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740results about How to "Improve interpretability" patented technology
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Computer-aided image analysis
InactiveUS6996549B2Improve abilitiesGreat dimensionalityMedical data miningImage analysisLearning machineComputer-aided
Digitized image data are input into a processor where a detection component identifies the areas (objects) of particular interest in the image and, by segmentation, separates those objects from the background. A feature extraction component formulates numerical values relevant to the classification task from the segmented objects. Results of the preceding analysis steps are input into a trained learning machine classifier which produces an output which may consist of an index discriminating between two possible diagnoses, or some other output in the desired output format. In one embodiment, digitized image data are input into a plurality of subsystems, each subsystem having one or more support vector machines. Pre-processing may include the use of known transformations which facilitate extraction of the useful data. Each subsystem analyzes the data relevant to a different feature or characteristic found within the image. Once each subsystem completes its analysis and classification, the output for all subsystems is input into an overall support vector machine analyzer which combines the data to make a diagnosis, decision or other action which utilizes the knowledge obtained from the image.
Owner:HEALTH DISCOVERY CORP +1
System and method for remote melanoma screening
InactiveUS20120008838A1Improve abilitiesGreat dimensionalityImage enhancementImage analysisImage analysisClassification result
A system and method are provided for diagnosing diseases or conditions from digital images taken by a remote user with a smart phone or a digital camera and transmitted to an image analysis server in communication with a distributed network. The image analysis server includes a trained learning machine for classification of the images. The user-provided image is pre-processed to extract dimensional, shape and color features then is processed using the trained learning machine to classify the image. The classification result is postprocessed to generate a risk score that is transmitted to the remote user. A database associated with the server may include referral information for geographically matching the remote user with a local physician. An optional operation includes collection of financial information to secure payment for analysis services.
Owner:HEALTH DISCOVERY CORP
Methods of identifying biological patterns using multiple data sets
InactiveUS6882990B1Improve abilitiesGreat dimensionalityImage analysisDigital data processing detailsSupport vector machineLearning machine
Systems and methods for enhancing knowledge discovery from data using multiple learning machines in general and multiple support vector machines in particular. Training data for a learning machine is pre-processed in order to add meaning thereto. Multiple support vector machines, each comprising distinct kernels, are trained with the pre-processed training data and are tested with test data that is pre-processed in the same manner. The test outputs from multiple support vector machines are compared in order to determine which of the test outputs if any represents a optimal solution. Selection of one or more kernels may be adjusted and one or more support vector machines may be retrained and retested. Optimal solutions based on distinct input data sets may be combined to form a new input data set to be input into one or more additional support vector machine. The methods, systems and devices of the present invention comprise use of Support Vector Machines for the identification of patterns that are important for medical diagnosis, prognosis and treatment. Such patterns may be found in many different datasets. The present invention also comprises methods and compositions for the treatment and diagnosis of medical conditions.
Owner:HEALTH DISCOVERY CORP +1
Methods of identifying patterns in biological systems and uses thereof
InactiveUS7117188B2Enhancing knowledge discoveryLarge amount of informationKernel methodsDigital computer detailsSupport vector machineData set
The methods, systems and devices of the present invention comprise use of Support Vector Machines and RFE (Recursive Feature Elimination) for the identification of patterns that are useful for medical diagnosis, prognosis and treatment. SVM-RFE can be used with varied data sets.
Owner:HEALTH DISCOVERY CORP +1
Method and apparatus for defining the effect of atrial arrhythmias on cardiac performance and directing therapy using a plurality of intrinsically and extrinsically derived signals
ActiveUS7963925B1Ensure complianceLess timelyElectrotherapyOrgan movement/changes detectionDigital signal processingPatient demographics
This invention describes methods and algorithms for processing a plurality of relevant signals / data intrinsic to a patient and / or derived from external diagnostic equipment for management of atrial arrhythmias. The intrinsic signals are acquired from intracardiac leads / sensors and analogous extrinsic data obtained from imaging equipment and patient demographics. These data are input into software algorithms that use digital signal processing to output informational data of clinical and technical relevance after comparisons are made to patients with access to this technology whose outcome under varying treatments is known. These combined data are used to define prognosis, make treatment suggestions, direct programming of cardiac devices and digitally convert intrinsically and extrinsically derived indices into a common metric. In a preferred embodiment, the intrinsically and extrinsically acquired data is utilized in the design of catheters and software algorithms for performing intracardiac procedures such as ablation of atrial arrhythmias.
