109 results about "Medical advice" patented technology

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

The invention discloses an AI nursing whiteboard system and method based on a large model agent, the system comprises a nursing whiteboard terminal, an AI intelligent chain platform and a database, the nursing whiteboard terminal comprises an AI nursing assistant, and a whiteboard control system is arranged in the nursing whiteboard terminal. Through the functions of integration of multi-source data, intelligent early warning, accurate question and answer and the like, various diagnosis and treatment data, such as vital signs, medical advices, nursing records and the like, of a patient can be rapidly integrated and presented in a clear and visual mode, a large amount of time and energy are saved, the patient can rapidly know the condition of the patient, and the diagnosis and treatment efficiency is improved. Vital signs and illness state changes of a patient can be monitored in real time, early warning can be immediately given out once abnormal conditions such as heart rate and blood pressure exceeding normal ranges occur, a nurse can know the abnormal conditions in the first time and take corresponding measures, the response speed to emergency situations of the patient is greatly increased, and when the nurse encounters a question in the aspect of medical knowledge, the nurse can timely take corresponding measures. Inquiry may be made by an AI care assistant.

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 discloses a multi-role collaborative management architecture based on RBAC + HIS synchronization, and the architecture comprises a role authority management module which builds a static role authority baseline based on an RBAC model, and associates the real-time organization architecture data of an HIS system through a dynamic rule engine, and generates a multi-level role authority tree; the data synchronization middleware adopts an API gateway to realize one-way data push between the HIS system and the psychological assessment platform, comprises a field-level encryption unit and an incremental data verification unit, and supports isolated transmission of patient medical records, medical advices and assessment tasks according to inpatient areas; the multi-terminal cooperation engine realizes authority mapping of the doctor workstation, the mobile nursing terminal and the patient self-service terminal through distributed session management, a conflict detection mechanism is built in, and a secondary authentication process is triggered when cross-inpatient-area operation is detected. According to the invention, dynamic authority management is realized, data secure transmission and multi-terminal cooperation are guaranteed, auditing tracing is enhanced, and business continuity when the system is abnormal is ensured.

The invention discloses a mobile rescue intelligent management method based on RFID automatic identification and AI artificial intelligence interaction, and belongs to the field of mobile rescue intelligent management. According to the invention, the problem of low efficiency of medicine and material inventory management of the rescue carriage in an inpatient area due to an existing manual inventory mode is solved, batch accurate identification can be carried out by adopting an RFID remote identification technology, batch inventory is realized to replace one-by-one checking, the inventory efficiency is effectively improved, and the inventory management cost is reduced. Manual electrocardiogram data checking is replaced by image visual recognition of electrocardiogram data, secondary recording is not needed, rescue data can be automatically recorded, rescue efficiency is improved, medical advice recognition is assisted by AI voice instead of manual recording of medical advice, the workload of nurses is reduced, the workload of secondary recording is reduced, and rescue efficiency is improved. And through a rescue scene full-process digital AI closed loop, digital tracing can be completed in the rescue process, and the problems that traditional handwritten records and data are not real-time and inaccurate are solved.

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 relates to the technical field of intelligent inquiry, in particular to a hospital intelligent inquiry method and system based on natural language processing, and the method comprises the following steps: based on the input text of a patient, analyzing the relation between text content and medical terms, identifying key symptom description, carrying out term adaptation, correcting context errors, and obtaining a context adjustment result. According to the method, the adaptation degree and correlation of medical terms are evaluated through accurately analyzed data, it is ensured that deduced medical suggestions are highly matched with patient symptoms, in the aspect of physical parameter analysis, through real-time monitoring and fluctuation analysis of physiological indexes of patients, health risks can be recognized earlier, medical responses can be adjusted in time, and the medical treatment effect is improved. Not only is the workload of medical staff relieved, but also the individuation level and efficiency of medical services are improved, and remarkable advantages are provided for accelerating the inquiry process and accurately positioning the health problem.

The invention relates to an intelligent oral disease risk prediction and analysis system, which comprises an oral health data acquisition module, a data preprocessing and standardization module, a multi-modal data fusion module, a risk prediction and suggestion module and a medical interaction interface module, the data preprocessing and standardization module is used for preprocessing original data and performing standardization operation, the multi-modal data fusion module is used for multi-modal data fusion, and the risk prediction and suggestion module is used for oral disease risk prediction and generation of personalized medical intervention suggestions. And the medical interaction interface module is used for checking a prediction report by a user, receiving medical suggestions and providing feedback information. According to the intelligent oral disease risk prediction analysis system, a multi-modal oral data feature extraction algorithm based on a convolutional neural network is proposed to perform feature extraction on data; and an oral disease risk prediction algorithm based on machine learning is provided to predict the oral disease risk.

