6957results about "Medical communication" patented technology

The invention provides a dynamic health adaptive monitoring method and system using artificial intelligence, and relates to the technical field of health monitoring, and the method comprises the steps: carrying out the noise reduction through a hierarchical diffusion probability model through multi-source heterogeneous health data, extracting preliminary features through combining with an improved attention auto-encoder, learning discriminative features through a contrast learning network, and carrying out the recognition of the discriminative features; inputting the discriminative features into an adaptive wavelet neural network for multi-scale feature decomposition, inputting the discriminative features into a dynamic graph neural network to obtain a health feature tensor, obtaining time sequence features by using a hierarchical memory enhancement network, and generating a health knowledge graph in combination with a dynamic routing capsule network and a causal reasoning network; and inputting the time sequence characteristics and the health knowledge graph into a deep Bayesian network for health risk assessment, and obtaining an adaptive monitoring scheme through a model prediction control network by using a hierarchical reinforcement learning network and combining a multi-agent collaborative optimization system and a deep deterministic strategy gradient network.

A physiological patient monitoring system with a healthcare professional-facing interface is disclosed. The healthcare professional interface may only display by default the most critical non-confidential patient information. To access the full range of features in the system, clinicians can unlock the device. Once unlocked, clinicians can access all of a patient's treatment history, and all the data may be consolidated and presented intuitively.

The invention discloses an intelligent nursing management system and method for a critical patient, and relates to the technical field of medical health monitoring, and the method comprises the steps: setting a multi-physiological signal collection channel, synchronously collecting multi-dimensional physiological signals of the patient, carrying out the preprocessing of the collected signals, and forming a standardized multi-modal physiological data flow; the preprocessed multi-modal physiological data are input into a deep learning model based on LSTM and a multi-head attention mechanism, intelligent fusion analysis of different physiological signals is achieved in combination with a dynamic weight adaptive algorithm, and a patient individualized physiological system coupling model is established; and based on an analysis result of the deep learning model, calculating an illness state risk assessment value of the patient in real time, and outputting a specific early warning level according to a set multi-level early warning threshold. According to the invention, the multi-stage and multi-dimensional early warning threshold system is constructed, so that the condition of the patient can be monitored and early warned in all directions. The monitoring quality of critically ill patients can be remarkably improved, and the missed judgment rate and the misjudgment rate are reduced.

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

The disclosed system for interacting with objects in an extended reality (XR) environment generated by an XR system may include (1) neuromuscular sensors configured to sense neuromuscular signals from a wrist of a user and (2) at least one computer processor programmed to (a) determine, based at least in part on the sensed neuromuscular signals, information relating to an interaction of the user with an object in the XR environment and (b) instruct the XR system to, based on the determined information relating to the interaction of the user with the object, augment the interaction of the user with the object in the XR environment. Other embodiments of this aspect include corresponding apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

The invention relates to the technical field of disease follow-up visit, in particular to a health state assessment system applied to disease follow-up visit, which comprises a data acquisition module, a data preprocessing module, a health state assessment module, a personalized health management suggestion module, a dynamic health risk prediction module and a visualization module, the data acquisition module acquires multi-dimensional data of a patient and performs data fusion and integration, the health state evaluation module evaluates the health state of the patient based on multi-level feature extraction and a deep learning model, and the personalized health management suggestion module combines a health state evaluation result and historical data to obtain a personalized health management suggestion result. Customized health management suggestions are generated through a rule engine, and the dynamic health risk prediction module predicts future health risks of patients and provides real-time early warning by using a time sequence analysis model. The visualization module displays health assessment and management suggestion results through a chart and an interactive interface, and the accuracy and individuation degree of health management are improved.

The invention relates to the technical field of data processing, and discloses an intelligent diagnosis and treatment management method and system for chronic respiratory diseases based on a large model. The method comprises the following steps: performing multi-dimensional data acquisition processing on a chronic respiratory disease patient to form a structured data set; constructing a large model and a knowledge graph by using a dual coding framework; performing risk hierarchical assessment based on the large model; generating a personalized diagnosis and treatment scheme according to an evaluation result; constructing a medical collaborative framework to execute curative effect monitoring; and customizing health education content and continuously optimizing the system. According to the application, accurate layering, personalized treatment scheme making, multi-stage medical collaborative management and dynamic curative effect monitoring of the chronic respiratory disease patients are realized, so that the defects of insufficient diagnosis and treatment accuracy, low resource allocation efficiency and discontinuous patient management in the prior art are overcome, and the overall efficiency of chronic respiratory disease diagnosis and treatment management is improved.

