33498results about "Medical data mining" patented technology

The invention provides a medical decision-oriented multi-modal data dynamic fusion and labeling method and system, and relates to the technical field of medical decision-making, and the method comprises the steps: collecting medical images, electronic medical records and physiological monitoring data of a patient, extracting multi-modal features such as a focus form, a disease type, a symptom, a treatment record and a physiological index, and carrying out the fusion and labeling of the multi-modal features; and constructing a dynamic fusion labeling model. According to the model, multi-modal features are coded, similarity is calculated, a feature association graph is constructed, feature aggregation is carried out, labeling is carried out in combination with a medical knowledge base, and a dynamic labeling vector is generated. And inputting the dynamic labeling vector into a medical decision support model, and based on similar case analysis and deep reinforcement learning, generating a diagnosis and treatment suggestion containing a treatment scheme and early warning information. By dynamically fusing multi-modal data and an online updating mechanism, the accuracy and efficiency of medical decision making can be improved, and personalized diagnosis and treatment are realized.

The invention provides a medical text big data intelligent labeling and knowledge graph construction method and system, and relates to the technical field of medical text processing, and the method comprises the steps: recognizing entities in a medical text through a pre-trained deep bidirectional coding model and a multi-level entity recognition network, and carrying out the normalization processing in combination with a medical knowledge base, according to the method, the semantic association degree between entities is calculated, a relation extraction result is optimized based on comparative learning, comprehensive relation representation is obtained through fusion of multi-granularity coding and a gating mechanism, the entity relation is determined in combination with an inference rule network, a medical knowledge graph is constructed based on an initial knowledge graph, and the accuracy of medical text entities and relation labeling can be effectively improved. And a more perfect medical knowledge graph is constructed, which is helpful for assisting medical diagnosis and decision making.

Systems and methods for improving reimbursement rates from payors for services or procedures provided by a medical care provider are disclosed herein. In some examples, reimbursement can be improved by improving the accuracy of medical coding of services and diagnosis codes through the use of machine learning techniques. In some examples, the system can suggest codes more likely to get reimbursed by a payor through analysis of patient data and / or insurance information. In some examples, the system can determine a likelihood of reimbursement through analysis of patient data and / or insurance information.

The invention discloses a medical diagnosis intelligent decision-making system based on multi-modal data fusion, and the system comprises the steps: obtaining a medical image, biological features and electronic medical record data of a patient through a patient information collection unit, and extracting feature vectors from different modal data through a feature processing unit; the core of the system is a modal fusion engine, and the module combines a deep neural network and a Bayesian inference engine to realize deep learning and probability-dependent modeling of an association relationship between features; carrying out weight optimization on different modal features by adopting an attention mechanism through a self-adaptive feature optimizer, and generating optimized feature representation; the intelligent diagnosis analysis unit calculates the diagnosis probability based on the optimized features, performs auxiliary diagnosis in combination with a case knowledge base when the confidence coefficient is insufficient, and outputs a diagnosis report containing diagnosis categories, basis and suggestions; according to the method, multi-source heterogeneous data are fused, deep learning and probabilistic reasoning are combined, intelligent medical auxiliary diagnosis is achieved, and diagnosis accuracy and interpretability are improved.

An intelligent pulmonary nodule grading method and system based on multi-modality feature fusion. The method comprises: using clinical multi-modality data of a physical examination crowd to acquire an ROI and a VOI of a pulmonary nodule on the basis of a chest CT examination image and an examination report, designing a multi-task feature extraction network based on an attention mechanism, and acquiring radiomics features and deep-layer image features from the ROI and the VOI; designing a cross-modality feature fusion method based on graph representation learning, designing a multi-modality information extraction method, acquiring a specific feature representation and graph structure of each modality, and then performing feature representation and graph structure fusion; and providing a pulmonary nodule grading GCN model optimization and clinical verification method based on self-supervised learning, so as to implement fine grading of pulmonary nodule malignancy having only slight differences. A new way is provided for the design of a fine-grained classification algorithm; and in the aspect of solving a clinical problem, provided is a classification algorithm optimization method and clinical verification scheme in which generative self-supervised learning and comparative self-supervised learning are organically combined.

