61 results about "Disease stages" patented technology

The invention provides a method for establishing a diagnosis and treatment system for digestive system disease multi-modal information. The method comprises the following steps: S1, collecting multi-modal information for labeling and preprocessing; s2, extracting a feature vector and embedding a label into the multi-modal information according to the labeled information; s3, splicing and mapping the feature vector and the tag to a unified dimension to obtain an enhanced feature vector; s4, fusing the enhanced feature vectors to form a multi-modal feature matrix, performing linear mapping and weighted aggregation on the multi-modal feature matrix to obtain global fusion vectors, and collecting to generate a fusion vector sequence; s5, enhancing the time sequence information of the global fusion vector sequence, enhancing the spatial information of the spatial relevance of the specific feature of the part, and performing interactive fusion to obtain a spatio-temporal joint feature; s6, performing classification prediction on the disease stage or the specific pathological type, and outputting a diagnosis result; and S7, performing semantic association on the diagnosis result and the medical knowledge graph, sharing data to an online health intelligent platform, and providing a personalized decision basis for clinicians.

The present invention relates to a multi-modal colorectal cancer pre-diagnosis information processing method, system, medium, and device. By converting image information into first-modal features, first clinical information into second-modal features, and family history information into third-modal features, and then performing feature fusion and inputting it into a neural network model of deep learning. Finally, the first pre-diagnosis report output can provide information on the disease risk level, stage feature prediction, canceration probability, and treatment plan of the current user. The multi-modal technology is applied to integrate various information sources such as the patient's personal information, image information, first clinical symptoms, and family history information, improving the comprehensive analysis of the colorectal diseases of the current user, enabling doctors to obtain a more comprehensive disease assessment, and thus achieving a more accurate risk level classification, disease stage prediction, and personalized treatment plan formulation, improving the efficiency and accuracy of the diagnosis and treatment process.

The invention relates to a multi-modal colorectal cancer pre-diagnosis information processing method and system, a medium and equipment, and the method comprises the steps: converting image information into a first modal feature, converting first clinical information into a second modal feature, converting family medical history information into a third modal feature, carrying out the feature fusion, and inputting the features into a deep learning neural network model. The finally output first pre-diagnosis report can provide disease risk level information, disease stage feature prediction information, disease cancerization probability information and disease treatment plan information of the current user, and a plurality of information sources such as personal information, image information, first clinical symptoms and family medical history information of the patient are integrated by applying a multi-modal technology; according to the method, comprehensive analysis of the colorectal disease of the current user is improved, so that a doctor can obtain more comprehensive disease assessment, more accurate risk grade division, disease stage prediction and personalized treatment scheme making are further realized, and the efficiency and the precision degree of the diagnosis and treatment process are improved.

The invention discloses a cardiovascular and cerebrovascular disease risk prediction method and system based on multiple modalities, and particularly relates to the technical field of medical prediction.The method comprises the steps that frequency domain decomposition is conducted on all modal signals of standardized time sequence data, and rhythm component time sequence signals corresponding to a preset physiological frequency band are extracted; performing Hilbert transform on the rhythm component time sequence signal to obtain an instantaneous phase of the rhythm component time sequence signal; based on the instantaneous phase difference between different modal rhythm components, stability quantification is carried out to obtain the phase synchronization intensity; constructing a multi-modal physiological rhythm synchronous dynamic network by taking the rhythm components as nodes and taking the phase synchronization intensity as a connecting edge weight; according to the method, through deep mining of multi-physiological-mode time sequence data in a continuous time window, phase synchronization dynamic changes among different-mode physiological rhythms are accurately captured, and the multi-mode physiological rhythm synchronous dynamic network construction and variation connected edge identification are combined; the normal fluctuation of the physiological system and the abnormal signal at the early stage of the disease can be effectively distinguished.

The invention discloses a return anxiety intervention method and system based on VR scene self-adaption and data feedback, and relates to the technical field of return anxiety intervention methods.The return anxiety intervention method comprises the steps that occupational attributes, disease stages and psychological state evaluation data of a user are collected, and physiological baseline information obtained by wearable equipment is combined; constructing an initial user portrait containing individual features and function states; matching a preset intervention path template according to the initial user portrait, and generating a personalized VR training scheme with a module sequence, a scene difficulty level and a training period; executing the personalized training scheme in a virtual reality environment, carrying out commuting simulation, task operation and social interaction training through an immersive office scene, and synchronously collecting multi-modal physiological response data of a user in a training process; the anxiety level of the user is dynamically evaluated based on physiological response data in the training process, and when it is detected that the anxiety state exceeds a preset tolerance range, the stimulation intensity of the current scene is automatically reduced, and a relaxation bootstrap program is started.