The invention discloses a neurology nursing potential safety hazard monitoring system and method, and relates to the technical field of medical information safety monitoring, and the system comprises a first collection processing unit which is used for collecting monitoring data of a target patient, inputting the monitoring data into a time sequence deep network for processing, and obtaining risk feature data; the second collecting and processing unit is used for collecting physiological state data of the target patient, constructing a risk assessment rule base based on a medical knowledge graph to analyze the physiological state data, and generating risk probability data; and the risk early warning unit is used for inputting the risk feature data and the risk probability data into a Bayesian fusion network, generating risk early warning information and sending the risk early warning information to the medical care terminal. According to the invention, collaborative analysis of multi-source information is realized through the Bayesian fusion network, the problem of low early warning accuracy of a traditional single monitoring method is solved, and the reliability of potential safety hazard monitoring is improved.

An apparatus and method for real-time assessment and mapping of quality of life indicators includes: selecting a set of quality of life dimensions, receiving objective quality of life indicators based on the quality of life dimensions, receiving subjective quality of life indicators based on the quality of life dimensions, performing a statistical analysis on the objective quality of life indicators and the subjective quality of life indicators, storing the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis in a database, and outputting the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis.

Embodiments described herein provide for a healthcare ecosystem of computing devices participating as nodes of a distributed ledger (e.g., blockchain). The distributed ledger includes NFTs representing corresponding patients, where the initial NET behaves logically as a token scaffold that represents a “digital twin” of the particular patient. The initial patient token represents personal profile data of patient personal data for the patient. The nodes may generate new data and / or new sub-tokens (or other blockchain entry type) representing new data, which the nodes associate with the token scaffold. The sub-tokens contain data for representing physical or digital specimens, health data, and data derived from the health data (e.g., cell lines, organoids, other model systems). As the nodes generate and associate new sub-tokens to the patient's token scaffold over time, the token scaffold graphs and facilitates integration of patient-specific data and evolves into a robust electronic health record (EHR) and digital twin.

The invention discloses a traditional Chinese medicine intelligent inquiry method and system based on a knowledge graph and medical case enhancement RAG, and the method comprises the following steps: collecting traditional Chinese medicine related data from a plurality of sources, and carrying out the data cleaning, formatting and standardization processing, and constructing a knowledge graph containing traditional Chinese medicine symptoms, disease causes, prescriptions, and the mutual relation of the symptoms, the disease causes, the prescriptions; expressing a knowledge structure in the graph in a triple form; searching a local sub-graph related to query based on query keyword extraction and generalization by utilizing a Leiden community detection algorithm; a mixed retrieval strategy is adopted, global search and local search are combined, recalled contents are sorted and scored, global and local retrieval results are fused, and a large language model is uniformly output. The system aims at improving the intelligent level and individuation ability of traditional Chinese medicine diagnosis, firstly, a knowledge graph covering entities such as traditional Chinese medicine theories, symptoms, pathogenesis and prescriptions and relationships of the entities is constructed, mass traditional Chinese medicine data are systematically integrated, deep understanding and semantic association of traditional Chinese medicine complex diagnosis and treatment logic are ensured, and the traditional Chinese medicine diagnosis and treatment efficiency is improved. Through deep combination of the structured knowledge base and the generated model, the diagnosis and treatment accuracy is improved, and the model fine tuning and updating cost is reduced.

Incident Summary:A computer-implemented method, computer program product and computing system for: monitoring a plurality of data signals associated with a patient within a medical environment; detecting one or more incidents defined within one or more of the data signals; and processing the one or more incidents defined within the one or more data signals to produce a summary of the one or more incidents so that a user may be quickly knowledgeable of these one or more incidents.

A system for remote guidance of medical procedures enables patients to be remotely trained by a healthcare provider on how to perform a medical procedure. The system implements Augmented Reality (AR) technology to enhance the remote training of the patient by the healthcare provider. The system includes a patient interface subsystem, a provider interface subsystem, a patient communication module, and a provider communication module. The patient interface subsystem includes an AR headset that enhances the training of the patient by providing video feed enhanced with AR instructional elements while simultaneously providing intelligent feedback during the training session. The provider interface subsystem allows the healthcare provider to remotely monitor and communicate with the patient and to provide the appropriate guidance to the patient during the training session. The patient communication module and the provider communication module allow for remote communication between the patient and the healthcare provider during the training session.