The invention provides a cerebral hemorrhage personalized treatment scheme optimization method and system based on big data analysis, and relates to the technical field of data analysis, and the method comprises the steps: constructing a cerebral hemorrhage patient multi-source heterogeneous clinical database, extracting feature subsets, carrying out semantic alignment, carrying out time sequence modeling and fusion, and obtaining a patient panoramic feature representation; constructing a cerebral hemorrhage diagnosis and treatment knowledge graph, adding the cerebral hemorrhage diagnosis and treatment knowledge graph to a preset graph attention network, distributing weights, obtaining knowledge-enhanced patient feature representation and taking the knowledge-enhanced patient feature representation as a state, generating an action space based on an optional treatment scheme, constructing a reward function and determining a candidate treatment scheme; performing medication suitability analysis, performing semantic conflict identification in combination with the patient allergy history and complication, and performing screening to obtain a suitable candidate treatment scheme set; and for each scheme, predicting a multi-dimensional prognosis index, constructing a multi-criterion decision matrix, combining personal influence factors, adjusting the weight of each index, modeling an optimization problem and determining an optimal treatment scheme.

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.

The invention discloses a pulmonary infectious disease prediction system based on multi-modal data fusion, and aims to improve the accuracy and reliability of disease prediction through comprehensive analysis of multi-dimensional data. The system comprises a data acquisition module, a feature extraction module, a dynamic fusion module and a prediction modeling module. The data acquisition module is used for acquiring clinical and medical images and environmental factor data of a patient in real time; the feature extraction module extracts feature vectors of all data sources; the dynamic fusion module performs adaptive weight distribution on the multi-modal features and fuses the multi-modal features into multi-modal feature representation; the prediction modeling module is used for carrying out dynamic change trend modeling on the multi-modal feature representation through time sequence analysis so as to generate a probability prediction value of suffering from pulmonary infectious diseases and a disease trend prediction result of the patient; the invention provides a comprehensive and efficient disease prediction method by utilizing the collaborative advantage of multi-modal data and combining an attention mechanism and time sequence analysis, and can be widely applied to the fields of medical diagnosis and public health management.

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 relates to the technical field of model construction, in particular to an obstetric delivery decision model construction method based on a knowledge graph. The method comprises the following steps that multi-dimensional information collection is conducted on a pregnant woman to be delivered, individualized data are obtained, and the multi-dimensional information comprises a medical record text, a laboratory examination result, an ultrasonic image and fetal heart rate monitoring data; performing standardization and structural processing on the individualized data to generate a standardized multi-modal data set; performing entity identification and relation extraction processing on the standardized multi-modal data set according to a preset obstetrical domain ontology model to obtain an initial knowledge graph; and carrying out priori knowledge-based mapping expansion on the initial mapping knowledge domain to obtain a multi-modal mapping knowledge domain. Through an attention mechanism and a decision support interface, the interpretability of the model is enhanced, and doctors can understand and trust decision suggestions.

One or more data streams may be used for controlling multi-system interaction. For example, a data stream may be received, and a surgical option associated with a surgical instrument may be selected based on the data stream. A control signal associated with the surgical instrument may be generated based on the selected surgical option. A surgical device may receive an external data stream from a source external to the surgical system. The device may derive, based at least on the external data stream, decision contextual information. The device may select a surgical option associated with a surgical instrument based on the decision context information. The device may generate a visual indication of the decision context information associated with selecting the surgical option. The device may generate a control signal associated with the surgical instrument based on the selected surgical option.

An aspect of the present disclosure provides an example method comprising: receiving an input query associated with a particular task domain of a plurality of available task domains; obtaining a machine-learned prompt component and a curated prompt component, wherein the machine-learned prompt component comprises a plurality of machine-learned prompt values for the plurality of available task domains, and wherein the curated prompt component comprises a plurality of exemplar prompt values corresponding to one or more embedded natural language exemplars for the particular task domain from domain experts; and generating an output responsive to the input query by processing a combined prompt and the input query using a pre-trained machine-learned model, wherein the combined prompt comprises the machine-learned prompt component and the curated prompt component.

