27 results about "Health intervention" patented technology

The application discloses a human health monitoring method and system based on proportion philosophy and individual self-adaption. A multi-level nested proportion model is constructed by non-invasively collecting multi-dimensional signals of a human body. A golden section 0.618 is taken as a harmonious anchor point to calculate an individual exclusive health benchmark zero point and a stable interval. Through real-time proportion comparison and fine ladder deviation analysis, the structure harmony and function state are judged. The health trend early warning is realized by combining multiple retests. The application breaks away from the limitation of unified medical indicators, realizes personalized, global and original health evaluation, can early identify imbalance and sub-health state, and is suitable for scenes such as daily monitoring, physical evaluation and health intervention effect judgment.

This specification provides a method and system for processing pet data, including: obtaining target-related information of a target pet, wherein the target-related information includes basic information, historical claims information, and hardware perception information of the target pet; the hardware perception information is collected from the target pet by hardware perception devices based on the target pet's environment; determining the target disease of the target pet based on the target-related information; and determining and pushing a target health intervention plan corresponding to the target disease to the owner of the target pet based on a pre-constructed target knowledge graph, wherein the target knowledge graph is used to represent the correspondence between diseases and health intervention plans. This aims to improve the pet's health status, ensuring the pet is as healthy as possible and reducing the pet insurance payout rate.

This invention provides a health management method, device, equipment, and storage medium based on assisted diagnosis. The method includes: standardizing patient medical record data, examination report data, and health monitoring data to construct a digital patient file; performing inference calculations through multiple intelligent analysis models, and adaptively weighting and fusing them according to the reliability coefficients and uncertainty measures of each model to generate a comprehensive probability ranking of multiple candidate results; generating assisted diagnostic results and recommended examination items based on this ranking; and calculating risk assessment indicators by combining the temporal characteristics of historical health data to generate personalized dynamic monitoring plans and health intervention suggestions. This invention improves output stability through multi-model fusion, avoiding the limitations of a single model; provides rich references for clinical diagnosis through multi-candidate ranking; and achieves dynamic risk assessment through temporal analysis, forming a complete closed loop from assisted diagnosis to health management, thus improving reliability and practicality.

The present application relates to the technical field of medical health management, in particular to a chronic disease patient health management system based on big data, comprising: constructing a full-dimension personal health record containing static and dynamic information. The data analysis module performs abnormal pattern mining on continuous sign data, and generates a trend graph by associating periodic test data with historical records. The data correlation module constructs a risk correlation matrix based on environmental exposure data, which quantifies the contribution of environmental behavior factors to health indicators. The risk modeling module integrates the above information. The prediction and intervention generation module uses a chronic disease progression prediction model to calculate and synchronously output individualized disease risk warning signals and the corresponding phased health intervention programs. The present application realizes the quantitative attribution of health risk factors and dynamic, phased precision health management.

The application provides a dynamic modeling method and system for multi-field skill AI automatic semantic labeling, and relates to information retrieval, medical health and cross-field skill fusion scene. Through nonlinear dynamic modeling and biomedical stability control, the semantic label automatic generation and real-time matching of talent skill and health intervention effectiveness are realized. The application innovatively integrates the sound wave frequency of fetal brain promoting music method, maternal action instruction and blood type nutrition scheme into the "skill-time" frequency domain space, combines the Gevrey smoothing operator and fractional derivative evolution equation, accurately separates the temporary fetal movement fluctuation and low-frequency core characteristics, and breaks through the characterization limitation of traditional models on nonlinear biological coupling effect. By introducing the "intervention effect-time" frequency domain component and local dependency constraint, the short-term effectiveness and long-term value of music intervention on fetal neurodevelopment are quantified, and the model distortion problem caused by extreme data or cross-field parameter resonance in traditional methods is solved.

