20113results about "Medical data mining" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.

A system for remotely monitoring and managing health statuses of a plurality of users includes software instructions storable on a memory device usable by a computing device, the software instructions causing a hardware processor of the computing device to receive a plurality of sets of health-related information from a plurality of mobile computing devices of a plurality of users. The health-related information includes physiological information derived from wearable devices of the users indicative of an onset of symptoms associated with an infection, contact tracing data, and diagnosis data. The hardware processor determines exposure levels based on at least the contact tracing data, and determines user-specific risk states based on physiological information, diagnosis data, and exposure levels.

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

A robotic surgical system includes a surgeon consol coupled to a patient consol, and the patient consol coupled to surgical instruments. A surgeon computer is coupled to or at the surgeon consol that is coupled to to one or more surgical instruments. A robotic surgery control system includes an artificial intelligence (AI) system with one or more deep learning algorithms. A feedback loop monitors and collects data from the one or more sensors. One or more cameras provide feedback to the robotic surgical system, and are configured to provide images of an anatomical object in at least a two dimensional (2D) arrangements of pixels / Deep learning algorithms of the AI system distinguish different anatomical objects from the images.

The invention discloses a chronic disease early detection method and system based on a multi-modal large model, and relates to the technical field of intelligent medical treatment and artificial intelligence, and the method comprises the steps: obtaining a multi-modal data stream of a target user in a target time window from a pathology database, and generating an original multi-modal data set; performing timestamp unification and numerical value standardization processing on the original multi-modal data set to obtain a time sequence feature sequence; inputting the time sequence feature sequence to the multi-modal large model to obtain an abnormal symptom feature; calculating the similarity between the abnormal symptom features and feature vectors of marked cases in a historical case library, and determining matched cases; a diagnosis result and a development process of the matched case are extracted, a disease risk level and a development trend corresponding to the original multi-modal data set are determined in combination with the medical knowledge graph, and a pathology assessment result is obtained; and generating an early warning signal containing the risk type and the intervention suggestion according to the pathological assessment result. By implementing the application, the accuracy of early detection of chronic diseases can be improved.

The invention discloses an infrastructure risk operation optimization management system based on AI driving, and belongs to the field of computer data processing and commercial management, and the system comprises a multi-modal causal twinning construction module which integrates on-site multi-modal data streams to construct a dynamic space-time causal map; the risk evolution deduction module is used for performing anti-fact simulation based on a causal atlas to construct a prospective risk model; the collaborative configuration optimization module is used for solving an optimal collaborative defense strategy according to the risk model; the instruction analysis and digital prescription generation module is used for analyzing the defense strategy into a job digital prescription for a specific risk scene; and the intervention efficiency attribution and evolution correction module performs attribution analysis according to the execution effect of the digital prescription and adaptively updates the causal atlas. According to the method, a comprehensive method of constructing a dynamic causal map for risk deduction, coupling resource constraints for collaborative optimization and performing closed-loop feedback on a correction model is adopted, and active prediction, accurate intervention and continuous learning optimization of capital construction risks can be realized.

The invention discloses a urinary surgery diagnosis and treatment data intelligent processing system based on artificial intelligence, and the system comprises the following modules: an original diagnosis and treatment data collection and preprocessing module which is used for collecting the data of a urinary surgery patient; the stage annotation text construction module is used for constructing a stage annotation text set; the image description structuring module is used for constructing a structured image description sequence; the cross-modal semantic fusion module is used for extracting a cross-modal joint semantic representation vector sequence; the named entity recognition module is used for outputting a structured medical entity set; the entity semantic graph construction module is used for constructing an entity semantic graph structure; the diagnosis and treatment relationship extraction module is used for outputting a diagnosis and treatment relationship set of a triple structure; the structured packaging module is used for outputting a structured diagnosis and treatment data unit; and the intelligent diagnosis and treatment decision module is used for performing illness state recognition, treatment path generation and reasoning suggestion output. According to the invention, multi-modal semantic modeling and graph neural reasoning are fused, and an intelligent diagnosis and treatment relation recognition system is constructed.

