112 results about "Diseases types" patented technology

The invention discloses a tunnel lining disease automatic identification method and system based on multi-source data fusion, and relates to the technical field of facility detection, and the method comprises the steps: collecting multi-modal time sequence data, carrying out the time-space alignment, and obtaining a time sequence multi-source data set; reconstructing a tunnel center line based on a vehicle pose and constructing a lining structure consistency coordinate framework, and performing structured projection and distortion correction on alignment data to obtain a multi-modal fusion data set; dividing a two-dimensional structure grid under the coordinate framework, extracting and fusing geometric, texture, depth and energy features, calculating a structure consistency damage index, and extracting a suspected disease area; and calculating a disease credibility index and judging a disease type in combination with multi-modal physical evidence, mapping a suspected disease region back to a three-dimensional space, completing disease boundary extraction and geometric quantization, and outputting structured disease information. According to the method, the structure expression and the structured alignment of the cross-modal data under the unified geometric reference are realized by constructing the consistent coordinate framework of the lining structure.

The invention relates to a tunnel three-dimensional disease intelligent identification system and method based on a large model, the system comprises a point cloud data acquisition module and a processor, and the processor comprises a data processing module, a three-dimensional tile optimization module and a disease identification module. The data processing module carries out standardization, noise reduction and registration processing on the received point cloud data; the three-dimensional tile optimization module constructs a multi-level tile pyramid structure based on the registered point cloud data, establishes a mapping relation between a space coordinate and a tile index, compresses tile data based on a curvature point cloud simplification algorithm and adjusts texture quality to form a three-dimensional tile image; calculating a comprehensive score of the tile quality to verify the quality of the three-dimensional tile image; a disease identification module extracts multi-modal fusion features of the preprocessed three-dimensional tile image and geometric features corresponding to disease types; fusing the multi-modal fusion feature and the geometric feature to obtain a joint fusion feature; performing field fine tuning on the joint fusion features; and obtaining a disease identification result.

The invention discloses a case resource integration data system based on big data analysis, and belongs to the technical field of medical data. The method comprises the following steps: acquiring hospital case data and corresponding disease type data to construct a resource integration range, acquiring a personal case information set provided by medical consultation of a patient, performing sensitive data extraction on the personal case information set to obtain a dynamic case parameter set, and sending the dynamic case parameter set to a data risk analysis module; the multi-source data acquisition module processes the personal case information set as follows; according to the method, a data integration-risk analysis-clinical intervention closed-loop system is constructed, preorder data standardization integration guarantees analysis reliability, accurate risk analysis provides a direction for intervention, multi-level alarm and pre-plan matching is achieved through linkage of the preorder data standardization integration and the accurate risk analysis, prediction diagnosis reports and intervention suggestions are automatically generated, invalid operations are reduced, the clinical decision-making efficiency is improved, and the system is suitable for large-scale popularization and application. And meanwhile, through dynamic threshold updating and system self-iteration optimization, the adaptability and practicability of the system are continuously enhanced.

The invention discloses a brain disease risk prediction method and system based on big data analysis, and belongs to the technical field of brain disease risk prediction. The method comprises the following steps: carrying out standardized preprocessing and tagged classification on brain disease related big data to generate a feature data set; mining specific disease characteristics and risk factors in the set, and carding an association rule; training a risk prediction sub-model for each disease type based on the data, and building a multi-sub-model hierarchical prediction system; and collecting to-be-predicted object data, matching a disease type, and calling the corresponding sub-model to complete risk assessment. The system comprises multiple modules for collaborative operation, and a full-process closed loop of data storage, feature processing, model management and result output is realized. According to the scheme, the pertinence, the accuracy and the efficiency of risk prediction are improved, the traceability of the whole process and the dynamic optimization of the model are realized, and reliable technical support is provided for early screening and risk early warning of brain diseases.