SOLUTION: To provide a cardiac monitoring system and a method for using such a system, in which preferable embodiments of the system and method detecting cardiac information through a wearable device and then recording the same, and extracting data features from the recorded cardiac information, the extracted data features being analyzed and used for clinical diagnosis.EFFECT: A biological monitoring system can smooth and / or enhance patient experience, for achieving more precise and timely diagnosis of cardiac arrhythmia.SELECTED DRAWING: Figure 11

The present disclosure generally relates to systems and methods for trigger risk, substance use, and / or undesirable behavior detection. In exemplary embodiments, a system is configured to be operable for identifying trigger risk(s), undesirable and / or desirable behavior(s) and / or substance (ab)use and / or addictive activity(ies) of one or more entity(ies) and determining and implementing action(s) or pre-empting and / or mitigating undesirable behavior(s) and / or encouraging and / or facilitating desirable behavior(s). The system comprises a plurality of digital and / or physical constructs and / or engines operable for providing various capabilities. The system is configured to be operable for providing a plurality of data from sensor(s), sensor array(s), device(s), system(s), and / or network(s) to the plurality of digital and / or physical constructs and / or engines.

The invention discloses an inquiry model training method and device based on a long thinking chain, and relates to the field of large models, semantic information is extracted through strategy network analysis of a model, and an initial step decision is generated in combination with context information in a historical memory library; sending the initial step decision into a reasoning path generator, and reasoning to generate a primary diagnosis disease source and an intermediate diagnosis step; sending the primary diagnosis source and the intermediate diagnosis step into a verification module, performing pathological logic verification according to a case diagnosis report and a medical knowledge base, and feeding back a verification result; the reasoning path generator updates the historical memory bank based on the feedback result, the preliminary diagnosis disease source and the intermediate diagnosis steps; the strategy network continues reasoning based on user feedback input and the updated context information in the historical memory bank, and finally an inquiry result is output. According to the scheme, technical means such as reinforcement learning, self-adaptive backtracking and memory enhancement are introduced into a long thinking chain reasoning framework, so that a large language model realizes multi-aspect comprehensive improvement in medical question and answer and auxiliary diagnosis scenes.

The invention relates to the technical field of cardiovascular disease treatment, and discloses a cardiovascular disease medical decision support method based on a large model, and the method comprises the steps: obtaining multi-modal cardiovascular disease medical data, constructing a multi-source heterogeneous data set, carrying out the preprocessing and feature fusion, and constructing a combined representation model based on the pre-training large model and the fusion of traditional and western medicine knowledge. And designing a hierarchical space-time diagram convolutional network to generate a dynamic physiological state code of the patient, and constructing a multi-target optimization decision tree to generate a treatment scheme containing western medicines, traditional Chinese medicine prescriptions and non-drug intervention. A federal learning framework is adopted to cooperatively train a local model, data security is guaranteed, a global model is optimized, and a curative effect attribution module is further arranged to correct decision deviation. According to the method, multi-modal data and traditional Chinese and western medicine knowledge are integrated, personalized treatment scheme generation is achieved, data safety is guaranteed, and the cardiovascular disease diagnosis and treatment level is improved.

The invention provides a psychological crisis multi-stage joint control method and system based on a psychological large model, and aims to realize real-time monitoring, accurate evaluation and intelligent intervention of psychological states through a multi-modal data fusion and deep learning technology. The system collects multi-source information such as texts, voices, videos, physiological signals and behavior data, performs cross-modal analysis by using models such as Transform, LSTM and CNN, constructs personalized psychological portraits, and analyzes and predicts the psychological state change trend in combination with a time sequence. According to the method, a psychological crisis dynamic grading model is adopted, the psychological state of a user is divided into a normal grade, a mild grade, a moderate grade and a severe grade, multi-grade intelligent intervention is provided based on different risk grades, and the multi-grade intelligent intervention comprises AI self-service adjustment, psychological counseling matching, social support enhancement, emergency medical intervention and the like. The psychological intervention strategy is optimized in combination with reinforcement learning, the intervention mode is dynamically adjusted according to user feedback, and individuation and adaptability are improved.