The invention relates to a method convenient for data consanguinity collection and analysis, which comprises the following steps of: obtaining original consanguinity data comprising a task execution log, application metadata and a cross-system dependency relationship, and carrying out standardization processing on the original consanguinity data to generate structured consanguinity information comprising an asset unique identifier, an upstream and downstream association relationship and a data operation type; structured consanguinity information is synchronously written into a graph database and a distributed data warehouse, the graph database stores real-time association topology, the distributed data warehouse stores full-amount historical versions, and a transaction consistency algorithm is adopted to ensure the atomicity of double-write operation, so that the data storage efficiency is improved. Single-asset-level consanguinity tracking is performed based on real-time topology of a graph database, global consanguinity analysis is performed based on batch computing power of a distributed data warehouse, a direct dependence path, a deep association network and a closed-loop link detection result are generated, and a closed-loop management mechanism from data acquisition, analysis to optimization is formed. And the problem that an analysis result is disjointed from an acquisition end in a traditional scheme is solved.

The invention discloses a scene interactive AI rehabilitation evaluation training and health monitoring system, and relates to the technical field of rehabilitation medical treatment and artificial intelligence, a semantic perception module is used for collecting and recognizing voice input, facial expressions, action tracks and eye movement paths of a user in a training process, and extracting context parameters; the knowledge-driven training generation module is used for calling a rehabilitation knowledge graph constructed by a graph neural network based on context parameters and individual training history, and generating a multi-path training scheme; training a feedback regulation engine, collecting posture offset, physiological stress and emotion feedback, and dynamically adjusting task difficulty, rhythm and prompt mode based on a dual-channel reinforcement learning model; the prediction module fuses training and monitoring data, and predicts a network identification function degradation risk through degradation driving; the cloud edge fusion platform is used for realizing task quick response and graph strategy iterative updating; according to the invention, the individuation, self-adaption and intelligent prediction capabilities of rehabilitation training are improved, and the rehabilitation effect and the system practicability are obviously optimized.

The invention relates to the technical field of medical artificial intelligence and knowledge graph construction, in particular to a rare disease knowledge graph construction method based on modal injection and multi-modal fusion. Comprising the following steps: S1, collecting multi-modal medical information including texts, images and genes; s2, standardization processing is carried out, and a three-layer metadata structure is constructed; s3, complementing missing modal data, and performing feature extraction and unified dimension conversion on the modal data to realize representation alignment in a shared semantic space; s4, performing multi-level semantic fusion to obtain a unified fusion semantic vector; and S5, constructing a double-layer structure system rare disease knowledge graph comprising an ontology layer and an instance layer. According to the method, multi-modal medical information of texts, images and genes is selected to construct the knowledge graph of the rare disease, the application range, coverage and accuracy of the knowledge graph are improved, correspondence adaptation of rare cases during clinical diagnosis and treatment of the rare disease can be achieved, and the method has high recognition capacity.

The invention discloses a medical intelligent decision-making method based on Deepseek and a time sequence causal knowledge graph, and the method comprises the following steps: 1, constructing an initial static medical knowledge graph, and generating a dynamic time sequence causal knowledge graph; 2, finely adjusting and training the DeepSeek model to enable the DeepSeek model to adapt to the medical field; and step 3, receiving and analyzing the text uploaded by the patient, performing intelligent triage and disease risk prediction, and realizing accurate matching of patient symptoms and target departments and intelligent prediction and early warning of potential diseases. The method aims at providing accurate triage and disease risk prediction for patients, constructing a scientific and efficient medical intelligent decision-making mechanism and optimizing medical resource allocation.

