1212 results about "Health information" patented technology

The invention relates to the technical field of health information monitoring and management, and discloses a health information monitoring and management system based on multi-source data fusion analysis, which comprises a physiological data acquisition unit, a fusion analysis engine, an intelligent decision management module and the like. The physiological data acquisition unit acquires a multi-source heterogeneous data stream, and a multi-layer fusion topology is constructed through preprocessing; the fusion analysis engine realizes data feature association and anomaly detection through feature association and mode recognition; and the intelligent decision management module generates a health state reference strategy and dynamically allocates data source weights. The real-time calibration module calibrates a signal time domain and adapts to an analysis frequency, the data weight optimization module evaluates an optimization strategy based on credibility, and the fault-tolerant processing module completes data verification and recovery in combination with the distributed cache unit. The system realizes efficient fusion, dynamic decision and reliable management of multi-source data, improves the accuracy of health monitoring and the robustness of the system, and is suitable for intelligent health management scenes.

The invention discloses an old people chronic disease risk prediction method and system based on a time sequence knowledge graph, and belongs to the technical field of medical health information, and the method comprises the following steps: S1, obtaining a health data set of an old people group; s2, on the basis of an ESOA-XGBoost algorithm, performing feature selection and sorting on each piece of health data in the health data set so as to determine correlation among variables, and generating a health data feature set; s3, constructing and training a time sequence knowledge graph prediction model including an STKGR-PR model and a T i PNN model, and predicting a relationship among the target entity, other entities and time sequence information through the time sequence knowledge graph prediction model; s4, predicting the old chronic disease risk of the old people group according to the trained time sequence knowledge graph prediction model; through the method, an elderly chronic disease risk prediction result with a good prediction effect and high accuracy can be obtained.

The invention relates to the technical field of intelligent medical treatment, in particular to an endocrine patient health management method and system integrated with an electronic medical record. The method comprises the following steps: performing multi-dimensional data acquisition of an endocrine patient through a hospital information system, and performing multi-dimensional portrait construction of the patient to obtain a multi-dimensional health portrait of the patient; performing time sequence data feature extraction on the multi-dimensional health portrait of the patient to obtain time sequence feature data; acquiring historical physiological data of the patient; establishing an individualized physiological rhythm model according to the historical physiological data of the patient to obtain the individualized physiological rhythm model; and performing non-uniform interpolation on the time sequence characteristic data according to the individualized physiological rhythm model to obtain interpolated data. According to the method, the individualized physiological rhythm model and the dynamic baseline model are constructed, the dynamic early warning threshold value and subtype layering are combined, and accurate risk assessment and individualized health information management are performed on each patient, so that the health management effect is remarkably improved.

The invention discloses an interactive personal health management system based on artificial intelligence and an operation method. Different from a part of existing modes, limited health data and brief basic information which are acquired by a single acquisition device and serve as a unique health information source of a user are not limited, but only serve as health basic information, and a concept and an implementation scheme of'health supplement information 'are provided. On the basis of obtaining the health basic information, through alternate or simultaneous use of multiple types of collection devices and multiple collection ways and modes, more health supplement information of the user is obtained, and a comprehensive AI health information source of the user is jointly constructed. The AI health information source is analyzed and calculated through an artificial intelligence health algorithm, once the AI health information source changes, the AI health assessment result is adjusted accordingly, an infinite loop mechanism is formed, the user health assessment result is promoted to be infinitely close to precision, and health information is interacted with the user in real time through the AI health digital human in a natural language.

The invention relates to the technical field of health information, in particular to a dementia risk prediction system based on an associated information processing model, and the system firstly extracts multi-modal hidden variable features through a data processing module, and generates an individualized risk feature matrix through causal reasoning and association rule optimization. And then, the system dynamically generates a personalized intervention path by adopting a reinforcement learning optimization algorithm, and realizes long-term risk prediction through a recurrent neural network. The system outputs personalized prediction reports including dementia risk assessment, trend analysis and intervention suggestions. Experiments show that the accuracy of dementia risk prediction, the dynamic adaptability of an intervention strategy and the efficiency of health management can be remarkably improved.