Owner:STUART SCHECTER LLC
Implementation method for fusing network question and answer system based on multi-attention mechanism
ActiveCN110134771AEnhanced Representational CapabilitiesImprove accuracyDigital data information retrievalNatural language data processingData setOriginal data
The invention discloses an implementation method of a fusion network question and answer system based on a multi-attention mechanism, which comprises the following steps of constructing a question andanswer system network model, preprocessing an original data set to obtain a standby data set, and performing text length distribution analysis; subjecting text in standby data set to one-hot vector representation, using a CBOW model to train one-hot word vector and forming a word2vec word list; adjusting the sequence length of each sentence in the text, and adding a sentence end mark; training the word2vec vector by using an ELMO language model to obtain an ELMO word vector; encoding the ELMO vector to obtain a sentence vector; performing coarse-fine granularity attention on the sentence vectors respectively to obtain memory vectors and attention vectors based on each word; carrying out vector splicing to obtain expression vectors based on words and sentences; and decoding an answer representing the vector generation question sentence. According to the method, the representation ability of sentences is improved through an ELMO language model; and various attention mechanisms are fused, so that the decision making accuracy of the system is improved, and the interpretability of the system is enhanced.
Owner:GUANGDONG UNIV OF TECH
Computer-aided image analysis
InactiveUS7383237B2Improve abilitiesGreat dimensionalityMedical data miningImage analysisLearning machineComputer-aided
Digitized image data are input into a processor where a detection component identifies the areas (objects) of particular interest in the image and, by segmentation, separates those objects from the background. A feature extraction component formulates numerical values relevant to the classification task from the segmented objects. Results of the preceding analysis steps are input into a trained learning machine classifier which produces an output which may consist of an index discriminating between two possible diagnoses, or some other output in the desired output format. In one embodiment, digitized image data are input into a plurality of subsystems, each subsystem having one or more support vector machines. Pre-processing may include the use of known transformations which facilitate extraction of the useful data. Each subsystem analyzes the data relevant to a different feature or characteristic found within the image. Once each subsystem completes its analysis and classification, the output for all subsystems is input into an overall support vector machine analyzer which combines the data to make a diagnosis, decision or other action which utilizes the knowledge obtained from the image.
Owner:HEALTH DISCOVERY CORP +1
Sparse dimension reduction-based spectral hash indexing method
InactiveCN101894130AImprove interpretabilityImprove search efficiencyCharacter and pattern recognitionSpecial data processing applicationsSearch problemPrincipal component analysis
The invention discloses a sparse dimension reduction-based spectral hash indexing method, which comprises the following steps: 1) extracting image low-level features of an original image by using an SIFT method; 2) clustering the image low-level features by using a K-means method, and using each cluster center as a sight word; 3) reducing the dimensions of the vectors the sight words by using a sparse component analysis method directly and making the vectors sparse; 4) resolving an Euclidean-to-Hamming space mapping function by using the characteristic equation and characteristic roots of a weighted Laplace-Beltrami operator so as to obtain a low-dimension Hamming space vector; and 5) for an image to be searched, the Hamming distance between the image to be searched and the original image in the low-dimensional Hamming space and using the Hamming distance as the image similarity computation result. In the invention, the sparse dimension reduction mode instead of a spectral has principle component analysis dimension reduction mode is adopted, so the interpretability of the result is improved; and the searching problem of the Euclidean space is mapped into the Hamming space, and the search efficiency is improved.