The invention discloses a disease type prediction method and system based on a medical bill and a pseudo-label mechanism, and the method comprises the steps: obtaining a medical bill set which comprises a medicine list, a doctor-seeing department and patient portrait information, and carrying out the standardization preprocessing, and generating standardized bill data; respectively inputting the standardized bill data into a dynamic reasoning knowledge base and a pre-trained large language model, generating a first pseudo-label disease set through a rule reasoning engine, and generating a second pseudo-label disease set through the guidance of a semantic understanding cue word template; and fusing the two pseudo-label disease category sets to obtain a fused pseudo-label, training a multi-label disease category classification model by taking the fused pseudo-label as a target, and finally realizing disease category prediction of the to-be-predicted medical bill data. According to the invention, through a dual pseudo-tag generation mechanism fusing rule reasoning and a large language model, the problem that the medical bill data lacks a real disease category tag is solved, the accuracy and reliability of disease category prediction are improved, and a reliable solution is provided for deep utilization of the medical bill data.

The invention relates to pattern recognition and anomaly detection in the technical field of computers, and discloses a laying hen disease early-stage AI early warning method and a laying hen disease early-stage AI early warning system. The method comprises the following steps: obtaining a laying hen behavior video stream, an excrement image and body temperature data, and carrying out preprocessing such as timestamp alignment and equipment number mapping to generate a training and reasoning input structure; then target tracking, key point coding, region segmentation, contour feature coding and body temperature fragment statistical coding are executed to extract a multi-source feature sequence; performing time window alignment, scale normalization, pattern recognition and anomaly detection processing on the multi-source features to realize disease type recognition; and finally, generating an early warning event based on rule triggering and threshold judgment, and forming closed-loop disposal through prevention and treatment scheme retrieval and instruction arrangement. According to the method, collaborative analysis and early abnormity early warning of multi-source heterogeneous data are realized, and the disease recognition accuracy and early warning timeliness are remarkably improved.

The invention discloses a multi-modal data processing method and device based on an attention mechanism, equipment and a medium. Living habits of a user and living and medical characteristics of genes are combined on the basis of image data and electronic medical records. The method is advantaged in that the problem of difficult fusion of heterogeneous multi-source data is solved through uniform characterization of features of four-modal data, adaptive adjustment of attention weight of each modal is carried out through the entity association relationship in the entity relationship knowledge graph, and fusion precision of full-modal fusion features is improved. Through the anti-illusion prediction model, disease type and probability prediction is carried out for individual differences (user tags and full-modal fusion features) of target users, and the accuracy of disease prediction results is improved. The multi-modal data processing method can be applied to user body evaluation in the financial field and disease type prediction in the medical field so as to improve the risk prediction accuracy in the financial field and the disease type prediction accuracy in the medical field.

The invention relates to a remote electrocardio telemetering method and system, a terminal and a medium, and belongs to the technical field of electrocardio monitoring. The remote electrocardio telemetering method comprises the steps that a cloud end distributes monitoring equipment and initial parameters according to patient information; the edge end receives real-time electrocardiogram data, obtains environment data in combination with the position of a patient, collects physiological data through a wearable sensor, constructs a spatio-temporal context sensing map, fuses the data to generate situation enhanced electrocardiogram signal representation, inputs a comprehensive risk prediction model to obtain a dynamically updated prediction risk value and uploads the prediction risk value; the cloud judges the predicted risk value, if the predicted risk value is larger than a threshold value, a matched disease type is called, and differential prompts are generated and pushed to the patient and the doctor in combination with the co-disease relation network; if the electrocardiogram data does not exceed the threshold value, carrying out deep analysis on the electrocardiogram data, identifying abnormity, determining an abnormity level, generating a personalized report in combination with treatment history, pushing the personalized report, and triggering a clinical response protocol. The method has the beneficial effect of feeding back the abnormal electrocardiogram condition of the patient in time.

