232 results about "Disease classification" patented technology

Systems, methods and devices for determination of disease class scores for patient tissue are disclosed. The disease class scores may be based on the probability or probability-like metric for a disease condition or outcome. The system includes an imaging apparatus and a computing system with instructions executable by a processor. The computer system may locate one or more points-of-interest on the pre-processed images of the patient tissue using a point-of-interest detector and generate one or more disease spatial maps including one or more probability or probability-like metric of disease classifications on the detected points-of-interest. The information in the disease spatial maps is aggregated to produce disease class scores.

A method for identifying diseases and pests of fruit trees under complex background based on cavity convolution is disclosed, At first, that original train set with sample labels is obtain by collecting images of diseases and insect pests of fruit tree leaves, and then the expanded train sample set is obtained by data augmentation operation, and then the average value is obtained, all the originaltraining set images are averaged, and then randomly scrambled to form a training data set; Then, the cavity convolution neural network model is established, and the disease classification training iscarried out by using the training data set. The characteristic image of the input image is extracted by the cavity convolution layer, and the nonlinear characteristic image of the input image is obtained by using the nonlinear activation function. Pooling layer is used to reduce the convolution layer weight parameters. Using multi-scale convolution kernel to obtain different features of the inputimage, and finally using Soft Max classifier to classify the disease types of the input image; The invention shortens the training time of the model, accelerates the recognition speed and improves the recognition accuracy.

The invention discloses an Alzheimer's disease classification method based on a depth neural network and multi-mode images, and the method comprises the steps: taking the 2D and 3D forms of the images of N classes of Alzheimer's diseases as the input layer, taking the probability of a classification result as the output layer, and constructing a depth neural network; carrying out the preprocessing of the images of N known classes, and obtaining a training sample; training the depth neural network through the training sample, optimizing the weight of network connection, and obtaining a final depth neural network; inputting the preprocessed to-be-classified image into the final depth neural network, and outputting a classification result. In order to make the most of the feature information of the brain of an AD patient, the method introduces the multi-mode image information: an MRI image, a PET image and a DTI image on the basis of a conventional single-mode medical image classification method, and the method achieves the fusion of non-image feature CSF information and gene information.

In some embodiments, disease classification system identifiers are received; disease classification system identifiers are translated into medical literature classification system identifiers; a medical literature database is filtered based on relevance to evidence-based medicine; and medical literature articles are identified from a medical literature database based on medical literature classification system identifiers. Various embodiments add, delete, and modify portions of the claimed methods and apparatuses. In other embodiments, genetic profiles of patients are received; genetic profiles are translated into medical literature classification system identifiers; a medical literature database is filtered based on relevance to evidence-based medicine; and medical literature articles are identified from a medical literature database based on medical literature classification system identifiers. Various embodiments add, delete, and modify portions of the claimed methods and apparatuses. Some embodiments include code on a computer readable medium.

The invention discloses a method and system for determining a disease diagnosis standardized coding recommendation list. The method comprises steps that an international disease classification database, electronic records and disease original diagnosis descriptions are obtained, the disease original diagnosis descriptions are preprocessed, the pre-processed disease original diagnosis descriptionsare inputted into a disease diagnosis classification prediction model, and a set of probability values of the pre-processed disease original diagnosis descriptions in each chapter of an internationaldisease classification database are outputted; a first-level candidate disease standard name database is established based on the set of probability values; a second-level candidate disease standard name database is established based on the first-level candidate disease standard name database; semantic similarity of disease standard names in the second-level candidate disease standard name database and the disease original diagnosis descriptions is calculated; the disease standard name coding recommendation list corresponding to the disease original diagnosis descriptions is determined according to the semantic similarity and provided for a coding main body for reference. The method is advantaged in that work efficiency of the coding main body can be improved.

The present invention belongs to the technical field of a neural network and relates to a WTA neural network based on a memristor array and application thereof. The WTA neural network and application thereof, namely a classifier model, are achieved by a design of the memristor array, thereby proposing a classification identification method that can be used for classifying a dermatology feature. the WTA model consists of a memristor, a MOSFET, a capacitor, a resistor and a power supply. On this basis, the classifier based on memristor array WTA neural network is exported. The purpose of the WTA neural network based on the memristor array and application thereof is to achieve the WTA neural network and theclassifier thereof by the memristor, and to explore the application of the memristor in the neural network and medical decision thereof. Compared to the conventional WTA neural network, the classifier designed by the memristor array in the present invention has excellent properties, and can be applied to the field of medical disease classification and the like.