A gait analysis is provided by passively sensing footstep-induced floor vibrations during walking using vibration sensors mounted on, within or under a floor. The approach is non-intrusive, scalable, and perceived as more privacy-friendly, making it suitable for continuous gait health monitoring in daily life. The floor vibration-based gait analysis framework estimates various gait parameters, including temporal parameters (step, stride, stance, swing time) and spatial parameters (step length, width, angle), and extracts gait health indicators (cadence / walking speed, left-right symmetry, gait balance, initial contact type).

The invention discloses a robot motion control and state monitoring method and device, and relates to the technical field of robot control, and the method specifically comprises the following steps: receiving robot information obtained based on a multi-modal sensor array, and carrying out the synchronous alignment processing of the robot information; performing health monitoring on the body state of the robot to obtain a health state vector, and analyzing the health state of the robot in combination with the robot information and the health state vector; according to the method, full-link closed loop from sensing, monitoring, modeling, decision making to execution is achieved, environment sudden change and body health information are fused in real time, safety and efficiency of motion are ensured through mixed decision making and joint compensation, and compared with traditional robot control depending on a pre-planned path, the method has the advantages that the robot control efficiency is improved. The method can quickly respond to environmental dynamics, assess health risks in real time and adaptively adjust the strategy, overcomes the problem of insufficient real-time performance and flexibility, realizes deep association of the state of the robot, and improves the motion precision of the robot in a complex sudden change scene.

The invention relates to the technical field of medical health information, in particular to a heart failure risk prediction method and system based on multi-source heterogeneous data fusion, and the prediction system comprises a data collection module, a data management module, a multi-modal feature extraction module and a dynamic risk prediction model module. An intervention strategy recommendation module; and a visualization and iterative optimization module. The prediction method is applied to the prediction system, patient health data is collected and obtained, a multi-source heterogeneous health database is constructed, physiological time sequence features, traditional Chinese medicine dialectical features and western medicine clinical features are extracted respectively to be subjected to structured coding processing, a multi-modal feature set is formed, the multi-modal feature set is divided into a training set, a verification set and a test set, and the training set, the verification set and the test set are combined. A Bayesian attention mechanism is introduced, dynamic prediction of the heart failure risk is achieved through the constructed depth time sequence model, an intervention scheme can be adjusted and formulated in the whole course of heart failure management, excessive medical treatment is reduced, meanwhile, disease progress is effectively restrained or delayed, and the life quality of a patient is improved.

A system for preparing documents in a standards compliant electronic health record (EHR). A document intake device is configured to receive an image file transmitted to the device using an image file transport protocol. An artificial intelligence / machine learning (AI / ML) model is configured to extract patient demographic and medical information contained within the image file as a text file with the patient demographic and medical information, and format the text file for use as a EHR by a transformation engine. The transformation engine is configured to convert the text file formatted for use as the EHR into the standards compliant EHR and transmit the standards compliant EHR as a Direct Secure Message to a Health Information Service Provider (HISP). The HISP configured to send the standards compliant EHR as a Direct Secure Message for storage as a patient record if a patient match is found.

To enable provision of assistance in maintaining the health of a particular person while reducing food loss.SOLUTION: A control system disclosed herein comprises an input unit for receiving user input, a processing unit for performing specific processing using a sentence generation model for generating a sentence according to input data, and an output unit configured to control an electronic device to output a result of the specific processing. The processing unit determines whether the user input includes a request for creating a menu for a particular person or not and, when affirmative, acquires ingredient stock information and health information of the particular person and controls the specific processing using an output of the sentence generation model that uses a text instructing creation of a menu according to the ingredient stock information, the health information, and information obtained from the user input as the input data.SELECTED DRAWING: Figure 4D

The invention discloses a personalized tooth modeling method and system based on digital oral cavity imaging, and relates to the technical field of digital modeling. The invention discloses a personalized tooth modeling system based on digital oral imaging. The personalized tooth modeling system comprises a data acquisition module, a model construction module, a model fusion module, a tooth gap labeling module, a tooth health prediction module and a nursing matching module. According to the method, the personalized tooth model is generated by synthesizing the tooth structure and activity range data of the user, and the deep cleaning scheme and the daily nursing scheme are accurately matched according to the tooth gap and tooth surface health condition, so that the nursing scheme of each user can be customized according to the unique oral cavity condition; by comprehensively acquiring the tooth and jaw structure, the tooth movement range and the health information of the user, the accuracy and comprehensiveness of the model are ensured, and sufficient data support is provided for subsequent cleaning and nursing schemes.