An Interferon Gamma Inducible Protein 16 (IFI16) mutant gene and its use as a marker for predicting, diagnosing, or prognosticating risk or severity of chronic liver disease is described. As a result of performing genomic analysis on NAFLD and NASH patient groups, it was confirmed that the frequency of IFI16 single-nucleotide variants (SNVs) including rs2276404, rs73021847, rs7532207, and rs6940 was increased, and the expression of the IFI16 mutant gene was increased depending on the disease stage of liver disease. The IFI16 SNV was highly expressed in infiltrating macrophages, playing a role in macrophage-induced inflammatory processes, and the IFI16 variant bound more strongly to dsDNA than wild-type IFI16, exacerbating the impaired mitochondrial DNA-sensing response signaling of the IFI16-PYCARD-CASP1 pathway. Thus, the IFI16 mutant gene may be used for predicting, diagnosing, or prognosticating risk or severity of chronic liver disease.

This invention provides a method, system, and apparatus for dynamic fusion of multi-scale health time-series features, relating to the field of dynamic feature fusion technology. The method includes: collecting multi-source health data from subjects, resampling it onto a time grid to obtain aligned data; extracting single-point feature vectors at each time point in the time grid to form a feature sequence; weighting the single-point feature vectors within a time window according to time decay weights, using a time window as the decreasing unit; calculating the moving average feature across multiple time scales; inputting the moving average feature into an attention scoring network to calculate the relevance score of each time scale within the current time window, and obtaining attention weights through normalization; and weighted fusion of the moving average feature to obtain a dynamic feature vector. This solution can adaptively adjust the focus on short-term or long-term information according to different users, different disease stages, and different scenarios, improving the model's generalization ability.

The present invention relates to methods of diagnosing melanoma in a subject, the method comprising detecting microRNA expression levels. The present invention also relates to methods of assessing the disease stage of melanoma and / or monitoring said melanoma stage. Also, the present invention relates to selecting a treatment or modifying a treatment based on the diagnosis or stage of melanoma. Further the present invention relates to a system for detecting and diagnosing melanoma in a patient and for determining the disease stage of melanoma.

According to the invention, atrial fibrillation and hypertension are combined, and a novel HFpEF double-strike animal model is developed. Specifically, the animal model disclosed by the invention can stably develop to a mild structure and functional disease stage within three weeks of double strike, reaches an end stage within two subsequent weeks and gradually dies, and the animal model is highly overlapped with the pathophysiological phenotype of the existing human HFpEF. The animal model stably and rapidly develops in a staged manner, is easy to implement, and provides a valuable tool for molecular mechanism research and drug development of disease occurrence and development.

Methods and systems for patient stratification include learning (404) interdependent biomarkers as integrated time-series machine learning models. A disease stage is identified (424) for a patient based on collected biomarker data. A treatment for the patient is performed (430) based on the identified disease stage and a predicted future response of the patient.

The application discloses an Alzheimer's disease stage recognition method and system based on an interpretable multi-modal, and the method comprises the following steps: acquiring and preprocessing sMRI images of Alzheimer's disease patients and corresponding clinical texts to generate a clinical data set; constructing a multi-modal enhancement fusion model comprising an image feature extraction channel, a text feature extraction channel, a Mamba global sequence module embedding an image feature extraction channel and a feature fusion module based on a gating mechanism; extracting image features and text features of the clinical data set, and performing cross-modal interaction by using a multi-head attention mechanism to generate a feature fusion sequence; inputting the feature fusion sequence into a convolution-based multi-layer perceptron for feature enhancement, performing an Alzheimer's disease classification task and generating a classification result; and using a test set of the clinical data set and a ten-fold cross-validation method to quantitatively analyze the model performance on the Alzheimer's disease classification task, and integrating post-hoc interpretability technology to analyze the model classification result.