The invention provides a collaborative reasoning method and system fusing a medical knowledge graph and a large model in the technical field of artificial intelligence. The method comprises the steps that S1, medical entities, medical relationships and medical attributes are extracted from a medical data set through a medical information extraction model to construct the medical knowledge graph; s2, monitoring the latest medical information through a medical information monitoring agent so as to update the medical knowledge graph; s3, creating a clinical decision collaborative reasoning model; s4, training and deploying the clinical decision collaborative reasoning model through the medical data set; s5, pushing the clinical decision collaborative reasoning model to a medical terminal through a federal gateway; and S6, the medical terminal inputs the query appeal carried by the query request into the clinical decision collaborative reasoning model to obtain a reasoning report. The method has the advantages that the reasoning ability, timeliness, interpretability and safety of medical reasoning are greatly improved.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

Provided is a method, including obtaining data associated with clinical trials, storing the obtained data into a repository by preprocessing the data to standardize diverse input formats into unified data model and organizing the stored data into a schema designed to integrate data of diverse input formats, indexing the stored data and analyses performed on the stored data, selecting one or more visualizations responsive to the query by selecting one or more visualizations as being responsive to the query based on metadata associated with each of the one or more visualizations, determining whether the stored data is associated with a plurality of metadata requirements of each of the one or more visualizations, dynamically generating executable code configured to generate the one or more visualizations responsive to the query, executing the generated executable code, and providing a response to the query.

Improved novel 3D imaging and reconstruction methods for quantitative and accurate 3D imaging used in multiple industries, segments, and applications within each. Improvement includes methods and hardware configured to improve performance and lower radiation and improve accessibility. Using simplified system matrix and datasets of little scatter interference in tomographic image acquisition and reconstruction, in some cases, using time continuity and spatial continuity to generate parameter data for substances within VOI for a number of key applications in medical and non-medical fields.

The invention discloses a block chain-based medical data management method, system and device, and a medium, and relates to the field of information management. The method comprises the following steps: acquiring medical data, analyzing content attributes and context information of the medical data through a first smart contract on a block chain, and generating a first sensitivity level identifier; based on the first sensitivity level identifier, performing differential encryption on the medical data through a second smart contract on the block chain, and generating verification information; partitioning the differentially encrypted medical data, constructing a data structure together with the verification information, and storing the data structure into a block chain; in response to the received access request, verifying an authority certificate in the access request and the type of the access request through a third smart contract on the block chain, and generating an access authorization certificate; and outputting target medical data corresponding to the access request based on the access authorization certificate. By implementing the technical scheme provided by the invention, the full-life-cycle safety management and control of the medical data from collection, storage to sharing is realized.

The invention discloses a medical whole-course intelligent management system based on a large model, and belongs to the technical field of large models. Comprising a multi-modal data acquisition module, a privacy calculation preprocessing module, a dynamic knowledge enhancement module, a time sequence data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction platform module, a dynamic intervention feedback module and a system security center module. The cross-mechanism data security sharing is realized, and the compliance of sensitive information processing is also ensured; a two-channel medical knowledge base is constructed, authoritative guidelines can be synchronized, newest clinical research data can be analyzed in real time, the knowledge base is kept in the newest state all the time, and the frontier scientific basis is provided for clinical decisions; dynamic modeling and trend prediction are carried out on long-term monitoring data of a patient by adopting a hybrid neural network model, and potential health risks and development trends can be identified more accurately.

The invention discloses an accurate health risk early warning analysis system and method based on multi-modal medical data fusion. The system comprises a multi-source data acquisition module, a preprocessing module, a dynamic fusion module, a risk assessment module, an interpretability module and a dynamic early warning module. According to the method, multi-modal data are collected, feature vectors are generated through preprocessing and cross-modal fusion, a comprehensive health risk index is calculated through a double-flow model (time sequence LSTM + static GNN), abnormal association is analyzed in combination with causal reasoning, a threshold value is dynamically adjusted, grading early warning is triggered, and finally the model is optimized through reinforcement learning. According to the scheme, deep fusion and dynamic evaluation of multi-modal data are achieved, the accuracy, timeliness and interpretability of risk early warning are improved, the method is suitable for scenes such as chronic disease management and intensive care, and powerful support is provided for clinical decision making.

