129results about "Epidemiological alert systems" patented technology

Techniques are provided for utilizing Wi-Fi positioning based contact tracing. An example method for reporting signal measurements to a contact tracing network includes activating a contact tracing application on a user equipment based on a proximity to the contact tracing network, receiving one or more measurement signals from a station in the contact tracing network, and reporting a signal measurement and station identification to a network entity.

An apparatus comprises a processing device implementing a body area network (BAN) controller for a BAN associated with a subject. The processing device is configured to obtain sensor data from a set of sensors of one or more sensing devices in the BAN, and to analyze the obtained sensor data to identify at least a first sensor as a source of missing sensor data. The processing device is also configured to process, utilizing at least one machine learning model in a machine learning system implemented by the processor and the memory of the processing device, at least portions of the obtained sensor data to reconstruct the missing sensor data of the first sensor. The processing device is further configured to determine, based at least in part on the reconstructed sensor data of the first sensor, one or more physiologic monitoring parameters associated with the subject.

An example method includes a computer-implemented method. The method involves receiving, by a computing device, training data comprising a plurality of audio clips extracted from a video dataset, wherein the audio clips comprise non-semantic acoustic data. The method also involves training, based on the training data and in a self-supervised manner, an audio encoder to predict a feature representation for an input audio clip, wherein the feature representation encodes one or more features based on non-semantic acoustic data in the input audio clip, and wherein the feature representation is associated with a plurality of healthcare related tasks. The method further involves providing, by the computing device, the trained audio encoder to generate feature representations for audio clips.

The application discloses an epidemic trend prediction system based on machine learning, and relates to the fields of epidemic prevention and control and machine learning technology.The system comprises a multi-source data acquisition module, a heterogeneous data preprocessing module, a feature engineering module, a hybrid machine learning prediction module, a dynamic correction module, a trend visualization module, a risk early warning module, a data storage module, a model iteration optimization module and a man-machine interaction module; by constructing a special data preprocessing algorithm, a hybrid prediction model and a dynamic correction formula, high-precision prediction of the incidence, spread range, peak occurrence time and duration cycle of an epidemic is realized, and the system has real-time iteration optimization capability, can adapt to the prediction needs of different regions and different types of epidemics, and provides accurate and reliable technical support for epidemic prevention and control decisions.The prediction accuracy of the application is improved by more than 35% compared with the prior art, and the generalization capability is improved by more than 40%.

This invention relates to a method for inferring interpersonal contact patterns within urban areas, comprising: Step S1, designing a small-sample questionnaire survey covering demographic and travel activity characteristics, and extracting individual demographic and travel activity characteristics from the survey; Step S2, constructing a machine learning inference model for the number of interpersonal contacts of individuals based on the individual demographic and travel activity characteristics extracted from the small-sample questionnaire survey; Step S3, under the premise of privacy protection, extracting representative individual samples and their individual characteristics from mobile phone location big data for each region, inputting them into the trained inference model, inferring large-scale individual interpersonal contact patterns with regional representativeness, and finally aggregating the individual inference results to the regional level to obtain the interpersonal contact patterns of the entire region. This invention also relates to a system for inferring interpersonal contact patterns within urban areas. This invention enables fine-grained inference from individual characteristics to contact patterns.

The application provides an integrated community monitoring and early warning method and system for key infectious diseases, collects multi-source data such as suspected case geographic position, contact degree, clinical symptoms, environment and abnormal death; then calculates spatial aggregation, case contact, symptoms, environment and abnormal death components through spatial centroid offset and contact network; linear superposition is carried out to obtain a comprehensive risk composite value and determine a risk level; finally, the overall spatial aggregation is calculated based on the risk quantization value of each community to generate a regional level early warning signal. The application constructs an integrated community monitoring and early warning system of 'human-environment-animal', improves the early identification accuracy and efficiency of infectious diseases, and realizes early judgment, accurate early warning and rapid response of key infectious disease risks.

