444 results about "Infectious illness" patented technology

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 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 discloses a rapid sequencing and traceability analysis system for input infectious diseases, which relates to the field of customs quarantine and comprises a sample preprocessing unit, a high-throughput sequencing unit, a data processing and quality control unit, a rapid comparison and annotation unit and a traceability analysis unit. According to the input infectious disease rapid sequencing and traceability analysis system, a complete link is formed from sample preprocessing, library construction, data cleaning, pathogen recognition and transmission map construction, module splitting and manual intervention are avoided, and tasks can be automatically completed in large-scale and emergency scenes.

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 biomedicine, in particular to a primer probe combination for broad-spectrum screening of common respiratory pathogens and application of the primer probe combination. Through screening and optimization, the respiratory pathogen nucleic acid detection primer probe combination for broad-spectrum screening of common acute respiratory infection and community-acquired pneumonia is obtained; the primer probe combination covers 18 respiratory tract pathogens, has the advantages of high accuracy, good specificity, strong anti-interference capability, good inclusiveness, high sensitivity, hyperjoint detection and the like, can realize parallel detection of 20 targets in cooperation with totally-closed single-tube PCR amplification, greatly improves early diagnosis of respiratory tract diseases with complex pathogen structures, and has wide application prospects. The method has very important significance on prevention, control and treatment of infectious diseases.

The invention discloses an infectious disease intelligent prediction and decision support system and method based on multi-source data, and relates to the technical field of infectious disease intelligent prediction, and the system comprises a prediction module and a decision module. Predicting the outbreak time period, the propagation scale and the propagation path of the target infectious disease; and the decision module is used for making a decision according to the predicted outbreak time period, the propagation scale and the propagation path of the target infectious disease. According to the method, the limitation of a traditional single data source is broken through by multi-source data integration, the multi-dimensional characteristics of infectious disease transmission can be comprehensively captured, and the accuracy and reliability of prediction are remarkably improved. The prevention and control strategy can be dynamically adjusted according to the real-time change of the epidemic situation, resource waste and excessive prevention and control are avoided, and the utilization efficiency of public health resources is improved.

The invention relates to the technical field of infectious disease monitoring and early warning, and discloses an intelligent influenza early warning system based on community multi-modal data fusion. The community-level multi-modal data fusion architecture is constructed, medical health data, environmental data, crowd activity data and network behavior data are integrated, spatial-temporal features are dynamically extracted and fused in combination with a deep learning model, and the problem of community monitoring blind areas caused by a single data source of an existing early warning system is solved; a long short-term memory network and convolutional neural network cascade architecture is utilized to capture a localized propagation rule, and the defect that a region-level prediction model cannot adapt to community heterogeneity is overcome; the risk score is generated in real time, the grading response instruction is triggered, a'monitoring-early warning-intervention 'closed loop is established, the early warning timeliness is remarkably improved, a basic-level response chain scission gap is filled, early prevention and control of flu outbreak are finally achieved, and public health resource consumption is reduced.

The invention relates to a drug relocation method, in particular to a drug relocation method using neighborhood information and a weighted fusion network. The invention aims to solve the problems that the existing drug relocation method is a shallow prediction model, the characteristics of drugs and disease nodes cannot be deeply known, and the relocation result of the existing drug relocation method is biased to common infectious diseases. The method comprises the following steps: extracting a data sample of the action relationship of drugs on diseases; in the drug-to-disease effect relation graph, learning interaction characteristics between drugs and disease nearest neighbors by using a weighted graph convolutional network; aggregating the learned characteristics of the drugs and the diseases; hidden high-order nonlinear features are obtained through a multi-layer fusion network; inputting the action relationship of the drugs in the training set on the diseases into the model, training the model, and obtaining a final prediction model after multiple iterations; and inputting the drug pairs in the test set into the prediction model to obtain a prediction result. The invention belongs to the technical field of drug relocation.

