1626results about "Epidemiological alert systems" patented technology

A system for remotely monitoring and managing health statuses of a plurality of users includes software instructions storable on a memory device usable by a computing device, the software instructions causing a hardware processor of the computing device to receive a plurality of sets of health-related information from a plurality of mobile computing devices of a plurality of users. The health-related information includes physiological information derived from wearable devices of the users indicative of an onset of symptoms associated with an infection, contact tracing data, and diagnosis data. The hardware processor determines exposure levels based on at least the contact tracing data, and determines user-specific risk states based on physiological information, diagnosis data, and exposure levels.

The invention provides an infectious disease early warning and monitoring method and system based on big data and deep learning, and relates to the technical field of infectious disease early warning, and the method comprises the steps: obtaining infectious disease historical data, crowd movement track data, medical treatment data and environment monitoring data as a training data set; establishing a space-time propagation link diagram and carrying out dynamic segmentation coding to generate a propagation characteristic sequence; training a deep learning model based on the propagation feature sequence to predict an infectious disease propagation inflection point and a high-risk area propagation probability; and when the propagation probability exceeds a preset threshold value, generating early warning information and pushing the early warning information to a monitoring terminal. According to the invention, accurate prediction and timely early warning of the transmission trend of infectious diseases can be realized, and the epidemic prevention and control efficiency is improved.

The invention provides a hospital infection prevention and control related data online management method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the dynamic graph calculation of an infection transmission path through a multi-modal deep learning model, and recognizing a high-risk department, equipment and personnel interaction mode, so as to obtain a real-time calculation result; generating a dynamic risk threshold based on the real-time calculation result, and generating a risk event vector; mapping the risk event vector into an executable supervision task list, and calculating a final task allocation path through a reinforcement learning algorithm; based on the final task distribution path, constructing a department-level risk portrait and a hospital-level risk topological graph; expected loss values of different prevention and control strategies are calculated through Monte Carlo simulation, and a department specific prevention and control scheme is generated; and when a multi-drug-resistant bacterium propagation risk is detected, automatically associating historical data of related departments, and calculating a final isolation region division scheme. The false alarm rate can be reduced.

The invention discloses an animal epidemic disease cross-border transmission risk analysis method based on big data, and particularly relates to the technical field of data analysis. Through a multi-modal data fusion algorithm of deep learning, multi-source heterogeneous data is integrated in real time to form a unified data set, a cross-border propagation path model based on an infectious disease propagation model and an epidemiological theory is constructed, a propagation path of epidemic diseases is accurately simulated in combination with spatio-temporal information and propagation characteristics, and on the basis, the propagation path of the epidemic diseases is accurately simulated. A machine learning algorithm is utilized to analyze the fusion degree of cross-border propagation data between different regions, and based on a multi-source data fusion evaluation result, a high-risk region and a propagation speed are predicted in real time, so that potential cross-border propagation risks are evaluated, and targeted prevention and control intervention measures are proposed; according to the method, the problems of data isomerism, timeliness difference and fusion processing in the prior art are effectively solved, the accuracy, the real-time performance and the scientificity of epidemic disease cross-border transmission risk analysis are improved, and accurate support is provided for epidemic prevention and control decisions.

The invention discloses a livestock epidemic disease early warning method and system based on big data analysis, and the method comprises the steps: generating a preliminary epidemic situation diffusion path through employing the farm distribution and traffic network data of a geographic information system in combination with a space-time propagation model, and carrying out the dynamic adjustment according to the real-time updated data; predicting a high-risk area by adopting a multi-source data fusion early warning model; the strain mutation risk is judged by analyzing the relevance between pathogen genomes and environmental parameters; deploying a deep learning model at an edge computing node, analyzing animal husbandry behaviors and voiceprint data in real time, and evaluating the animal husbandry health state; and integrating mutation risk analysis and animal husbandry health assessment results to generate a comprehensive epidemic situation early warning report. Through multi-dimensional data analysis and edge intelligent calculation, accurate early warning of animal epidemic situations is realized, and a scientific basis is provided for epidemic situation prevention and control.

