seasonal variations, and sensor decay, thereby reducing false positives and negatives. Installation flexibility is achieved by selecting and deploying a subset of models tailored to available data at the sensor's location, ensuring optimal operation regardless of specific environmental conditions or sensor configurations. The adaptive sensing system is used in environmental control systems for dynamic routine management, sensor health management, a triage system for complex event analysis, and multifaceted compliance assurance solution. Further, the adaptive sensing system is used for automated configuration, model deployment, and maintenance by leveraging edge computing units and a central control unit.