280 results about "Disease monitoring" patented technology

The invention provides a platform technology with rich ability to flexibly perform, create, deploy, maintain, and update a wide range of panels, assay, array, and / or sequence of tests for a wide range of substances and pathogens. The invention provides a unifying framework for widely-ranging miniature sensor implementation, fluidic / gas interfacing, electrical interfaces and optical interfaces, and further by collocating, allowing the integration a large number highly-selective sensors and chemical sensors—together as needed with appropriately selected supplemental sensors (for example temperature, pH, selective ions, etc.), into a common readily-manufacturable framework. The diverse sensor arrays give rise to statistical enhancing through novel statistical processing approaches. The invention is deployable and useable in a wide range of situations previously unavailable, and addresses many otherwise problematic aspects of field testing for food safety, water safety, epidemic outbreaks, routine diagnosis, and disease monitoring.

Real-time and individualized disease monitoring is central to rapidly evolving medical sciences and technologies, but for the vast majority of patients, disease progression and treatment are monitored only in an irregular and discontinuous fashion. Consequently, disease progression and relapse are often allowed to proceed too far before they are detected, compromising the possibility of any effective treatment. For one patient, this can mean becoming refractory to the few early drug treatments that are available; for another, missing early detection may be deadly. This invention provides a method for the detection of early signals of disease and recovery thereof comprising a universal yet personalized health-monitoring solution using cell phones or other wearable smart device data that generate extensive real-time data. The invention further provides a system and method to provide answers to a variety of questions related to the patient health status and health trajectory. Its flexibility and generality is designed for a preferred application to rare disorders and rare questions for which other analytical system are lacking.

Energy efficient methods and systems for using multi-dimensional activity sensors with implantable cardiac devices are provided. In certain embodiments the output of a passive activity sensor (used for rate responsive pacing) is used to trigger temporary use of a relatively high power multi-dimensional activity sensor. In other embodiments, the output of a relatively low power oxygen saturation sensor is used to trigger temporary use of a relatively high power multi-dimensional activity sensor. This description is not intended to be a complete description of, or limit the scope of, the invention.

Devices, systems and methods are provided for the real-time detection of disease through constant or periodic monitoring of biomolecules in an individual without laboratory sampling. Biomolecule detector devices may be worn in contact with skin, or be implanted in fluid communication with a biological fluid to provide information and identity of disease-related circulating biomolecules in an individual.

The invention relates to a winter jujube disease identification method based on a deep convolutional neural network and a disease image. The method includes the following steps that: an original winter jujube diseased fruit RGB color image collected by an Internet of Things is converted into a YUV color model, and preprocessing is performed; the a rectangular region of interest of disease spots which contains a disease image is extracted, the rectangular region of interest is segmented through using a K-means clustering algorithm, so that a YUV color disease spot image can be obtained; and a three-channel layered convolutional neural network model is constructed and is trained by using training data, and a jujube disease image to be recognized is inputted into the trained model so as to be subjected to disease category identification. The method of the invention can be applied to an Internet of Things-based greenhouse winter jujube disease monitoring system and can obtain accurate disease identification results.

Provided is an automatic identification method of a foliar disease image of a greenhouse vegetable. The automatic identification method of the foliar disease image of the greenhouse vegetable comprises the steps of carrying out image collection on a foliar disease of the greenhouse vegetable, automatically generating a threshold, carrying out estimation by using a two-dimensional maximum entropy principle and combining the average grey degree grade and the intra-neighborhood grey degree grade of an image, optimizing the automatically-generated threshold by using a differential evolution algorithm, using an average value of results obtained through more than 30 times of differential evolution algorithm optimization which is independently carried out to serve as a threshold for image segmentation, carrying out segmentation on the known foliar disease image of the greenhouse vegetable by using the threshold, obtaining an image of the area of a disease speck, analyzing features of the disease speck, obtaining feature parameters such as the color, the texture and the shape of the disease speck of the foliar disease image of the greenhouse vegetable, carrying out fusion on the features of the disease speck, and carrying out disease type feature identification. The automatic identification method of the foliar disease image of the greenhouse vegetable can achieve rapid and effective diagnosis of the foliar diseases in a greenhouse without damage to sick leaves of the greenhouse vegetable, and can be well applied to disease monitoring of the greenhouse vegetable.