The invention provides a three-dimensional simulation and infection scene decision optimization system and method based on digital twinning, and relates to the technical field of intelligent medical treatment and digital twinning crossing technologies. The three-dimensional simulation and infection scene decision optimization system and method based on digital twinborn comprises the following modules: a data acquisition module, a path optimization module, a risk analysis module, a disinfection scheduling module and a collaborative prevention and control module, by arranging an air microorganism sampler, ultra-wideband positioning equipment and a temperature and humidity sensor, pathogen concentration, personnel trajectory and environmental parameters are collected in real time. Through a multi-source sensor fusion algorithm and a space-time attention mechanism, weight fusion and noise filtering are carried out on pathogen concentration, personnel tracks and environmental parameters in a hospital environment, the problems of information isolation and noise interference in traditional data collection are solved, and the space-time relevance of multi-modal data is remarkably enhanced.

The invention relates to the technical field of epidemic situation transmission prediction, in particular to an infectious disease transmission prediction method and system based on a dynamic space-time diagram neural network, and the method comprises the following steps: extracting space-time features from historical case data through a dynamic space-time diagram learning module, and generating an adjacent matrix representing an infectious disease transmission network; predicting a future epidemic situation development trend based on the adjacent matrix through an infectious disease dynamic modeling module; wherein the dynamic space-time diagram learning module comprises a time sequence convolutional network, a multi-head self-attention mechanism and a long short-term memory network; the infectious disease dynamic modeling module comprises a time sequence convolutional network, a graph neural network and a gating circulation unit.According to the infectious disease transmission prediction method and system based on the dynamic space-time graph neural network, by introducing virtual nodes and a dynamic graph structure, the response ability to external factors is improved, and the prediction accuracy is improved; data missing and external influence factors can be effectively processed, and a real propagation mode can be embodied from the generated propagation network.

The invention relates to the technical field of public health safety, in particular to an infectious disease transmission path prediction method and device and a medium, and the method comprises the following steps: extracting an initial tetrad of an event from infectious disease outbreak data, mapping the initial tetrad into a numerical value, and obtaining a numerical value tetrad; dividing the numerical tetrad according to time to obtain a time sequence knowledge graph; performing initialization embedding representation on entities and relationships in the time sequence knowledge graph; constructing a relational graph attention network, and learning structure information in the time sequence knowledge graph to obtain entity embedding and relation embedding; calling a gating circulation unit, and extracting time sequence information of the time sequence knowledge graph; and combining the structure information with the time sequence information by adopting a decoder, generating a prediction result, and realizing infectious disease transmission path prediction based on the prediction result. According to the method, the transmission path of the infectious diseases is predicted by adopting the time sequence knowledge graph, and compared with a traditional public health prevention method, the method has higher dynamic nature and multi-dimensional correlation analysis capability.

The invention relates to the technical field of livestock and poultry epidemic disease management, and particularly discloses an intelligent livestock and poultry epidemic disease early warning and partitioned prevention and control management system and method, and the method comprises the steps: laying an integrated intelligent sensor network in a livestock and poultry farm, and collecting environment parameters, feeding management data and animal health data in real time through a low-power-consumption Internet of Things technology; transmitting the collected multi-dimensional data to a cloud platform by utilizing an edge computing technology; based on multi-dimensional data, a deep learning and multi-source data fusion analysis technology is adopted, a dynamic disease risk prediction model is constructed, and generalization ability of an adversarial generative network optimization model in a complex environment is introduced; by arranging the integrated intelligent sensor network, environmental parameters, feeding management data and animal health data in the livestock and poultry farm are comprehensively collected in real time, the early monitoring capability of epidemic diseases is remarkably improved, potential epidemic disease risks are found in time, and precious time is won for subsequent prevention and control work.

The invention discloses an adverse drug reaction event recognition method and system based on multivariate knowledge mixed retrieval enhancement, and the method comprises the steps: extracting drug entities from a to-be-recognized clinical disease course record, segmenting the disease course record, and obtaining a drug entity set and a sentence set; for each extracted drug entity, retrieving drug concept knowledge having a hyponymy relationship with the drug entity and drug adverse reaction knowledge having an adverse reaction relationship with the drug entity in a pre-constructed multivariate knowledge base; for each sentence obtained through segmentation, searching suspected adverse drug reaction events meeting a first similarity requirement and drug field text knowledge meeting a second similarity requirement in a pre-constructed multivariate knowledge base; and finally, calling a large language model, taking all the retrieved knowledge as reference knowledge, and identifying the adverse drug reaction event from the to-be-identified clinical disease course record. According to the invention, the accuracy and reliability of adverse drug reaction event identification can be improved.