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

The invention discloses a garden landscape plant health assessment method, and belongs to the technical field of resource assessment. Comprising the following steps: step 1, constructing a time sequence database of environmental factors and plant growth states; step 2, according to different plant species and growth stages thereof, dynamically calculating and generating a health reference value adapted to the current environmental condition; step 3, in the optimal evaluation time window, performing preliminary evaluation on the plant health condition, extracting original health indexes, and performing correction in combination with an environment disturbance compensation factor; step 4, performing double comparative analysis on the corrected health assessment result, eliminating false anomalies caused by environmental fluctuation, and calculating an anomaly confirmation credibility index; step 5, grading the plant health conditions, visually displaying the plant health conditions in a thermodynamic diagram form, and marking the environmental interference degree; and step 6, based on an evaluation result, automatically generating a targeted maintenance suggestion, and designing a corresponding maintenance effect verification scheme.

The invention discloses an AI-based traditional Chinese medicine dialectical auxiliary diagnosis method and system and a medium thereof, and relates to the technical field of intelligent auxiliary diagnosis, and the method comprises the steps: collecting physiological monitoring data, tongue condition data and pulse condition signals of a patient, obtaining patient symptom information, mapping the patient symptom information to a preset traditional Chinese medicine pathogenesis classification model, and generating an initial symptom feature vector; calling a knowledge base containing traditional Chinese medicine prescription rules and compatibility medication taboo, performing data space-time alignment processing and dynamic combination calculation on the initial symptom feature vector, and generating a personalized candidate prescription set conforming to the traditional Chinese medicine compatibility taboo; according to dynamic combination calculation, a solution algorithm based on a constraint satisfaction problem is adopted, and multi-dimensional association rule matching is carried out in combination with the mapping relation between syndromes and modern medical symptoms; and outputting a prescription medication recommendation scheme containing traditional Chinese medicine compatibility, dosage and decoction methods based on the candidate prescription set. The method supports the formulation of an accurate personalized diagnosis and treatment scheme, and meets the requirements of high-quality traditional Chinese medicine personalized auxiliary diagnosis and treatment.

The invention discloses an auxiliary scheme generation method and system for dysphagia rehabilitation, belongs to the technical field of medical rehabilitation intelligent evaluation, and is used for solving the problem of poor optimization of a swallowing training path. According to the method, myoelectricity, tongue pressure, throat images and acoustic signals in the swallowing process are collected through a multi-channel sensing device, a unified multi-mode swallowing behavior feature vector is constructed, obstacle stage recognition and functional grade judgment are achieved in combination with a pre-training model and historical data, and a structured evaluation result is output; matching training actions with high adaptation degree based on the knowledge graph, fusing muscle group matching degree, edge weight and function level factors to generate a personalized training path, and setting strength, frequency and rhythm parameters; in the training process, the motion and myoelectricity deviation is monitored in real time, a prompt signal is output, and the behavior completion degree is recorded; and the rehabilitation progress map is dynamically updated according to feedback, and if the deviation exceeds the limit, the path is automatically reconstructed and doctor intervention is synchronized, so that the rehabilitation efficiency and safety are improved.

The invention provides a gynecological disease diagnosis and treatment method and system fused with an electronic medical record, and relates to the technical field of medical informationization, and the method comprises the steps: collecting the multi-modal diagnosis and treatment data of a patient, and constructing a personalized medical database through a self-encoding network; deploying a plurality of agents based on a dynamic causal network, and generating an initial treatment scheme in combination with deep reinforcement learning; through real-time data acquisition and a multi-objective optimization algorithm, the scheme is dynamically optimized by fusing expert decision features. According to the invention, personalized precise treatment of gynecological disease diagnosis and treatment can be realized, the treatment effect is improved, and the complication risk is reduced.

The invention belongs to the technical field of knowledge visualization, and discloses a traditional Chinese medicine acupuncture knowledge visual display system based on a knowledge graph. Comprising the steps that a traditional Chinese medicine ancient book data acquisition unit, a clinical case data acquisition unit and an ultrasonic image data acquisition unit acquire data and integrate the data into traditional Chinese medicine acupuncture data, and the traditional Chinese medicine acupuncture data is preprocessed to obtain perfect traditional Chinese medicine acupuncture data; constructing a knowledge graph based on the perfect traditional Chinese medicine acupuncture data; constructing an optimization network model to optimize the knowledge graph to obtain a high-quality knowledge graph; constructing an incremental learning framework, updating the high-quality knowledge graph based on the incremental learning framework, and outputting a real-time updated knowledge graph; constructing an interactive three-dimensional human body model based on the real-time updated knowledge graph; based on the real-time updated knowledge graph and the interactive three-dimensional human body model, outputting an executable medical strategy for the application case, and sending the executable medical strategy to the student terminal; and the teaching quality is improved while knowledge visualization is realized.

