302 results about "Health problems" patented technology

This invention presents a multisensor-connected, AI-enabled telehealth system for assisting healthcare providers with differential diagnosis and patients with early health concern detection. The system comprises a multi-sensor medical device with at least seven sensors, a secure cloud-based platform, and an interactive telehealth module. The device preprocesses and securely transmits patient information to the cloud platform, where an ensemble of deep learning models analyzes the data to generate ranked potential diagnoses with likelihood scores. The telehealth module facilitates communication between providers, patients, and the cloud platform, presenting visualizations and receiving feedback. The system continuously updates and fine-tunes its models using incremental learning algorithms, adapting to new data while retaining previous knowledge. It also generates alerts for providers and patients when deviations from normal physiological patterns are detected, accompanied by explainable AI visualizations.

An AI-based system and method for providing warning of potential health concerns. The invention involves receiving patient information from various sources, including one or more of sensor data, patient-reported symptoms, and medical records. The patient information is processed using AI models to identify potential health concerns. The AI models may identify user-specific patterns and / or metrics associated with potential health concerns. Tiered alerts may be generated and provided to a user based on the AI analysis. Recommended actions may be generated and provided to the user based on the alert.

The invention relates to the technical field of health event prediction, in particular to an AI-based abnormal health event prediction system, which comprises a data acquisition module, a data preprocessing module, a causal analysis and health trend deduction module, a dynamic risk assessment and hierarchical management module, a feedback and optimization module and a user interaction and suggestion module, the data acquisition module collects health data of a user in real time; the data preprocessing module is used for preprocessing the collected health data; the causal analysis and health trend deduction module deduces the health trend of the user; the dynamic risk assessment and level-to-level management module dynamically assesses the health risk of the user; and the feedback and optimization module optimizes the health trend prediction model. According to the method and the system, the user is helped to take proper actions to prevent or deal with potential health problems, refined and personalized health management is realized, and the overall health level of the user is improved.

The invention provides a medical large model intelligent inquiry reasoning method and system, and the method comprises the steps: obtaining the input information of a user, and carrying out the processing of the input information, including intention recognition, entity recognition and historical information summarization; sequentially performing pre-retrieval, multi-modal database retrieval and knowledge reordering according to the information obtained after processing; generating an inquiry strategy according to the information obtained after knowledge reordering, and performing information retrieval enhancement generation on the medical large model according to the information obtained after knowledge reordering and the inquiry strategy; and generating content according to the generated medical large model. According to the method, reasoning and replying of symptom description and health problems input by the user are achieved through the generative medical large model and multi-modal data retrieval, the method is suitable for intelligent health management and medical consultation in the medical field, and the intelligent level of medical services and the user experience are effectively improved.

The invention discloses a chronic disease patient information management system and method based on intelligent AI, and relates to the technical field of information management. The information management system comprises a patient information management module, a data acquisition module, a data management module, a data analysis and evaluation module, a risk early warning module, a health management module and a remote medical service module. According to the chronic disease patient information management system and method based on the intelligent AI, learning and analysis are carried out based on comprehensive health data of the patient, so that accurate assessment of the health risk of the patient is realized, the current health data of the patient is integrated, and multi-dimensional information of medical history and living habits of the patient is combined, so that the assessment result is more comprehensive and accurate, and meanwhile, the assessment efficiency is improved. The health risk possibly faced by the patient in the future is predicted, early warning is given out in advance, sufficient time is provided for medical institutions to formulate intervention measures, potential health problems can be found and handled in time, and disease deterioration is prevented.

The invention relates to the technical field of health management, in particular to a psychological risk early warning method and system based on multi-dimensional emotion data of a user. The method comprises the following steps: acquiring user multi-dimensional emotion data corresponding to a user emotion expression image, a user emotion voice signal and a user emotion physiological index, and performing multi-source time sequence alignment processing on the user multi-dimensional emotion data to obtain user multi-dimensional time sequence synchronous emotion data; an emotion three-level feature extraction framework is constructed, modal emotion feature extraction and cross-modal fusion are carried out, and cross-modal fusion psychological features of the user are obtained; and obtaining user environment data and user social network data, and performing emotional psychological dynamic graph reasoning and psychological risk early warning on the user cross-modal fusion psychological features based on the user environment data and the user social network data to generate corresponding user emotional psychological risk early warning response measures. According to the invention, early recognition and intervention of psychological health problems can be realized.