The invention discloses a remote-sensing crop disease identification method based on time phase and spectrum information and a habitat condition. Multi-time-phase visible light-near infrared and thermal-infrared remote sensing images in a study area within a monitoring period are obtained and pretreatment is carried out on the images; with the remote sensing images after pretreatment, planting information of target crops is extracted by combining a certain kind of classification algorithm; and according to the visible light-near infrared and thermal-infrared remote sensing images, habitat information of disease monitoring is obtained, a disease area and a disease type of the target crops in the study area are determined by combining the time phase information of the target crops and the obtained habitat information, and severity of the disease is determined by using the spectrum information based on the determined disease area and disease type. According to the technical scheme, the remote sensing diagnosis precision of the crop disease can be improved effectively; and a reverse identification problem of remote sensing crop disease monitoring can be solved to a certain extent.

An agriculture and forestry pest and disease monitoring and management system uses the Internet of Things technology, relates to the technical field of system integration and software development, ishighly integrated and excellent in function completeness and forms a relatively complete entirety from a hardware acquisition end to a server receiving end and further to a client side (a remote PC side and a mobile phone APP). The system realizes that the client side can remotely control a camera of the hardware acquisition end through a server to move up and down and acquire meteorological data.The system realizes remote control on a hardware device through the server, controls the camera to go up and down, can take video images in multiple angles, and realizes multi-point monitoring and multi-user access. Intellectualization is reflected fully, a problem that agriculture and forestry experts have to go to farmlands to perform observation is solved, and the experts can check anytime anywhere. An early warning part also reminds people to make corresponding measures. Mass data obtained provide a sufficient data basis for later analysis of crop optimal growth environment, and so on.

The invention discloses a management system and method of intelligent livestock breeding. An intelligent fodder feeding module in the system is used for detecting real-time edible condition of materials of livestock in real time and supplying fodders of predicted fodder put volume according to livestock individual growth data, the number of feeders is reduced, the breeding cost is controlled, thescientific nature, standardization and degree of automation of the livestock breeding are improved; an intelligent environment control module collects environmental parameters in a breeding environment, and the livestock breeding environmental parameters are adjusted in the most suitable growth range; a disease monitoring module monitors the disease condition of the livestock to reduce the epidemic situation infection probability of the livestock; a growing prediction module predicts the growth tendency and best slaughter time of the livestock according to the individual growth data; a qualitytraceability module records, stores and inquires the breeding information in the livestock breeding process; and the intelligent management of the livestock breeding is realized together, and the purposes that the number of the feeders is reduced, the breeding period is shortened, the breeding cost is controlled and the livestock breeding is scientific and standard are achieved.

The invention provides a regional-scale monitoring method for crop diseases based on multi-temporal satellite images. The method makes full use of spectral information and temporal information in time sequence image data, combines technologies of GIS, GPS and RS, introduces spectral information divergence analysis to the crop disaster monitoring field, and provides a method and technology for large-scale monitoring the diseases by using satellite-earth synchronous data in a certain regions. The method can effectively reduce field operation cost of disease monitoring, expands a conventional disease monitoring modes from point to surface, and provides convenience for government departments and agricultural administrative departments to master and to know important information such as generation, serious degree and the like of regional-scale diseases timely and accurately.

The invention provides a disease prediction method. The method comprises the steps that the disease monitoring data, the weather data and the public opinion data are acquired; the disease monitoring data, the weather data and the public opinion data are preprocessed; a multilayer LSTM model is constructed; training and performance verification are performed on the multilayer LSTM model so as to obtain the optimized multilayer LSTM model; and the prediction time point is predicted by using the optimized multilayer LSTM model so as to obtain the disease prediction result of the prediction time point. The invention also provides a disease prediction device, a computer device and a readable storage medium. High-accuracy disease prediction can be realized.

The invention discloses a crop information fusion method and a disease monitoring system, and relates to the technical field of agricultural information monitoring. The method comprises the following steps of: A, acquiring temperature, humidity and illumination information of a crop and acquiring an image of spots of leaves of the crop; B, performing weighted average on the acquired temperature, humidity and illumination information as environment information by using a weighted average method and processing the image of the spots of the leaves of the crop; and C, forming characteristic description with fusion property in combination with current environment information and the image of the spots of the leaves of the crop by using a multi-sensor information fusion technology. According to the crop information fusion method and the disease monitoring system, automatic monitoring of crop diseases can be realized, the information query speed is high, and the system has low application barrier.