The invention relates to the technical field of postoperative rehabilitation nursing management systems, in particular to a postoperative rehabilitation nursing management system based on data analysis, which comprises a data acquisition module; the physiological health analysis module is used for generating a health state evaluation result based on the physiological data, and the physiological health analysis module comprises a dynamic weighted information entropy collaborative matrix, nonlinear smooth prediction, multi-level abnormal deviation detection, personalized dynamic threshold adaptation and progressive health state grading collaborative evaluation; the health evaluation module is used for obtaining a rehabilitation level based on the health state evaluation result; the system comprises a dynamic weighting information entropy collaborative matrix algorithm, a rehabilitation recommendation module, a nursing plan module, a health information module and an expert suggestion module, realizes real-time weighting of complex association among physiological data through the dynamic weighting information entropy collaborative matrix algorithm, overcomes the problem that a traditional fixed weight model is difficult to adapt to dynamic change of data, and improves the accuracy of data processing through dynamic adjustment of collaborative matrix weight. And the health state change of the patient is accurately captured.

A health manager may receive health information from a user, the health information including current health data associated with the user and anonymized user identification information. A health manager may, based on the health information and the anonymized user identification information, access, from a secure health datastore, health history for the user. A health manager may, based on the health information and the health history, generate a contextual health query for a foundation model system. A health manager may provide the contextual health query as an input to the foundation model system. A health manager may receive, from the foundation model system, a health analysis for the user based on the contextual health query.

The invention provides a small carrier for manned and logistics and an unmanned intelligent driving system of the small carrier. The system is applied to the technical field of automatic driving and comprises a sensor module used for collecting the information of a carrier and the environment of the carrier, a path planning module used for performing target detection, road recognition, obstacle detection and positioning and map construction according to the information of the carrier and the environment of the carrier by using a multi-modal sensor fusion method, and obstacle avoidance and automatic path navigation are realized based on an improved path planning algorithm. The cloud management platform is used for monitoring the state of the carrier in real time, storing navigation data and vehicle health information through the cloud and supporting multi-vehicle collaborative scheduling; and the voice interaction module supports voice instruction input and is used for analyzing the voice instruction of the user, dynamically responding and re-planning the path by using the path planning module. According to the invention, the transportation efficiency and the intelligent level of the small-sized carrier for manned and logistics are improved.

The invention discloses a health monitoring system based on pulse signal multi-domain feature extraction and deep learning, and the system is characterized in that a multi-dimensional feature extraction module employs an FMCW radar and a sensor device to collect and obtain pulse signals, carries out the multi-level processing and feature extraction of the obtained pulse signals, and extracts features related to a health state; the feature association mapping module matches and associates the pulse signal features of different frequency bands with the health state; the state recognition module automatically recognizes and classifies pulse signal features and health states based on a deep learning model; and the health information feedback module analyzes the health state of the user according to the health state recognition result of the state recognition module, provides health feedback and generates corresponding personalized health guidance information. By using the system, comprehensive analysis of health conditions can be realized, continuous and highly-targeted health tracking and evaluation services can be provided, and modern personalized and intelligent traditional Chinese medicine health monitoring and management requirements can be met.

Provided is a process that includes receiving, via computing system, a template form comprising one or more unpopulated health information elements and a set of populated heath information elements; determining, with a generative language model, generated information based on the set of populated heath information elements of the template form, wherein the generated information relates to a first health information element of the one or more unpopulated health information elements; sending, with the computing system to a user computing device, a message prompting the user to accept the generated information; and responsive to receiving permission from the user computing device, storing the generated information in memory.

The invention provides an electronic health record automatic coding method and system, and the method comprises the steps: segmenting an electronic health record text into a plurality of text segments, and obtaining a plurality of unstructured text contents; obtaining structured key medical information based on a preset large language model and the plurality of unstructured text contents; generating a preliminary coding result based on a preset coding rule base and the structured key medical information; and obtaining a final coding result based on a preset verification model, a preset auditing model and the preliminary coding result so as to complete automatic coding of the electronic health record. According to the method, the key health information in the non-structural text is coded, and the coding result is verified, so that the accuracy of the coding result is improved.