Owner:ZHEJIANG UNIV
Computer-aided detection system utilizing temporal analysis as a precursor to spatial analysis
ActiveUS20070165920A1Improve analysisImprove interpretabilityImage enhancementImage analysisGraphicsSpatial analysis
A computer-aided detection system for evaluating tissue based on a series of timed images acquired both before and after a contrast agent is administered performs a temporal analysis of the tissue and then a spatial analysis in which the tissue is categorized. After the temporal and spatial analysis is performed, the results can be displayed. The displayed results can include both tissue characterization results, underlying curves used to determine the characterization, and confidence data regarding how good the characterization is expected to be. The confidence data can be provided, for example, by using variations in color schemes, displaying numerical confidence levels, or providing graphical features such as piecewise linear models.
Owner:GENERAL ELECTRIC CO
Credit assessment model training method, and credit assessment method and apparatus
InactiveCN107301577AImprove performanceInterpretableBuying/selling/leasing transactionsNonlinear modelLinear model
The invention discloses a credit assessment model training method, and a credit assessment method and apparatus. The training method comprises the steps of obtaining original training behavior data of a training user in a business system; extracting original training features in the original training behavior data; performing feature combination on the original training features according to a GBDT model to generate corresponding training cross combination features; and performing training on a logistic regression model according to the training cross combination features to build a credit assessment model. According to the method, the original training features are trained through the nonlinear GBDT model to generate the corresponding training cross combination features, and the linear LR model is trained through the training cross combination features to build the credit assessment model, so that the credit assessment model has not only high performance of the nonlinear model but also interpretability of the linear model.
Owner:ALIBABA GRP HLDG LTD
Computer-aided image analysis
InactiveUS20060224539A1Improve abilitiesGreat dimensionalityMedical data miningImage analysisLearning machineComputer-aided
Digitized image data are input into a processor where a detection component identifies the areas (objects) of particular interest in the image and, by segmentation, separates those objects from the background. A feature extraction component formulates numerical values relevant to the classification task from the segmented objects. Results of the preceding analysis steps are input into a trained learning machine classifier which produces an output which may consist of an index discriminating between two possible diagnoses, or some other output in the desired output format. In one embodiment, digitized image data are input into a plurality of subsystems, each subsystem having one or more support vector machines. Pre-processing may include the use of known transformations which facilitate extraction of the useful data. Each subsystem analyzes the data relevant to a different feature or characteristic found within the image. Once each subsystem completes its analysis and classification, the output for all subsystems is input into an overall support vector machine analyzer which combines the data to make a diagnosis, decision or other action which utilizes the knowledge obtained from the image.
Owner:HEALTH DISCOVERY CORP +1
Item recommendation method for combining user comment content and grades
InactiveCN106202519AHigh precisionEasy to predictWeb data indexingBuying/selling/leasing transactionsInterpretabilityCold start
The invention discloses an item recommendation method for combining user comment content and grades. A model for combining the user comment content with user grades is provided for solving the cold start problem and the poor interpretability problem in a recommendation system. By means of the rich information contained in comments, the prediction accuracy is greatly improved; particularly, when data is quite sparse, the cold start problem and the poor interpretability problem can be well solved. The method mainly considers that descriptions of item characteristics are contained in user comment information, and potential characteristics in numerical value grades correspond to item characteristics in comment information through a mapping function. A model can be well built for user preferences, and therefore prediction and recommendation can be well carried out even if data is quite sparse.
Owner:GUILIN UNIV OF ELECTRONIC TECH
Convolutional neural network power system intelligent fault detection method and system based on Spearman level correlation
ActiveCN109614981AImprove reliabilityRealize analysisCharacter and pattern recognitionDesign optimisation/simulationNODALElectric power system
The invention provides a convolutional neural network power system intelligent fault detection method and system based on Spearman level correlation, and the method comprises the steps: setting a phasor measurement unit at a regional network node, and carrying out the measurement of data; performing Spearman correlation analysis on the acquired data, and proposing an image generation method basedon an analysis result; establishing an equivalent fault network, verifying the relation between the fault characteristics and the Spearman level correlation, and demonstrating the feasibility of the method; taking the generated image as an initial convolutional layer, and establishing a convolutional neural network architecture based on Spearman level correlation; and verifying the rationality andsuperiority of the method based on PSCAD / EMTDC according to the established architecture. A plurality of types of electric quantity data are comprehensively used for fault diagnosis, the position ofa fault in the power system can be quickly and accurately identified through the convolutional neural network, the problems that the power system has volatility and the traditional detection method isinaccurate due to addition of a distributed power supply and the like are solved, and the robustness and the self-adaptability of the power system are higher.
