92 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.

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 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.

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.

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

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 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.

The invention relates to the technical field of insurance and intelligent data processing, and discloses an internet health insurance underwriting method and device based on disease characteristics, and the method comprises the following steps: S1, obtaining to-be-underwritten historical insurance data and current insurance application data; s2, constructing a health insurance special word vector library based on the historical insurance data; s3, inputting the feature vector into a pre-trained hierarchical disease recognition model, and obtaining an output disease severity score; s4, generating an underwriting risk prompt according to the disease severity score, and sending the underwriting risk prompt to an underwriting operation terminal; and S5, collecting an artificial underwriting decision result for the underwriting risk prompt, and taking the artificial underwriting decision result as a new training sample to carry out incremental updating on the hierarchical disease recognition model. According to the method, the non-standard disease description analysis precision is improved by fusing the claim co-occurrence word vector and the vertical domain attention mechanism.

The present application relates to a bridge degradation identification and maintenance decision support method and system based on a knowledge graph, belonging to the technical field of intelligent operation and maintenance of transportation infrastructure and engineering information processing, and solving the defects of weak data fusion capability, opaque causal modeling and non-traceable decision support of the prior art. The method comprises: acquiring and preprocessing multi-source heterogeneous data to generate a standardized data set; performing semantic alignment to extract candidate entities and relationships; constructing a knowledge graph ontology and loading it as a knowledge graph; using an adaptive quantile threshold to binarize the factor intensity and disease severity sequence, and fusing a one-point smoothing mutual information and a Spearman correlation coefficient to learn the causal weight; completing the bridge factor influence relationship and calculating the weight through graph reasoning; and finally calculating the bridge degradation risk score and outputting the high-confidence influence path based on the shortest path algorithm. The present application realizes multi-source data fusion and explicit modeling of the degradation causal chain, and provides a scientific basis for bridge maintenance decision-making.

The invention discloses a biomarker for aortic dissection detection and an application of the biomarker. The biomarker is one or more of MDK, NTN1, CCL21, LIPC, DNASE1L3, TFPI2, CTSG, HAMP, CXCL14, PLA2G2A, SCUBE1, GZMK, HP1BP3, SRP14 and TOP1. The biomarker is obtained by screening plasma of a patient with aortic dissection through plasma proteomics, and shows relatively high sensitivity and specificity. ELISA verification proves that the biomarker can be used as an early diagnostic biomarker of the aortic dissection, and can also be used as one of indexes for evaluating the disease severity of AD patients, so that a new direction is provided for clinical early diagnosis and long-term prognosis evaluation of the aortic dissection.

Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic system to assess pulmonary function and risk of mortality in patents with coronavirus disease 2019. The CAD system processes thoracic X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients. An alternative approach is to use an automatic CAD system to grade COVID-19 from computed tomography (CT) images to determine an accurate diagnosis of lung function.

The embodiments of the present specification provide a training method, a classification method, an apparatus and a computer device of an image classification model. The method comprises: obtaining mild medical images and severe medical images respectively labeled with disease types; training an initial image classification model based on a severe medical image subgraph and a disease type corresponding to the severe medical image subgraph to obtain a primary image classification model; inputting a mild medical image subgraph into the primary image classification model to obtain a predicted disease type of the mild medical image subgraph; if the predicted disease type of the mild medical image subgraph is the same as the disease type of the mild medical image to which the mild medical image subgraph belongs, training the primary image classification model using the mild medical image subgraph to obtain an image classification model. By focusing on the influence of disease severity on lesion distribution density, without labeling the medical image subgraph, the accuracy of the image classification model in identifying the disease type in the medical image can be improved to a certain extent.

The invention discloses an application of KL-6 alone or in combination with other immune inflammation markers in complex silicosis recognition and lung function injury evaluation. The invention firstly discloses application of KL-6 in preparation of a product for complex silicosis identification and lung function injury evaluation. The invention further discloses application of the KL-6 combined with other immune inflammation markers in preparation of products for complex silicosis recognition and lung function injury evaluation. According to the invention, KL-6 is taken as a core, LDH and ESR are combined to construct a diagnosis scheme, and complex silicosis and dust exposed persons or simple silicosis can be distinguished; meanwhile, KL-6 is in significant negative correlation with lung function parameters and can assist in evaluating the severity of diseases and the condition of lung function impairment. The method is rapid and accurate in diagnosis, easy and convenient to operate, controllable in cost, capable of achieving batch detection, suitable for health monitoring of high-risk occupational people and capable of providing efficient technical support for early recognition of complex silicosis, lung function injury evaluation and clinical decision making.

The application provides a satellite communication-based offshore first-aid information transmission method and system. A temporary dedicated satellite communication link is constructed, and the temporary dedicated satellite communication link is parameter-optimized to obtain an optimized communication link. A field environment is three-dimensionally reconstructed, a disease severity description is analyzed, a disease development is predicted, and a disease prediction result is generated. The distribution strategy of special medical supplies is optimized by considering factors such as weather changes and sea current directions, and an optimal distribution path plan is obtained. Pattern recognition and trend prediction are performed, a potential risk warning is generated, and updated medical needs are formed. The optimal distribution path plan is re-evaluated and optimized, and an updated optimal distribution path plan is generated. The technical scheme provided by the application improves the efficiency and accuracy of offshore first-aid response, and ensures that medical supplies can be timely and accurately delivered to the first-aid site.

Disclosed are method and apparatus for identifying biomarkers and in particular for identifying biomarkers for use in making clinical assessments, such as early diagnostic, diagnostic, disease stage, disease severity, disease subtype, response to therapy or prognostic assessments. In one particular example, the techniques are applied to allow assessments of patients suffering from, suspected of suffering from, or with clinical signs of SIRS (Systemic Inflammatory Response Syndrome) being either infection-negative SIRS or infection-positive SIRS.

Methods of assessing disease severity in a subject with multiple sclerosis using item response theory and the Expanded Disability Status Scale are described Related methods of assessing the effect of a therapeutic, and selecting subjects for treatment or participation in a clinical trial are also described.

The application discloses a sample detection system and a sample detection method, and the sample detection system comprises a conveying device, an identification device and an uncapping device, the conveying device comprises a guide rail mechanism and a tube seat, the guide rail mechanism is provided with an identification station and an uncapping station; the identification device comprises an identification detection mechanism and a carrying manipulator; the identification detection mechanism comprises an identification module, the carrying manipulator can grab or release a sample tube, and the carrying manipulator can also drive the sample tube to lift or rotate; the uncapping device comprises an uncapping tube holding mechanism and an uncapping manipulator. The identification module obtains image information of the sample tube, obtains whether blood samples in the sample tube have hemolysis, jaundice and chyle phenomena and disease severity information through analysis, thereby identifying abnormal samples which have a greater influence on detection errors, and in addition, the above information can also be used for correcting analyzer analysis results, thereby reducing analysis errors and improving analyzer working efficiency.

A computer-implemented method of predicting disease severity in a subject includes receiving, at a generalized additive model, input data corresponding to a set of one or more features of the subject, and generating a predicted disease severity score for the subject as an output of the generalized additive model, wherein generating the predicted disease severity score for the subject includes processing the received input data using the generalized additive model.