The present application relates to the technical field of data processing and strategy optimization, and particularly relates to a diet structure health intervention method and system based on physical data of obese children. The present application firstly acquires physical data and nutrition data and groups children; further acquires intervention synchronism of each physical data according to deviation characteristics of each physical data compared with corresponding preset physical standard and similarity of changes in time domain of deviation characteristics of all physical data; further adjusts the diet structure of the target child according to the difference in intervention synchronism of each physical data of the target child and the children in the same group at the current period, in combination with the difference between the nutrition data and the preset recommended diet structure. By analyzing the individualized differences in the physical angle and the diet intake angle of the children, the diet structure of the children is continuously adjusted, the diet intervention adaptability is improved, and the intervention effect is ensured.

This invention relates to a method and apparatus for predicting and analyzing health intervention factors based on the Eight Principles of Traditional Chinese Medicine (TCM) diagnostic methods. The method includes: constructing a deep learning model, denoted as the first prediction model, for predicting TCM interventions based on the four diagnostic methods of TCM; constructing a first dataset through volunteer data collection and expert group classification; training the first prediction model based on the first dataset; after model training, feeding the user-input four diagnostic vector X into the first prediction model for prediction, and performing health intervention factor analysis based on the model's output prediction vector Y and an intervention factor library to obtain a corresponding health intervention report, which is then fed back to the current user. This invention can improve the personalized prediction level of health intervention factors and enhance the accuracy and completeness of health intervention factor analysis.

This application discloses a central robot and its home-based health and elderly care implementation method. The method includes: acquiring a user's health events based on multimodal perception data from multiple non-visual sensors, detection data from health monitoring devices, and a digital twin space; activating visual sensors when preset conditions are met, and performing fusion processing with the digital twin space and the user's health profile to obtain the user's scene context; generating health intervention content based on the scene context and a pre-stored health knowledge graph, and mobilizing corresponding execution modules for multimodal output; determining the user's interaction information based on the perception data from the sensing modules and the digital twin space, and adjusting the corresponding execution modules for multimodal output. Implementing the technical solution of this application can avoid errors in perception from a single device or module, reduce the risk of user privacy leakage, provide more professional closed-loop health services, and eliminate the need for complex user operations.

This invention relates to an AI-based full-lifecycle health intervention method, comprising: constructing a metaverse virtual space for health management; creating and maintaining a dynamic virtual health record for each patient, serving as their digital twin and core data hub; providing rehabilitation activities to patients in the space through virtual avatars configured for users, supporting real-time multi-party interaction, with multi-dimensional health data generated during the process being collected in real time and updated to the record; using a multimodal time-series prediction model to perform real-time analysis and trend prediction of the health data, generating risk prediction information; and triggering and executing personalized health intervention measures based on this information. This invention achieves the integration and continuous management of patients' full-dimensional health data, completing the transformation from a passive response to a proactive early warning disease management model, and providing Alzheimer's patients with an immersive, proactive, and full-cycle comprehensive health management solution by constructing an online and offline linked closed-loop intervention system.

The application discloses a low-perception health guidance method based on behavior rhythm resonance of an office table scene, and is applied to a system comprising an office table and office equipment carrying a visual sensor. The method comprises the following steps: acquiring a user visual data stream through the visual sensor, and establishing a reference health posture model locally; identifying a repetitive micro-motion cycle of the user, and analyzing characteristic changes to establish a personal behavior rhythm model; combining the two models, and predicting the fatigue risk of a specific muscle group in real time through a time sequence prediction algorithm; when the risk exceeds a threshold value, generating a multi-mode guidance instruction set synchronized with the current action cycle of the user, so as to guide the user to perform micro-motions for relaxing target muscle groups. The application realizes non-interfering, personalized and accurately targeted preventive health intervention, and significantly improves the compliance and acceptance of the intervention.

The application discloses a personalized health intervention opinion recommendation method and system based on a double attention mechanism, belongs to the technical field of health intelligent intervention, and comprises the following steps: generating a corresponding user portrait by using preprocessed user full data, and obtaining an initial personalized health suggestion based on a health intervention industry large model; using two global and local attention modules of the double attention mechanism to separately pay attention to the user portrait and the initial personalized health suggestion for feature extraction through adaptive weight parameters; converting and combining the weighted and feature-extracted initial personalized health suggestion and the user portrait by using a classifier model, determining a series of probability distributions of personalized health intervention opinion recommendations based on feature vector similarity, and determining a health intervention opinion recommendation result based on the probability distributions. The application not only inherits the knowledge accuracy of a vertical industry model, but also fully pays attention to individual differences, and provides a health intervention suggestion with strong executable force.