The invention relates to the technical field of medical health monitoring, and discloses a remote monitoring system with intelligent voice, which comprises an intelligent monitoring module, a communication network module, a cloud platform module and a user terminal module, the intelligent monitoring module comprises a multi-mode sensor unit, a voice interaction unit and an edge calculation unit; by arranging the intelligent monitoring module, intelligent recognition and analysis of user voice are facilitated, natural voice instructions are supported, the problem that a traditional system is complex in operation is solved, meanwhile, multi-modal data are fused, a user personalized baseline is dynamically adjusted based on historical data of a user, and the user experience is improved. Therefore, the method can adapt to users of different ages and disease types, generates the environmental factors to correct the personalized baseline, and can adjust the physiological parameter weight according to the users of different disease types, thereby improving the accuracy of the personalized baseline, effectively reducing the probability of missing report and false report, and improving the intelligence.

The invention relates to the technical field of biomedical information, in particular to an umbilical cord mesenchymal stem cell donor matching database construction method based on HLA (human leukocyte antigen) typing, which comprises the following steps of: recording and storing data of a donor in a database to finish initial construction of the database; regularly rechecking the donor data record stored in the database, and updating the state identifier of the donor data record when the rechecked data is changed by a preset degree; and receiving a matching request containing the human leukocyte antigen typing data of the target patient and the target disease type, performing donor screening in the database by using a preset comprehensive matching degree model based on the matching request, and optimizing parameters of the comprehensive matching degree model through a machine learning model based on the matching record and the clinical curative effect data corresponding to the matching record. According to the method, the accuracy and effectiveness of umbilical cord mesenchymal stem cell treatment are improved, and meanwhile, the system has continuous self-evolution ability.

The invention discloses a pathological image classification method and system for assisting pathological diagnosis, and aims to solve the problems that the traditional pathological diagnosis is low in efficiency and the accuracy depends on artificial experience. According to the method, a pathological image is collected, denoising, enhancement and other preprocessing operations are executed, a pre-trained deep learning model is utilized to automatically extract image features, and classification results of benign, malignant or specific disease types are output in combination with a classification algorithm, so that efficient and accurate automatic diagnosis is realized; the system integrates image acquisition, preprocessing, feature classification and result display modules, supports multi-user concurrent access and cloud deployment, is equipped with model updating and user interaction functions, and can continuously optimize model performance based on new data. In addition, remote pathological diagnosis is supported, and balanced distribution of medical resources is promoted through a digital system; the method can significantly reduce the workload of pathologists, reduces the risk of human misdiagnosis, and is suitable for rapid classification and diagnosis of various pathological images such as tissue slices, cell smears and the like.

The invention discloses a video transmission method and device for bridge detection, equipment and a storage medium. The method comprises the following steps: acquiring video data acquired by an unmanned aerial vehicle and disease identification data including information such as disease types, positions and confidence coefficients; segmenting the video data to obtain a plurality of fragments, packaging the fragments and the disease identification data to obtain a video stream data packet and a disease identification data packet, and transmitting the video stream data packet and the disease identification data packet; analyzing the video stream data packet, grouping and recombining to obtain a recombined video frame, and analyzing the disease identification data packet to determine a boundary to obtain an identification result; cooperative detection data are obtained through time sequence synchronous calibration, the disease information and the video frames are subjected to superposition rendering, and an enhanced video stream containing a disease labeling box, a type text and a confidence coefficient identifier is generated. According to the method, the packet loss resistance and the synchronization precision are improved, an end-to-end closed-loop transmission system is constructed, and the accuracy and the efficiency of bridge remote detection are effectively improved.

The invention relates to the technical field of sepsis, in particular to a medicine regulation and control method and system for sepsis patients. Comprises: constructing a drug simulation model based on a historical diagnosis and treatment library; acquiring a feature feedback packet of the user to be treated, and setting a primary regulation strategy according to the feature feedback packet and the drug regulation model; acquiring a monitoring data packet according to a preset feedback time node, and judging whether to correct the first-level regulation and control strategy or not according to the monitoring data packet; rapid drug intervention for a user to be treated is achieved by setting drug sub-strategies of various disease types, meanwhile, a digital twinborn model for the user to be treated is constructed by collecting a real-time feedback data packet of the user to be treated and carrying out optimization fitting on an initial simulation sub-model, refined and real-time regulation and control of drug dosage are achieved, and the treatment effect of the user to be treated is improved. Therefore, the treatment efficiency is improved.