Owner:NORTHEASTERN UNIV
System and method for remote melanoma screening
InactiveUS8543519B2Improve abilitiesGreat dimensionalityImage enhancementImage analysisLearning machineMelanoma
A system and method are provided for diagnosing diseases or conditions from digital images taken by a remote user with a smart phone or a digital camera and transmitted to an image analysis server in communication with a distributed network. The image analysis server includes a trained learning machine for classification of the images. The user-provided image is pre-processed to extract dimensional, shape and color features then is processed using the trained learning machine to classify the image. The classification result is postprocessed to generate a risk score that is transmitted to the remote user. A database associated with the server may include referral information for geographically matching the remote user with a local physician. An optional operation includes collection of financial information to secure payment for analysis services.
Owner:HEALTH DISCOVERY CORP
Method for analysing seismic data with attribute functions
ActiveUS20120150447A1Improve interpretabilityElectric/magnetic detection for well-loggingDispersed particle separationThinningGeophysics
A method for stratigraphic analysis of seismic data, known as GeoLeaf, comprises: selecting a seismic data volume comprising a plurality of samples; selecting a number of horizons (TH, BH1 TPH, BPH, SMH) within the seismic data volume; selecting a scale of interest within the seismic data volume, which ranges from 500 m up to the size of the entire seismic data volume; applying an attribute function to at least one of the selected number of horizons (TH, BH, TPH, BPH, SMH) at the selected scale of interest; and displaying a stratigraphic attribute, which represents a geologic feature, such as a seismic fades attribute (FM), an unconformity attribute (Zmax / Z) and / or a thinning attribute (Z1 / Z2).
Owner:SHELL OIL CO
Method and apparatus for following and inspecting an edge or border
InactiveUS6909799B1Improve interpretabilityPromote resultsInvestigating moving sheetsArc welding apparatusEngineeringImage capture
To follow and inspect a common edge between two sheet metal blanks which are to be welded together, a line pattern is projected across the edge by a projector and is imaged by a camera. The image captured is analyzed in order to derive from the trace of the lines information on the gap between the blanks, or on the track of the edge. The pattern projected has several lines with different light intensities. When the photographed image is processed, it is then possible—even if there is variation in the reflection characteristics of the sheet metal blanks—to select a line which is neither too faint nor too bright in the image obtained and which can therefore be used for meaningful analysis.
Owner:PRECITEC VISION +1
Biomarkers for screening, predicting, and monitoring prostate disease
InactiveUS20070092917A1Improve abilitiesGreat dimensionalityMicrobiological testing/measurementMaterial analysisLearning machineAbnormal tissue growth
Gene expression data are analyzed using learning machines such as support vector machines (SVM) and ridge regression classifiers to rank genes according to their ability to separate prostate cancer from BPH (benign prostatic hyperplasia) and to distinguish cancer volume. Other tests identify biomarker candidates for distinguishing between tumor (Grade 3 and Grade 4 (G3 / 4)) and normal tissue.
