128 results about "Disease severity" patented technology

The invention relates to a hierarchical diagnosis and treatment resource allocation system based on an AI priority pre-auditing mechanism, and belongs to the technical field of medical resource allocation, and the system comprises a user input module which is used for receiving initial information of a patient based on an AI applet, and the initial information at least comprises a symptom description, a medical history record, examination data and a diagnosis and treatment stage; the AI intelligent pre-inquiry module is used for performing multiple rounds of questions and answers on the patient according to the initial information to extract an illness state index, inputting the illness state index into a preset risk layering model, and determining an illness state severity / urgency index of the patient; the resource distribution module comprises a special number source pool reserved for experts, and the resource distribution module is used for opening expert numbers for the patients whose illness condition severity / emergency index is greater than an illness condition threshold value; and the referral guidance module is used for recommending a replacement doctor seeing scheme for the patient who does not accord with the open expert number. According to the invention, the technical problem of insufficient fairness and efficiency of an illness state emergency degree registration distribution mechanism caused by structural mismatching of expert resources is solved.

The invention relates to the field of agricultural intellectualization, in particular to a crop disease and insect pest real-time identification and analysis system based on deep learning, which comprises an image acquisition unit, a mobile control unit, an image identification unit, a disease evaluation unit and a disease and insect pest prediction unit, the core innovation of the invention lies in that an image recognition unit introduces a Riemannian geometry multi-scale manifold learning framework and comprises a manifold construction module, a manifold feature fusion module and a manifold constraint optimization module, and the manifold construction module maps crop image features to a Riemannian manifold space; the manifold feature fusion module fuses multi-scale feature manifolds through geodesic line connection and parallel transmission; and the manifold constraint optimization module executes network parameter optimization in a Riemannian space. The disease evaluation unit evaluates the severity of the disease based on the identification result; the disease and pest prediction unit predicts the disease development trend based on historical data and environmental information, the disease and pest recognition precision is remarkably improved, and particularly the rare disease recognition capability is improved by 23%.

The invention relates to a bridge degradation identification and maintenance decision support method and system based on a knowledge graph, belongs to the technical field of traffic infrastructure intelligent operation and maintenance and engineering information processing, and solves the defects of weak data fusion capability, opaque causal modeling and non-traceable decision support in the prior art. The method comprises the following steps: acquiring and preprocessing multi-source heterogeneous data to generate a standardized data set; semantic alignment is carried out, and candidate entities and relations are extracted; constructing a knowledge graph body and loading the knowledge graph body into a knowledge graph; a self-adaptive quantile threshold value is adopted to binarize the factor intensity and the disease severity sequence, and a smooth point mutual information and a Spearman correlation coefficient are fused to learn a causal weight; complementing the influence relation of factors on the bridge through graph reasoning and calculating the weight; and finally, calculating a bridge degradation risk score, and outputting a high-confidence influence path based on a shortest path algorithm. According to the method, multi-source data fusion and degradation causal chain explicit modeling are realized, and a scientific basis is provided for a bridge maintenance decision.

The present invention uses ex vivo human airway cultures to assess the human transmissibility and replication competence of influenza and coronavirus strains. By comparing pandemic influenza A subtype H1N1 and highly pathogenic avian influenza H5N1 as reference strains, the transmissibility risk of various viruses was evaluated and categorized. Additionally, an in vitro model evaluated virus-induced impairment of alveolar fluid clearance (AFC) as an indicator of disease severity. The study revealed correlations between bronchus viral replication, human transmission, AFC impairment, and clinical disease severity across different influenza and coronavirus strains.

The invention discloses a multi-classification auxiliary prediction method and system for metabolism-related fatty liver diseases, electronic equipment and a storage medium, belongs to the field of medical data processing, and is used for solving the problem that a traditional MAFLD binary prediction model cannot fully reflect continuous pedigree of disease severity. The method comprises the following steps: acquiring clinical data related to classification prediction of metabolism-related fatty liver diseases; performing label coding, missing value filling and feature screening on the clinical data; and inputting the screened features into a TabNet classification model optimized by a particle swarm algorithm, and outputting a risk classification result of the metabolism-related fatty liver disease. According to the method, the severity of the fatty liver can be more accurately evaluated, the liver health condition of a patient is comprehensively reflected, and more accurate treatment guidance is provided for clinical diagnosis.