A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.

The invention discloses a hospital information intelligent analysis and decision-making system based on a multi-modal large model, and relates to the technical field of hospital information analysis and decision-making. The system comprises a data acquisition module, a preprocessing and fusion module, a large model construction and training module, an intelligent analysis module, a decision support module, a knowledge graph construction and application module, a data security and privacy protection module, a system evaluation and optimization module, a multi-hospital cooperation and data sharing module, a mobile application module and the like, and all the modules cooperate to realize hospital information intelligent processing and decision making. The system integrates multi-modal data, assists in precise diagnosis, recommends a treatment scheme, predicts resource demands, monitors medical quality, assists medical research, manages patient health and the like, comprehensively improves the intelligent level of hospitals, optimizes medical services and benefits doctors and patients.

The invention provides a clinical decision-making method and system based on a large language model and a knowledge graph, and belongs to the technical field of artificial intelligence and intelligent diagnosis and treatment crossing. The method comprises the following steps: S1, training and deploying a created generative large language model and a knowledge extraction model; s2, extracting medical knowledge from the medical data set through a knowledge extraction model; s3, constructing a medical knowledge graph based on the medical knowledge; s4, acquiring an input medical question, inputting the medical question into the generative large language model, querying medical knowledge corresponding to the medical question by the generative large language model through the medical knowledge graph, generating a medical answer based on the medical knowledge, recording a decision basis chain in the query process, and feeding back the medical answer and the decision basis chain; and S5, recording a question and answer log including the medical questions, the medical answers and the decision basis chain. The method has the advantages that the accuracy, the reliability, the timeliness and the safety of clinical decision making are greatly improved.

The invention relates to a digital chronic disease intelligent management platform based on an AI model and multi-dimensional data fusion, clinical diagnosis and treatment data, wearable equipment monitoring data, medication record data and environment monitoring data are acquired through a data acquisition module, and after standardized preprocessing is performed through a data fusion processing module, deep analysis is performed through an AI analysis module, and the data fusion processing module performs data fusion processing; in combination with medical knowledge of the knowledge base module, the intelligent decision-making module generates a personalized management scheme, and the personalized management scheme is implemented through the intervention execution module and the intelligent interaction module. Multi-dimensional health data are processed through an AI large model, a complex mode and an association relationship are automatically learned, and accurate disease prediction and risk assessment are realized; the pertinence of the scheme and the compliance of a patient are greatly improved; and real-time interaction and personalized guidance are provided, the participation degree and the self-management ability of the patient are effectively enhanced, and a benign health management cycle is formed.

The invention discloses an inquiry model training method and device based on a long thinking chain, and relates to the field of large models, semantic information is extracted through strategy network analysis of a model, and an initial step decision is generated in combination with context information in a historical memory library; sending the initial step decision into a reasoning path generator, and reasoning to generate a primary diagnosis disease source and an intermediate diagnosis step; sending the primary diagnosis source and the intermediate diagnosis step into a verification module, performing pathological logic verification according to a case diagnosis report and a medical knowledge base, and feeding back a verification result; the reasoning path generator updates the historical memory bank based on the feedback result, the preliminary diagnosis disease source and the intermediate diagnosis steps; the strategy network continues reasoning based on user feedback input and the updated context information in the historical memory bank, and finally an inquiry result is output. According to the scheme, technical means such as reinforcement learning, self-adaptive backtracking and memory enhancement are introduced into a long thinking chain reasoning framework, so that a large language model realizes multi-aspect comprehensive improvement in medical question and answer and auxiliary diagnosis scenes.