The invention provides a multichannel LSTM (Long-short term memory) neural network influenza epidemic situation prediction method based on an attention mechanism, and belongs to the technical field ofepidemic disease monitoring. The multichannel LSTM neural network influenza epidemic situation prediction method comprises the following steps: firstly, carrying out preprocessing, normalization andfeature selection on data in a data set, dividing the selected data into weather-related data and influenza epidemic situation-related data, and generating a training set; then establishing a multichannel LSTM neural network model comprising an attention mechanism; inputting training set data into the model for training, and performing MAPE (mean absolute percentage) evaluation to obtain a trainedmultichannel LSTM neural network model; processing the test data to obtain a test set; inputting the test set data into the trained LSTM neural network model for testing; and finally, performing inverse standardization processing on a test output result to obtain an influenza epidemic situation prediction value. According to the multichannel LSTM neural network influenza epidemic situation prediction method based on an attention mechanism, the problem of low prediction accuracy of the existing influenza epidemic situation prediction technology is solved. The multichannel LSTM neural network influenza epidemic situation prediction method based on an attention mechanism can be used for influenza prediction of different regions.

The livestock automatic identification door of the present invention comprises a livestock weighing instrument, an ear tag reader-writer, a data controller, a channel door, a radio frequency antenna and an entrance and exit infrared transmitter receiver, and is characterized in that: the livestock weighing instrument is installed underground, and the channel door is installed At both ends of the livestock weighing instrument, a radio frequency antenna is installed on the top of the passage door, a pair of infrared transmitter receivers are installed at the entrance and exit of the passage door, and the ear tag reader and data controller are on the side of the passage door. The radio frequency antenna and ear tag The reader-writer is connected, and the ear-tag reader-writer, livestock weighing instrument and entrance-exit infrared transmitting receiver are all connected with the data controller. It mainly solves identification, weight monitoring, disease prevention and control monitoring and early warning, collection of production and operation information in the process of livestock breeding, and provides remote guidance and services for livestock breeding through data processing.

The invention discloses a crop disease occurrence range monitoring method and system, and relates to the technical field of agricultural monitoring. The method comprises the steps of obtaining crop disease data and meteorological data of a target region within the preset period of time, and building a relation model between a disease occurrence degree and a meteorological factor; obtaining a meteorological factor of the target region at the year to be predicted; obtaining the disease occurrence degree of the target region at the year to be predicted through the relation model; according to the disease occurrence degree, obtaining a remote-sensing image of the target region, conducting preprocessing on the remote-sensing image, extracting the area of crops in the target region, and determining a mapland monitored within the crop disease occurrence range; determining a remote-sensing index SI of crop disease monitoring, and according to the remote-sensing index SI, drawing a map within the mapland monitored within the crop disease occurrence range. According to the crop disease occurrence range monitoring method and system, starting from a disease occurrence mechanism, monitoring and map drawing can be carried out on the suspected disease occurrence region by means of the satellite remote-sensing image and disease spectral features, and the significant decision basis is provided for large-range disease prevention and control management.

The invention discloses a spatio-temporal data prediction method based on a graph convolution network. The method comprises the following steps: obtaining spatio-temporal data as an object of a prediction task; processing the obtained spatio-temporal data to obtain a data set; constructing a spatio-temporal data prediction model based on a graph convolution network; taking the obtained training sample and the verification sample as inputs for constructing a model, executing a training algorithm to obtain model parameters, and determining a spatio-temporal data model based on a graph convolutional network; and inputting time and space to be measured, and executing the spatial-temporal data model based on the graph convolutional network, thereby obtaining an expected prediction result. The method can effectively capture the time attribute and spatial structure features in the spatio-temporal data, thereby improving the accuracy of a spatio-temporal data prediction task, and has very important application values in various fields of disease monitoring, traffic management and the like.

Disclosed are luminescent compositions having luminescent particles coated by a surface capping agent. Luminescent particles include rare earth doped phosphors, semiconductor quantum dots, and organic phosphors. Surfactants include macromolecules, polypeptides, polysaccharides, and polymers. Rare earth doped phosphors have host compositions and rare earth dopants, wherein the host compositions include NaYF4, LaF3, YF3, CeF3, CaF2, CsCdBr3, and Y2O3, and wherein the rare earth dopants include Cs, Pr, Nd, Sm, Er, Gs, Tb, Dy, Ho, Er, Tim, Yb, and combinations of two or more of these. The refractive index mismatch of the luminescent compositions and surrounding medium is less than about 0.1. Also disclosed are methods of making the luminescent compositions, luminescent devices and displays containing the luminescent compositions, uses of the luminescent compositions in particle imaging velocimetry, and uses of the luminescent compositions as contrast agents for disease monitoring.