The invention discloses a mild cognitive impairment disease classifying method based on a brain network and brain structure information, and belongs to the field of medical image disease classification. The method includes the steps that individual cortex characteristics are calculated first, and difference between individual cortices is calculated with a specific kernel function to construct the structural brain network; any number of parameter types are selected from network characteristics and original MRI data to be combined, characteristics are selected from each combined datum through a stepwise judgment method, and the top ten characteristics appearing the most frequently are used as classification characteristics; a support vector machine based on a radial basis function is adopted for classification. The interference on characteristic selection by different combining modes of the cortex characteristics and network characteristics is fully considered, and robustness and stationarity of characteristic selection are ensured. The characteristics with high sensitivity can be selected fast and effectively, and classification precision is improved.

The invention relates to a low-power consumption portable electrocardiograph monitor and a control method thereof, which belongs to the field of medical appliances. The portable electrocardiograph monitor comprises an electrocardiosignal collection unit, an electrocardiosignal analysis and storage unit and an upper computer. The control method comprises the following steps of: step 1: carrying out digital filtering to collected signals by using a wavelet entropy optimal threshold denoising method; step 2: carrying out rejection of baseline drift to the signals processed by step 1 by using a morphological filtering method; and step 3: carrying out disease classification to the signals processed by step 2 by using a support vector machine algorithm. The invention has the advantage of high integrity, and has the characteristics of portability, compactness and economy.

The present invention discloses an image classification method based on a semi-multi-mode fusion feature reduction frame. Namely, a relation between a brain function and structure network feature vectors is maintained to better capture complementation information of a plurality of mode data and allow disease classification accuracy to be further improved. The method provided by the invention fullyexcavates reference information provided by structure network data, selects valid feature data from a brain function network feature vector matrix, and adds a new constraint on the basis of a known K-support normal form to remain distances of different mode feature data. Analysis and experiment results show that the image classification method based on the semi-multi-mode fusion feature reductionframe is better than known KSN and NF-KSN methods in the prior art. Consistency network connection selected in the invention comprehensively considers correlation of the structure and the function network layer with the diseases and is not limited in the function network layer, and the selected consistency network connection taken as disease biomarkers is higher in reliability.

The invention relates to a disease data processing method and device, electronic equipment and a computer readable medium, and relates to the field of medical big data processing. The method comprisesthe steps: obtaining disease data, wherein the disease data comprise at least one disease symptom tag; Performing word segmentation processing on the disease data to generate a vocabulary set; Constructing a symptom set through the vocabulary set, wherein the symptom set comprises at least one disease symptom tag; And inputting the symptom set into a diagnosis model to obtain a disease classification identifier, wherein the diagnosis model is an artificial neural network model. According to the disease data processing method and device, the electronic equipment and the computer readable medium, the disease prediction accuracy can be improved, and a better aid decision is made for diagnosis of clinicians.

The invention relates to a fever disease deep-learning auxiliary diagnosis system based on text medical history for children. The system includes a medical history input module, a text structuralizingmodule, a characteristic extraction module and a result output module, wherein the medical input module is used for receiving the medical history in a free text form; the text structuralizing moduleis linked with the medical history input module and adopts a natural language processing method for processing the medical history in the free text form to obtain structuralized data; the characteristic extraction module is linked with the text structuralizing module and used for extracting diagnosis characteristics from the structuralized data; the result output module is linked with the characteristic extraction module and used for utilizing a neural network to carry out prediction of disease classification on the current medical history records according to the extracted diagnosis characteristics and a diagnosis model learned from the past medical history records, and then outputting diagnosis results. Compared with the prior art, on the basis of a natural language processing and diagnosis model of deep learning, the diagnosis accuracy of the system can exceed a grassroots doctor level, the system can function stably, and therefore a real help can be offered to grassroots medical workers.

The invention provides a CNN and transfer learning-based disease intelligent identification method and system, which can reduce interference of a picture background, can achieve high recognition accuracy under the condition of a limited sample number, and supports higher training sample multi-classification operation efficiency. The disease image identification method comprises the following steps: image preprocessing: normalizing the size of the image, quickly positioning a disease region by using Fast-RCNN, and eliminating background interference; image feature extraction: using a triplet similarity measurement model to extract image features, and then using SIFT features as compensation features to perform weighted fusion; and disease classification and recognition: learning a first image feature of a normal plant image by adopting a deep convolutional neural network, then learning a second image feature of the disease plant image by using transfer learning, and finally performing classification and recognition by combining the first image feature and the second image feature.

