580 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 discloses an infectious disease multi-point triggering intelligent early warning method based on multi-source data fusion, and relates to the technical field of public health, and the method comprises the following steps: 1, collecting and integrating multi-source data, building data interfaces with all levels of medical institutions and grassroots public health units, collecting diagnosis and treatment data and abnormal health condition information, and storing the diagnosis and treatment data and abnormal health condition information; accessing online platform data, screening and removing invalid data, and constructing a regional diagnosis and treatment database to provide a comprehensive data basis for subsequent analysis; according to the invention, by integrating multi-source data, the accuracy and real-time performance of the infectious disease early warning system are significantly improved; according to the method, symptom description is preprocessed and vectorized by using a natural language processing technology, a similar mode between symptoms and examination data is deeply mined in combination with standardized examination data, so that the characteristics of infectious diseases are accurately extracted, a potential characteristic set of the infectious diseases is identified through clustering analysis and an association rule mining algorithm, and a characteristic knowledge base is constructed.

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 relates to the technical field of health management, and provides a health management system and method.According to the health management system, in the prevention and control of infectious diseases, real-time data drives model and strategy updating, for example, high-risk area disinfection and personnel protection are rapidly enhanced and propagation is restrained when an epidemic situation suddenly occurs; with ease of an epidemic situation and precise reduction of prevention and control intensity, through continuous data monitoring and analysis, the spreading change trend of infectious diseases in a hospital can be caught sensitively, prevention and control measures can be enhanced rapidly, high-frequency disinfection is achieved, personnel protection is upgraded, and resources are allocated emergently in the rapid spreading stage of the early stage of the epidemic situation, so that epidemic situation spreading is effectively restrained; and as the epidemic situation enters a stable or slowed-down stage, management and control can be accurately and moderately relaxed, resources are reasonably allocated, excessive investment is avoided, the dynamic adjustment mechanism always ensures that the management strategy is closely matched with the actual epidemic situation, and the flexibility and accuracy of a hospital for coping with infectious diseases are greatly 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 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 embodiment of the invention discloses a new infectious disease monitoring and early warning method and device based on multi-point triggering. The method comprises the following steps: acquiring clinical data of a patient in HIS, LIS, PACS, operation and electronic medical records from a medical institution; setting a screening strategy of new infectious diseases; identifying suspected cases according to the clinical data of the patient; identifying abnormal diagnosis and treatment behaviors of the patient from clinical data of the patient based on big data analysis; determining a suspected case level according to the suspected case and the abnormal diagnosis and treatment behavior; obtaining relevant survey data of suspected cases; judging whether the suspected cases have aggregated cases or not according to the related survey data of the suspected cases; and if the suspected cases have aggregated cases, determining an infectious disease transmission route, and performing case management. By implementing the method provided by the embodiment of the invention, real-time monitoring and early warning of infectious disease epidemic situations and other public health events can be realized, and the processing efficiency and accuracy of new infectious diseases are 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 infectious disease prevention and control, in particular to an infectious disease epidemic situation space-time aggregation analysis method and system, and the method comprises the steps: constructing a case growth trajectory propagation model and an epidemic situation propagation and diffusion model through a data warehouse; predicting an epidemic situation diffusion path and a time-space coverage change graph by using the case growth trajectory propagation model and the epidemic situation propagation and diffusion model; and fusing the predicted epidemic situation diffusion path and the time-space coverage change map with the spatial data of the key public place, and calculating to obtain an epidemic situation influence index of the key public place. By constructing the case growth trajectory propagation model and the epidemic situation propagation and diffusion model, the diffusion path and the space-time coverage change of the epidemic situation can be predicted more accurately, a more scientific basis is provided for prevention and control work, and according to the predicted epidemic situation diffusion condition and the key public occasion epidemic situation influence index, the prediction of the epidemic situation is realized. Medical resources, manpower, materials and the like can be configured more reasonably, and the prevention and control efficiency is improved.

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.

Provided herein are elastin-like polypeptides, endosomal escape peptides, and compounds comprising an elastin-like polypeptide chemically or recombinantly conjugated to one or more endosomal escape peptides. Also provided herein are compositions comprising a compound provided herein and an agent, methods of delivering an agent, and methods of treating or preventing a disease (e.g., a cardiovascular disease, a lung or respiratory disease, a musculoskeletal disease, a hematological disease, an immune disorder, an infectious disease, a genetic disease, cancer, a neurological disease, a psychiatric disorder, a metabolic disorder, or an inflammatory disease).

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.

A system includes a memory that stores instructions; and a processor that executes the instructions. When executed by the processor, the instructions cause the system to: obtain real-time data of potential routes for transporting a patient with an infectious disease within a hospital; apply a prediction model to the real-time data to minimize the potential for spreading the infectious disease while transporting the patient; and suggest a route among the potential routes with a minimized potential for spreading the infectious disease.

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 discloses an infectious disease prediction and transmission link speculation method and system based on a random forest, and belongs to the technical field of infectious disease transmission risk prediction, and the specific steps are as follows: data acquisition and preprocessing: collecting case information from official websites and social media platforms of a plurality of provincial and urban health departments throughout the country, cleaning and preprocessing the data set; predicting the illness time of the patient: constructing and training a model by using a random forest algorithm, and accurately predicting the infection time of the patient; evaluating an algorithm prediction result, including evaluating model performance through classification prediction accuracy and correction accuracy; the disease transmission relation speculation comprises the steps of constructing a dynamic transmission model based on the predicted infection time and generating an infection time sequence diagram, the transmission path of diseases in urban population is revealed, and a scientific basis is provided for infectious disease control and public health decision making.

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.

A method for determining a risk for an infectious disease for a patient using a cognitive intelligence platform is disclosed. The method includes receiving, from an authoritative source, factors indicative of a person being infected by the infectious disease, wherein the factors include medical information pertaining to the infectious disease. The method also includes determining whether any of the factors are present in a patient graph of the patient, wherein the patient graph includes medical information pertaining to the patient and the determining is performed by matching the medical information pertaining to the infectious disease to the medical information pertaining to the patient. The method also includes, responsive to determining the factors are present in the patient graph of the patient, determining the risk for the infectious disease for the patient, and performing a preventative action based on the risk for the infectious disease.

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.