The invention relates to a multi-mode-based self-adaption rheumatism glove control method and a multi-mode-based self-adaption rheumatism glove control system. The method comprises the following steps: acquiring a multi-source data set including impedance data, thermodynamic distribution data, joint movement data, pressure distribution data and the like; respectively performing inflammation activity quantification, joint function evaluation and infection risk evaluation processing according to the data set to obtain an inflammation score, a function index and a risk identifier, and generating a state vector; based on the state vector and a historical state vector sequence loaded from a historical database, carrying out illness state stage identification processing to obtain an identification result, and generating a control instruction according to the identification result; and performing thermal therapy-electrical stimulation cooperative processing based on the control instruction, generating a pressure target value according to the state vector, performing pressure regulation and control processing, and generating a treatment log. According to the method, multi-source data are comprehensively analyzed, personalized control instructions and pressure regulation and control strategies are generated, accurate and personalized treatment schemes can be provided, and the treatment experience and rehabilitation effect of rheumatism patients are improved.

The invention discloses an Alzheimer's disease preclinical risk quantitative evaluation method and system, and belongs to the technical field of deep learning. The method comprises the steps of data preprocessing, model construction, model training and evaluation, image thermal region mapping and the like. According to the method, a quantitative evaluation model covering NC, SCD, MCI and AD stages is constructed based on a cognitive feature and neural image bimodal enhanced fusion method and a Grad-CAM-based interpretive loop, the classification precision is improved, a key region of interest of neural image MRI in the Alzheimer's disease stage is mapped, and the classification accuracy is improved. Therefore, reference is provided for auxiliary diagnosis of Alzheimer's disease development.

The present invention discloses a Parkinson's disease progression prediction method based on the BCL model, which relates to the technical field of medical imaging data, and includes: S1, obtaining clinical data including the H&Y score and basic patient information, as well as image data including MRI images; S2, performing format conversion on the MRI images and performing data annotation in combination with the H&Y score; S3, preprocessing the images after data annotation; S4, using principal component analysis to reduce the dimension of the preprocessed images and extract the low-dimensional features of the images; S5, constructing a curriculum learning framework and a classification model based on a balanced weight scheduler to train the features after dimension reduction; S6, predicting the progression of Parkinson's disease in patients and evaluating the prediction performance of each classification model. The present invention can provide a more accurate assessment of the patient's disease stage, reduce the data dimension while improving the computational efficiency and generalization ability of the model, and enhance the model's learning ability for difficult samples.

The invention discloses a double-flow data fusion-based rice blast incubation period diagnosis and prediction method and model, and the method comprises the steps: inputting a double-flow fusion time sequence feature vector into a gating circulation module for time sequence data prediction, and obtaining a hidden state outputted by the gating circulation module, the gating circulation module comprising a plurality of preset DDC-PGRUs; the hidden state output by the gating circulation module is respectively input into two parallel full connection layer branches, and a diagnosis result of a disease stage to which the rice belongs at the current moment and a prediction result of the disease degree are respectively output; the DDC-PGRU unit comprises a trend gate and a potential gate, the change rate of an input time sequence feature vector at adjacent moments is introduced in the calculation process of the trend gate, and the energy accumulation value of the input time sequence feature vector in a long-time window is introduced in the calculation process of the potential gate; according to the method, synchronous mechanism modeling of the disease evolution direction and the gleying intensity is realized.

This application relates to a method, device, equipment, and medium for dynamic prediction of Alzheimer's disease course. The method includes: acquiring multidimensional detection data of a patient; determining the patient's disease subtype and disease stage based on the multidimensional detection data and clinical diagnostic criteria; performing time-series analysis on the multidimensional detection data according to the disease subtype and the disease stage to obtain the patient's disease progression pattern; predicting the patient's future disease trajectory using a preset disease trajectory prediction model based on the disease progression pattern to obtain the patient's Alzheimer's disease course prediction result; and generating a recommended intervention plan for the patient based on the Alzheimer's disease course prediction result and preset clinical intervention rules. This method enables dynamic prediction of the Alzheimer's disease course and provides personalized intervention suggestions for patients, improving clinical treatment outcomes.

A method, computer system, and a computer program product for personalized disease progression modeling is provided. The present invention may include receiving patient data, wherein the patient data is comprised of at least genetic data and clinical observations. The present invention may include building a personalized disease progression framework using one or more patient groupings and one or more disease progression pathways. The present invention may include determining a disease stage and progression pattern for each of the one or more patient groupings using a personalizes disease progression model.

