2171results about "Therapies" patented technology

The invention provides a neurology clinical nursing potential safety hazard analysis method and device, and relates to the technical field of neurology clinical nursing, and the method comprises the steps: inputting a risk feature matrix generated based on multi-source heterogeneous nursing data into a rule engine and graph neural network model, and outputting a preliminary screening risk event set; taking the primary screening risk event set and the risk feature matrix as input, and constructing a causal conduction map through a causal discovery algorithm; performing risk conduction quantitative integration on each risk event in the primarily screened risk event set based on a causal conduction map to generate a quantitative risk list; based on the dynamic risk priority number and the conduction path chain, reversely tracing to a root cause node along a directed conduction edge, and generating a targeted intervention strategy packet bound with the root cause node; the targeted intervention strategy package is pushed to the nursing responsible person terminal, the strategy execution effect and the risk evolution data are recorded, and a closed-loop disposal database is generated, so that the dynamics and the effectiveness of potential safety hazard analysis of clinical nursing of the neurology department are improved.

The invention relates to the technical field of medical rehabilitation, and particularly discloses an anorectal postoperative rehabilitation nursing evaluation method and system based on a sensor. According to the scheme, the system comprises a multi-source physiological information sensing module, a data preprocessing and feature extraction module, a multi-modal data fusion analysis module, a dynamic risk assessment and early warning module and a man-machine interaction and decision support module, multi-dimensional physiological signals are continuously collected, a rehabilitation state index is generated through fusion, the complication risk is assessed in real time, and the rehabilitation effect is improved. Early warning and accurate nursing decision support are realized, and comprehensiveness and timeliness of rehabilitation evaluation are improved.

The invention provides a dynamic myopia management lens prescription customization method and system, and relates to the technical field of lens design and customization. According to the dynamic myopia management lens prescription customization technology provided by the invention, ophthalmic basic data of a patient is acquired; then periodically follow-up the patient to obtain ophthalmology measurement follow-up data of the patient; the joint evaluation model dynamically updates a myopia risk evaluation grade of the patient based on ophthalmology basic data and ophthalmology measurement follow-up visit data; and finally, dynamically updating the lens prescription based on the myopia risk assessment level of each time by the joint assessment model. According to the technical scheme, the combined evaluation model can perform dynamic lens prescription adjustment according to follow-up visit ocular axis progress and choroid change conditions, dynamic myopia management lens prescription customization, whole-course tracking of a myopia cycle and individualized prevention and control management are realized, and thus the effectiveness and continuity of myopia prevention and control are improved.

The invention discloses a mild cognitive impairment brain rehabilitation training device and system in the technical field of cognitive nerve rehabilitation, and the device comprises a multi-modal data fusion module, a personalized training task generation module, a multi-sensory stimulation presentation module and a real-time adaptive regulation and control module. And the personalized training task generation module is used for dynamically generating a personalized narrative training task fusing the user interest theme and the cognitive training target by utilizing a reinforcement learning algorithm based on the user cognitive weak domain evaluation result and the user personal interest and life narrative content obtained through natural language processing. Clinical data of a patient and real-time electroencephalogram and behavior data are fused, a narrative training task deeply fused with life experience and interest of the patient is dynamically generated, the difficulty of the training task is automatically adjusted according to real-time physiological indexes, meanwhile, seamless connection with a medical system is achieved through digital biomarkers, and the training efficiency is improved. And the precision and compliance of rehabilitation training are obviously improved.

The invention relates to the technical field of treatment scheme optimization, and provides an emergency treatment critical patient first-aid path optimization method and system based on big data, and the method comprises the steps: building a standardized event sequence through extracting historical diagnosis and treatment data of a hospital information system, and analyzing the time sequence correlation between events through a graph structure mining technology, key diagnosis and treatment nodes and high and low frequency paths are identified, and a layered first-aid path state transition network is constructed based on the key diagnosis and treatment nodes and high and low frequency paths. When a real-time patient is processed, the system matches an electronic health record of the patient with a network core state, dynamically plans a subsequent standard transfer path, and avoids a low-frequency risk path marked as a taboo, so that an emergency path optimization scheme of risk avoidance and path dynamic optimization is realized, and the treatment efficiency of emergency critical patients is improved. The treatment effect is improved, and the bad prognosis risk is reduced.

The invention relates to the technical field of medical prediction, in particular to a Web-based big data driven stroke risk prediction method, which comprises the following steps: S1, multi-scale data acquisition and alignment: acquiring physiological data, behavior data and health state data of a user; s2, causal path identification: generating a causal feature sub-graph with confidence rating; s3, feature fusion modeling: performing response modeling on fluctuation features in the real-time physiological data, performing trend feature extraction on behavior data, and dynamically allocating fusion weights of the fluctuation features and the trend features to generate unified risk characterization features; s4, risk evolution simulation: simulating the influence of different intervention strategies on the stroke risk, and generating a risk evolution trajectory; and S5, intervention scheme generation: generating a personalized intervention scheme. According to the invention, the accuracy of risk prediction is improved, and the change of the health condition of the patient can be responded in real time.