One or more embodiments of the invention provide a medical examination report interpretation method and device based on a large language model, and the method comprises the steps: obtaining a to-be-interpreted medical examination report image, and carrying out the optical character recognition of the medical examination report image, identifying a medical examination report text from the medical examination report image; performing information extraction on the medical examination report text to extract a target text related to a medical examination result from the medical examination report text, and organizing the target text into a structured text according to a preset structured text format; and inputting the structured text into a large language model, and reasoning based on the structured text by the large language model to generate a medical suggestion text corresponding to the medical examination result.

The invention relates to the technical field of wisdom medical treatment and artificial intelligence, and discloses a health suggestion report generation method and device, computer equipment and a storage medium, which are applied to a health assessment and health suggestion system of wisdom medical treatment, and the method comprises the following steps: collecting multi-dimensional time sequence health data of a user; preprocessing the multi-dimensional time sequence health data to generate a structured time sequence feature text; identifying an abnormal mode label based on the structured time sequence feature text through a lightweight language model; performing knowledge matching and knowledge adjustment on the basis of the abnormal model label through the associated medical knowledge base, and generating target health description information and target medical suggestion information; and generating a health suggestion report according to the abnormal mode label, the target health description information and the target medical suggestion information. The method has the advantages that the health assessment accuracy is improved, the personalized suggestion generation capability is enhanced, and the dynamic threshold adjustment is realized.

The invention relates to the technical field of intelligent hospital guidance, and discloses an intelligent medical decision and intervention method and system based on a multi-source data fusion large language model. The method specifically comprises the steps of collecting medical dialogue data from real dialogue data of a plurality of medical centers, establishing a database, and performing fine adjustment on a pre-trained large language model based on the medical dialogue data to obtain a large language model in a medical dialogue scene. The real-time medical dialogue data is analyzed according to the large language model in the medical dialogue scene, the doctor's advice content is generated, the mood and frequency of the doctor's advice content are adjusted according to the illness state and the emotional state of the patient, the capabilities of the large language model in the aspects of semantic comprehension, natural interaction and regionalization adaptation are remarkably improved, and the user experience is improved. A more efficient, accurate and personalized solution is provided for communication between the patient and the medical staff, communication obstacles between the patient and the medical staff can be relieved, and powerful technical support is provided for intelligence of medical services.

The invention discloses an artificial intelligence-based whole doctor's advice reasonable medication auditing method and platform, and the method comprises the steps: integrating the structured data and unstructured text of a patient, carrying out the preprocessing according to a time sequence, determining a physiological safety range, and constructing a panoramic taboo doctor-seeing portrait; after deep information extraction and multi-source data fusion, a panoramic image of a patient is formed and is subjected to taboo comparison with medicine advised by a doctor, and a hard interception warning is triggered to avoid medicine conflict; further establishing a digital twinborn model, setting medical advices subjected to taboo comparison as a baseline scheme, simulating dynamic changes of physiological indexes within 72 hours, and assisting doctors to adjust drug doses in combination with clinical experience; finally, an individualized medication safety baseline is determined according to the simulation curve, and accurate simulation, dynamic early warning and scientific decision making are achieved. According to the scheme, the treatment safety and accuracy are remarkably improved, full-process intelligent medication auditing support is provided for complex patients, and the scheme has important clinical application value.

The invention provides a hospital adverse event risk identification method and system based on causal reasoning, and the method comprises the steps: responding to an operation request of a user for reporting a hospital adverse event based on the optimization of medical advice data processing and a medical quality management process in an operation risk identification scene, inputting the detailed information of the event through a formatted form, and carrying out the operation of the event. Through multi-node, multi-auditor and multi-department cooperative auditing, an adverse event form reported by a user is subjected to approval, flow and summary analysis, medical advice data is subjected to structured storage by adopting visual enhancement OCR, dynamic knowledge graph verification and a time sequence causal reasoning model, a time window division algorithm is adopted, and medical advice data is obtained. The causal discovery algorithm constructs an unplanned re-operation association network, early warning of adverse events of operation complications is carried out, and compared with a traditional mode, the medical advice processing efficiency, the transcription error rate and the unplanned operation detection rate are all greatly improved.