The invention relates to the technical field of health early warning, in particular to a human health early warning method and system based on physical examination data analysis, which comprises the following steps: collecting physical examination data of a human body, analyzing index changes of blood pressure, blood sugar, cholesterol and body mass index of the human body, sorting the physical examination data of the human body through time sequence sorting, and generating a time sequence sorting result. According to the method, time sequence sorting and spatial dimension evaluation are performed on the physical examination data, so that the health risk can be accurately identified and the early warning level can be dynamically evaluated, the data analysis precision is improved, the risk prediction dimension is increased, early warning is more timely and accurate, and the accuracy of early warning is improved by performing layout evaluation on the physical examination data points in a finite dimension space. Potential health abnormalities can be identified on multiple levels, and multi-dimensional analysis provides a new way for identifying complex health problems, so that health management is more active and predictable, health monitoring and risk management are optimized, and the health condition of a patient is improved.

The invention discloses an online health consultation method and system based on intelligent AI and a storage medium, and the method comprises the steps: obtaining the physical condition description data of a user to generate consultation information, and obtaining a plurality of corresponding question and answer pairs according to a preset large language model through the consultation information; selecting related basic health data from the basic health archive of the user, obtaining abnormal features through feature extraction, and selecting question and answer pairs corresponding to the abnormal features to obtain user corpora to supplement consultation information; generating a health condition representation of the user in a large language model by utilizing the supplemented consultation information and the abnormal features, and accessing a related domain knowledge graph to generate a health condition representation of the user in the knowledge graph; and learning representation is carried out on the fusion representation of the health condition of the user through a multi-layer perceptron, and answer data is selected and output. According to the invention, accurate analysis of the consultation content of the user is realized, more targeted and more accurate consultation feedback in the aspect of medical health is provided for the user, and the health problem of the user is explored in time.

The invention discloses a method for managing medical risks, and particularly relates to the technical field of medical management, and the method comprises the steps: S1, deploying a multi-modal noninvasive biosensor, and collecting the physiological data of a patient; s2, integrating data sources, and performing cleaning and standardization processing to ensure data consistency and accuracy; s3, performing feature extraction and correlation analysis by using a deep belief network and a graph neural network, and identifying a potential risk mode; and identifying a potential risk mode through high-dimensional feature extraction and complex correlation analysis. And by utilizing self-supervised learning and a generative adversarial network, hidden risks can be efficiently detected, and potential health problems can be warned in advance. A personalized risk assessment and multi-level response mechanism provides accurate intervention measures for each patient, and the quality and efficiency of medical services are improved. Meanwhile, according to the method, continuous data collection is achieved through the noninvasive sensor network, it is guaranteed that the patient is not interfered in the monitoring process, and the comfort level and compliance of the patient are improved.

The invention provides a pet health monitoring method, device and equipment, a medium and a product, and is applied to the technical field of pet health management.The method comprises the steps that pet monitoring data of multiple modes is acquired; fusing the pet monitoring data of the multiple modes to obtain fused monitoring data, and analyzing the fused monitoring data by adopting a large model to obtain a health analysis result corresponding to each mode; according to a health analysis result, determining a health anomaly probability corresponding to each mode, and performing weighted summation on the health anomaly probabilities by adopting weight information corresponding to each mode to obtain a health risk value; according to the health risk value, pet health early warning is carried out, pet health monitoring is carried out according to the data of multiple modes, the requirements for comprehensive monitoring and early warning of pet health are met, the pet monitoring data of multiple modes are fused and analyzed through a large model, and the requirement for accurate recognition of potential health problems of pets is met.

A network management system (NMS) configured to attribute a health issue of an access point (AP) to a classifier or sub-classifier and remedy the health affecting a user's experience. The NMS obtains network data associated with an AP device of a site. The NMS also determines whether the network data satisfies one or more criteria corresponding to a classifier of a service level expectation (SLE) metric for determining a health issue of the AP device. In response to determining the network data does not satisfy the criteria associated with the classifier, NMS attributes the health issue of the AP device to the classifier. In response to attributing the health issue of the AP device to the classifier, NMS performs a remedy action based on the classifier.