A disease prediction method comprises the steps of acquiring the disease monitoring data, weather data and public sentiment data; preprocessing the disease monitoring data, the weather data and the public sentiment data; constructing a multi-layer GRU model; training and performing performance verification on the multi-layer GRU model to obtain the optimized multi-layer GRU model; and predicting the prediction time point by utilizing the optimized multi-layer GRU model to obtain the disease prediction result of the prediction time point. The invention further provides a disease prediction device, a computer device and a readable storage medium. According to the invention, rapid and high-accuracy disease prediction can be realized.

The invention provides a container for detecting nucleic acid reaction, which has the advantages of simple and convenient operation and low cost, and a method for detecting the nucleic acid reaction by using the container. The container for detecting the nucleic acid reaction is integrally sealable, and is divided into different chambers by spacers; a liquid flowing channel is formed above the spacers; and liquid in the different chambers can flow among the chamber only through the fluid flowing channel. The container has the advantages of totally enclosed detection, prevention of cross contamination in the detection of the nucleic acid reaction, false negative prevention, simple operation and low cost. The container and the method can be widely used for infectious disease monitoring, human genetic disease monitoring, antenatal and premarital checking, medicolegal expertise or blood relationship identification, husbandry and food industry, environment monitoring and the like.

The invention discloses an on-site epidemiology intelligent investigation system. The system is composed of a handheld mobile terminal and a server, wherein the handheld mobile terminal and the server are interconnected via a network; the handheld mobile terminal is a mobile working application carrier, comprises a one-dimensional and two-dimensional code scanning and communication interface and a data line interface, and further comprises the following functional modules: a data display module, a data acquisition module, a graphic image acquisition module, a data local storage module and a wireless communication functional module; and the server comprises an application server, a data server and a file server, and further comprises the following functional modules: an epidemic situation management module, a knowledge base management module, a disease monitoring management module, a system management module and a forum management module. Users can choose to use a PC (Personal Computer) terminal mode or a handheld mobile terminal mode for data acquisition and query works according to site conditions, the data is transmitted in a wire transmission form or wireless transmission form, the use flexibility and compatibility of the system can be ensured, and the system exerts the effects of practical investigation works to the greatest extent.

The invention discloses a tunnel state detection, monitoring and management system, and the system comprises an acceptance detection module which processes the data at a key position of a tunnel so asto recognize a defect in the tunnel, and carries out the two-dimensional display on the processed data; a key monitoring module which is used for displaying the monitoring information of different monitoring objects obtained by monitoring the structure at the key monitoring position of the tunnel according to different display modes; and a tunnel visualization display module which is used for carrying out three-dimensional modeling on the tunnel by utilizing the relevant information of the tunnel subjected to disease monitoring, creating a three-dimensional model of the relevant diseases of the tunnel in real time, and displaying the three-dimensional model in a tunnel space according to the position coordinates of various diseases in the tunnel. The system can improve the informatization, intelligence and visualization level of a road network system.

An Internet-based disease monitoring system may include a network-based disease sensor device, which is coupled to a sensor, such as a spirometer. A remote server may be coupled to the network-based disease sensor device to provide analysis of input signals from the sensor. The remote server may be able to provide various services for grouping or handling functions relating to such input signals. A service may provide the ability to aggregate input signals from multiple sensors coupled to sensor devices and provide predictive modeling or statistical analysis, which may then be used to adjust future input signals. A service may also contain instructions how to handle billing based on usage of the network-based disease sensor device.

The present disclosure relates to biomarker sensor arrays, to circuits including the sensor arrays, to systems including the arrays, and to methods of forming and using the arrays, circuits, and systems. The arrays, circuits, and systems can be used to detect a variety of materials, including chemical, biological, and radioactive materials. The arrays and circuits can be used for, for example, screening tests, disease diagnostics, prognostics and disease monitoring.

The invention discloses a test strip for detecting an anti-cyclic citrullinated peptide antibody by colloidal gold chromatography and a preparation method thereof. The test strip comprises a baseplate (7) and a nitrocellulose membrane (3), a conjugate pad (2), a sample pad (1) and a water-absorbing pad (6), which are sequentially stuck on the baseplate (7) by mutual lap joint. A detection region (4) and a quality control region (5) are arranged on the nitrocellulose membrane, the detection region is used for coating a cyclic citrullinated peptide antigen, the quality control region is used for coating the antibody which is used as a quality control material, and the conjugate pad is used for coating two different colloidal gold labeled antibodies. The test strip introduces the branch cyclic citrullinated peptide and carries out process improvement on pretreatment of the sample pad and the conjugate pad, and the invention provides the anti-cyclic citrullinated peptide antibody test strip with high sensitivity and strong specificity, which can quickly screen positive samples and further play an important role for early auxiliary diagnosis of RA and disease monitoring.