An intelligent medical follow-up visit system based on a large language model comprises a user interface layer, a voice recognition module, a natural language processing module, a medical knowledge base, a data management module, an intelligent recommendation module, a doctor assistance module, a data analysis module and a safety and privacy protection module. Personalized medical suggestions and health management schemes are provided for the user, the quality and efficiency of medical services are improved, the user is helped to better manage own health, doctors are helped to make diagnosis and treatment suggestions, and the working efficiency and accuracy of the doctors are improved; the system ensures that personal health data of a user is fully protected through the security and privacy protection module, and the personal health data conforms to related privacy laws and regulations, in short, the intelligent medical follow-up visit system based on the large language model is remarkably improved in the aspects of understanding patient requirements, personalized suggestions, data analysis, knowledge bases and the like, and the system has a wide application prospect. The efficiency and quality of medical follow-up visit can be better improved.

The invention discloses a complex medical quality management and control index automatic calculation method based on an intelligent agent. According to the index semantic model construction method provided by the invention, the automatic conversion of the medical quality control indexes from a natural language to structured semantics is realized, so that the index definition has computability and mobility, the dependence of manual analysis and script configuration is eliminated, and the standardization, generalization and reuse efficiency of the index definition is remarkably improved. Through multi-source data semantic packaging and an MCP service abstraction mechanism, semantic unification and interface standardization of multi-source heterogeneous data such as electronic medical records, inspection information, disease course records and medical advice management are achieved, and a semantic data layer capable of achieving cross-system access is constructed; the problems of data dispersion, field isomerism and interface incompatibility in a traditional system are effectively solved.

The invention provides an information generation method and device, an information model training method and device, equipment, a medium and a product. The method comprises the steps of obtaining a clinical data set and a standard knowledge base; for each piece of clinical data, executing the following thinking chain data generation operation: based on a standard knowledge base, generating thinking chain data from patient condition information to diagnosis information and doctor's advice information in the clinical data by utilizing a large language model technology; and for each piece of clinical data, taking patient condition information in the clinical data as input data, taking diagnosis information and doctor's advice information in the clinical data and thinking chain data of the clinical data as label data, and performing supervised training on a preset large language model to obtain an information generation model. The information generation model obtained through training of the method has the medical thinking ability, the thinking decision process of a doctor can be restored, meanwhile, the model illusion phenomenon is avoided, and the accuracy, reliability and interpretability of the information generation model are greatly improved.

The invention relates to a disease information management method and system based on multi-source data, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: forming multi-source heterogeneous data through employing the obtained infectious disease data and chronic disease data, and carrying out the preprocessing with the corresponding data features as a reference, on one hand, risk prediction and clustering analysis are performed on infectious disease related data through a logistic regression model to obtain risk assessment information, and a propagation trend is predicted through the model; on the other hand, chronic disease data are predicted and evaluated through the model, a medical advice result is obtained, and finally disease control information management is conducted in combination with all the data. Therefore, a set of perfect disease prevention and control information management is realized, on one hand, more timely and accurate infectious disease detection and early warning are realized, and the efficiency and effect of major infectious disease prevention and control work are improved, and on the other hand, chronic disease management is perfected, and the problems of diagnosis and treatment errors and lagging of patient treatment consciousness caused by incomplete data are avoided.

A method includes receiving input data for multiple patients, including medical data, situational data, and environmental data. The input data is stored and curated within a data store. The input data is processed through rule-based algorithms to automatically generate a real-time patient assessment for a target patient, which is fed back into the data store. A comprehensive care artificial intelligence (AI) system implements a real-time dynamic predictive AI model to process data within the data store to automatically identify causal relationships pertinent to the real-time patient assessment for the target patient, and automatically generate a real-time dynamic healthcare recommendation for the target patient, which is fed back into the data store. Both the real-time patient assessment for the target patient and the associated real-time dynamic recommendation for the target patient are conveyed as outputs.