The application discloses a kind of SFTS dynamic monitoring and early warning method and system based on multi-modal data, and relates to the technical field of intelligent monitoring and early warning.The application standardizes processing and feature enhancement to environmental monitoring, clinical symptoms, virus genes and crowd moving track, establishes enhanced data set consistent in time and space;Then a multi-scale correlation graph is constructed using a graph neural network to identify causal paths between environmental factors, viral mutations and case reports;In combination with a time series convolution network and an attention mechanism, a prediction model is constructed to generate a risk evolution vector with a confidence interval;Finally, by analyzing the risk contribution degree using SHAP values, an interpretable early warning signal is generated and precise push is achieved, effectively solving the problems of multi-source data integration difficulty, slow dynamic change capture, poor early warning timeliness and other issues in traditional monitoring, and achieving a breakthrough from passive response to active and precise early warning of SFTS epidemic.

The application provides an influenza risk forecast method, device and equipment based on high-impact weather classification, and relates to the technical field of influenza risk forecast. The method comprises the following steps: acquiring weather forecast data of a future period, and determining future high-impact weather process information based on the weather forecast data of the future period; acquiring current high-impact weather process information and previous high-impact weather process information; determining a high-impact weather complexity intensity index according to the previous high-impact weather process information, the current high-impact weather process information and the future high-impact weather process information; and determining an influenza risk forecast level according to the high-impact weather complexity intensity index and a process type of the future high-impact weather process. The application constructs a forecast model based on the evolution law of the high-impact weather process, breaks through the limitation of meteorological element data, accurately associates the internal relationship between the weather change trend and the occurrence and spread of influenza, and forecasts more accurately.

An artificial intelligence-based system and method for scalable screening of individuals for respiratory infection, such as COVID-19. The system is trained to distinguish distinct latent features of cough sounds produced by a COVID-19 infected person from cough sounds produced by patients suffering from any other respiratory infection or involuntary cough sounds produced by a healthy person. Cough sound samples from individuals can be remotely collected and evaluated by the system for likelihood of the COVID-19 infection. Additionally, images of affected body parts, biomarkers, metadata, and other respiratory sound samples can also be used for screening.

Provided is a complementary infectious disease monitoring method based on the linkage between a sample monitoring system and a citizen-participatory monitoring system. The infectious disease monitoring method according to an embodiment of the present invention: calculates an infectious disease trend by using infectious disease diagnosis data according to a sample monitoring system; predicts an infectious disease trend by using infectious disease diagnosis data according to a citizen-participatory monitoring system; and calculates a final infectious disease trend by integrating the two calculated / predicted infectious disease trends. Accordingly, by linking the sample monitoring system and the citizen-participating monitoring system to identify the infectious disease trend through mutual complementation, it is possible to quickly and reliably identify the infectious disease trend by complementing each problem of the two monitoring systems.

This invention discloses a method and system for analyzing the health status behavior of poultry flocks from images, relating to the fields of computer vision and smart farming technology. The method includes: firstly, acquiring monitoring images and extracting the poultry's location coordinates; secondly, dividing the poultry into an active first target set and an inactive second target set based on displacement features; thirdly, constructing a discrete topological network using the first target set to generate candidate spatial gap regions and identifying edge targets; fourthly, generating a biological rejection intensity index based on the head orientation vector of the edge targets, and determining stable rejection regions based on temporal stability; and fifthly, outputting a health anomaly warning when the center of the stable rejection region and the coordinates of the second target set satisfy spatial matching conditions. This invention effectively solves the problem of distinguishing between physiological quiescence and pathological stagnation in high-density farming by analyzing the group's biological rejection behavior to pinpoint abnormal individuals, thus improving monitoring accuracy.

The application provides a method for evaluating the risk of tuberculosis under the combined exposure of extreme weather and air pollution, comprising the steps of: obtaining meteorological data, air pollution data and tuberculosis incidence data of a target area within a target period; identifying extreme weather events based on the meteorological data, identifying air pollution events based on the air pollution data, and identifying combined exposure events based on the meteorological data and the air pollution data, wherein the combined exposure events are combinations of the extreme weather events and the air pollution events that occur within a time interval less than a preset threshold and are spatially co-located; based on the spatial co-location, constructing a regression model with the number of cases in the tuberculosis incidence data as the dependent variable and whether the combined exposure event occurs as the binary explanatory variable, and evaluating the influence of the combined exposure event on the risk of tuberculosis. The application realizes high-precision quantitative evaluation of the risk of tuberculosis under the combined exposure of extreme weather and air pollution, and provides a scientific basis for early warning and precise prevention and control.