The dynamic monitoring system for the senile cardiac patient comprises a multi-mode sensor module, a data processing module, an analysis module and an alarm module. The multi-modal sensor module collects parameters to generate a multi-modal physiological signal; the data processing module generates a preprocessing signal; the analysis module performs real-time analysis on the pre-processed signal based on a preset elderly electrocardiogram characteristic model to generate a graded alarm instruction; the alarm module triggers corresponding local reminding, remote notification or first-aid linkage response according to the graded alarm instruction, wherein the first-aid linkage response automatically sends the patient position and key physiological data to a specified first-aid center through the first-aid communication module; wherein the self-adaptive adjustment of the multi-mode sensor module comprises the step of dynamically optimizing signal gain according to a skin contact state. According to the dynamic monitoring system for the senile cardiac patients, the problems that dynamic monitoring of the senile cardiac patients is poor in real-time performance, high in false alarm rate and delayed in first-aid response can be solved.

The invention provides a multifunctional health monitoring and early warning system based on radar and vision fusion, and relates to the technical field of intelligent early warning, and the system comprises a feedback receiving module which receives a radar feedback signal and an image feedback signal; the monitoring identification module traverses the target monitoring area to determine monitoring data, and carries out monitoring identification; the abnormity judgment module is used for carrying out fusion analysis on the first monitoring data set and the second monitoring data set, carrying out bidirectional abnormity judgment and generating an early warning instruction; and the risk analysis module uploads the abnormal judgment result to a cloud platform for risk analysis, and sends early warning information to a remote terminal according to the risk level. According to the invention, the technical problem that the accuracy of early warning information is affected due to high false alarm rate caused by environmental infection due to dependence on a single sensor in health monitoring in the prior art can be solved, risk assessment is carried out by combining radar and visual fusion, the accuracy of anomaly recognition is improved, and the accuracy of early warning information is improved. And the early warning real-time performance and accuracy are improved.

The invention relates to the technical field of vascular surgery, in particular to a vascular surgery interventional intracavity treatment postoperative complication prediction and management system which integrates data acquisition, processing, model training, prediction analysis, comprehensive decision and early warning management and is specially designed for vascular surgery interventional intracavity treatment. The system monitors the postoperative physiological indexes of a patient in real time through multiple sensors, combines clinical data and disease history, carries out physiological sequence prediction and image analysis by utilizing RNN and CNN models, and accurately evaluates the risk of complications. The comprehensive decision-making module generates a personalized management scheme, and the early warning management module monitors in real time and pushes early warning and suggestions in time when abnormity occurs. The system not only improves the prediction accuracy and the management effect, but also optimizes the medical resource configuration, enhances the patient experience, contributes to the development of precise medical treatment and intelligent management, and effectively solves the key problem of postoperative complication prediction and management.

Systems and methods are disclosed for preserving patient privacy while transmitting health data from one geographic region to another geographic region for data analysis. One method includes receiving patient-specific health data including patient privacy information at a first region; removing the patient privacy information from the patient-specific health data to generate anonymous health data; storing the patient privacy information at the first region; and transmitting the anonymous health data to a second region for analysis.

A computer-implemented method, computer program product and computing system for: monitoring one or more operations within a medical environment; generating an operations management report based, at least in part, upon the one or more operations monitored within the medical environment; and providing the operations management report to a user, wherein the operations management report includes one or more of: an operations management score, an explanation of the operations management score, and a justification for the operations management score.

The present disclosure generally relates to systems and methods for the use of digital agents. In exemplary embodiments, a system is configured to be operable for creating, developing, formulating, programming, informing, educating, influencing, facilitating, modifying, controlling, directing, understanding, using, and / or learning from / with / using one of more digital construct(s) of one or more entity(ies) and / or group(s) that are based and / or focused on one or more motivation(s), ethic(s), moral(s), reputation(s), and / or their root cause(s) of the one or more entity(ies) and / or group(s). The one or more digital construct(s) are configured to have a primary function of providing information about the one or more motivation(s), ethic(s), moral(s), reputation(s), and / or their root cause(s) of the one or more entity(ies) and / or group(s) to the one or more digital construct(s) and / or to another digital construct(s), system(s), device(s), sensor(s), sensor array(s), and / or network(s).