A remote healthcare method is applied to a healthcare server and includes the steps of receiving detected information from a physiological parameter detecting instrument, reading a patient identification code and a physiological parameter from the detected information, reading an identification datum and a medical history datum matched with the patient identification code from a history database according to the identification code, reading a disease matched with the disease classification code and a corresponding pathological symptom from the disease database according to at least one disease classification code in the medical history data, screening a corresponding matched pathological symptom from the pathological symptoms according to the read physiological parameter to create a self-perceived list, transmitting the self-perceived list to the physiological parameter detecting instrument, and waiting for a receipt of the selected option of the pathological symptom for determining an appropriate medical dosage or medical tool.

The invention provides a medical text information processing method and device and a computer readable storage medium. The method comprises: preset disease related text information and current medicalrecord data are acquired; wherein the preset disease-related text information represents various disease-related symptom descriptive languages and corresponding symptom crowd information; performingfeature coding on the preset disease-related text information and the current medical record data by utilizing a prediction classification model to obtain disease text features and medical record datafeatures; wherein the prediction classification model is obtained through model training according to preset disease-related text information and historical medical record data, and the correspondingrelation between the medical record data and disease classification is represented through the preset disease-related text information; and carrying out feature fusion and classification processing on the disease text features and the medical record data features through an attention mechanism to obtain a classification result.

The invention relates to an Alzheimer's disease multi-classification diagnosis system based on deep study. The Alzheimer's disease multi-classification diagnosis system comprises an image characteristic extracting module, an index characteristic selecting module, a vector linear merging module and a disease classification and diagnosis module, wherein the image characteristic extracting module is used for extracting characteristic vectors of a cerebral three-orthogonal plane MRI image according to a neural network model; the index characteristic selecting module is used for selecting checking indexes according to medical pertinent literatures to form index characteristic vectors; the vector linear merging module is used for adopting a multivariate data linear merging method based on canonical correlation analysis to merge the characteristic vectors of the image and the index characteristic vectors; and the disease classification and diagnosis module is used for inputting the merged vectors to a multi-classification classifier to distinguish the three stages of the Alzheimer's disease. The Alzheimer's disease multi-classification diagnosis system disclosed by the invention can assist the multi-classification diagnosis of the Alzheimer's disease.

The invention provides a neural fingerprint extraction and classification method. The method comprises an image acquisition step, a brain structure annotation step and a neural fingerprint extractionstep in sequence. The method has the advantages that information mining is performed on magnetic resonance images in different sequences, a unified fingerprint extraction and establishment method forall brain areas is formed through unified structure annotation establishment and positioning, a comprehensive neural fingerprint system is constructed, neural fingerprint standard libraries for different sexes and different ages are established, and therefore brain health assessment, disease classification and parting and disease prediction can be performed on a to-be-tested target; through the establishment of the neural fingerprint standard libraries, quantitative assessment and classification can be performed on brain development, brain senescence, neural diseases and mental diseases of a to-be-tested object, and talent assessment, advantage assessment, senescence assessment, rehabilitation assessment, assistant diagnosis, disease prediction and other technologies can be formed; and a standard technical foundation is laid for advantage cultivation, brain healthcare and diagnosis and prevention of brain diseases of human talents.

The invention relates to a data processing technology, and provides a big data-based medication scheme recommendation method and device, computer equipment and a storage medium. The method comprises the steps: obtaining and carrying out the structural processing of the case symptom information of a patient, and obtaining the target case symptom information; analyzing the target case symptom information to obtain a target entity, and determining the disease classification of the patient based on the target entity; traversing a preset mapping relationship between diseases and diagnosis and treatment according to the disease classification to obtain a target diagnosis and treatment scheme; obtaining drug information carried by the target diagnosis and treatment scheme to obtain an initialized medication scheme; based on a pre-trained drug rule network model, evaluating whether the initialized drug use scheme meets a preset drug use requirement or not; and when the evaluation result is that the initial medication scheme does not accord with the preset medication rule, adjusting the initial medication scheme to obtain a target medication scheme. According to the invention, the accuracy of medication scheme recommendation can be improved, and the construction of smart medical treatment and smart cities is promoted.