Disclosed are an equipment failure mode predetermination and residual life prediction coupling system and method. The equipment failure mode predetermination and residual life prediction coupling system realizes coupling of equipment failure mode predetermination and residual life prediction, continuous collection of health information of equipment, predetermination of the failure mode and prediction of the residual life based on the state of health of the equipment in operation monitored and perceived in real time by sensor sets.

Health information about an individual patient is collected from public and private systems into an electronic patient record. Health records can also be collected and maintained for clinical trial purposes. The records are represented as NFTs stored on a blockchain and securely exchanged in compliance with privacy, authentication, and healthcare data protocols.

The invention discloses a multi-agent collaborative decision-making system and method based on knowledge Token, and the method comprises the following steps: S1, constructing a knowledge base architecture, executing the content Hash calculation, and generating an original fingerprint in an original fidelity layer; s2, extracting content, packaging the content into knowledge Token, and writing identification, semantics, source, authority, value, contract, association, traceability and health information; s3, activating a knowledge layer, constructing a vector index and mapping knowledge graph nodes; s4, unifying an index layer, matching task requirements, and screening and verifying candidate Tokens to obtain an authorization set; s5, the multiple agents generate local decisions and fuse values and semantics to form a collaborative result; and S6, binding a Token identifier after consistency verification, generating a collaborative decision and recording data consanguinity information. According to the method, multi-agent collaborative decision-making is realized through the knowledge Token and the knowledge graph, and the decision-making accuracy and traceability are improved.

The present invention relates to the field of health information processing technology, and specifically discloses an artificial intelligence-based student psychological data analysis and early warning system. The system sets a data set complexity determination module, a data slice determination module, a GPU compatible matching module, and a psychological data analysis and early warning module. The psychological health monitoring platform collects multi-source student psychological data sets, evaluates their comprehensive complexity, and determines the number of psychological data slices and their respective complexities based on this. The platform monitors the execution data of each GPU in real time, evaluates its initial execution quality indicators and compatible complexity. Subsequently, through data processing, the comprehensive complexity of the psychological data slices is matched with the compatible complexity of each GPU, and the allocation of psychological data slices to GPUs is completed. Finally, the platform uses this allocation relationship to analyze the students' psychological data, and ultimately achieves analysis and early warning of the students' psychological data.

The invention discloses a double-flywheel system and an intelligent mine field system and method.The double-flywheel system comprises a sensing system and a planning system, the sensing system fuses acquired multi-dimensional environment data of a mine field through the bird's-eye view angle technology and a deep learning algorithm, a mine field 3D environment model is established, and environment sensing data is output; the planning system performs planning path prediction on each mine car by using a deep learning algorithm and a Monte Carlo tree search algorithm based on vehicle management information, task scheduling data and environment perception data fed back by the motorcade management platform, and generates an optimal driving path. The vehicle management information comprises vehicle posture and health information, vehicle position state information, vehicle operation task priority information and mine field safety standard information. According to the invention, through deep cooperation of sensing and planning and integration of motorcade management information and safety standards, the vehicles can work according to the optimal path more accurately, the working yield of the mine field is improved, and the development of the intelligent mine field is promoted.

Example methods and systems for dynamic site selection in a global server load balancer (GSLB) environment are described. In one example, a computer system may obtain first health information from a first entity and second health information from a second entity. The first health information may be generated based on multiple first traffic flows between (a) multiple first client devices and (b) a first pool of backend servers. The second health information may be generated based on multiple second traffic flows between (a) multiple second client devices and (b) a second pool of backend servers. In response to receiving a request to access the service, the computer may select a selected site based on the first health information and / or second health information. A response may be generated and sent to cause a third client device to access the service by directing a third traffic flow towards the selected site.

The invention discloses a generative artificial intelligence auxiliary method and system for lower lumbago rehabilitation, and the implementation method comprises the following steps: S1, collecting health data of a patient in real time through a data collection module, and processing the health data; s2, the generative artificial intelligence auxiliary system converts the dialogue content between the patient and the doctor into a structured text record through an automatic voice recognition module and a natural language processing module, and the structured text record is integrated into an EHR system; s3, analyzing the health information of the patient by using the generative AI model, and generating a simplified medical language version; s4, generating a personalized rehabilitation scheme based on the health data; s5, the rehabilitation scheme is pushed to the patient through a user interface, and the generative artificial intelligence auxiliary system dynamically adjusts the rehabilitation scheme in real time according to feedback of the patient; and S6, continuously optimizing the generative AI model by continuously collecting patient feedback data. According to the invention, a personalized rehabilitation scheme can be automatically generated.