The invention discloses a medical insurance cost prediction and optimization method based on big data analysis, and belongs to the technical field of medical data processing. The method comprises the following steps: constructing a multi-source data acquisition module, and integrating data of an HIS system, a medical insurance platform and wearable equipment by using an FHIR interface and a block chain; a BERT-BiLSTM-CRF model is adopted to fuse multi-modal data, a dynamic model group containing Transform anomaly detection and LSTM-ARIMA time sequence prediction is established, and the prediction error rate is reduced to 12% (reduced by 28% compared with that of a traditional method); a multi-objective optimization system is designed, medical quality and cost control are balanced based on an improved NSGA-II algorithm, the cost of a single disease is reduced by 18%-25%, and the quality standard reaching rate exceeds 95%; a hybrid cloud and homomorphic encryption module is deployed, and the data leakage risk is reduced by 90%; a policy sandbox system is constructed, medical insurance policy simulation deduction is supported, and the response time is shortened to 72 hours. According to the invention, the problems of data islands, low prediction precision and privacy risks are solved, and the whole-process intelligent management and control of medical insurance fees is realized.

The invention discloses a livestock feed proportioning method based on livestock growth, and relates to the technical field of feed proportioning, and the livestock feed proportioning method comprises the following steps: collecting the body temperature and motion track data and environmental temperature and humidity data of livestock individuals, monitoring the concentration of amino acids in digestive tracts, preprocessing the collected data, and generating a standardized biological data set; analyzing the compensation amount of vitamins and trace elements according to the metabolic fingerprint spectrum, and constructing a multi-objective optimization function in combination with an individual growth curve; solving the multi-objective optimization function through a quantum annealing algorithm, and matching local raw material inventory data of block chain evidence storage to generate an optimization strategy; and executing the optimization strategy and detecting the mixing uniformity, and when the mixing uniformity index exceeds a preset mixing uniformity threshold, starting the compensation device to adjust the optimization strategy. According to the invention, through a cooperation mechanism of dynamic nutritional requirement identification and multi-constraint optimization, the precise regulation and control capability of livestock breeding is significantly improved.

The invention relates to the technical field of virtual simulation, and particularly discloses a sudden major respiratory infectious disease on-site epidemiological investigation virtual simulation experiment system which comprises a virtual scene management module, a data management module, a disease transmission simulation module, an epidemiological analysis module, a virtual on-site interaction module, a learning support module and an analysis and evaluation module. The virtual scene management module is used for creating and managing a simulation experiment environment and simulating an epidemic background and a development process of sudden major respiratory infectious diseases; the data management module is used for simulating the process of collecting case information, environment information and social contact data when students perform on-site epidemiological investigation; according to the system, big data analysis, the GIS technology and an AI intelligent analysis tool are integrated, a disease transmission path and a development trend are simulated, and virtual simulation not only helps students to master how to support a decision through scientific data, but also can improve the emergency response capability and the comprehensive analysis capability of the students in a complex epidemic situation.

The invention relates to the technical field of medical or health data information, in particular to an intelligent health old-age care safety monitoring system, which comprises an old-age information acquisition module for acquiring face and basic information; the body health monitoring module predicts health parameters through LSTM, and if the health parameters exceed a threshold value or change abnormally, one is added to the alarm frequency; the psychological health monitoring module uses a StyleGAN to generate an enhanced data set and performs emotion classification through a convolutional neural network, and if the emotion deviates from a baseline, one is added to the alarm frequency; the tumble posture monitoring module judges tumble through posture estimation, and if tumble is detected, one is added to the alarm frequency; the position anomaly monitoring module identifies abnormal movement or residence, and if an anomaly is detected, the number of alarms is increased by one. And guardians and medical staff can obtain alarm information and monitoring reports in time according to the alarm times, so that the efficiency of monitoring the health and safety of the elderly is improved.