The invention relates to the technical field of medical care informatics, in particular to a respiratory infectious disease risk early warning method and system. The method comprises the following steps: acquiring a respiratory tract sample of a mutant virus infected person; the method comprises the following steps: extracting a mutant virus from a respiratory tract sample, carrying out genome sequencing on the mutant virus, positioning starting and ending positions of a spike protein coding gene in a genome by virtue of a biological information comparison method, extracting a spike protein coding sequence, and translating the spike protein coding sequence into an amino acid sequence; inputting the amino acid sequence into an immune escape prediction model to predict the immune escape score of the mutant virus; and performing risk assessment based on the immune escape score in combination with clinical monitoring information, and outputting a respiratory infectious disease risk early warning value. According to the invention, the risk of respiratory infectious diseases can be early warned better.

The invention relates to a medical data screening system and method based on AI auxiliary interrogation, the screening system at least comprises a client APP, an AI client and a cloud which are mutually connected, compared with the prior art, the system fully combines the advantages of AI, cloud storage, a user intelligent terminal and an offline diagnosis and treatment end, and realizes data acquisition, classification, fusion and interaction; the upstream of the AI client side can allow a patient to input basic data and image data, the downstream of the AI client side can call a cloud database, pathological data can be accurately screened through feature comparison, and pathological similarity suggestions are provided; in the high-incidence period of infectious diseases, the screening system or the screening method can be used for shunting hospitals, limited medical resources are fully allocated, and cross infection is avoided; by means of big data screening and online and offline diagnosis and treatment combination, the pathology screening accuracy is improved, accurate diagnosis and treatment suggestions can be given, and the medical diagnosis service experience is improved.

The invention relates to the technical field of epidemic disease monitoring, and discloses an infected population tracing method and system. The system comprehensively utilizes various data sources such as medical records, public health reports, social media dynamic and geographic position data and the like to realize real-time monitoring and accurate prediction of infectious disease epidemic situations. The system identifies high-risk areas and key propagation nodes through an advanced machine learning algorithm and a mathematical model, and provides accurate prevention and control measure suggestions. The method improves the traceability efficiency, optimizes the resource allocation, effectively supports public health decisions, and provides technical support for epidemic prevention and control. The system can guide a targeted intervention strategy by identifying key nodes and super propagators in a propagation chain, so that the overall propagation risk is reduced.

The application discloses a disease transmission link prediction method and system based on a dynamic hypergraph neural network, aiming to comprehensively depict virus transmission paths and risk levels from multiple dimensions. First, data is collected from a gene database, mobile device logs and clinical texts, virus variation characteristics, population contact relationships and symptom semantic information are extracted respectively, and a dynamic hypergraph that integrates virus strain nodes, individual nodes and regional nodes is constructed. The hyperedge is defined according to molecular similarity, space-time contact and semantic overlap, and the weight is dynamically adjusted according to the transmission intensity of the infectious disease. A multi-modal hypergraph neural network with variation perception is introduced to jointly model and adaptively fuse multiple types of hyperedges and multi-scale features, output node propagation embedding vectors and predict potential transmission chains, perform prevention and control responses, and realize real-time deployment and feedback loop in an edge-cloud collaborative manner, ensuring efficient and accurate infectious disease transmission modeling and intervention control while ensuring data privacy.

[00121] The present invention relates to a label-free, high throughput method for determining an infectious virus titer in an aqueous sample comprising infectious virus, wherein the method comprises detecting the kinetics of morphological cell alteration, such as cell rounding, following viral infection, determining a parameter defining the curve of the morphological cell alteration as a function of time post infection and determining the infectious virus titer based on a standard curve of the infectious virus at a known infectious titer.

The invention discloses an intelligent patient safety risk reminding method and system based on PACS and RIS, and the method comprises the steps: collecting the multi-source medical data of HIS, EMR, LIS and the like in real time through an HL7 and DICOM multi-protocol fusion technology, constructing a dynamic evaluation model covering the core risks of allergy, metal implants, infectious diseases and the like, and carrying out the native integration of a hierarchical reminding mechanism at PACS and RIS workstations. The system adopts a Bayesian network model to realize dynamic adjustment of risk levels, a differentiated reminding interface is embedded through a webpage style technology, and risk reminding of each level forcibly requires a doctor to confirm and process. And the closed-loop feedback module records the medical care operation and returns the medical care operation to a related system, and continuously optimizes the risk assessment model. The problems that an existing system is isolated in data, single in reminding function and incomplete in risk coverage are solved, the clinical risk management level is remarkably improved, and the medical accident rate is reduced.