The application discloses a health monitoring and early warning method and system based on multi-source data fusion, relates to the field of medical information technology, and comprises the following steps: performing health semantic mapping and medical correlation marking on multi-source monitoring health data to form a correlation health data set; identifying health data missing items with missing data in the correlation health data set and outputting user health completion data; extracting the correlation relationship of the user health completion data and constructing a health status correlation graph; identifying the state change result of the health data node in the health status correlation graph in the time dimension and the correlation influence transmission path in the data correlation relationship, generating health warning data; converting the health warning data into early warning prompt information and outputting health intervention suggestions. Through the cooperation of medical correlation marking processing and cross-source inference supplement, the application realizes the correlation consistency construction and data integrity improvement of multi-source health data.

This invention provides a functional medicine proactive health management method and system for large-scale model fine-tuning engineering. The method includes the following steps: S101, pre-training a general large-scale model using functional medicine literature and clinical guidelines; S102, fine-tuning the general large-scale model using medical tasks to form a medical-specific large-scale model; S103, integrating LSTM and Transformer temporal algorithms, and introducing autoencoder model reconstruction error as an anomaly scoring indicator; S104, integrating reinforcement learning and generative adversarial networks to output personalized health intervention suggestions based on user health risks; S105, collecting user health behavior data in real time through IoT wearable devices and portable detection instruments, preprocessing the health behavior data to generate a structured health dataset, and inputting it into the medical-specific large-scale model for user health assessment and management. This invention achieves a closed-loop process from data collection, accurate assessment, dynamic early warning to personalized intervention, meeting the needs of functional medicine proactive health management.

The application discloses a health analysis method and device. The method comprises the following steps: obtaining historical health observation data of a plurality of individuals, including time series data of health characteristic indexes, health intervention measures, health risk results and individual inherent health data; constructing a causal inference model comprising an encoder and a decoder, wherein the encoder adopts a variational gated recurrent neural network and is used for extracting noise-robust latent variables from the time series data; training the causal inference model based on a preset loss function comprising a regularization term based on enhanced inverse propensity weighting, obtaining a health analysis model, and analyzing to-be-analyzed health observation data of a target object based on the health analysis model. By using the technical scheme, the calculation complexity of model training can be reduced, the stability of the throughput and response speed of online inference service can be improved, and the memory occupation during model inference can be reduced, so that the model can be deployed on a resource-limited device.

This application relates to a method and system for full-life-cycle medical and health management, belonging to the field of smart healthcare technology. The method includes: by constructing a closed-loop system covering the entire process from data collection and risk assessment to intervention optimization, this application effectively solves the problems of insufficient accuracy, low efficiency, and high cost in traditional health management. By leveraging the analysis mechanism of medical-specific training and the nine-square grid intelligent matching, it realizes personalized health intervention based on individual and family collaborative data, improving service matching and security. By continuously tracking execution data and quantifying changes in health indicators, the intervention plan can be dynamically evaluated and optimized in a closed loop. While improving user compliance and health outcomes, it reduces manual dependence and repetitive costs, providing a scientific, efficient, and scalable solution for full-life-cycle health management.

This invention relates to the field of health management technology, specifically to a health service management method integrating detection, assessment, intervention, and follow-up. This method includes health information collection and electronic record establishment; multi-dimensional precise health detection and dynamic data monitoring; multi-disciplinary comprehensive health risk assessment and level determination; personalized precise health intervention program customization and goal setting; tiered and categorized intervention implementation and full-process professional guidance and supervision; full-cycle health data tracking and real-time feedback recording; comprehensive review of intervention effects and dynamic optimization and adjustment of the program; and long-term management of health consolidation and risk warning. This method can effectively address the pain points of existing health services such as fragmentation and lack of personalization, achieving seamless connection and data interoperability across all stages of detection, assessment, intervention, and follow-up, accurately matching individual multi-dimensional health differences, and improving the pertinence and effectiveness of health management.