The invention provides a hospital infection propagation path tracing method based on multi-source data fusion, and belongs to the technical field of infection propagation paths in hospitals, and the method comprises the steps: building a data set through collecting hospital multi-source infection monitoring data, constructing a multi-layer network propagation model comprising physical contact, spatial proximity and time co-occurrence, and carrying out the tracing of the multi-source infection propagation path. Community detection is carried out by using a random block model, infection propagation path identification is converted into graph coloring problem solving, an individual behavior heterogeneity model is established, and SEIR model propagation parameters are corrected by fusing individual characteristic parameters such as the immune state of a patient and the protection level of medical staff; a propagation situation awareness model based on a hybrid expert mechanism is adopted to optimize propagation parameter estimation, and a propagation parameter weight coefficient is obtained through a game optimization mechanism and Nash equilibrium solution for iterative optimization. And finally, outputting a high-confidence infection propagation path sequence and a propagation source positioning result by adopting a propagation path backtracking algorithm in combination with spatial-temporal correlation analysis.

The invention relates to the technical field of model construction, in particular to a construction method of a disease feature recognition and evaluation model based on the Internet of Things. The method comprises the following steps that multi-modal physiological data of a patient are collected in real time through Internet of Things medical equipment, data preprocessing is conducted on the multi-modal physiological data of the patient, standard patient physiological data are generated, and the standard patient physiological data are stored in a distributed database; extracting gene sequencing data based on the biological sample library; performing global health risk factor perception on the standard patient physiological data to generate user level health risk perception data; and carrying out feature fusion on the user level health hazard perception data and the gene sequencing data to construct a disease feature matrix containing a space-time dimension. Through multi-modal data fusion, spatio-temporal evolution modeling, deep learning architecture and personalized risk assessment, the precision and timeliness of disease feature recognition assessment model construction are improved.

The invention relates to the technical field of crop disease and insect pest monitoring, and particularly provides a crop disease and insect pest intelligent monitoring system based on image recognition and machine learning, and the system comprises a reference model generation subsystem which obtains a continuous state flow in a crop growth period through a multispectral imaging array; performing micro-variation field comparison on the real-time image of the continuous state flow and the ideal form reference vector, and outputting a biological stress difference chart; the mutual exclusion association engine subsystem purifies a pathological response map, inputs the pathological response map into a pathological characteristic dissociation network, and outputs an environmental immune type disease mark set; and the disease source trajectory deduction subsystem diffuses a power chain through organisms on the basis of the initial infection focus coordinates in combination with a real-time wind direction vector field and a plant density topological graph. According to the method, the problem of high false positive rate in a complex farmland scene is solved through a dual denoising mechanism of growth rhythm decoupling and environmental interference stripping; and finally, monitoring dimension jump from single-point identification to group prevention and control is realized.

The invention relates to a diabetic symptomatic ICAS prediction method and system based on machine learning. The method comprises the following steps: collecting an HRMRI image sample of a target patient; performing high-risk plaque feature analysis on the HRMRI image sample to obtain high-risk plaque features; performing image omics feature extraction and screening on the HRMRI image samples to obtain image omics features; based on an XGBoost algorithm, training the high-risk plaque features and the radiomics features to obtain a prediction model; and performing symptomatic ICAS attack prediction on a to-be-predicted patient through the prediction model to obtain a prediction result. The accuracy and the stability of the clinical model in predicting the new symptomatic intracranial atherosclerotic disease of the diabetic patient can be improved, and the clinical model adapts to individual specificity.

The invention relates to the technical field of infectious disease on-site simulation, in particular to a respiratory infectious disease on-site epidemic AI simulation experiment system based on environment evaluation. Performing virus region diffusion analysis based on a virus propagation risk assessment result, the environment data and the contact condition of nearby facilities, performing path virus risk assessment based on a path virus region diffusion analysis result, a personnel evacuation condition and a virus attenuation condition, and acquiring an evacuation path according to a path virus risk assessment result. By evaluating the risk of the virus in the path, the path with relatively low virus transmission risk can be accurately identified, and the path is selected for personnel evacuation in the period of infectious disease outbreak, so that the contact opportunity between evacuated personnel and the virus can be reduced to the greatest extent, the possibility of infectious disease infection is effectively reduced, and the life health and safety of the public are guaranteed.