The invention discloses a diabetic retinopathy classification method by using a super lightweight SqueezeNet network. The method comprises the following steps: preparing lots of SLO (Scanning Laser Ophthalmoscope) fundus photographs aiming at each type of diabetic retinopathy and performing preprocessing and data amplification; establishing a super lightweight SqueezeNet deep convolutional neuralnetwork containing a fire module; training the deep convolutional neural network based on the lots of fundus photographs, and enabling the final output value of the deep convolutional neural network to accord with the classification results of the fundus photographs; automatically carrying out disease classification by utilizing the trained deep convolutional neural network. According to the method disclosed by the invention, due to application of the lots of fundus photographs comprising diagnostic markers, operations of automatically learning needed features from a training case library andperforming classification judgment are realized by virtue of super lightweight deep learning network and a few parameters, and data features for judgment and deep convolutional neural network parameters are continuously corrected in the training process, so that the classification accuracy and reliability in realistic application scenarios can be greatly improved.

The invention provides a hospitalization service performance evaluation method of medical institutions. The 20th chapter in codes of international classification of diseases ICD-10 is used as main disease classification MDC, for the diseases in each MDC, grouping is conducted continuously according to the top three of the ICD codes of disease categories, standardized processing is conducted on all indexes through the disease category grouping DCGs method, the medical technology difficulty and the service capacity, efficiency and quality of hospitals are evaluated, and therefore the total hospitalization service performance is obtained. The method involves a hospitalization service capacity evaluation method, a hospitalization service efficiency evaluation method, a hospitalization service quality evaluation method and a total hospitalization service performance evaluation method. Through the methods, a hospital performance evaluation model based on hospitalization medical record home page information is established according to core contents of medical service performance, and the purpose of analyzing and studying hospital service performance is achieved.

The embodiment of the invention provides a disease coding method and a disease coding device. The method comprises the steps of acquiring a structured electronic case; carrying out word segmentation on the target field of the structured electronic case to obtain a plurality of keywords, wherein the target field at least comprising a doctor diagnosis description field, a disease part field, a symptom field and an examination result field; searching at least one candidate international disease classification code matched with the plurality of keywords from the medical knowledge base; inputting the candidate international disease classification codes into a machine learning model to obtain a score rank of the candidate international disease classification codes; outputting candidate international disease classification codes and the score ranks thereof; recommending at least one candidate international disease classification code to the user according to the score ranking of the candidateinternational disease classification codes. Therefore, the technical scheme provided by the embodiment of the invention can solve the problem of low accuracy of manual coding by experience in the prior art.

The invention discloses an Alzheimer's disease classification and prediction system based on multi-task learning, and relates to an Alzheimer's disease classification and prediction system. The objective of the invention is to solve the problem that an existing Alzheimer's disease classification system cannot judge whether a mild cognitive impairment individual will be transformed into Alzheimer'sdisease or not. The system comprises a an image processing main module, a clinical index processing main module, a neural network main module, a training main module and a detection main module; wherein the image processing main module is used for acquiring a head image, preprocessing the acquired head image to obtain a preprocessed image, and inputting the preprocessed image into the training main module and the detection main module; wherein the clinical index processing main module is used for selecting clinical indexes, acquiring feature vectors of the clinical indexes and inputting the feature vectors of the clinical indexes into the training main module and the detection main module; and the neural network main module is used for building an Alzheimer's disease classification and prediction model. The system is applied to the technical field of intelligent medical detection.

The invention provides a deconvolution-guided semi-supervised plant leaf disease identification and segmentation method, which uses a small amount of disease category labels and disease spot pixel-level labels to achieve disease category identification and disease spot region segmentation through deconvolution. According to the method, a category prediction label of an unmarked sample is generatedthrough a consistency regularization and entropy minimization method; image mixing is carried out on the marked sample and the unmarked sample, and semi-supervised disease classification is carried out by utilizing the newly generated image; and up-sampling is performed on the category information, and semi-supervised scab segmentation is performed by using a small number of pixel-level marks. Inthe process of model training, model parameters are updated by using exponential weighted average, so that the model is more robust in test data. The method is suitable for identifying and segmentingplant leaf diseases with insufficient label samples, integration of identification and segmentation is achieved, the model has high generalization capacity in leaf images with insufficient light andforeign matter shielding, and the identification and segmentation speed can meet the real-time requirement.