A system for the treatment of chronic diseases, consisting of: a data acquisition unit configured to capture patient health information in real time from wearable or networked health monitoring devices, electronic health records, and patient-reported outcomes; a central processing unit configured to preprocess the captured data by normalizing, aggregating, and coding the data for further analysis; a feature extractor operationally connected to the central processing unit, the feature extractor being capable of extracting key features from preprocessed data, including demographic data, clinical history, biomarker levels, and lifestyle;a predictive modeling engine communicating with the feature extraction module, wherein the predictive modeling engine is configured to use machine learning techniques to generate recognition models from historical patient data, the models predicting disease progression, treatment outcomes, and potential complications; a visualization interface connected to the predictive modeling engine, wherein the visualization interface is configured to display the results of the predictive analytics to healthcare providers and patients to enable actionable insights for the management of chronic diseases; and a feedback mechanism integrated into the visualization interface, configured to allow users to input real-time feedback on the predicted outcomes, the feedback being used to refine the predictive models over time.

According to the physical examination item recommendation method, device and equipment based on the large language model and the computer readable storage medium, interactive physical examination questions and answers are performed with the target user according to the initial risk factors and the related question list corresponding to the candidate disease information list, the collection efficiency of the dynamic health information of the target user is improved, and the user experience is improved. And mining dynamic health information. In combination with dynamic health information, symptom information and health risk factors are further mined, and a disease information list of the target user is enriched, so that personalized physical examination item recommendation of the target user is realized. And the basic health information of the target user is combined with the dynamic health information, and the potential disease information of the target user is determined comprehensively, so that the physical examination item information more adaptive to the target user is obtained according to the comprehensive target disease information list, and the recommendation efficiency and recommendation accuracy of the physical examination item information are improved.

Systems or techniques that facilitate automated role-based access control for patient health information security and compliance are provided. In various embodiments, a system can access historical data corresponding to medical systems. In various aspects, the system can generate, based on the historical data, automated role-based access control rules. In various instances, the system can employ a trained machine learning model to detect, based on the automated RBAC rules, user access anomalies from audit logs of the medical systems. In various embodiments, the system can visually render the detected user access anomalies on a graphical user interface.

A system recommends to a healthcare professional (HCP) (230) medical literature that is of relevance to the HCP's patients. The system communicates with the HCP (230) and accesses electronic health record (EHR) documents in a database (210) associated with the HCP's patients. The system analyzes the contents of the EHR documents to query a medical-literature database (212) for publications that are deemed relevant to the EHR documents. The extracted publications are then presented to the HCP.

The invention relates to the technical field of old people health management, and discloses a personalized health auxiliary management system based on artificial intelligence, which comprises a multi-mode sensing module, an intelligent preprocessing module, a dynamic knowledge graph module, a personalized inference engine module and a self-adaptive communication and interaction module. The multi-mode sensing module is used for acquiring physiological, environmental and behavioral data; the intelligent preprocessing module desensitizes and cleans data through federal learning, and extracts core features; the dynamic knowledge graph module constructs and updates an exclusive graph containing basic and dynamic nodes and associated edges; the personalized inference engine module generates a layered intervention scheme; and the adaptive interaction module adapts to the cognitive ability and synchronizes the scheme. According to the invention, health information integration and dynamic intervention are realized, the management accuracy and compliance are improved, and the method is suitable for home non-clinical scenes.

The invention relates to a control method of an intelligent health cabin system and the intelligent health cabin system. The method comprises the following steps: acquiring environment information inside and outside a vehicle and health information of people in the vehicle; calculating an in-vehicle human health index, an out-vehicle environment quality index and an in-vehicle air health index according to the in-vehicle and out-vehicle environment information and the in-vehicle personnel health information, and further calculating to obtain a target health cabin index; taking the target health cabin index as a coupling target to obtain a control signal of each adjusting module; and the operation states of the adjusting modules are controlled according to the control information corresponding to the adjusting modules. According to the method disclosed by the invention, the adjustment modules are jointly adjusted, so that the adjustment of the cabin in the vehicle better conforms to human engineering, the health of the environment in the vehicle is greatly improved, and the comfort of people in the vehicle is improved.