The invention relates to the technical field of animal monitoring, and provides a wild animal epidemic disease monitoring, prevention and control method and system based on artificial intelligence, and the system collects the video stream, the shell temperature, the air pathogen concentration, the sound characteristics, the VOCs spectrogram and other data of wild animals in real time through arranging multi-mode sensor nodes. Real-time reasoning is carried out through a wildness degree AI model in the edge calculation unit, the health state and epidemic disease risk of animals are evaluated, a multi-source risk knowledge graph and a graph neural network are combined, an epidemic situation occurrence probability threshold value is dynamically adjusted by the system, and accurate prevention and control instructions such as risk area division, isolation early warning and material putting schemes are generated; after the epidemic disease risk is confirmed, the system sends early warning information to a prevention and control center through various communication links, and continuously optimizes a prevention and control strategy through reinforcement learning. According to the method, intelligence and precision of epidemic disease prevention and control of wild animals are achieved, complex ecological environment changes can be coped with in real time, and prevention and control efficiency and accuracy are remarkably improved.

The invention discloses a pig farm abortion attribution diagnosis method based on a porcine reproductive and respiratory syndrome risk propagation knowledge graph, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing a pig farm porcine reproductive and respiratory syndrome attribution knowledge graph, pre-defining a risk propagation mode according to a porcine reproductive and respiratory syndrome risk propagation mechanism, searching a path according with the risk propagation mode through graph mode matching, and carrying out the diagnosis of the abortion attribution of a pig farm. Integrating into a risk sub-graph; performing representation learning on the risk sub-graphs by adopting a graph attention network fused with PRRS risk propagation knowledge, and quantifying the contribution degree of each risk sub-graph to the abortion rate of the pig farm in combination with context representation learning and a time difference attenuation mechanism; and based on the contribution proportion of each risk event in the attention score decomposition risk sub-graph, generating a quantitative attribution result, and outputting a diagnosis result including risk event identification, a risk propagation link and a quantitative attribution contribution degree. Risk attribution of the porcine reproductive and respiratory syndrome in the pig farm is realized, and contribution of specific attribution risk points to the abortion rate of the pig farm is quantified.

Systems and methods for outbreak analysis by performing taxonomic classification of the first LRS data to assign one or more taxonomic identifiers to each record in the first LRS data; identifying a plurality of species-specific candidate genomes from a reference genome database based on the assigned taxonomic identifiers; aligning the first LRS data with each of the plurality of species-specific candidate genomes to determine a first species identity of a microorganism species present in the sample; identifying a first nearest strain of the identified microorganism species present in the first sample by aligning the first LRS data with a plurality of strain-specific candidate genome dataset.

The present disclosure relates to a computer-implemented process for generating and providing simulated user absorption information pertaining to users and based on target profiles and target situations, thereby providing user targeted and situationally targeted content recommendations. It is an object of the present disclosure to provide a technological solution to the long felt need in small scale content recommendation systems caused by the technical problem of generating situationally targeted and user profile targeted content recommendations for users of an interactive electronic system.

The invention relates to the technical field of health risk assessment, in particular to a system and a method for preventing respiratory tract infection of children, an environmental data analysis module is used for collecting air temperature, humidity and air quality parameters focused on a pediatric activity place and carrying out data cleaning and standardization processing; according to the invention, through deep processing of environment monitoring data of a pediatric activity place, including hyperfine data cleaning and precise standardization, the reliability of analysis is comprehensively guaranteed, and a foundation is built for subsequent model operation. Innovative daily mean value calculation and optimized time sequence analysis are closely attached to the environment change rhythm of the pediatric department, and the dynamic trend of environment parameters is clearly drawn. A composite machine learning strategy of a random forest and a support vector machine is fused, pediatric exclusive features are deeply embedded, the precision and adaptability of a disease prediction model are greatly improved, and the complexity and variability of pediatric data are leisurely handled.