The invention belongs to the technical field of biomedicine, and particularly relates to application of GPR161 as an acute distress syndrome diagnostic marker and a therapeutic target. Experiments prove that the expression level of GPR161 in peripheral blood mononuclear cells of a patient with the acute respiratory distress syndrome is remarkably increased, and the expression quantity of the GPR161 is positively correlated with the severity of the disease, so that the GPR161 gene or GPR161 protein can be used as a molecular marker for screening or diagnosis or prognosis evaluation of the acute respiratory distress syndrome; a reagent for inhibiting GPR161 gene expression or protein activity can be used for preparing a medicine for treating the acute respiratory distress syndrome. The invention provides a new strategy for diagnosis, monitoring and targeted drug development of ARDS, and has important clinical application value.

Provided herein are systems and methods for identifying a disease or disorder of a patient, identifying if a patient is likely to respond to a treatment for the disease or disorder, and / or predicting the clinical outcome of the disease or disorder of a patient. Systems and methods described herein may be directed to patients with different chronic conditions, inflammatory conditions, and / or autoimmune conditions. Systems and methods described herein may be directed to patients with a dermatological condition.

The invention provides an airway atomization flow intelligent drug delivery control method and system, and relates to the technical field of medical instruments.The method comprises the steps that basic information of the age, the weight and the illness state severity of a patient is obtained, and physiological parameters of the respiratory rate and the tidal volume of the patient are collected in real time through a sensor; inputting the basic information and the physiological parameters into a fuzzy adaptive control algorithm, and initializing an individual administration rule base according to the basic information; based on the real-time change trend of the respiratory rate and the tidal volume, three initial control index values including an atomization flow value, a drug concentration value and a time interval are dynamically generated through a membership function. According to the invention, through the full-process design of data acquisition, intelligent modeling, accurate execution and data closed loop, intelligentization of atomization treatment is realized.

The invention discloses a potato late blight severity grading calculation system and method based on YOLOv8-UNet3Plus, and the system comprises an image collection module which is used for collecting a shadow-free potato leaf image through employing a bidirectional light source and a fixed pose camera; the leaf positioning module is used for optimizing a YOLOv8 network by using a space and channel redundant convolution ScConv and a bidirectional feature pyramid network BiFPN, positioning a plurality of potato leaves in the potato leaf image by using the optimized YOLOv8 network, and outputting bounding box information of each leaf; the scab segmentation module is used for optimizing the UNet3Plus network to obtain a lightweight UNet3Plus network, performing pixel-level segmentation on each leaf area by using the lightweight UNet3Plus network, and distinguishing a healthy area from a disease area; the grading calculation module is used for calculating the disease spot area proportion and dividing the disease severity grade of each leaf; according to the method, lightweight construction of the leaf positioning and scab segmentation network is realized, and the model parameter quantity, the calculation quantity and the terminal deployment cost are remarkably reduced.

The application provides a tobacco virus classification model construction method based on tobacco leaf hyperspectrum, and belongs to the technical field of tobacco virus classification models. Fractional differential spectrum enhancement processing is adopted, persistent homology eigenvalues and Betti number sequence values are calculated through spectrum topological manifold embedding of enhanced spectrum data, dimension reduction is performed through kernel principal component analysis to obtain topological embedding feature vectors, fusion feature vectors are output through a multimodal attention fusion module in combination with thermal infrared temperature field data and chlorophyll fluorescence kinetic curve data, data enhancement is performed on minority class samples by using a spectral generative adversarial network, the fusion feature vectors are input into a virus classification and recognition model adopting a focal loss function and a dynamic network adjustment mechanism for training, virus type classification and disease severity grade recognition of tobacco leaves to be tested are realized, and the problem that different types of tobacco viruses cannot be accurately recognized and distinguished in the early stage of disease is solved.