The invention discloses an elderly disease health question and answer method based on multiple agents and a knowledge graph, and belongs to the crossing field of artificial intelligence and medical information technologies. Structured representation of medical knowledge is realized by constructing a multi-modal medical knowledge graph and through multiple entities and association relationships thereof. In combination with a domain customized large language model and a multi-agent dynamic routing mechanism, a two-stage training strategy is adopted to optimize a base model, including all-parameter pre-training and parameter efficient fine tuning based on a low-rank decomposition technology, so that the professionality and reliability of the model are effectively improved. In a multi-agent collaborative architecture, the system dynamically selects an optimal processing path according to input characteristics, including knowledge graph query, professional answer generation or external knowledge retrieval, and maintains context continuity of multiple rounds of conversations through a dynamic abstract compression algorithm. According to the method, the multi-agent collaborative architecture is combined with the knowledge graph reasoning ability, and the advantages of relation query and semantic matching are integrated through the mixed retrieval strategy.

The invention discloses a disabled health care auxiliary management system based on Internet of Things, and relates to the technical field of medical health management, and the system comprises the following components: a health Internet of Things acquisition module which connects a wearable health monitoring device, a smart home health monitoring device and an environment sensor with a cloud server through a wireless network, the intelligent health analysis module is introduced, advanced algorithm formulas are combined, real-time physiological index data and living environment data from the health internet-of-things acquisition module are deeply mined and analyzed, and the real-time health risk assessment mechanism can accurately capture tiny fluctuations of physiological indexes of a patient, so that the accuracy of health risk assessment is improved. According to the health risk assessment system and method, the historical health data and living habits of the patient are combined, possible health problems are predicted in advance, early warning is sent to the patient, family members and medical staff in the first time in the modes of short messages, APP pushing and voice prompting, and the accurate and timely health risk assessment provides powerful support for early discovery and intervention of diseases.

An intelligent (self-learning) sensor-aware and / or context-aware subsystem comprising (i) a System-on-a-Chip (SoC), (ii) a radio transceiver, (iii) a microphone, (iv) a voice processing module, (v) a (bio-inspired) neuromorphic event camera or a hyperspectral camera, (vi) a first set of computer implemental instructions in artificial neural networks (ANN) (which may include a transformer model / diffusion model or Poisson flow generative model++ (PFGM++)), (vii) a second set of computer implementable instructions to analyze and interpret contextual data and (viii) an autonomous artificial intelligence (AI) agent is disclosed.

The invention relates to the technical field of psychological health monitoring, in particular to a self-injury behavior early warning and intervention method and system based on multi-modal physiological data and AI.The method comprises the steps that physiological signals, behavior data, text data and environment parameters of a user are collected in real time based on a contact sensor and non-contact sensing equipment, and the physiological signals, the behavior data, the text data and the environment parameters of the user are obtained; generating a multi-source original data set; performing time synchronization and spatial correlation analysis on the multi-source original data set to form a fusion feature vector; inputting the fusion feature vector into a preset multi-modal deep learning model, obtaining a self-injury behavior risk score value, and dividing risk levels based on the self-injury behavior risk score value; and triggering a hierarchical intervention strategy of AI psychological counseling, emergency contact notification and medical resource linkage according to the risk level. The method has the effect of improving the precision and quality of teenager mental health assessment and self-injury behavior early warning results.

Disclosed herein is an artificial neural network-based medical image diagnosis assistance apparatus for assisting in diagnosing a medical image based on a medical artificial neural network. A medical image diagnosis assistance apparatus according to an embodiment of the present invention includes a computing system, and the computing system includes at least one processor. The at least one processor is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network about a first medical image, to detect user feedback on the first analysis result input by a user, to determine whether the first analysis result and the user feedback satisfy training conditions, and to transfer the first analysis result and the user feedback satisfying the training conditions to a style learning model so that the style learning model is trained on the first analysis result and the user feedback.

The invention provides a method for establishing a diagnosis and treatment system for digestive system disease multi-modal information. The method comprises the following steps: S1, collecting multi-modal information for labeling and preprocessing; s2, extracting a feature vector and embedding a label into the multi-modal information according to the labeled information; s3, splicing and mapping the feature vector and the tag to a unified dimension to obtain an enhanced feature vector; s4, fusing the enhanced feature vectors to form a multi-modal feature matrix, performing linear mapping and weighted aggregation on the multi-modal feature matrix to obtain global fusion vectors, and collecting to generate a fusion vector sequence; s5, enhancing the time sequence information of the global fusion vector sequence, enhancing the spatial information of the spatial relevance of the specific feature of the part, and performing interactive fusion to obtain a spatio-temporal joint feature; s6, performing classification prediction on the disease stage or the specific pathological type, and outputting a diagnosis result; and S7, performing semantic association on the diagnosis result and the medical knowledge graph, sharing data to an online health intelligent platform, and providing a personalized decision basis for clinicians.