This invention provides a health risk prediction and intervention optimization method driven by nutritional and physiological multimodal data. The method uses the image capture time as a node, discretely distributing other multimodal data around the image data to form multiple temporal data snapshots. Features are extracted from the modal data in the temporal data snapshots, and the extracted features are fused with medical evidence from an evidence-based matching library to output a feature vector. A neural network predicts the values ​​and trends of the modal data in the next time period based on the feature vector. Simultaneously, the neural network model is corrected and optimized based on the prediction results and / or modifications to the feature vector. This invention, by fusing nutritional and physiological multimodal data, overcomes the limitations of single-modal information, and combines temporal dynamic feature extraction and evidence-based matching to significantly improve the accuracy of health risk prediction and provide quantitative basis for personalized nutrition and health interventions.

This application provides a method and system for intelligent generation of individualized nursing plans based on big data, belonging to the field of intelligent generation technology for individualized nursing plans. It involves collecting multi-source health data of the target nursing subject, performing feature encoding on the multi-source health data to generate a structured health record; performing similarity mapping on the health status of the target nursing subject according to a diagnostic mapping table and the structured health record to obtain a set of health statuses, and determining an individualized status index based on the health status set; detecting status differences based on real-time monitoring data and the structured health record to generate a status change trend vector; performing fusion analysis on the status change trend vector and the individualized status index to generate a dynamic health intervention index; and generating an individualized nursing plan for the target nursing subject based on the dynamic health intervention index. This application can achieve dynamic adaptation of nursing plans to the patient's real-time health status, improving the accuracy and timeliness of nursing plans.

This invention discloses a health management system and method based on online and offline collaborative medical resources, relating to the fields of medical information technology and artificial intelligence technology. This invention achieves early identification and quantitative assessment of health risks by constructing a personal multimodal time-series health database and a data-driven early warning mechanism. It utilizes an online data collection and intelligent analysis system deployed across various IoT devices in the user's living environment to establish a personal health baseline. By calculating the deviation of various core indicators from the baseline, it performs weighted analysis and comprehensive evaluation of the synergistic changes in multi-dimensional indicators. This allows for the objective identification of potential health deterioration trends from changes in daily patterns and the generation of quantitative early warning reports. When these reports are synchronized to the offline professional conditioning service subsystem, therapists obtain a long-term, real-world profile of the user's condition and develop targeted, phased conditioning plans, realizing a paradigm shift in health intervention from experience-driven to data-driven.

This invention relates to the field of health management technology and discloses a data mining method for personalized health management solutions. The method includes: acquiring multi-source heterogeneous health record data of target users; performing spatiotemporal alignment processing on the multi-source heterogeneous health record data to generate a standard health data sequence; constructing a dynamic health entropy value to divide health level intervals; inputting the health level intervals into a preset health intervention knowledge base; determining intervention measure primitives that match the health feature vector set; associating and mapping the intervention measure primitives with the dynamic health entropy value to construct an intervention mapping matrix; optimizing the intervention measure primitives in the intervention mapping matrix by minimizing the dynamic health entropy value to generate a personalized health candidate set; and performing expected effect deduction on the personalized health candidate set to obtain an optimized health management solution. This invention can improve the efficiency of data mining for personalized health management solutions.

The application provides a health intervention list generation method and device, electronic equipment and a storage medium, applied to the field of artificial intelligence technology, medical intervention weights are determined according to medical symptom data of a target object, the more serious the illness indicated by the medical symptom data, the greater the medical intervention weight, so that the medical intervention weight can be determined according to the actual physical condition of the target object; when the target object is inclined to a strong medical order and a high-risk physical condition, the medical intervention weight is large, and medical intervention projects are mainly used; when the target object is inclined to a sub-health and stable physical condition, the medical intervention weight is small, and life intervention projects are mainly used. The adaptive intervention of 'treating the symptoms (emphasizing medicine) in an emergency and treating the root cause (emphasizing life)' can be realized, and the rationality of health intervention list recommendation is improved.