The invention relates to methods for determining the stage of a disease, particularly an ocular neurodegenerative disease such as Alzheimer's, Parkinson's, Huntington's and glaucoma, comprising the steps of identifying the status of microglial cells in the retina and relating that status to disease stage. Methods for identifying cells in the eye are also provided, as are labelled markers and the use thereof.

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.

Provided herein are antibodies or fragments thereof that specifically bind to the microtubule binding region (MTBR) of tau, and uses thereof. Further provided are methods of detecting MTBR species in blood or cerebrospinal fluid, as well as the use of such detection for diagnosing, prognosing or staging pathological characteristics and / or clinical symptoms of tauopathy, and selecting a treatment suitable for a given disease stage.

The application relates to the field of biomedical technology, and discloses a Parkinson disease subtype staging inference system based on a corneal nerve image and a SuStaIn algorithm, which comprises an image data acquisition module, a health control module, an abnormality division module, a SuStaIn model construction module and an output module. The image data acquisition module acquires an original image data set, and extracts core effective features after preprocessing the original image data set. The health control module calculates the Z scores of each feature in the corresponding core effective features of Parkinson disease patients through a standardization method. The abnormality division module divides abnormality grades based on the calculation results of the Z scores, and constructs a feature event matrix. The SuStaIn model construction module constructs a disease progression model based on the feature event matrix, inputs the patient feature Z score matrix, sets related parameters, completes model training, and then determines the optimal subtype number through multi-index evaluation. The output module outputs the model prediction results. The application realizes accurate identification of Parkinson disease patient subtypes and objective inference of disease stages, and provides strong support for early diagnosis, individualized treatment and drug research and development of diseases.

Provided herein are compositions, methods, and kits related to chymotrypsin production for characterizing proteins, assessing, and monitoring disease stages and phases, predicting the likelihood of disease progression, predicting and monitoring responses to disease therapies, and treating disease conditions.

The invention discloses a supramolecular fluorescent sensing array and application thereof in preparation of a preparation or a kit for early diagnosis of Alzheimer's disease, the supramolecular fluorescent sensing array comprises a plurality of sensing units, and each sensing unit is composed of a macrocyclic main solution and a fluorescent indicator solution; a macrocyclic main body used in the macrocyclic main body solution is selected from at least one of any macrocyclic main bodies; a fluorescent indicator used in the fluorescent indicator solution is selected from at least one of any fluorescent indicators. The supramolecular fluorescence sensing array can accurately distinguish cerebrospinal fluid samples of alzheimer disease transgenic rats in different disease course stages, and a new method is provided for early-stage accurate diagnosis and disease course monitoring of the alzheimer disease.

The application provides a method for establishing a digestive system disease multi-modal information diagnosis and treatment system, S1, collecting multi-modal information for labeling and preprocessing; S2, extracting a feature vector and embedding a label to the multi-modal information according to the labeled information; S3, splicing and mapping the feature vector and the label to a unified dimension to obtain an enhanced feature vector; S4, fusing the enhanced feature vector to form a multi-modal feature matrix, performing linear mapping on the multi-modal feature matrix, obtaining a global fusion vector after weighted aggregation, and generating a fusion vector sequence; S5, enhancing the time sequence information of the global fusion vector sequence and the spatial information of the spatial correlation of the part-specific feature; interactive fusion to obtain a space-time joint feature; S6, classifying and predicting the disease stage or specific pathological type, and outputting a diagnosis result; S7, realizing semantic association between the diagnosis result and a medical knowledge graph, sharing data to an online health wisdom platform, and providing personalized decision-making basis for clinical doctors.

The invention discloses a multi-database data management method and system for IgA nephropathy treatment, and relates to the technical field of data management. According to the method, multi-source clinical data is collected, a structured data set is formed based on data quality scoring and conflict resolution, the disease course stage of a patient is dynamically recognized based on a biomarker and a glomerular filtration rate, and differentiated collection, storage, synchronization and access strategies based on the disease course stage of the patient are specifically generated; multi-database collaborative management is executed, and the management effect is iteratively optimized through the data collection integrity rate, the data synchronization timeliness rate, the storage resource utilization rate and the response time; the problem that the IgA nephropathy multi-source database management and synergy efficiency is low is effectively solved, and the accuracy and efficiency of IgA nephropathy multi-source data management are remarkably improved.