This invention belongs to the field of bioinformatics and relates to a method for predicting protein post-translational modifications based on multimodal deep learning. First, multimodal feature extraction is performed on the input protein sequence and 3D structural data to obtain sequence feature vectors and structural feature vectors. Second, a cross-modal attention mechanism and an adaptive gating network are used for feature fusion. Then, the fused features are combined with disease type information, and the predicted probabilities are fine-tuned using a disease-specific coding network. Next, a multi-task learning framework is used to predict the site probabilities of various protein post-translational modification types in parallel. Finally, gradient backpropagation is used to calculate feature importance, and a comprehensive report is output based on variation impact analysis. This invention achieves high-precision, interpretable, and disease-aware protein post-translational modification prediction, providing an important computational analysis tool for revealing the molecular mechanisms of diseases and discovering precise drug targets.

Providing a CDKN2a companion diagnostic for interferon therapy for bladder cancer. [Solution] A method for treating bladder cancer, comprising the steps of: diagnosing bladder cancer in a human; measuring the CDKN2A expression level in the human; and subsequently injecting an interferon expression-inducing agent into the human. Non-muscle-invasive bladder cancer (NMIBC) represents the most common disease state in patients with newly diagnosed bladder cancer. Those with high-grade (HG) tumors are at significant risk of both recurrence and progression.

Systems and methods of the disclosure may include a computer-implemented method, the computer-implemented method including: receiving, at a computer system, nucleic acid sequencing data derived from a methylation assay performed on a biological sample associated with at least one subject; computing, using a processor associated with the computer system, a beta value matrix based on the nucleic acid sequencing data, wherein the beta value matrix comprises one or more missing beta values; addressing, using the processor, the one or more missing beta values in the beta value matrix using a missing beta value completion approach; identifying, using the processor, one or more principal components in the completed beta value matrix; and training, using the one or more principal components in combination with a predetermined set of clinical variables, a classifier to predict a survival outcome for a target subject associated with a disease type.

To provide improved methods for treating lysosomal storage disorders (LSDs).SOLUTION: The present invention provides acetyl-leucine, or a pharmaceutically acceptable salt thereof, for use in a method of treating a lysosomal storage disorder (LSD) or one or more symptoms associated with a LSD in a subject in need thereof, wherein the LSD is not Niemann-Pick Type C.SELECTED DRAWING: Figure 9A

The invention discloses a disease treatment method for old airport lanes in a high heat area. The method comprises the following steps: S1, disease evaluation and classification treatment; s2, planning a processing scheme; s3, high-temperature-resistant composite cover surface treatment; and S4, interface reinforcement and drainage optimization. According to the method, the pavement disease type and severity are determined by evaluating and classifying the diseases, the treatment scheme is determined according to the pavement disease type and severity, local structure repair is performed according to the treatment scheme, and compared with an existing cement concrete plate replacement repair mode, the method has the advantages of being short in construction period and low in cost.

A device for predicting and diagnosing risk by cardiovascular disease type according to an embodiment comprises at least one processor, wherein the at least one processor predicts risk by cardiovascular disease type using a diagnostic model. The diagnostic model includes a first model that analyzes an inputted fundus image to predict cardiovascular risk; and a second model that receives a cardiovascular risk score predicted by the first model and disease type-specific factors to predict risk by cardiovascular disease type. Wherein the at least one processor is characterized by providing diagnostic information based on the cardiovascular risk predicted by the first model and the risk by cardiovascular disease type predicted by the second model.

The invention discloses a method for constructing a multi-modal live pig early disease early warning model based on spatio-temporal information enhancement, and the method comprises the steps: collecting the multi-modal data of a pig, including an image, an audio, a sensor, a spatial layout and the day age information of the pig; constructing a time-space aligned multi-modal data set; extracting embedded vectors of different single-mode data to obtain spatio-temporal information; performing multi-modal feature interaction to obtain weighted information of different-modal feature interaction; performing feature fusion on weighted information of different modal feature interaction to obtain fused feature representation, and performing deep extraction on the fused feature representation to obtain feature representation; performing end-to-end training on the multi-modal model by using the training set; inputting the data of the test set into the trained multi-modal model to obtain feature representation, and identifying and positioning abnormal pigs and judging disease types. According to the method, potential health problems can be found earlier, the misdiagnosis rate is reduced, and a solid guarantee is provided for pig health management and sustainable development of animal husbandry.