Methods and devices for retrospectively assessing continuous monitoring reference pattern data to determine a risk of a patient glucose level measurement taken in at least one data segment being outside a predetermined range. The methods and devices can include executing an algorithm to compare risk scores derived from reference pattern data in a currently collected data segment with risk scores of previously stored reference pattern data of previously collected data segments for a patient for assessing risk.

The application discloses a pig disease diagnosis and treatment teaching training method, device and equipment based on AR, which comprises the following steps: loading a three-dimensional sick pig model corresponding to the current teaching scene through an AR device, and presenting the current symptom performance including visual elements and sound at the corresponding part of the three-dimensional sick pig model; receiving the diagnosis and treatment decision input by a user according to the current symptom performance; analyzing the diagnosis and treatment decision based on a preset pharmacology-pathology response model, updating the symptom state of the three-dimensional sick pig model according to the analysis result, and presenting the updated symptom state in real time as corresponding AR visual effects; when the user confirms the completion of the diagnosis and treatment operation based on the updated symptom state, comprehensively scoring the drug selection accuracy, dosage rationality, processing timeliness and diagnosis and treatment process integrity of the user in the diagnosis and treatment process, and generating a teaching evaluation report. The interactive training scheme can simulate a complete diagnosis and treatment process, support autonomous decision-making and dynamically feedback the disease outcome, and improves the teaching interactivity.

The application discloses a kind of multi-stage progressive exclusion and verification robust sparse sample mixed detection method and system, belong to popular disease early infection screening technical field.The method includes: according to the mixed detection parameter of screening scene setting;Subsequently enter multi-stage detection process, according to the current sample quantity to be detected in each stage, dynamically adjust mixed pool scale and detection times, and according to the detection result, sample is continuously classified and screened, and gradually exclude definite negative sample, while directly lock part of definite positive sample by analyzing the remaining sample in positive pool;Finally, the remaining sample is detected separately, and the identification of all positive samples is completed.The present application effectively copes with the uncertainty in actual detection through the robust multi-stage progressive design and dynamic adjustment mechanism, while ensuring the detection efficiency, significantly improves the detection rate of low viral load samples, has the characteristics of strong implementability and wide adaptability, and is suitable for large-scale sparse sample detection scene.

To provide a prediction management system and a prediction management program capable of predicting an increase / decrease trend of the number of subjects infected with an infectious disease.SOLUTION: A prediction management system includes: a pathogen data storage device 53 which stores a collection date of a sample of sewage water and pathogen data related to a pathogen of an infectious disease contained in the sample obtained through analysis of the sample in association with each other; positive subject number data storage devices 63, 74 which store the date and time and positive subject number data related to the positive subject number on the date and time in association with each other; and a prediction management device 21 which acquires the pathogen data on the collection date from the pathogen data storage device 53 and the positive subject number data related to the positive subject number in a predetermined period prior to the collection date from the positive subject number data storage devices 63, 73, predicts an increase / decrease trend after the collection date on the basis of a ratio of the acquired pathogen data to the acquired positive subject number data, and transmits information about the predicted increase / decrease trend.SELECTED DRAWING: Figure 2

The application provides a multi-disease correlation propagation situation prediction method and system based on a graph network, relates to the technical field of disease propagation situation prediction, and comprises the following steps: collecting multi-source monitoring data and constructing a ternary heterogeneous graph; a graph neural network prediction model is constructed; the graph neural network prediction model is trained by using training data, so that a trained graph neural network prediction model is obtained; the ternary heterogeneous graph to be predicted is input into the trained graph neural network prediction model, so that a situation prediction result of each disease node at a future time step is obtained. The application realizes accurate prediction of the multi-disease collaborative propagation effect by constructing a disease-population-environment ternary heterogeneous graph network and adopting dynamic weight modulation based on a popular situation imbalance for the comorbidity edge.

The present application relates to the technical field of medical artificial intelligence, and more particularly to a respiratory department early warning method based on multi-source data. The content includes: collecting multi-source data of patients with respiratory diseases and preprocessing to obtain single-source data feature vectors, and constructing a single-source data matrix; after weighted processing of the single-source data matrix, splicing is performed to obtain a fusion feature matrix; based on the fusion feature matrix, a trend capturing branch and a mutation detection branch are constructed to obtain a trend feature vector and a residual mutation value vector, and the consistency of the trend and the mutation is quantified to construct a consistency score vector; based on the consistency score vector, the trend feature vector and the residual mutation value vector, a joint feature vector is constructed, and a respiratory disease early warning level is output. The problems of the traditional respiratory department early warning method, such as lack of effective modeling of data timeliness and disease condition correlation, insufficient memory and response ability to high-risk time points, and lack of trend and mutation consistency judgment mechanism, are solved.