The invention relates to the technical field of intelligent medical assistance, and discloses an intelligent environment control system and method for an asymptomatic patient, and the method comprises the steps: obtaining biological signal data of a user, and environment data and equipment state data of an environment where the user is located; dynamically evaluating the interaction capability of the user based on the biological signal data, and selecting an interaction channel based on the interaction capability; historical behavior data of the user is obtained, and prediction control options of the user are generated in combination with the environment data; acquiring actual control options of the equipment, fusing the predicted control options with the actual control options to obtain fusion options, and presenting the fusion options to the user through the interaction capability; and identifying a control instruction sent by the user through the interaction channel, and executing a corresponding environment control operation. The method can dynamically adapt to the interaction capability of the user in different illness state stages, simplifies the operation process, improves the control accuracy and convenience, and helps the patient to adjust the surrounding environment and equipment autonomously.

The invention discloses a Parkinson's disease progress prediction method based on a BCL model, and relates to the technical field of medical image data, and the method comprises the following steps: S1, obtaining a model comprising Hamp; y score and basic information of the patient, and image data including an MRI image; s2, performing format conversion on the MRI image, and combining with Hamp; y, scoring and carrying out data annotation; s3, preprocessing the image after data annotation; s4, performing dimensionality reduction on the preprocessed image by using principal component analysis, and extracting low-dimensional features of the image; s5, constructing a curriculum learning framework and a classification model based on a balance weight scheduler, and training the dimensionality-reduced features; and S6, predicting the progress of the Parkinson's disease of the patient, and evaluating the prediction performance of each classification model. According to the method, more accurate patient disease stage evaluation can be provided, the calculation efficiency and generalization ability of the model are improved while the data dimension is reduced, and the learning ability of the model for difficult samples is enhanced.

The present invention provides a blood gene detection method for tumors for medication guidance, which belongs to the field of bioinformatics technology, and specifically includes: based on disease indicators and development data of disease indicators, determining the matching situation of the patient's disease indicators in different dimensions with preset disease stages, and determining based on the matching situation that when the matching coefficient between the patient and the preset disease stage meets the requirements, determining the position and number of gene mutation points corresponding to the target drug, and using the gene mutation data corresponding to the gene mutation points to determine the risk coefficient of drug resistance variation of the target drug, obtaining the patient's medication duration and the risk coefficient of drug resistance variation, and determining whether it is necessary to perform gene testing on the patient in combination with the patient's drug resistance risk and the matching coefficient, thereby improving the efficiency of gene testing for the patient.

Methods of treating Alzheimer's disease and other neurodegenerative and / or neurocognitive and / or neurodevelopmental diseases are described. The methods comprise the administration of compounds that modulate an activity of cell division control protein 42 (Cdc42), such as the interaction between Cdc42 and intersectin (ITSN). Exemplary modulator compounds include thioureas, disulfonamides of fused aromatic systems (e.g., benzofuran), and acyl hydrazones, among others. Some of the modulator compounds act as activators of Cdc42, while others act as inhibitors. In some cases, the modulator compound has dual functionality and the ability of the modulator compound to act as an inhibitor or activator depends on whether or not Cdc42 is already activated in a particular disease stage or biological environment by an upstream activating signal of Cdc42.

The invention belongs to the technical field of neuroimaging and artificial intelligence auxiliary diagnosis, and particularly relates to a cognitive disorder comprehensive evaluation system based on brain PET-MR (positron emission tomography-magnetic resonance), which comprises a data acquisition module, an image processing module, a multi-modal fusion analysis module and an intelligent evaluation module. According to the scheme, the structure and function indexes of the same brain region are integrated to construct the multi-valued features, the single-valued correlation features are obtained through PCA dimension reduction, the brain region atrophy gap statistical features are accurately obtained through the dynamic grid generation and structure anomaly quantification method, cross-modal correlation information is reserved, direct correlation between the structure and the function is achieved, and the structure and the function of the brain region can be accurately obtained. Highly correlating the screening characteristics with the biomarkers; a classification model structure is constructed according to time steps to complete dynamic disease course simulation, and in combination with brain region atrophy gap statistical characteristics and basic clinical data cooperative training, precise division of disease stages and accurate identification of disease subtypes are realized, and key decision support is provided for clinical intervention.