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 relates to the technical field of artificial intelligence and medical diagnosis, in particular to a disease diagnosis prediction method and system based on a graph neural network. The disease diagnosis prediction method based on the graph neural network comprises the five steps of heterogeneous medical knowledge graph construction, adaptive node embedding representation, hierarchical graph attention network modeling, incremental learning dynamic graph updating and multi-dimensional feature input and result output. The invention discloses a disease diagnosis and prediction system based on a graph neural network. The system comprises a multi-source data acquisition module, a heterogeneous graph construction module, a self-adaptive embedding module, a graph network calculation engine, a dynamic updating module, a disease prediction module and a feedback optimization module. According to the method, the multi-modal heterogeneous knowledge graph is constructed to integrate the multi-dimensional data of the patient, and the hierarchical graph attention network and the dynamic incremental learning are combined, so that the accurate prediction of the disease risk and the visual explanation of the pathological association path are realized.

The invention provides a health management data mining method and system based on deep learning, and the method comprises the steps: firstly obtaining a target health data set, including physiological monitoring, behavior activity records and medical history texts, of a target user, and then carrying out the cross-modal noise filtering and feature alignment processing of the target health data set; the method comprises the following steps: generating a standardized health feature sequence containing a synchronous timestamp and the like, performing hierarchical feature abstraction by using parallel time sequence convolution branches and space attention branches through a cascaded spatial-temporal feature extraction network to obtain a multi-granularity health state characterization vector, inputting the multi-granularity health state characterization vector into a pre-training model, and training the multi-granularity health state characterization vector to obtain a multi-granularity health state characterization model. According to the method, a personalized health management scheme containing disease risk levels and the like is generated, finally, according to dynamic monitoring period parameters in the personalized health management scheme, the data acquisition frequency and the feature alignment strategy are adjusted in real time, a closed-loop health management data flow is formed, and precise and personalized health management is achieved.

The invention provides a multi-level attribution and recommendation method and system for medical management decisions, and relates to the technical field of medical management decisions, and the method comprises the steps: extracting graph node evolution characteristics through a multi-level medical data knowledge graph and a decision knowledge base by employing a time sequence diagram convolutional network, constructing a decision influence propagation graph through a cross-layer interactive attention network, and carrying out the analysis of the decision influence propagation graph; calculating the contribution degree of each decision node to the medical effect, generating an attribution analysis result, determining a decision influence hierarchical system according to the attribution result, and generating and evaluating candidate decision paths in combination with a multi-objective optimization network, a hierarchical reinforcement learning network and a Monte Carlo search tree network. In conclusion, accurate attribution analysis and multi-objective optimization of the management decision can be realized, the hospital resource allocation efficiency is effectively improved, the operation cost is reduced, scientific decision support is provided for a hospital management layer, and the hospital fine management level and the overall operation efficiency are remarkably enhanced.

The invention provides a construction method of a comprehensive intelligent early warning system for a traumatic patient, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: carrying out the processing of multi-modal data through a feature engineering framework, and extracting multi-scale time sequence features and correlation features; carrying out abnormal state identification and complication risk prediction by adopting a deep learning prediction framework, and carrying out feature fusion through an attention mechanism; constructing a medical knowledge graph to obtain vector representation of domain rule knowledge; and performing integrated verification through a bidirectional mapping mechanism to generate a prediction result. According to the invention, accurate early warning of disease development of the trauma patient can be realized, the early warning accuracy and interpretability are improved, and effective support is provided for clinical decision making.

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 discloses a multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for internet medical treatment, which comprises the following steps: S1, performing feature extraction and fusion on multi-modal diagnosis and treatment data through a nonlinear mapping algorithm based on quantum state optimization; s2, constructing a self-adaptive dynamic relation network, and capturing a complex interaction relation between cross-domain features in the diagnosis and treatment data; s3, fusing implicit features in the sparse medical data with semantic association; s4, optimizing the diagnosis and treatment decision process by applying a causal inference technology; s5, processing anomalies in the diagnosis and treatment data by using a fusion generative adversarial network and sparse tensor decomposition technology; s6, constructing a decentralized encrypted federated learning network to protect the privacy of the patient; s7, predicting a disease evolution path in real time to form dynamic decision support; and S8, performing accelerated analysis on the high-dimensional feature mode in the diagnosis and treatment data in combination with quantum calculation. The method has the advantages of high data fusion capability, high diagnosis precision and high real-time decision support capability.

The present disclosure relates to a method for detecting a state of wear of a cardiac support system. The method comprises a read-in step and a determination step. During the read-in step, a sensor signal representing an operating state of the cardiac support system is read in. During the determination step, a wear signal is determined using the sensor signal and a comparison rule. The wear signal represents the wear condition.