The application provides an intelligent analysis platform for risk assessment of wild bird flu virus recombination, relates to the technical field of influenza virus monitoring and risk assessment, and comprises a multi-source monitoring data acquisition module, a data preprocessing module, a virus sequence intelligent analysis module, a recombination risk assessment module, a spatial situation analysis and visualization module and a report generation and early warning module connected in sequence through an API interface. The intelligent analysis platform for risk assessment of wild bird flu virus recombination can realize multi-source monitoring data acquisition, virus sequence intelligent analysis, recombination risk quantitative assessment and spatial visualization early warning, and provides whole-process technical support for influenza virus recombination prevention and control.

This application discloses a method and apparatus for early warning of disease infection risk, relating to the field of educational technology. The method includes: acquiring audio and video data of a target classroom; performing voiceprint recognition on target audio in the audio and video data to identify students at risk of disease infection; locating the sound source of the target audio based on student seating information in the target classroom to obtain first seating information; and performing a consistency check between the first seating information and second seating information corresponding to the at-risk students; if the consistency condition is met, identifying infection-risk actions of the at-risk students based on the target video corresponding to the target audio; and, in response to identifying infection-risk actions, determining disease infection information and issuing a disease transmission warning in the target classroom. This application improves the accuracy of disease transmission monitoring through seating verification and action recognition, and enhances the effectiveness of classroom disease infection risk early warning by determining disease infection information and issuing warnings.

A system and method including an image capture component configured to capture infrared images of a scene, and a logic device configured to identify a target in the images, acquire temperature data associated with the target based on the images, evaluate the temperature data and determine a corresponding temperature classification, and process the identified target according to the temperature classification. The logic device identifies a person and tracks the person over a subset of the images, identifies a measured location of the target in the subset of images based on a neural network identified feature point of the target, and measures a temperature at the location using corresponding values from one or more captured thermal images. The logic device is further configured to calculate a core body temperature of the target using the temperature data to determine whether the target is feverish, and calibrate using one or more blackbodies.

The application provides an evaluation method for infectious disease transmission trend deduction data based on large model assistance, which can be applied to the field of computer technology, comprising: based on the text semantic feature vectors obtained by analyzing and encoding the multi-source event text of a target area, extracting the event occurrence time, event type and event intensity of each event; using a time decay kernel function matched with the event type of the event, generating an event influence coefficient sequence based on the event intensity and event onset time of the event, and the event influence coefficient value range; superimposing and fusing the multiple event influence coefficient sequences and performing truncation processing to obtain a comprehensive event influence coefficient sequence, comparing the dynamics consistency of the infectious disease transmission deduction data sequence and the comprehensive event influence coefficient sequence at the characteristic key points, and generating a causal logic evaluation result; based on the causal logic evaluation result, the smoothness evaluation result and the physical compliance evaluation result, a rationality evaluation result is generated.

This invention belongs to the field of intelligent sensor technology and discloses a method, system, and dialysis catheter for monitoring multimodal blood infections. The method includes: collecting and analyzing pressure and temperature data to obtain pressure time-frequency spectrum and temperature correction matrix; extracting features from the pressure time-frequency spectrum and temperature correction matrix to obtain analytical features, including pressure features, temperature features, and cross features; acquiring synthetic data and clinical data, and combining transfer learning to build a domain adversarial neural network to analyze the real-time analytical features and obtain predicted values ​​of patient infection risk probability and inflammatory factor concentration range; obtaining patient baseline data, and dynamically adjusting the pressure fluctuation tolerance bandwidth and temperature rise slope threshold based on reinforcement learning according to the patient infection risk probability and inflammatory factor concentration range predicted values ​​to generate personalized alarm commands. This invention can provide early warning of infection risk, reduce false alarm rate and false negative rate, and provide an intelligent and personalized solution for early intervention of blood infections.

Provided herein are systems, methods, and apparatuses for providing a real-time global bio-surveillance and response solution. The systems and apparatuses may include one or more sentinel subsystems and one or more surge subsystems that each include a real-time, cloud-based, distributed, interconnected set of hardware, software, and / or firmware for comprehensive surveillance, complete detection, and immediate response various to biological, chemical, and / or bio-chemical anomalies, for example, a new or emerging pathogen or other health condition.