The invention discloses an intelligent medical advice recommendation method and system based on fusion of patient heterogeneous data, and relates to the technical field of health data communication. Multi-modal dynamic association and standardized mapping are carried out on heterogeneous data of patients from different sources to form a preliminary modal vector; performing statistical aggregation on each mode in a time window, dynamically adjusting the weight in combination with quality evaluation, and finally fusing to generate a unified semantic vector, so as to ensure the integrity, the time sequence and the reliability of patient information; and then screening and scheduling local and cloud models in combination with a hospital resource environment, dynamically selecting an optimal model to execute a generation request, and importing a unified semantic vector to complete doctor's advice recommendation. The problems of data isomerism, information timeliness and model availability can be effectively solved, the accuracy, the response speed and the clinical applicability of the generated intelligent medical advice are all improved, meanwhile, the manual intervention requirement is reduced, and the system reliability and the clinical decision-making efficiency are improved.

The invention relates to the field of medical information processing, in particular to a medicine accurate query system based on artificial intelligence, and the system comprises a document analysis module which is used for analyzing the past and current diagnosis and treatment medical advice and diagnosis and treatment data of a patient to obtain a structured document analysis result; the query module is used for performing rule matching and reasoning calculation based on the analysis result to generate medication consistency test data; the scheme generation module is used for converting the inspection data into an executable drug administration plan; and the scheme correction module is used for adjusting the drug administration plan in real time based on the execution feedback data. Through medical advice clause logic analysis and medication time axis construction, time logic and conditional logic extraction is realized, and the reasonability of medical advices is ensured through conflict detection; furthermore, rule mapping is carried out in combination with changes of diagnosis and treatment monitoring indexes, a recommended medication plan is generated, dynamic correction is carried out under execution feedback, and accurate query based on reasoning can be realized under complex medical record information.

A patient support apparatus for concurrently transporting a patient and a medical device includes a base and an intermediate frame coupled to the base and further includes a support assembly coupled to the intermediate frame. The support assembly supports the medical device for concurrent movement with the patient support apparatus. The support assembly includes a track extending between first and second track ends with at least one curvilinear region defined therebetween and includes a carrier for selective movement between the ends. The support assembly also includes a mount coupled to the track and configured to support the medical advice. The support assembly further includes a retainer operatively attached to the carrier with the carrier operable between an engaged state to inhibit relative movement of the carrier along the track and a disengaged state to permit movement of the carrier along the track.

The invention relates to an online and offline integrated hepatopathy diagnosis and treatment system and a use method, and the system comprises an online platform which is used for carrying out preliminary triage based on the symptoms and hepatopathy history of patients, guiding the patients to select proper hepatopathy medical institutions, and carrying out online treatment appointment registration on the patients through the online platform based on the hepatopathy medical institutions, medical suggestions and treatment schemes are obtained; the offline liver disease diagnosis and treatment module allows the patient to go to a liver disease medical institution for offline liver disease treatment based on medical suggestions and treatment schemes; the hepatopathy medical institution arranges experts in advance for face diagnosis and further examination according to the time when the patient arrives at the hospital based on the treatment scheme. Patients input personal symptoms and liver disease histories through an online platform, then intelligent triage is carried out by utilizing an intelligent triage algorithm and historical liver disease data according to the input personal symptoms and liver disease histories, and most suitable liver disease medical institutions and expert resources are quickly matched. And the patient does not need to blindly run among various hospitals, so that the time is greatly saved.

The embodiment of the invention provides a personalized medical advice generation method and device, electronic equipment and a storage medium, and relates to the technical field of information processing. The personalized medical advice generation method comprises the following steps: acquiring disease symptom information and personal diagnosis and treatment information of a patient; wherein the disease symptom information comprises one or more of disease information and disease symptom information, and the personal diagnosis and treatment information comprises one or more of tongue diagnosis information, face diagnosis information, pulse diagnosis information and palm diagnosis information; acquiring basic doctor's advice content matched with the disease symptom information; obtaining customized medical advice content matched with the personal diagnosis and treatment information; and generating a personalized doctor's advice according to the basic doctor's advice content and the customized doctor's advice content. According to the embodiment of the invention, the technical effect of providing personalized medical advices for different patients can be realized.

This invention discloses a medical problem consultation system and method based on a large model. It includes a dialogue consultation module that processes input text and inputs it into a finely tuned large model for inference, outputting medical advice; a professional knowledge base module that processes input text and documents and inputs them into a framework based on the large model for further processing, outputting medical advice and related professional knowledge; an image analysis module that receives medical images and inputs them into an image classification model to obtain the corresponding disease name, then inputs the disease name into the finely tuned large model, which generates corresponding medical knowledge based on the disease name; and an image classification model that receives the input medical images, performs feature extraction, fusion, weighting, and classification to obtain the corresponding disease name. This system can simultaneously address the problems of young people lacking time for in-person consultations and the elderly lacking awareness of early disease symptoms, enabling early prevention, early detection, early diagnosis, and early treatment of diseases.