The invention provides an intelligent underwear data processing method and system based on artificial intelligence. The method comprises the steps that intelligent underwear collects physiological and environmental data of a user through a built-in sensor; the edge computing chip preprocesses the collected data to obtain first data, performs sensitivity grading marking to obtain second data, performs processing according to sensitivity grades to obtain third data, and selects fourth data needing to be uploaded from the third data; the server receives and decrypts the fourth data, integrates the multi-source data according to a timestamp to construct a health data set, inputs a preset model for analysis, and sends a result to the intelligent underwear; the edge computing chip provides health suggestions through a feedback device according to the analysis result, and collects a user feedback optimization model; and the server simulates the health state of the user by using a digital twinning technology, predicts the health problem and formulates a management scheme. According to the invention, the accuracy of intelligent underwear data analysis is improved, and the security and privacy protection of the user data in the transmission and processing process are enhanced.

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

The invention relates to the technical field of water quality monitoring, in particular to a distributed raw water quality real-time monitoring system which comprises an underground water sampling module, a sample analysis module, a water quality feedback terminal and a data storage terminal. And finally, the water body quality corresponding to the underground water quality in the target raw water body area is fed back to a system interface for interface display, the water quality evaluation can guarantee the safety and sustainability of the water resource, and the water quality evaluation efficiency is improved. Health risks and economic losses caused by water quality pollution are avoided, and underground water quality evaluation has direct influence on public health. Various health problems may be caused by drinking or using the water for a long time, the drinking water safety of residents can be guaranteed, health risks can be found and treated in time, and therefore the health of the residents is protected.

The invention provides a system and a method for cow health early warning, which are applied to the technical field of animal husbandry health monitoring, perform individual identification and behavior recording by collecting cow activity data, environmental parameters and management operation data, analyze a behavior mode and identify health problems in combination with environmental management data, and have the advantages of realizing automatic monitoring and improving the efficiency of cow health early warning. Non-contact data acquisition, individual identification, individual behavior recording, integration of environment parameters and management event data, analysis based on behavior and environment management data, identification and indication of a behavior mode of a health problem, and early health early warning are realized.

The invention provides a graded early warning method and system for a home health risk event of the aged, and the method comprises the steps: 1, obtaining health risk index data of the aged at home through intelligent monitoring equipment, carrying out the classification and summarization of the obtained index data according to a home health risk scene, and determining a home health risk monitoring index; 2, identifying home health events of the monitored person by detecting the change of the monitoring indexes, and classifying the events into different grades according to the event types and degrees; and step 3, performing graded early warning according to different grades, namely performing pop-up warning on the corresponding terminal according to the identified monitoring information when the health event reaches the triggering condition of the corresponding early warning grade. According to the invention, through classification and grading of the monitoring data, possible health problems of the elderly can be identified in an early stage, timely alarm can be given for sudden health events, and the life quality and survival opportunities of the elderly are improved.

The embodiment of the invention provides a nurse station ward information collection method and system based on a wireless network. A flexible electronic skin network is integrated in a patient mattress, pressure and temperature data are captured through distributed piezoresistive units, a multi-modal thermodynamic diagram is generated, and data synchronization is achieved through a wireless ad hoc network protocol. And driving an air cushion bed actuator to perform gradient compensation according to a matching result of the pressure gradient track and the physiological parameters, transmitting a compressed hierarchical data packet to a nurse station gateway by adopting a remote wide area network protocol, reconstructing a three-dimensional pressure model and generating an early warning instruction. According to the technical scheme provided by the embodiment of the invention, the occurrence probability of health problems such as bedsore caused by long-time bed lying is remarkably reduced, and the nursing quality of the patient is improved.

The invention relates to the technical field of health management, and discloses a health state determination method and system and a program product. The method comprises the following steps: acquiring physical physiological data and physical environment data of a monitored person; matching the physical and physiological data with the physical environment data according to the time attribute and the space attribute, and determining an association relationship between the physical and physiological data and the physical environment data; and based on the association relationship, performing data processing on the physical physiological data and the physical environment data locally to generate health state information. By implementing the technical scheme of the invention, the health problem of the monitored person can be found in time, and the health state information is generated.

The invention discloses a single-channel air supply type mine ventilation device, and relates to the technical field of mine ventilation devices. The single-channel air supply type mine ventilation device comprises a ventilation pipe, one side of the ventilation pipe is fixedly connected with an air extracting pump, one end of the air extracting pump is fixedly connected with an air supply pipe, the outer surface of the air supply pipe is fixedly connected with a connecting pipe, the outer surface of the connecting pipe is provided with an air outlet groove, and one end of the connecting pipe is fixedly connected with a removing mechanism. According to the mine ventilation device, when the mine is ventilated through the clearing mechanism and the air supply type ventilation device, air needs to be exhausted from the outside to enter the mine, but during air exhausting, dust and cotton fibers can enter the ventilation device from the outside and can shield the ventilation position, and the dust at the ventilation opening is cleared away through the clearing mechanism during air supply; the concentration of suspended particles in air can be reduced by removing dust, the quality of air in a mine is improved, and the risks of respiratory diseases and other health problems of workers are reduced.