The invention belongs to the field of infectious disease community transmission multi-subject simulation, and relates to an infectious disease community transmission multi-subject simulation method and system containing asymptomatic infectors. The method comprises the following steps: establishing a virtual community group which comprises a plurality of communities and a plurality of destinations; distinguishing subjects in the virtual community group by using the health state and the space state, wherein the subjects comprise a susceptible subject S, an exposed subject E, an asymptomatic infection subject A, an infection subject I and a rehabilitation subject R; a motion sequence of all subjects in the virtual community group is established, the infectious disease transmission process is simulated according to the motion sequence, and the motion sequence comprises movement, infection and state updating, infection detection and isolation and community sealing control; and obtaining a simulation result, and evaluating the effectiveness of the infectious disease prevention and control policy. According to the method, rapid evaluation of different prevention and control strategies on infectious disease transmission conditions, social and economic influences caused by infectious diseases and the like can be realized, and beneficial reference is provided for a macroscopic regulation and control policy for restraining infectious diseases.

The invention discloses a mask prevention and control system based on a respiratory infectious disease virus field, and belongs to the technical field of infectious disease prevention and control. The mask prevention and control system based on the respiratory infectious disease virus field comprises the following modules: a data acquisition module, a transmission module, a data analysis and prediction module, a user interface module, a mask prevention and control optimization module and a public health data platform. According to the mask prevention and control system based on the respiratory infectious disease virus field, the virus concentration, environmental conditions and user health data are monitored in real time through the high-density sensor network and the intelligent chip, the virus propagation trend and a high-risk area are accurately predicted based on a machine learning algorithm, early warning information is issued in time, and the safety of a user is improved. According to the health state and the surrounding environment of the user, the protection mode of the mask is dynamically adjusted, personalized protection is provided, and targeted protection suggestions are generated in combination with the health data and the environment data of the user to help the user to reduce the infection risk.

The invention relates to the technical field of infectious disease risk prediction, in particular to an infectious disease risk prediction analysis method and system based on electronic cases. Comprising the following steps: S1, preprocessing a multi-modal electronic case, cleaning structured data, and performing structured conversion on unstructured data; S2, constructing dynamic risk characteristics, quantifying basic disease risks, evaluating a key symptom change rate, and calculating a dynamic risk comprehensive index; S3, predicting infectious disease risks, and evaluating dynamic relevance between a patient and a group infectious disease condition. S4, optimizing a risk judgment threshold value, calibrating the threshold value through a Kappa coefficient, iteratively optimizing a model and features in combination with clinical feedback, forming a standardized scheme, improving data quality through multi-modal data integration, achieving accurate risk assessment through two-dimensional layering, dynamically calibrating the threshold value, optimizing and enhancing model adaptability through clinical feedback, and improving the reliability of the model. A standardized scheme guarantees application consistency, and accuracy and clinical practicability of infectious disease risk prediction are effectively improved.

The invention discloses a disease transmission link prediction method and system based on a dynamic hypergraph neural network, and aims to comprehensively describe a virus transmission path and a risk level from multiple dimensions. The method comprises the following steps: firstly, collecting data from a gene database, a mobile equipment log and a clinical text, respectively extracting virus variation characteristics, a crowd contact relationship and symptom semantic information, and constructing a dynamic hypergraph fusing virus strain nodes, individual nodes and region nodes; the hyperedge is defined according to molecular similarity, space-time contact and semantic overlap, and the weight is dynamically adjusted along with the infectious disease transmission intensity. A multi-modal hypergraph neural network of variation perception is introduced, joint modeling and adaptive fusion are performed on multi-type hyperedge and multi-scale features, a node propagation embedded vector is output, a potential propagation chain is predicted, prevention and control response is executed, and real-time deployment and feedback closed loop are realized in an edge-cloud cooperation mode. And efficient and accurate infectious disease transmission modeling and intervention control are completed on the premise of ensuring data privacy.