The invention relates to the technical field of data analysis, in particular to a chronic disease follow-up visit data AI analysis method and system based on 5G. The method comprises the following steps: acquiring a follow-up report uploaded by a patient through a low-delay data transmission channel provided by a 5G network; analyzing the follow-up visit report, and determining a self-evaluation scale, physiological index detection data and a medication record; analyzing the self-evaluation scale, and determining the lifestyle of the patient; historical complication data of the patient is called from the database, and according to the physiological index detection data, the lifestyle, the historical complication data and the medication record, multi-modal data integration is carried out through a 5G network to obtain a structured data set; based on the dynamic knowledge graph, analyzing the structured data set, and determining the risk evolution condition of the patient; and acquiring real-time behavior information of the patient, and generating a dynamic intervention scheme according to the risk evolution condition of the patient and the real-time behavior information of the patient. The emergency event processing efficiency is improved, and medical advice discarding caused by scheme rigidity is reduced.

The embodiment of the invention provides a hospitalized patient falling risk assessment method, device and equipment and a storage medium, the method is applied to the technical field of hospitalized patient management, and the method comprises the steps: obtaining multi-dimensional assessment information corresponding to a target patient; the multi-dimensional evaluation information comprises evaluation information corresponding to basic daily life activity information, evaluation information corresponding to fall risk evaluation scale information and evaluation information corresponding to a medical advice medication list; inputting the multi-dimensional assessment information into a risk assessment model for risk prediction, and obtaining a target risk prediction result; generating a target nursing scheme based on the target risk prediction result and the multi-dimensional evaluation information; and managing the target patient based on the target nursing scheme. According to the embodiment of the invention, the falling risk assessment efficiency of the inpatient can be improved, and the falling occurrence rate of the inpatient is reduced.

The invention discloses an off-line acquisition and on-line synchronization method for medical system data. A platform comprises six modules including an identity authentication and initialization module and a doctor advice data off-line synchronization module. A nurse logs in the PDA by scanning a work number plate two-dimensional code at a nurse station, medical advice data are synchronized to a local database, medical advice is executed by means of'small nurse point 'software in an off-line state of a ward, and operation data are temporarily stored and processed by a cache module. And after the network is recovered, the platform automatically and bidirectionally synchronizes the data, and during the period, the generative adversarial network and the Transform fusion processing module carry out feature extraction, enhancement, verification and integration on the data. The method comprises six steps of identity authentication, medical advice pre-storage, offline execution, network monitoring and data synchronization. The problem that a traditional scheme is limited by network signals is solved, it is guaranteed that medical advice execution is efficient and stable, and the accuracy and reliability of medical data collection synchronization are improved.

Genome-wide data is obtained, and data cleansing, data sparsity processing and bioinformatics feature extraction are performed on the obtained genome-wide data; model construction is performed based on the sparsity-processed genome-wide data and the bioinformatics features to obtain a preliminary hybrid model; model training, regularization, and interpretability enhancement are performed on the preliminary hybrid model to obtain a trained model weight and an interpretability analysis corresponding to the trained model weight; learning and uncertainty estimation are performed based on the trained model weight and to-be-predicted genome-wide data on the hybrid model to obtain an integrated prediction result and uncertainties corresponding to the integrated prediction result; and personalized medical advice and decision assistance are performed based on the integrated prediction result, the interpretability analysis, and the uncertainties corresponding to the integrated prediction result.

A system includes a data acquisition engine that receives input data for multiple patients, including medical, situational, and environmental data, which is stored and curated within a data store. A patient assessment engine of the system processes the input data through rule-based algorithms to automatically generate a real-time patient assessment for a target patient, which is fed back into the data store. The system includes a comprehensive care artificial intelligence (AI) system that implements a real-time dynamic predictive AI model for processing data within the data store to automatically identify causal relationships pertinent to the real-time patient assessment for the target patient, and automatically generate a real-time dynamic healthcare recommendation for the target patient, which is fed back into the data store. A dashboard generator of the system provides output conveying both the real-time patient assessment for the target patient and the associated real-time dynamic recommendation for the target patient.