The invention discloses a doctor service method based on a medical large model and a related device. The method comprises the following steps: acquiring service request data of a target doctor in a target service scene; wherein the target service scene is any one of the following medical service scenes: diagnosis decision suggestion, treatment scheme recommendation, medical record generation, medical record quality control and medical record information management; and analyzing the service request data based on the large medical model to obtain a medical service suggestion output by the large medical model to the target doctor in the target service scene. In this way, medical service suggestions can be automatically provided for doctors.

A method, system and devices for digital contact tracing security and privacy with proximity-based ID exchange with distance-bounding. The method is performed by a first wireless communication device and provides for exchanging IDs with a second wireless communication device. A rolling proximity identifier A associated with the first wireless communication device is sent to the second wireless communication device. A rolling proximity identifier B associated with the second wireless communication device is received from the second wireless communication device. A cryptographic challenge response authentication with time-based distance-bounding is performed based on a hash value determined from the rolling proximity identifiers in accordance with a hash function. The rolling proximity identifier of the second wireless communication device is only stored in memory in response to a successful cryptographic challenge response authentication.

The invention relates to the technical field of preventive medicine, and discloses a mouse-borne disease intelligent monitoring and early warning device based on multi-source data fusion, which comprises an environment sensor module, a mouse activity monitoring module, a pathogen detection module, a human case data acquisition module, a data fusion processing unit and an early warning and decision support unit, the data fusion processing unit is used for integrating and analyzing multi-source heterogeneous data from the above modules, and the early warning and decision support unit generates early warning information and prevention and control suggestions based on an analysis result. According to the method, the environment data, the mouse activity data, the pathogen detection data and the human case data are integrated, a multi-dimensional monitoring system is constructed, space-time correlation analysis is performed in combination with a deep learning algorithm, the prediction accuracy of disease outbreak is remarkably improved, the mouse recognition accuracy is high, and the pathogen detection specificity is excellent.

The method involves using a digital badge device worn on the chest of a user to visually convey personal attributes to others in line of sight. It begins by configuring the badge device to communicate wirelessly with the user's mobile device. Next, a personal attribute of the user is determined based on input from the user or device-captured data. This attribute could relate to the user's affiliation, emotional state, physical activity, and more. Finally, a pre-defined color and / or symbol code representing the determined attribute is displayed on the badge device's screen for others to see. This provides an easily interpretable visual cue to communicate specific personal attributes to different users. By employing this method, the badge wearer can convey information about themselves without the need for verbal communication. The method offers a convenient and efficient way to signal personal attributes in situations where direct interaction may be limited or impractical.

The invention relates to the technical field of medical health big data analysis, and discloses a disease trend analysis and early warning system and method based on big data, and the method comprises the steps: 1, obtaining multi-source medical data through a hospital information platform, carrying out the preprocessing of the data, building a correlation mapping relation, and forming a standardized data warehouse; 2, constructing a spatio-temporal data cube, and forming a three-dimensional relation graph of time, space and diseases; 3, establishing a disease analysis model according to the multiple influence factors, and performing dynamic analysis on the disease trend and the high-incidence diseases based on the disease analysis model; and 4, according to an analysis result, identifying a disease critical area, generating area early warning information and feeding back the area early warning information to a corresponding terminal. According to the method, the disease monitoring accuracy and real-time performance are effectively improved, the early warning capability is improved, resource configuration is fully integrated, and the disease response efficiency is improved.

The invention discloses a multi-agent real-world clinical curative effect evaluation and accurate decision-making system, and relates to the technical field of medical data processing. According to the invention, a research demand analysis agent generates a research scheme after understanding the input content of a user and transmits the research scheme to a data management agent, a statistical analysis modeling agent, a result report generation agent, a data security agent and the data management agent privacy and encrypt data uploaded by the user; meanwhile, additional feature construction is carried out according to the research requirement of a user to form brand new analysis data used by a subsequent statistical analysis modeling agent, and after the statistical analysis modeling agent obtains the data, an analysis result is obtained by calling external software and is transmitted to a result report generation agent; and generating a clinical evaluation report together with the research scheme transmitted by the research demand analysis agent. According to the invention, full-process automatic, specialized and safe research support is realized.