This invention relates to a grading method for diabetic retinopathy based on a local-global interactive dual-branch network, belonging to the fields of deep learning, medical image analysis, and computer-aided diagnosis. It includes: inputting the generated initial feature sequence into a global scanning module; using dilated reparameterized convolution to capture multi-scale structural prior information; inputting the feature map with structural prior information into a core dual-branch LoGo module; the local flow extracting fine-grained lesion features through depthwise separable convolution; the global flow generating spatially variable convolution kernels based on the global context through contextual hybrid dynamic convolution; and performing bidirectional cross-modulation through an adaptive multi-scale feature interaction aggregation module to sharpen local details for global semantics and filter local noise from the global context, ultimately outputting a disease severity grade. This invention explicitly simulates the cognitive strategies of clinicians, significantly improving grading accuracy while maintaining computational efficiency.

Embodiments of computerized deep-learning systems and methods for processing medical videos to assess disease severity of a patient are disclosed. In one or more embodiments, an encoder is configured for encoding a plurality of video frames corresponding to the medical videos to obtain respective frame embeddings corresponding to respective frames of the plurality of video frames, wherein the encoder has been pre-trained using self-supervised learning. One or more video frame classifiers, each comprising an attention-based deep learning network, processes the respective frame embeddings and computes, on a frame-by-frame basis, respective frame-level inferences corresponding to the respective frames. A video analyzer comprises an attention-based deep learning network and uses the respective frame-level inferences and the respective frame embeddings to calculate at least one disease severity assessment of the patient.

The present invention discloses an intelligent TCM ear diagnosis auxiliary system, comprising: an ear data acquisition module for collecting pathological image data, temperature percentage data, and humidity percentage data from the patient's ear and performing preprocessing; a pathological image data analysis module for matching and analyzing the pathological image data to generate disease diagnosis data; a temperature and humidity percentage data analysis module for integrating and analyzing the temperature percentage data and the humidity percentage data to generate disease severity data; an accuracy analysis module for integrating and analyzing the disease diagnosis data and disease severity data to generate accuracy data; and a disease output module for outputting a diagnosis result and error level based on the disease diagnosis data and accuracy data. The present invention improves the authenticity and accuracy of diagnosis and reduces the incidence of misdiagnosis.

The present invention discloses a set of diagnostic markers for aortic dissection and their applications. The markers include the genes MMP8, ALOX15, HP, FXYD2, SIGLEC8, and CCL2. The genes MMP8, ALOX15, HP, FXYD2, SIGLEC8, and CCL2 protected by the present invention can be used as biomarkers for AD and as indicators for assessing disease severity in AD patients, enabling efficient diagnosis of patients with aortic dissection.

The application relates to the technical field of pest and disease identification, in particular to a dynamic early warning and prevention and control system based on forestry pest and disease identification, which obtains crown layer leaf attribute data of target forest area tree species and excitation source physical characteristic parameters of a patrol unmanned aerial vehicle, determines aerodynamic pressure required for turning over of the crown layer leaves according to the leaf attribute data, determines aerodynamic excitation control parameters in combination with the excitation source physical characteristic parameters and a final parameter mapping model, controls the unmanned aerial vehicle to force the leaves to turn over by using a downward airflow to obtain leaf back image data, and adaptively corrects the control parameters according to an effective leaf back exposure rate; image recognition is performed on the leaf back image data to obtain pest and disease characteristic data, and a leaf stiffness index is calculated through time domain analysis; pest and disease severity is generated according to the pest and disease characteristic data and the leaf stiffness index, and a graded early warning is performed; the application can effectively obtain leaf back image information, and realizes early and accurate identification and dynamic early warning of pests and diseases.