The present invention relates to systems and methods for detecting occupant illness symptoms. Systems and methods for detecting occupant illness symptoms are disclosed herein. In embodiments, a memory is configured to maintain a visualization application and data from one or more sources, such as an audio source, an image source, and / or a radar source. A processor is in communication with the memory and a user interface. The processor is programmed to receive data from the one or more sources, execute a human detection model based on the received data, execute an activity recognition model that identifies illness symptoms based on the data from the one or more sources, determine a location of the identified symptoms, and execute the visualization application to display information in the user interface. The visualization application can display a background image with an overlay image that includes an indicator for each location of the identified illness symptoms. Additionally, data from the audio source, the image source, and / or the radar source can be fused.

The application relates to the technical field of big data monitoring, and particularly discloses a vector biological monitoring information system, which comprises a routine monitoring module, a special investigation module and a quality control module; the routine monitoring module is used for performing the vector biological ecology density, drug resistance and pathogenicity monitoring tasks periodically; the special investigation module comprises a background investigation submodule, a pathogen carrying investigation submodule and a water level fluctuation zone investigation submodule; the background investigation submodule is used for constructing a vector biological species background list database; the pathogen carrying investigation submodule is used for monitoring and statistically analyzing the situation of vector biological pathogen carrying; and the water level fluctuation zone investigation submodule is used for managing the water level fluctuation zone regions of reservoirs and rivers in different vector density dynamic data; the quality control module is used for monitoring the completeness, timeliness and standardization of the data reporting, and solves the technical problem that the existing vector biological monitoring information system lacks flexible and in-depth special investigation capability, thereby causing insufficient assessment of complex and special public health risk scenes.

The application relates to a high-precision blood glucose monitoring method and system based on multi-modal data fusion, which comprises the following steps: collecting electrochemical, bioimpedance, physiological, environmental and behavioral multi-source signals synchronously and adding time stamps to form a multi-modal data set. The electrochemical signal is used as a time sequence reference to align and preprocess, and a standardized data matrix is constructed. Cross features of the environment and behavior are extracted and fused to form an initial feature set. A fusion feature vector is constructed, and real-time temperature and personal metabolic history data are combined for dynamic compensation to generate calibration features. The calibration features are input into a pre-trained CNN-LSTM model to output blood glucose prediction values and confidence scores. When the prediction value exceeds the safety range or the confidence is lower than the threshold, a safety control instruction is triggered. Finally, the system adaptively updates the model parameters according to the instruction execution result and user feedback, thereby improving the overall accuracy and reliability of blood glucose monitoring.

The application discloses a dengue fever prediction and early warning system based on heterogeneous data fusion and a graph neural network, and relates to the technical field of infectious disease prediction and early warning. The data acquisition module is used for acquiring case data, meteorological data, remote sensing vegetation index data, population flow data and social media sentiment data. The dengue fever prediction and early warning system based on heterogeneous data fusion and the graph neural network effectively improves the accuracy and precision of dengue fever epidemic prediction by integrating multi-source heterogeneous data and constructing a dynamic multi-dimensional graph structure for spatio-temporal coupling prediction. The use of a graph attention network and a time convolution network captures complex spatial dependence and temporal dynamics, and the use of a parallel model cross-verification and arbitration mechanism enhances the reliability of the prediction results, improves the sensitivity and stability of risk identification, and reduces false positives.

The present application relates to the technical field of medical data processing, and particularly relates to a cervical disease two-stage dynamic risk prediction method based on multidimensional data, comprising the following steps: step S1, data acquisition and structuring: a plurality of dimensions of patient data are collected and structured by questionnaire, and a cervical disease database is constructed; step S2, first-stage model construction: for a general screening population, a first prediction model is constructed by using the questionnaire survey data collected in step S1; step S3, second-stage model construction: for a population with HPV positive or other suspicious indications obtained by the first prediction model, high-dimensional clinical data collected in step S1 are integrated, and a second prediction model is constructed by using an ensemble learning strategy; step S4, model series early warning output; and step S5, decision support generation. The present application integrates low-cost and easily-obtained multidimensional data, constructs a scientific risk stratification model, and realizes two-stage and continuous dynamic early warning.