Provided herein are methods of treatment and medicaments for treating Niemann-Pick Disease Type C (NPC) with hydroxypropyl beta-cyclodextrin (HPBCD), including in subjects with infantile-onset NPC. Provided herein are also methods of preventing NPC in in a human subject in need thereof with HPBCD wherein the human subject is 0-2 or 0-6 years of age or has an age on onset of NPC of 0-2 or 0-6 years. Provided herein are also methods of preventing NPC in in a human subject in need thereof with HPBCD for at least 2 years, at least 4 years, or at least 6 years, wherein the human subject is 0-2 or 0-6 years of age or has an age of onset of 0-2 or 0-6 years.

The invention provides a primer design, a disease typing diagnosis method and device, a composition, a kit and application thereof, and the primer design method comprises the following steps: obtaining a variety of reference data including a TRDV reference gene sequence and a TRDJ reference gene sequence; aiming at each kind of reference data, performing the following operations: aligning a plurality of reference gene sequences according to sites, determining a conservative score list of each site, obtaining conservative scores of a plurality of conservative intervals according to the conservative score list of each site, selecting K conservative intervals with higher conservative scores, K being a natural number greater than or equal to 1, and selecting K being a natural number greater than or equal to 1; generating K groups of primer combinations aiming at the K conservative intervals; and screening the primers in the K groups of primer combinations, evaluating the screened K groups of primer combinations, and obtaining a final primer combination according to an evaluation result.

The invention provides a graph neural network modeling and causal interpretation method for mental disease recognition. The method comprises the following steps: constructing multi-modal data; performing label labeling on the multi-modal data according to the disease type, and forming a data set by labels and the multi-modal data; constructing an initial model, wherein the initial model comprises a graph construction module, a graph neural network module, a causal interpretation module and a prediction and interpretation output module; training the initial model by using the data set to obtain a graph modeling model for disease classification; and inputting the multi-modal data of the patient into the graph modeling model to obtain a prediction result of the patient. The invention also provides a graph neural network modeling and causal interpretation system. The method provided by the invention is suitable for personalized diagnosis and intervention path analysis, and has good practical application potential.

The application provides a disease-specific emergency whole-process auxiliary decision-making method based on multi-modal perception and AI, relates to the technical field of medical emergency, and comprises the following steps: acquiring multi-modal data of an emergency scene, constructing a feature dependency graph, and identifying disease types and symptom characteristics; indexing disease diagnosis and treatment data based on a symptom characteristic vector, determining reference cases and treatment paths; combining clinical norms to construct an action space, applying Monte Carlo search to predict expected returns, and generating an optimal treatment plan, which can improve the accuracy of emergency disease identification, shorten the treatment decision-making time, and improve the emergency intervention effect.

This invention relates to the field of intelligent identification technology for swine diseases, and discloses a method for identifying and making decisions about swine diseases by integrating body temperature and image features. The method separates the independent basis components of each disease in a mixed infection by performing non-negative matrix decomposition on the body temperature time spectrum. It then uses the Hungarian algorithm to establish a cross-time point correspondence between the basis components, and uses an optimal transmission algorithm to associate lesion instances with the body temperature basis components to generate a feature sequence of disease components. Finally, it uses a time-series perceptual graph neural network to perform independent inference on the symptom evolution time-series graph, outputting the disease type identification confidence and disease stage inference confidence for each pathogen. Based on a multi-pathogen joint decision rule base, it generates a coordinated treatment plan.