The application discloses a family doctor robot based on bioelectricity and magnetic resonance principles, and relates to the technical field of intelligent medical equipment, and solves the technical problems of single detection dimension, low diagnosis accuracy and lack of personalized health intervention of existing family medical equipment. The robot integrates a bioelectricity detection module, a magnetic resonance detection module, an intelligent analysis processing module, an interactive execution module and a cloud service module, collects bioelectricity signals of multiple parts of the human body through a flexible sensor, realizes non-invasive structure and metabolism detection of local tissues of the human body in combination with low-field magnetic resonance, completes multi-dimensional health data fusion analysis, preliminary diagnosis of diseases and risk assessment relying on an AI diagnosis model, simultaneously outputs a personalized intervention scheme and executes basic health services, and can also realize continuous tracking of health data and docking of remote medical treatment. The application realizes non-invasive, multi-dimensional and intelligent health monitoring and preliminary diagnosis and treatment in a family scene, has high detection accuracy, is convenient to operate and has strong adaptability, and greatly improves the professionalism and convenience of family health management.

The application provides a one-dimensional three-dimensional double-state three-order health promotion method and system based on essence, qi and spirit, and the method comprises the following steps: obtaining three-dimensional parameters, wherein the three-dimensional parameters comprise essence dimension data, qi dimension data and spirit dimension data; constructing a three-dimensional health vector through the three-dimensional parameters, determining a self-healing force pivot state parameter according to the three-dimensional health vector; analyzing the self-healing force pivot state parameter, and determining a double-state intervention mode according to the analysis result, wherein the double-state intervention mode comprises active intervention and passive intervention; mapping the double-state intervention mode to a specific technical path and an operation strategy, combining a space-time characteristic vector, and outputting an executable three-order intervention scheme. The application can automatically adjust the intervention mode and intensity according to the change of the individual health state, improve the matching and continuity of health intervention, realize more flexible health promotion, and can be applied to the scenes of chronic disease management, health promotion and individualized intervention, and realize stable maintenance of health promotion.

This application discloses an intelligent method and system for detecting psychological and physiological health, belonging to the field of health detection technology. The method achieves synchronous acquisition of dual-modal signals from the same source using a high frame rate camera, generates accurate evaluation coefficients through a multi-dimensional real-time verification algorithm, and adaptively calibrates them. It employs an attention mechanism dynamic correlation fusion algorithm to strengthen the intrinsic correlation between physiological and psychological indicators, combines an improved Bi-Mamba deep neural network to capture temporal features, and establishes a risk mapping model. Based on a three-dimensional evaluation system, it generates multi-scenario adapted detection reports. The system includes six modules: dual-modal acquisition, multi-indicator extraction, dynamic verification and calibration, deep correlation fusion, temporal risk modeling, and scenario-based reporting. This invention achieves synchronous acquisition, dynamic verification, deep fusion, and timely early warning of physiological and psychological signals, significantly improving detection accuracy and scenario adaptability, and providing precise data support for psychological and physiological health intervention.

The application relates to the technical field of medical treatment, in particular to a health intervention scheme generation system and method for a high-uric-acid population. The system comprises a first acquisition module configured to acquire multi-modal data of a current user; an input module configured to input the multi-modal data of the current user into a health intervention scheme generation model, wherein the health intervention scheme generation model is obtained by training a preset language model based on multi-modal data of multiple target users; and a generation module configured to output a high-uric-acid intervention scheme of the current user through the health intervention scheme generation model, and output the high-uric-acid intervention scheme of the current user when the high-uric-acid intervention scheme of the current user meets a preset output condition. Thus, the problems in the prior art, such as heterogeneous follow-up data, difficulty in integration, existence of logical contradictions or abnormalities, inability to effectively utilize time-series information, lack of automatic evaluation of treatment schemes, non-standard diagnosis and treatment, and difficulty in using cloud-based large models, are solved.