The invention relates to a historical building-oriented real-scene three-dimensional GIS-based health monitoring data prediction method, and belongs to the technical field of building monitoring, and the method comprises the steps: obtaining modeling data and monitoring data, and constructing a building geometric model based on the modeling data; matching the monitoring data with the building geometric model so as to map the monitoring data to a corresponding position of the building geometric model; based on a preset prediction and early warning model, learning the monitoring data, and predicting and outputting the change trend of the monitoring data in a specified time period in the future; and analyzing monitoring data corresponding to the building geometric model based on a pre-constructed health analysis model, and outputting a structural damage assessment result of the building geometric model. The method has the advantages that the historical building is efficiently monitored, potential health problems of the historical building are found in time, and the maintenance efficiency of the historical building is improved.

The invention particularly relates to an old people health condition monitoring system based on a wristband oximeter. The system comprises a state determination module; a motion index module; a fatigue index module; and an analysis processing module. According to the system, the wrist strap type oximeter obtains the exercise step number, monitors key physiological parameters such as the blood oxygen saturation degree, the pulse rate and the perfusion index, and deeply analyzes the parameters in different exercise states to obtain the exercise index, the fatigue index and the cooperation index, so that the multi-dimensional comprehensive monitoring of the health condition of the old people is realized; the physical state of the old people can be reflected more accurately, and potential health problems can be found in time.

The invention provides a health education resource RPA interaction system and method based on a knowledge graph, and the system comprises a multi-source data fusion module which is used for summarizing health data of medical literatures, electronic cases and monitoring equipment, calling a pre-training model for entity recognition and relation extraction, and constructing a dynamically updated health knowledge graph; the RPA process management module is used for generating an automatic process according to the health knowledge graph and allowing a user to dynamically adjust a generation rule; the multi-modal interaction module provides texts, languages and / or visual charts for the user for real-time interaction according to the generated automatic process; and the feedback optimization module is used for optimizing the knowledge graph and the RPA process according to the user behavior log. According to the invention, the RPA system is combined with the AI, the health problem of the patient is intelligently analyzed, task scheduling is automatically carried out, personalized and accurate health education service is provided, and the intelligence and practicability of health education are improved.

The invention discloses a health information management platform for an inflammatory bowel disease patient. The health information management platform comprises the following steps: step 1, user registration and information input; 2, data preprocessing and feature extraction; 3, constructing and training a model; step 4, a real-time prediction and early warning mechanism; according to the scheme, by actively monitoring the health data of the patient and applying the real-time analysis capability of the machine learning model, potential health problems can be recognized in advance, timely early warning is achieved, the risk of acute symptom attack is reduced, and therefore the life quality of the patient is improved, multi-dimensional data analysis is utilized, and the accuracy of the patient is improved. Customized health management suggestions are provided for each patient, individual requirements of the patient are met, the control ability of the patient to the illness state of the patient is enhanced, the treatment effect is improved, the system can provide more basis treatment suggestions for doctors through continuous learning and optimization, clinical decisions are helped to be improved, and the accuracy and effectiveness of overall treatment are improved.

The invention relates to the technical field of health education, and particularly discloses a digital one-stop health education platform based on artificial intelligence and a construction method thereof, and the platform comprises a health data management module which is used for collecting health data of a user from a plurality of data sources, and automatically generating a health file of the user in combination with personal health information inputted by the user; the health portrait generation module is used for generating a personalized health portrait based on the health file, and performing personalized demand analysis according to the health portrait in combination with a target set by a user; according to the invention, based on the established health portrait, customized health education content is obtained according to personal requirements, and a health scheme is adjusted at any time according to health data changes, so that individuation and dynamics of health management are ensured; the user can ask questions at any time to obtain real-time health guidance and professional suggestions; by means of artificial intelligence and big data analysis, the health trend can be predicted, early warning can be given out when potential health problems are found, and effective health intervention measures are provided.