The invention relates to an audio-electric cooperation ear vagus nerve stimulation method, earphone and system based on multi-mode disease information, and belongs to the technical field of vagus nerve stimulation, and the method comprises the steps: obtaining the disease information of a user; determining a disease type based on the disease information; determining a treatment scheme based on the disease type, wherein the treatment scheme comprises stimulation parameters of the electrical stimulation module and music to be played; and playing the to-be-played music by using the earphone, and controlling the electrical stimulation module to stimulate the ear vagus nerve based on the stimulation parameters. According to the invention, two adjusting modes of music and ear vagus nerve stimulation are combined, and the synergistic effect of the two adjusting modes is brought into full play, so that the effect of more efficient and accurate emotion and cognition adjustment is achieved.

The invention provides a diagnosis method, device and equipment for multi-modal medical data fusion based on AI, and the method comprises the steps: constructing multi-source medical data, and constructing a dynamic association network based on the multi-source medical data; performing hierarchical feature interaction processing on the dynamic association network, updating node feature representation through bidirectional information conduction between nodes, and generating a cross-modal enhanced feature set; inputting the cross-modal enhanced feature set into a pre-trained diagnostic reasoning model to generate fusion diagnostic features; determining candidate disease types, corresponding support feature items and non-support feature items according to the overall matching degree and the sub-item matching degree of the fusion diagnosis features and the standardized disease typical feature spectrum; establishing a feature traceability chain of a diagnosis basis according to source features traced by the support feature items in the cross-modal enhanced feature set, and constructing a feature association map based on the feature traceability chain and a node association relationship of the dynamic association network; and outputting a diagnosis result according to the candidate disease type, the feature tracing chain and the feature association map.

The invention relates to the technical field of artificial intelligence, in particular to a psychiatric patient emotional fluctuation intelligent monitoring system based on a machine learning algorithm, which takes an electronic medical record of a patient as an entrance, automatically extracts the shortest emotional staying duration corresponding to a disease type, and calls similar historical data to carry out differential fine tuning on a pre-trained network. Enabling each patient to obtain an exclusive monitoring model; and meanwhile, by taking an abnormal proportion as a unified scale, online credibility evaluation is performed on an identification result, and acquisition parameters, an updating period and a compression ratio are reversely adjusted, so that a personalized psychological patient emotion identification model is output by the same hardware platform. The intelligent monitoring system provided by the invention can adapt to different diseases, different disease courses and individual differences by combining medical record information and real-time data of a patient, so that the problem that an existing system cannot accurately recognize emotion fluctuation due to a fixed mode is effectively solved, and the accuracy and reliability of emotion monitoring are remarkably improved.

The invention discloses a cultural relic disease intelligent diagnosis system based on multispectral fusion and AI, and the system comprises a multispectral data collection module, a spectral feature extraction module, a cross-modal data association module, an AI disease recognition and analysis module, a diagnosis result generation module, and a system interaction control module. Transmitting to a spectral feature extraction module to extract spectral features; the cross-modal data association module establishes spectral feature and cultural relic image pixel mapping and generates an association data set; the AI disease recognition and analysis module processes the data set and recognizes disease types and region boundaries; the diagnosis result generation module integrates the information to generate a report containing disease position, area and severity; and the system interaction control module displays the report and realizes storage and external calling. Through multi-module cooperation, multispectral and AI technologies are fused, the historical relic disease diagnosis precision and efficiency are improved, and comprehensive data support is provided for historical relic protection.

The invention relates to a nursing simulation method and system, electronic equipment and a computer program product. The method comprises the following steps: determining a to-be-simulated case, and obtaining initial physiological parameters, a disease type and a patient type indicated by the to-be-simulated case; the physiological simulation human body is driven by a physiological engine to conduct state initialization according to the initial physiological parameters; acquiring professional knowledge corresponding to the disease type from a professional knowledge base through a dialogue engine, and acquiring personality description data corresponding to the patient type from a role knowledge base; when the voice data are collected, current physiological parameters are obtained from the physiological engine through the dialogue engine, and the current physiological parameters are matched with the current state of the physiological simulation human body; and calling a large language model to generate a reply text for the voice data based on professional knowledge, personality description data, voice data and current physiological parameters through a dialogue engine, and controlling a loudspeaker to perform voice playing according to the reply text. By adopting the method, the nursing simulation effect can be improved.