The present invention discloses a method for optimizing infectious disease intervention measures based on reinforcement learning, comprising: adjusting parameters of an improved Covasim environment; training an intelligent agent: (1) the intelligent agent obtains state information s from the improved Covasim environment; t When the number of infected people exceeds the preset threshold, the intervention intensity value a is output according to the status information. t , according to a t Act on the environment and get the corresponding reward information r t , get the next state information s t+1 , the data group (s t , a t , r t , s t+1 ) is stored in the experience replay area; (2) when the data stored in the experience replay area reaches the preset data threshold, a Mini‑batch is randomly sampled to update the network parameters of the agent; (3) steps (1)‑(2) are repeated until the preset maximum training time is reached to obtain the trained agent; the trained agent outputs corresponding intervention measures according to the current epidemic status of the infectious disease.

The invention provides a post-war health guarantee and infectious disease prevention system, and relates to the technical field of epidemic disease monitoring. The system comprises a baseline calibration module, a node identification module, a migration modeling module, a path analysis module and a priority analysis module. According to the method, dynamic identification of infectious disease risk personnel is achieved by calculating the deviation degree of the individual physiological state, potential infected persons are found in the early stage, the missing report condition is reduced, and the epidemic situation diffusion path is simulated and predicted by identifying the social contact network of the risk personnel, calibrating propagation nodes and combining the war area road damage condition. The actual crowd migration condition in the war area environment is effectively reflected, road failure analysis and supply chain network optimization are combined, medical material demand levels are graded and classified, the response speed of medical resources in the war area is increased, the treatment sequence is dynamically adjusted through real-time physiological data monitoring in combination with the injury condition change trend, and the treatment efficiency of the medical resources in the war area is improved. And optimal utilization of medical resources is realized.

The invention provides a home health early warning method based on artificial intelligence and related equipment, and is applied to the technical field of medical data processing. The method comprises the following steps: preprocessing patient bracelet monitoring data, medical text data and environment data to generate a target physiological data sequence, medical text features, environment features and climate data features; processing the environment characteristics and the climate data characteristics to generate infectious disease monitoring characteristics; processing literatures in the knowledge base data based on a literature evidence intensity grading system to generate updated knowledge base data; processing the target physiological data sequence, the medical text features and the infectious disease monitoring features based on a target health early warning model to generate target health risk features; and processing the target health risk features based on the target health early warning model and the updated knowledge base data to generate patient health early warning information.

The invention relates to a disease information management method and system based on multi-source data, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: forming multi-source heterogeneous data through employing the obtained infectious disease data and chronic disease data, and carrying out the preprocessing with the corresponding data features as a reference, on one hand, risk prediction and clustering analysis are performed on infectious disease related data through a logistic regression model to obtain risk assessment information, and a propagation trend is predicted through the model; on the other hand, chronic disease data are predicted and evaluated through the model, a medical advice result is obtained, and finally disease control information management is conducted in combination with all the data. Therefore, a set of perfect disease prevention and control information management is realized, on one hand, more timely and accurate infectious disease detection and early warning are realized, and the efficiency and effect of major infectious disease prevention and control work are improved, and on the other hand, chronic disease management is perfected, and the problems of diagnosis and treatment errors and lagging of patient treatment consciousness caused by incomplete data are avoided.

The invention provides an isolation device, and relates to the technical field of infectious disease prevention and control isolation, the isolation device comprises an isolation monitoring base, the top end surface of the isolation monitoring base is provided with a treading groove, and the center of the bottom surface of the inner end of the treading groove is fixedly provided with a proximity sensing assembly; through fixed installation of a matching strip block and a door plate of a room door, when an isolated person opens the room door of the room for prevention and control isolation of infectious diseases, first, information is wirelessly transmitted to a monitoring center, and second, information is transmitted to the monitoring center; therefore, an isolation prevention and control worker in the monitoring center can know the current behavior of opening the door of the isolated person at the first time, and the problem that key behaviors such as approaching or opening the door cannot be found and processed in time due to the lack of behavior supervision on the isolated person when the person is isolated in a single room is solved. And the hidden danger of virus transmission caused by isolation failure exists.

The application discloses a set of molecular targets, primer compositions, methods and applications for identifying respiratory pathogens, the respiratory pathogens are novel coronavirus, influenza A virus and respiratory syncytial virus, and nucleotide sequences of the molecular targets are shown as SEQ ID NO:1-3. The application provides molecular targets, primers and probes for detecting three respiratory pathogens, and establishes a triple real-time RT-RAA detection technology, which can realize rapid identification and differentiation of three respiratory infectious disease pathogens with similar clinical symptoms within 30 minutes, reduces the operation threshold of the existing detection technology, realizes the target of rapidly, simply and accurately identifying and differentiating the three viruses, meets the needs of early clinical diagnosis of infection, provides technical support for precise medication, and has wide application value.