The invention discloses an exercise health monitoring method and system of a smart watch, and relates to the technical field of smart watches, and the method comprises the steps: carrying out the time synchronization of continuous data, and enabling all monitoring indexes to be aligned on the same time axis; carrying out real-time analysis and anomaly detection on the heart rate and blood oxygen saturation data by utilizing a machine learning algorithm; the heart rate variability index of the user is evaluated, the blood oxygen saturation data and the body surface temperature data are integrated, and a pressure evaluation model is constructed; in combination with height and weight information input by the user, constructing a deep learning model to identify a motion mode of the user; the heart rate and blood oxygen health indexes of the user are tracked and analyzed by utilizing a time sequence analysis technology, the future health trend is predicted, and potential health problems are explored. By arranging the data prediction module, the heart rate, blood oxygen and other health indexes of the user can be tracked and analyzed, the future health trend is predicted, and potential health problems are explored.

A system and method provide artificial intelligence (AI)-based diagnostic guidance for a patient. The system receives captured radiological scan images of the patient, and input components such as results of physical examination of the patient and laboratory data of the patient. The system provides patient imaging information based on the recognized patterns of the received radiological scan images and the three-dimensional reconstructed scans, identifies problems based on predetermined AI criteria and deep-learning based on patient-specific patterns, responses, and the input components, determines risk factors of the patient which cause heath issues, provides probability estimation of diagnoses that predict probability of occurrence of the health issues to the patient based on the risk factors of the patient, prioritize the problems to a hot list based on the probability estimation of diagnoses.

The present invention relates to the field of power grid monitoring technology, and specifically discloses a distributed power grid node health monitoring and prediction method, the method comprising: obtaining distributed power grid nodes, marking them as each power grid node, and obtaining health monitoring data of each power grid node; processing the equipment status data to obtain the equipment status abnormality evaluation index of each power grid node, and comprehensively analyzing to obtain the data accuracy evaluation value of each power grid node; screening to obtain each normal monitoring power grid node according to the data accuracy evaluation value of each power grid node; and providing evaluation feedback according to the health status evaluation value of each normal monitoring power grid node. By providing a distributed power grid node health monitoring and prediction method, the present invention can timely discover and handle health problems of power grid nodes, help prevent power outages or safety accidents caused by equipment failures, can more reasonably allocate power resources, improve the operating efficiency of the power grid, and help provide better power output and stability.

The invention discloses a mental stress assessment and intelligent dredging method and system, and belongs to the technical field of digital medical treatment and health informatics. Multi-modal physiological signals are continuously collected through the wearable device, medical diagnosis level pressure state evaluation is carried out based on the personalized physiological baseline, and personalized pressure indexes and levels are generated; secondly, when it is diagnosed that the pressure level exceeds the standard, the system serves as an intelligent decision support system, the environment and schedule information after privacy protection processing is fused, and an optimal grooming action is dynamically selected from a predefined intervention action library and executed; finally, the system serves as a continuous learning system, the intervention efficiency is evaluated in real time according to feedback data of the pressure index after execution, the decision model is updated, and collaborative self-optimization of the diagnosis strategy and the intervention strategy is achieved. According to the method, medical diagnosis, personalized treatment decision and adaptive learning are integrated, and the systematicness, accuracy and intelligent level of mental stress related health problem management are improved.

The invention relates to the field of human body physiological data monitoring, in particular to an exercise physiological data monitoring method which comprises a monitoring system, and the monitoring system comprises an acquisition module, a conveying module, a storage display module, a data processing module and a customized training module. The monitoring system comprises an acquisition module, a conveying module, a storage display module, a data processing module and a customized training module which are electrically connected with one another, the acquisition module monitors the heart rate of a monitored person in real time through the sports bracelet, personalized training suggestions are provided for athletes or sports enthusiasts through accurate physiological data monitoring and analysis, and the monitoring effect is good. According to the exercise physiological data processing and analysis method, training plans are optimized, the exercise performance is improved, the exercise injury risk is reduced, the health conditions of individuals are monitored through the exercise physiological data, potential health problems are found in the early stage, scientific exercise and diet plans are made, chronic diseases are prevented, and development of related technologies is promoted through research on the exercise physiological data processing and analysis method.

The technology disclosed herein provides a digital health privacy platform (Platform) and digital health passport (Passport) that together equip consumers to avoid unauthorized surveillance as they engage with their many authorized and variously located healthcare providers in information exchange and transactions for healthcare goods and services across episodes of care for diverse health problems while managing health-related self-maintenance, legal and financial matters.