Owner:HEALTH DISCOVERY CORP
A recommendation system and a recommendation method based on attention mechanism
InactiveCN109087130AImprove interpretabilityImprove recommendationsMarketingSpecial data processing applicationsFeature vectorSelf attention
The invention provides a recommendation system and a recommendation method based on an attention mechanism, wherein the merchandise in the user history and the merchandise to be predicted are mapped to the feature vector of the merchandise through the feature embedding layer, according to the self-attention mechanism, the attention layer learns the user's expression to obtain the user's feature vector, a prediction value for the merchandise from a user is output according to the feature vector of the merchandise and the feature vector of the user through a fusion output layer. The invention provides a recommendation system based on attention mechanism, which adopts a neural attention mechanism to automatically distribute weights to each merchandise in a user's history record, so that the interpretability is enhanced. The invention outputs a prediction value of the user to the merchandise according to the characteristic vector of the merchandise and the characteristic vector of the userthrough a fusion output layer. The neural attention mechanism can be used to simulate user behavior more reasonably and get finer grained weights so as to obtain better recommendation effect.
Owner:SHENZHEN INST OF ADVANCED TECH
Electroencephalogram signal recognition fuzzy system and method with transfer learning ability
ActiveCN104523268AImprove performanceImprove interpretabilityDiagnostic recording/measuringSensorsSystems approachesFuzzy method
The invention discloses an electroencephalogram signal recognition fuzzy system and method with the transfer learning ability. In a traditional intelligent recognition method, a training set and a testing set of a model are assumed to conform to the same data distribution, and therefore the good classification performance can be obtained only when data in a training domain and data in a testing domain conform to the same distribution. The electroencephalogram signal recognition fuzzy method helps epileptic electroencephalogram signal recognition under the transfer learning environment by means of the transfer learning strategy. The 0-order TSK type fuzzy system modeling technology with the direct-pushing type transfer learning ability is built based on the fuzzy system. The technology has the transfer learning ability and is not confined to the assumption of the uniform data distribution of the training domain and the testing domain, a certain difference is allowed to exist between the data in the training domain and the data in the testing domain, the good performance is kept on the condition of the same data distribution of the training domain and the testing domain, and the recognition effect of the finally-obtained model under the diversified electroencephalogram signal recognition problems is greatly improved.
Owner:JIANGNAN UNIV
Information recommendation method based on convolutional neural network and joint attention mechanism
InactiveCN110765260AImprove accuracyImprove interpretabilitySemantic analysisNeural architecturesFeature vectorFeature extraction
The invention relates to an information recommendation method based on a convolutional neural network and a joint attention mechanism, which is used for effectively utilizing potential semantic information of text and overcoming inherent defects of a feature extraction method of traditional machine learning. According to the method, feature vectors of the evaluation text processed by a CNN deep neural network is processed by a layer of attention mechanism, so that the attention weight of key points of interest in the evaluation text is increased. The vector sets of users and projects and thescore of the previous attention mechanism respectively use a layer of attention mechanism to acquire attention mechanism weight vectors of the users and the projects respectively. Point multiplication is carried out on the attention mechanism weight vectors and vector sets of the users and the projects respectively to obtain final representation, the users, the projects and the evaluation text are combined to obtain the final representation, and score prediction is made. Compared with traditional recommendation technology, the method has the advantages that recommendation can be performed more effectively, the recommendation quality is improved, and the interpretability of recommendation is enhanced.
Owner:BEIJING UNIV OF TECH
Product recommendation method and device and storage medium
InactiveCN110020913AImprove accuracyIncrease diversityFinanceBuying/selling/leasing transactionsKnowledge graphComputer science
The embodiment of the invention provides a product recommendation method and device and a storage medium. In some exemplary embodiments of the present application, a meta-path set associated with therecommended service scene is selected from the insurance knowledge graph in combination with the recommended service scene, and for the target client, a similar client list of the target client and asimilar product list of the insurance products purchased by the target client are generated according to the entity characteristics contained in the plurality of meta-paths and the service relationship existing between the entity characteristics; collaborative filtering recommendation is performed according to the client similarity list and the similar product list to obtain a first product set capable of being recommended to the target client, a recommendation strength score of each product in the first product set, a second product set and a product recommendation strength score of each product in the second product set; according to the recommendation strength score of each product in the first product set and the product recommendation strength score of each product in the second product set, the target product recommended to the target user is determined. According to the product recommendation method, the insurance knowledge graph is utilized to fully express various semantic information, and the accuracy, the diversity and the interpretability of the product recommendation result are improved.