The invention discloses a cerebral apoplexy risk prediction method and system based on multi-modal data fusion. The method comprises the following steps: A1, a data acquisition and preprocessing step; a2, a feature embedding and multi-modal fusion step; a3, a self-attention mechanism optimization feature fusion step; a4, a multi-mode cooperative training step; and A5, dynamic prediction and real-time updating. By integrating medical image data, gene data, electronic health record (EHR) and biomarker data, the risk of cerebral apoplexy can be comprehensively evaluated, and the limitation of a single data source is solved. According to the method, independent feature extraction and embedding processing are carried out on various data through a deep learning model, deep information in each data mode can be captured, and more detailed and accurate risk assessment is provided.

The invention relates to the technical field of medical rehabilitation, in particular to an artificial intelligence-based orthopedic full-cycle rehabilitation management system, which comprises a rehabilitation demand evaluation module, a three-dimensional pathological modeling module, a multi-modal training generation module, a rehabilitation effect prediction module, a dynamic parameter adjustment module and a full-cycle management module. Multi-dimensional data such as bone mineral density, joint motion range, gait mechanics and psychological state of a patient are collected, an artificial intelligence algorithm is combined for analysis, a personalized rehabilitation scheme is generated, and rehabilitation parameters are predicted and adjusted in real time. The system can dynamically correct the training intensity, the nutritional supplement dosage, the psychological intervention mode and the rehabilitation schedule, and the safety and reliability of data are guaranteed through the block chain technology; according to the system, biomechanics, nutriology, psychology and environmental factors are comprehensively fused, the system has strong adaptive ability and full-period management ability, real-time optimization can be performed according to the rehabilitation process of a patient, and the rehabilitation effect of the orthopedics department and the satisfaction degree of the patient are remarkably improved.

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 an intelligent system and method for early screening of depression based on electroencephalogram-eye movement multi-modal data fusion. According to the system, in a virtual reality situation, emotion and cognitive response of a subject are stimulated through an elaborately-designed cognitive task, and brain electrical activity and sight behavior data are synchronously collected by adopting a wearable EEG device and a high-precision eye tracker. After signal preprocessing and feature extraction, deep fusion of multi-modal data is realized by using a weighted fusion formula or a graph neural network, the weight of each modal is automatically adjusted, interaction features between electroencephalogram and eye movement are extracted, and then the depression risk of a subject is accurately judged through a classifier. Experiments show that the method can effectively capture weak physiological abnormalities of mild depression patients, has the advantages of high sensitivity, high accuracy and real-time online screening, and meets the requirements of non-invasive, portable and intelligent clinical application.

The invention provides a knowledge and data fused medical content image-text generation system and method. Relates to the technical field of digital medical treatment and artificial intelligence. The multi-modal feature extraction module is used for extracting multi-modal features from the multi-modal data; the multi-modal feature fusion module is used for carrying out cross-modal dynamic fusion on the multi-modal features to generate cross-modal high-consistency fusion features; the model training optimization module is used for configuring a soft and hard target loss function and a parameter optimizer; the information analysis and reasoning module is used for carrying out semantic matching on the fusion features, the disease classification and the clinical path based on a dynamic medical knowledge network, and generating disease prediction probability distribution and a semantic reasoning path; and the image-text report generation module is used for generating an image-text report containing a diagnosis conclusion and an interpretation basis according to the prediction probability and the semantic reasoning path. According to the invention, intelligent and precise generation of medical contents can be realized.

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 disease follow-up visit, in particular to a health state assessment system applied to disease follow-up visit, which comprises a data acquisition module, a data preprocessing module, a health state assessment module, a personalized health management suggestion module, a dynamic health risk prediction module and a visualization module, the data acquisition module acquires multi-dimensional data of a patient and performs data fusion and integration, the health state evaluation module evaluates the health state of the patient based on multi-level feature extraction and a deep learning model, and the personalized health management suggestion module combines a health state evaluation result and historical data to obtain a personalized health management suggestion result. Customized health management suggestions are generated through a rule engine, and the dynamic health risk prediction module predicts future health risks of patients and provides real-time early warning by using a time sequence analysis model. The visualization module displays health assessment and management suggestion results through a chart and an interactive interface, and the accuracy and individuation degree of health management are improved.