The invention provides a mobile communication terminal and a remote blood oxygen monitoring system, wherein the mobile communication terminal includes a displaying module, as well as a blood oxygen collecting module for collecting analog current signal data of the blood oxygen and outputting the analog current signal data; and a blood oxygen data processing module connected with the blood oxygen collecting module via a data line and used for receiving the analog current signal data output by the blood oxygen collecting module, converting the analog current signal data into blood oxygen digitalsignal data, calculating blood oxygen parameters according to the blood oxygen digital signal data and outputting the parameters to the displaying module to display. The mobile communication terminalcan effectively improve the portability of the blood oxygen detecting device and the reliability of data transmission, can be used as an access terminal of the remote blood oxygen monitoring system,and can provide accurate blood oxygen detection and disease monitoring serviced for the patients at any place and any time.

The invention discloses an electrocardiosignal processing method. The electrocardiosignal processing method includes the following steps that A, an original electrocardiosignal set theta is collected and preprocessed to construct an input electrocardiosignal signal theta'; B, a sample F' is extracted, a hidden layer is trained, and a self-learning set phi is output; C, a label vector G of the self-learning set phi is detected, and a node is output; D, an input layer, the hidden layer and an output layer of an artificial neural network are stacked, so that a complete electrocardiosignal disease monitoring artificial neural network is established. Thus, basic waveform positioning is not needed, dependence on artificially selected characteristics is avoided, the requirement for priori knowledge is low, important characteristics in data can be automatically and abstractly input, and the dimensionality of an input electrocardiosignal sample is reduced through PCA whitening dimensionality reduction applied during preprocessing.

The present disclosure generally relates to improved systems and methods for generating, managing, and sharing digital scripts—which are accessible to patients and / or health care providers and may be accepted by any appropriate prescription dispensary—as well as a comprehensive, real-time source of patient prescription history in compliance with and furtherance of the Meaningful Use regulations. The systems and methods may support the multi-directional flow of script information to improve disease monitoring and treatment, to mitigate patient health risks like undesirable drug interactions, to identify social factors like patient ability to afford prescriptions, and to prevent prescription fraud like forged refills.

The invention relates to the technical field of biology, in particular to a loop-mediated isotherm amplification (LAMP) kit for detecting mycoplasma pneumoniae (Mp). The kit contains 4 LAMP primers, the LAMP primers and an LAMP reaction solution form a detection system together, and the nucleotide sequences of the 4 LAMP primers are shown as SEQ ID No. 1-4. The kit can be used for quickly and sensitively detecting the Mp, and the lowest detection limit is 100 copies. The kit is easy to use and low in cost, the reaction result is easy to observe, and the kit has good specificity, is very suitable for disease monitoring, field emergency and detection of clinical specimens and facilitates large-range popularization and application.

A system that requests clinical data from a patient and then provides the clinical data wirelessly to be reviewed or analyzed with a certified medical algorithm and / or decision tree. The certified algorithm and / or decision tree can provide a summary of the data, indication of severity, “smart” decision or intervention protocol to a nurse, doctor, and the like. In addition, the system with the certified algorithm and / or decision tree can provide the summary of the data, indication of severity, “smart” decision or intervention protocol to the nurse, doctor, and the like within a short time frame, e.g. within 5 minutes from the data being provided by the patient. The certified decision and / or protocol can be agreed to or changed by an individual, e.g. a doctor, which can then be forwarded or sent back to the patient. If the patient doesn't respond to the certified decision within a short time frame, e.g. within 3 minutes from the data being provided to the patient, then a series of triggers can take place, e.g., a text message to a caregiver and the like.

The invention discloses an active monitoring and early warning system of infectious diseases in a medical institution. The active monitoring and early warning system comprises a diagnosis data rule base, an inspection index rule base and an image and pathologic examination rule base. According to the active monitoring and early warning system, influences, caused by personal factors, on monitoring of the infectious diseases in the medical institution are reduced to the greatest extent; a manual monitoring mode is changed so that disease monitoring can be seamlessly integrated with an existing information system of a hospital; intelligent monitoring and whole-course monitoring of the infectious diseases are realized; the missing report rate and late reporting rate of the infectious diseases, especially major infectious diseases, are reduced; the monitoring management efficiency on the infectious diseases of the medical institution is improved.