This invention discloses the application of platelet-reactive protein-4 (THBS4) in the diagnosis and treatment of endometriosis. Through bioinformatics integration analysis, clinical sample validation, and in vitro and in vivo experiments, this invention confirms that THBS4 is significantly highly expressed in both ectopic lesions and peripheral blood of patients with endometriosis, and its expression level is positively correlated with disease severity. The area under the receiver operating characteristic (AUC) curve for THBS4 alone in diagnosing endometriosis is 0.930, significantly superior to cancer antigen 125 (CA125); the AUC for the combined diagnosis of THBS4 and CA125 reaches 0.968. Simultaneously, silencing the THBS4 gene effectively inhibits the proliferation, migration, and invasion of human endometrial stromal cells, and significantly reduces the volume and fibrosis area of ​​ectopic lesions in a mouse model of endometriosis. This invention provides a highly sensitive and specific new biomarker for the non-invasive or minimally invasive early diagnosis of endometriosis, and provides new targets and candidate drugs for non-hormone-dependent targeted therapy.

The invention uses ex vivo human airway cultures to assess the transmissibility and replication ability of influenza and coronavirus strains. By comparing the pandemic influenza A H1N1 and the highly pathogenic influenza A H5N1 as reference virus strains, the spreading risks of various viruses are evaluated and classified. In addition, an in vitro model is used to assess the degree of alveolar fluid clearance (AFC) impairment caused by the virus as an indicator of the severity of the disease. The research reveals the relevance among the virus replication ability, human transmission, AFC damage degree and clinical disease severity of different influenza and coronavirus strains in bronchus.

To analyze an image of a body to determine whether the image involves a level of change of a severity of a medical condition.SOLUTION: Methods, systems and computer programs for monitoring skin condition of a person. In one aspect, a method can include: obtaining data representing a first image, the first image depicting skin from at least a portion of a body of a person; generating a severity score that indicates a likelihood that the person is trending towards an increased severity of an auto-immune condition or trending towards a decreased severity of an auto-immune condition; comparing the severity score to a historical severity score, where the historical severity score is indicative of a likelihood that a historical image of the user depicts skin of a person having the auto-immune condition; and determining based on the comparison, whether the person is trending towards an increased severity of the auto-immune condition or trending towards a decreased severity of the auto-immune condition.SELECTED DRAWING: Figure 1

The invention discloses a wheat stinking smut infection degree detector and method based on an array type gas sensor. The method comprises the following steps: S1, acquiring sensor response data of wheat samples with different infection degrees by using an instrument; s2, measuring the trimethylamine content of the wheat sample, and taking the measured TMA concentration as a reference basis of the disease degree; s3, according to the TMA content distribution condition, dividing the wheat samples into three types, namely healthy, mild infection and severe infection; a sensor response signal is used as model input, the disease level corresponding to the TMA measured value is used as a model label, and a data set is constructed; s4, based on a deep learning framework, establishing a wheat stinking smut infection degree grading model; and S5, collecting gas sensor response data of a to-be-detected wheat sample, inputting the data into the trained model, automatically judging the severity of the disease by the model, and outputting a classification result. The volatile gas characteristics of the wheat sample can be analyzed in real time without destroying the wheat sample, and the disease assessment result is output in combination with the intelligent analysis model.

The application discloses a kind of quantitative evaluation methods of fish bubble disease degree under TDG supersaturation stress, first place fish under supersaturation stress under microscope respectively observe the amount of attached bubble of tail fin, anal fin, ventral fin, pectoral fin and dorsal fin and bleeding symptom, and take the photo of each fin;According to the total area S of photo measurement each fin, bubble coverage area S1, bleeding area S2;Calculate the bubble amount severity of each fin α1 and bleeding symptom severity α2;According to the size of α1 and α2, scoring is carried out, and the score t of each fin is calculated;According to the score of each fin, the comprehensive score T of fish is calculated;Determine the degree of fish bubble disease under TDG supersaturation stress.The application fills the vacancy of technical system of quantitative evaluation of fish bubble disease severity, and enriches the theoretical basis of water conservancy total dissolved gas supersaturation on fish impact evaluation.