Owner:PEOPLE'S INSURANCE COMPANY OF CHINA
Energy constraint heterogeneous reservoir thickness identification system
ActiveCN102736107AImproved Thickness PredictionHigh precisionSeismic signal processingUltrasound attenuationWell logging
The invention relates to an energy constraint heterogeneous reservoir thickness identification system. The system comprises the steps of acquiring accurate reservoir data parameters by single well logging; acquiring an optimal wavelet; constructing a reservoir basic wavelet function; constructing a basic function of energy constraint reservoir thickness spectrum, wherein psi is a wavelet function, t is time, b is the time difference between a top and a bottom interface, a is a scale parameter of different time and the scale parameter varies with frequency, and c and d are cosine square fringing attenuation functions; building the energy constraint reservoir thickness spectrum of a seismic trace near the well; carrying out three dimensional data space thickness prediction; with the aid of the machine three dimensional display system software of a computer, carrying out stereoscopic display on the reservoir thickness data obtained by the prediction.
Owner:PETROCHINA CO LTD
Early warning method and device applied to information system
ActiveCN106909487AIncrease flexibilityImprove adaptabilityHardware monitoringWeight coefficientOriginal data
The invention discloses an early warning method and device applied to an information system. The method comprises the steps that original monitoring data of a monitored node within a preset period of time is collected, original data description is constructed according to preset characteristic indexes; according to the original data description, comprehensive indexes are determined, wherein the comprehensive indexes include weight coefficients corresponding to index components; according to the original data description and the comprehensive indexes, training data is determined, according to the training data, a prediction model is trained, the prediction model is determined, and target prediction data of a target node is determined according to the prediction model; actually obtained data of the target node is determined and converted according to the comprehensive indexes, and the conversion result is compared with the target prediction data; when the comparison result conforms to preset characteristics, corresponding information system early warning information is sent out. According to the method, flexibility and adaptability of information system risk early warning and the early warning result accuracy can be improved.
Owner:北京盛世全景科技股份有限公司
Tree alternating optimization for learning classification trees
PendingUS20200372400A1Reduce classification errorsImprove accuracyCharacter and pattern recognitionOther databases indexingAlgorithmDecision tree
Computer-implemented methods for learning decision trees to optimize classification accuracy, comprising inputting an initial decision tree and an initial data training set and, for nodes not descendants of each other, if the node is a leaf, assigning a label based on a majority label of training points that reach the leaf, and if the node is a decision node, updating the parameters of the node's decision function based on solution of a reduced problem, iterating over the all nodes of the tree until parameters change less than a set threshold, or a number of iterations reaches a set limit, pruning the resulting tree to remove dead branches and pure subtrees, and using the resulting tree to make predictions from target data. In some embodiments, the TAO algorithm employs a sparsity penalty to learn sparse oblique trees where each decision function is a hyperplane involving only a small subset of features.
Owner:RGT UNIV OF CALIFORNIA
An intelligent vehicle driving decision method based on generative countermeasure network
ActiveCN109131348AImprove enforceabilityImprove generalization abilityControl devicesOptimal decisionDecision control
The invention discloses an intelligent vehicle driving decision method based on a generating countermeasure network, which comprises establishing a driving decision model and driving decision control.The invention processes the driving image based on the generated antagonistic network, can process the vehicle driving path planning under the non-ideal road condition, and improves the executabilityof the end-to-end neural network. The invention extracts the most essential characteristics of driving images through the generated antagonistic network processing, maps the driving data of differentsources into a unified virtual domain, realizes the application of reinforcement learning to a real vehicle, improves the generalization of the network and adapts to the ability of different samples.For the input of the driving image, the input image used each time is the first several frames of the current time stamp video image. The predicted images obtained by this method can be used to judgethe driving decision-making planning to a greater extent. As the basis for predicting the optimal decision of the vehicle, the invention builds a bridge from the reinforcement learning to the real vehicle application.