The invention discloses a generative agent-based Ebola virus social simulation system and a generative agent-based Ebola virus social simulation method, which are used for dynamically simulating the interactive influence of social behaviors and virus propagation by integrating an SEIR infectious disease model and a generative agent framework. The system comprises a social factor interaction module, an experimental scene configuration module and a dynamic analysis module, supports high-fidelity simulation of scenes such as management scheme deviation and resource allocation optimization, and can quantitatively evaluate social chain reactions (for example, the infection rate is reduced by 30%-40%) of prevention and control measures. According to the method, the defect that a traditional model ignores social complexity is overcome, a prospective experimental platform is provided for policy making of social sensitive infectious diseases such as Ebola, and the method is adapted and expanded to other infectious disease research.

The invention belongs to the technical field of biological detection, and particularly relates to a primer probe pair, a microfluidic chip and a kit for detecting pathogens of vector infectious diseases based on a TaqMan microfluidic chip technology as well as a preparation method and application of the primer probe pair. Based on a TaqMan microfluidic chip technology, the invention provides a detection method for simultaneously detecting 28 kinds of pathogens of the disease-borne infectious diseases, and the related pathogens of the disease-borne infectious diseases comprise viruses, bacteria and parasites, and are wide in range and large in difference. The detection method provided by the invention has a low detection limit (5-20 copies / [mu] L); when the method is applied to an actual sample, the sensitivity, specificity, accuracy and precision are high, and compared with a gold standard method, the consistency is good; and compared with a traditional qPCR method, the method has the advantages that the detection time efficiency and the manpower and material consumption are greatly reduced, and the method is particularly suitable for disease monitoring and emergency disposal scenes with undefined pathogen spectrum, need of rapid and comprehensive troubleshooting and the like, and has a good application prospect.

The present disclosure relates to a vaccine comprising at least one peptide antigen conjugate having the formula selected from PEG-[E1]-A-[E2]-[U]-H and H-[U]-[E1]-A-[E2]-PEG, wherein E1 is an N terminal extension, E2 is a C terminal extension, A is peptide antigen, H is hydrobhobic block, wherein one or more drug molecules (D) are optionally attached to each H directly or via a suitable linker X1; U is a linker, [ ] denotes the group is optional and - denotes that the two adjacent groups are directly attached to one another by a covalent bond or indirectly to one another via a suitable linker X. The vaccine is useful in treating or preventing a cancer, an autoimmune disease, an allergy, or an infectious disease.

The present invention relates to an infectious disease transmission zone identification method, and an apparatus using same. The method is an infectious disease transmission zone identification method implemented by a processor, comprising the steps of: receiving at least one stay area for an infectious disease patient; receiving, from a spatial atmospheric pressure measurement sensor, measured atmospheric pressure of the stay area and a first area connected to the stay area so that air flows therebetween; determining that the stay area has either negative pressure or positive pressure on the basis of the atmospheric pressure of the stay area and the first area; and determining a transmission zone for the infectious disease according to whether the stay area has negative pressure or positive pressure.

In certain aspects, the invention provides methods and compositions for heritable immunization and their use in infectious disease control. The invention also provides methods of preparing organisms and model organisms that can heritably transfer immunization to offspring.

The invention relates to application of a mir-331-3p inhibitor in preparation of a medicine for inhibiting malaria mosquito transmission, and belongs to the technical field of biological medicine. Aiming at the technical problems that the existing anti-malarial drug mainly acts on the later stage of plasmodium development and is lack of an effective propagation blocker, the invention provides the miR-331-3p inhibitor which is used for blocking the formation of plasmodium oocysts by inhibiting the interaction between microRNA and a mosquito target gene spetzle. Experiments show that the inhibitor can reduce the mosquito infection rate by about 50%, and has the characteristics of high targeting property, simplicity in operation and the like. The invention provides a novel drug development target for blocking malaria transmission, can effectively supplement the prevention and control blind area of the existing anti-malarial drug, and has important application value in the field of mosquito-borne infectious disease control.