In some aspects, a method, a system, or a non-transitory computer-readable storage medium are described for training one or more models to predict ulcerative colitis (UC) severity based on human-interpretable image features extracted from a whole-slide image, including acts of accessing a plurality of annotated whole-slide images associated with a plurality of UC patients, wherein each of the plurality of annotated whole-slide images includes at least one annotation describing a cell-type label or a tissue-type segmentation for a portion of the whole-slide image, extracting a plurality of human-interpretable image features based on cell-type labels and tissue-type segmentations associated with the plurality of annotated whole-slide images, training a statistical model based on the plurality of human-interpretable image features to predict the UC severity for a whole-slide image, and storing the trained model on at least one storage device.

The application discloses a Parkinson disease severity evaluation method and device based on feature fusion, and the method comprises the following steps: inputting a training set of a target patient into a bidirectional long short-term memory network trained by all patient data to obtain shared parameter features; selecting a Markov boundary of a unified Parkinson disease rating scale (UPDRS) of the target patient from the shared parameter features by using an incremental association Markov boundary algorithm; inputting the training set of the target patient into a bidirectional long short-term memory network trained by target patient data to obtain specific parameter features; fusing the Markov boundary of the UPDRS and the specific parameter features, and inputting the same into a full connection layer network to obtain the Parkinson disease severity of the target patient. The above technical scheme learns information from other tasks to make up for the insufficient amount of target patient data, and effectively improves the accuracy of Parkinson disease severity prediction.

The invention relates to the technical field of data analysis, can be applied to the field of medical health, and provides a vaccine recommendation method and device, equipment and a storage medium. According to the method, the matching degree of a target user and candidate vaccines is determined by obtaining validity scores corresponding to the candidate vaccines, adverse reaction probabilities corresponding to the candidate vaccines, risk probability values of target infectious disease infection of the target user and disease severity, and the target infectious disease is detected according to the matching degree of the target user and various candidate vaccines. When the method is applied to the field of medical health, the accuracy of vaccination can be improved, and the vaccination safety of the user can be guaranteed.

In the present invention, cyclic dinucleotides or their compositions are used as biological response modifiers to enhance the immune response of subjects with deficient immune systems, with a low or absent response to vaccine antigens, or with a deficient immune response to pathogens. The formulations of the present invention are capable of modifying the biological response in such a way as to eliminate the lack of response, resulting in the development of a high-avid immune response that prevents disease caused by infectious agents, particularly viruses with the ability to evade immune surveillance mechanisms and, consequently, suppress the induction of an effective, high-avid adaptive response.The present invention also comprises cyclic dinucleotide formulations, which can be administered alone, in combination with other biological response modifiers, or in vaccine formulations with antigens. The proposed invention further includes a method for preventing or treating acute respiratory infections, preventing disease severity, and blocking transmission by inducing an immune response at the point of entry (nasopharyngeal mucosa), preventing or reducing person-to-person transmission of pathogens / viruses in preventive vaccine formulations, as well as in formulations used for prophylaxis before and after exposure to acute respiratory infections.

The invention discloses an acute necrotizing pancreatitis severe prediction model construction method based on a machine learning method. The acute necrotizing pancreatitis severe prediction model construction method comprises the following steps: S1, collecting patient data based on an inclusion standard and an exclusion standard; s2, data preprocessing and feature selection; s3, obtaining an optimal differential diagnosis prediction model in combination with radiomics characteristics and a plurality of machine learning algorithms; s4, obtaining an evaluation index through prediction performance comparison; and S5, constructing a pancreas model, a pancreas surrounding model and a combination model, and producing an ROC curve interpretation result. The present invention develops and verifies a machine learning model for distinguishing between severe and moderate ANPs (i.e., ANSP and ANMSP). These models will be based on radiomics features extracted from pancreatic parenchyma portal vein phase CECT images, peripancreatic necrotic lesions, and combinations thereof. By evaluating the diagnostic performance of the models, early recognition of disease severity is realized, and support is provided for clinical decisions related to treatment.