Owner:DALIAN UNIV OF TECH
Driverless vehicle motion trail planning system and method for structured road
InactiveCN111273668AImprove interpretabilityEasy to operatePosition/course control in two dimensionsVehiclesControl engineeringTrajectory planning
The invention relates to a driverless car motion trail planning system for a structured road. The driverless car motion trail planning system comprises a sensing module, a positioning module, a lane changing decision module, a motion planning module and a trajectory tracking module. The lane changing decision module outputs a decision action according to the data acquired by the sensing module andthe positioning module; and the motion planning module outputs an optimal trajectory to the trajectory tracking module according to the decision action. Deep reinforcement learning is used for makinga decision, a trajectory planning module, a perception module, a control module and other modules based on decision action dynamics are independently processed, the interpretability and operability of the decision planning process are greatly improved compared with an end-to-end method, and the invention can be well adapted to a previous driverless automobile system architecture.
Owner:FUZHOU UNIV
Colon cancer biomarkers
InactiveUS20050165556A1Improve abilitiesGreat dimensionalityImage analysisKernel methodsLearning machineFuzzy support vector machine
Owner:HEALTH DISCOVERY CORP +1
Strain Image Display Systems
InactiveUS20100134629A1Improve data qualityImprove interpretabilityImage enhancementImage analysisQuality dataImaging quality
A method of displaying strain image data for an imaged object, the method comprising: capturing strain image data defining deformation of said object over an imaged region of said object; processing said strain image data to determine local image quality data, said local image quality data comprising a measure of the quality of said strain image data varying over said imaged region; and displaying a representation of said strain image data for said imaged region of said object, using said local image quality data to provide a visual indication of the quality of said displayed representation varying over said imaged region or to improve a quality of said displayed representation of said strain image data.
Owner:CAMBRIDGE ENTERPRISE LTD
Multivariate time series multilayer space-time dependence modeling method based on deep learning
ActiveCN111651504AEffective filteringImprove accuracyDigital data information retrievalNeural architecturesAlgorithmEngineering
The invention belongs to the field of deep learning, and discloses a multivariate time series multilayer space-time dependence modeling method based on deep learning. According to the method, a novelattention mechanism is introduced to carry out finer-grained processing on space-time dependence features extracted from different layers in the neural network; the model provided by the invention iscomposed of a stacked long-short-term neural network-convolutional neural network (LSTM-CNN). The network is composed of a spatial attention mechanism based on a CNN, a CNN-based channel attention mechanism, a time attention mechanism and an autoregressive assembly. The concept of multi-layer space-time dependence is introduced; a CNN-based channel attention mechanism and a CNN-based spatial attention mechanism are used to pay attention to space-time dependence characteristics of different layers respectively. According to the method, filtering of redundant information and effective extractionof features having greater influence on a prediction result are realized, the purpose of improving the prediction result is achieved, and the method is excellent in performance on multivariate time series data in different fields and can be extended to a task of unit time series prediction.
Owner:HUNAN UNIV
ICD automatic coding method and system for diagnosis reason visualization
ActiveCN111540468AImprove accuracyMeet usage habitsMedical data miningMedical automated diagnosisMedical recordDiagnostic data
The invention discloses an ICD automatic coding method and system for diagnosis reason visualization. The method comprises the following steps: acquiring medical record data from a medical record document library, and constructing a multi-label classification data set; preprocessing the data set, and converting the multi-label classified data set into a plurality of single-label classified data sets; completing training of a hierarchical attention neural network model based on the data sets of the plurality of single-label classifications; inputting doctor writing diagnosis data and illness state description data into the trained hierarchical attention neural network model so as to obtain predicted ICD codes and names; and according to the predicted ICD name, extracting a corresponding sentence from the disease description data as a diagnosis reason for visualization. According to the method, doctor writing diagnosis and medical record description data are used as mode features at thesame time, doctor writing diagnosis is ingeniously used as supervision information in the classification process, traditional multi-label classification is converted into a simpler single-label classification problem, and therefore the accuracy of model coding is improved.
Owner:CHONGQING UNIV
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