This application provides a dietary recommendation method and system based on multi-dimensional data analysis. The method includes: obtaining a set of modern nutritional attributes and a set of traditional Chinese medicine (TCM) theoretical attributes of the target food; obtaining a TCM constitution suitability score by matching the TCM theoretical attribute set with the user's TCM constitution type using a first rule; obtaining a disease nutritional suitability score by matching the modern nutritional attribute set with the user's list of diagnosed diseases using a second rule; determining whether there is a dietary recommendation conflict based on the TCM constitution suitability score and the disease nutritional suitability score; if a dietary recommendation conflict exists, assessing the severity of the disease and the deviation of the constitution for the user based on the list of diagnosed diseases and the TCM constitution type, respectively, to obtain corresponding disease severity indicators and constitution deviation indicators; and generating dietary recommendations for the target food based on the disease severity indicators and the constitution deviation indicators, thereby improving the accuracy and rationality of dietary recommendations.

The invention belongs to the technical field of medicines, and discloses application of a compound M0 as shown in a formula (I) and pharmacodynamically acceptable salts thereof in preparation of medicines for preventing, relieving and / or treating autoimmune demyelination diseases. On a mouse experimental autoimmune encephalomyelitis model, the compound M0 shows a very good treatment effect, can effectively prevent and treat pathological changes and disease progression of diseases, relieve the severity of the diseases and inhibit LPS-stimulated microglial BV2 cells from releasing inflammatory mediators, and provides a new drug and a new structure for clinical treatment. # imgabs0 #

The application discloses a Parkinson disease severity recognition method based on tensor ring decomposition and region segmentation, relates to the technical field of machine learning recognition, and comprises the following steps: collecting VGRF signals, accurately dividing the VGRF signals according to target personnel indexes, gait window indexes, foot indexes, set region indexes and sampling point time indexes, constructing a five-order time domain tensor, and improving the learning ability for large-dimension tensors; converting the five-order time domain tensor into a frequency domain five-order tensor, extracting low-rank structures in the frequency domain five-order tensor as core tensors, reducing the data dimension of calculation, and improving the learning power of a classification model; extracting statistical features of patients in gait, feet and each set region in the core tensors, further maintaining the correlation features between each dimension of data on the basis of reducing the data dimension of calculation, and then accurately distinguishing the severity of Parkinson disease of the patients and improving the accuracy of the classification model in recognizing the disease severity of the patients.

The invention provides a classification model training method and device for unbalanced medical images, electronic equipment and a storage medium, and relates to the field of biological information. The method comprises the steps that a training batch is acquired, the training batch comprises multiple frames of medical images, and each frame of medical image has a corresponding disease severity label; according to the disease severity labels, the categories of the disease severity in the training batch are counted, each category corresponds to one image sample set, and each image sample set comprises at least one frame of medical image; calculating a prediction error according to the image sample set to obtain an error set; performing normalization and smoothing according to the error set to obtain a category-level error; and according to the category-level error, updating parameters of the classification model to obtain a target classification model. Therefore, by constructing a disease-oriented sample space and designing a brand-new disease-oriented loss and a corresponding classification model based on the space, the classification result of the unbalanced medical image is improved.

This invention discloses a method for improving a mouse model of ANCA-associated vasculitis induced by anti-GBM antibodies, relating to the field of animal model construction. The method involves subcutaneously injecting mice with 20 μg rMPO on Day 0; administering 10 μg rMPO on Day 7; subcutaneously injecting 250 μg / kg GCSF daily from Day 11 to 15; and injecting 100 μl of goat anti-GBM serum via the tail vein on Day 16 to obtain the improved mouse model. This method improves the existing anti-GBM antibody-induced ANCA-associated vasculitis mouse model by injecting GCSF. This model enhances the disease severity in ANCA-associated vasculitis mice, specifically reflecting the disease in renal pathology and proteinuria, making the differences more obvious and effectively improving the success rate of model establishment.