The invention discloses an aquaculture comprehensive guarantee method and system based on multi-modal data acquisition, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting aquaculture multi-modal data and an aquaculture image of an aquaculture region, carrying out the aquaculture feature capture of the aquaculture image, and outputting a visual feature group; inputting the visual feature group into a MobileNet lightweight model, and outputting a health state label and a confidence score; based on the health state label and the confidence score, outputting disease type data through a residual network structure optimized by transfer learning; constructing a graph neural network according to the disease type data, and generating a dynamic regulation and control scheme; and the control center executes the dynamic regulation and control scheme, carries out regulation and control effect detection and target comparison on the aquaculture area of the dynamic regulation and control scheme, judges whether the regulation and control effect is effective or not, and generates a feedback regulation instruction. According to the invention, through multi-modal data fusion and intelligent decision closed loop, dynamic and accurate guarantee of aquatic product health management is realized.

The invention provides a medical decision-oriented multi-level knowledge graph construction and semantic reasoning method, and relates to the technical field of knowledge graphs, and the method comprises the steps: carrying out semantic segmentation and standardization processing on medical text corpora, extracting medical entities and attributes thereof, and constructing an incidence matrix; a reasoning path is mined based on recursion deep search, and an optimal path is selected by using an attention mechanism; and performing decision verification and optimization in combination with medical rules. According to the method, the accuracy and reliability of medical decision making can be improved, efficient mining and application of complex medical knowledge are achieved, and effective support is provided for clinical diagnosis and treatment.

The invention discloses a health care service data management method based on machine learning, and relates to the technical field of data management. The method comprises the following steps: collecting health care service data in real time, constructing a health care knowledge graph, and carrying out feature dynamic alignment on the health care knowledge graph by adopting agency attention and multi-scale contrast learning to obtain a dynamic alignment feature vector; based on the dynamic alignment feature vectors, training CatBoost, XGBoost and a random forest model through Bayesian optimization, and obtaining a health prediction model through stacking generalization fusion of a logistic regression model; inputting the dynamic alignment feature vector into a health prediction model, and outputting to obtain a prediction result; corresponding service measures are executed according to the prediction result, new data are collected again after the service measures are executed, the health care knowledge graph is updated, and therefore management of health care service data is achieved.

The invention discloses an oral cavity data monitoring and early warning method and system based on deep learning, and the method comprises the steps: solving a problem that the focus recognition is inaccurate because the image collection process of an oral cavity endoscope of a patient is affected by various environment and equipment parameters; according to the method, synchronous binding of collected images and equipment parameters and intelligent image preprocessing are proposed, historical time sequence images and variation trend features are combined, lesion space and time features are extracted through combination of a lightweight convolutional neural network and a long-short term memory network, feature weighted reasoning is realized by using a context awareness attention fusion network, and the focus space and time features are extracted. According to the method, the model parameters and the risk threshold value can be dynamically adjusted, the focus recognition accuracy and the early warning individuation are improved, and clinical grading treatment and intelligent health management are effectively assisted.

The invention discloses a multi-agent real-world clinical curative effect evaluation and accurate decision-making system, and relates to the technical field of medical data processing. According to the invention, a research demand analysis agent generates a research scheme after understanding the input content of a user and transmits the research scheme to a data management agent, a statistical analysis modeling agent, a result report generation agent, a data security agent and the data management agent privacy and encrypt data uploaded by the user; meanwhile, additional feature construction is carried out according to the research requirement of a user to form brand new analysis data used by a subsequent statistical analysis modeling agent, and after the statistical analysis modeling agent obtains the data, an analysis result is obtained by calling external software and is transmitted to a result report generation agent; and generating a clinical evaluation report together with the research scheme transmitted by the research demand analysis agent. According to the invention, full-process automatic, specialized and safe research support is realized.

The invention discloses a venous thrombosis risk assessment method based on a large language model, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: collecting thoracic surgery diagnosis and treatment data of a patient, carrying out the space-time alignment, generating a standard diagnosis and treatment data flow, and carrying out the homomorphic encryption of the standard diagnosis and treatment data flow, and forming an encrypted patient data package; inputting the encrypted patient data packet into a multi-task large language model, performing feature extraction and semantic coding by a feature coding layer, performing time sequence modeling and risk probability calculation by a risk quantification layer, and outputting a venous thromboembolism risk level of a patient; and performing feature decoupling and potential space mapping on the encrypted patient data packet to obtain thrombus semantic potential features. Through the multi-task large language model, the dual machine learning algorithm and the homomorphic encryption, the accuracy of venous thrombosis risk early warning is improved, and the safety of the risk assessment process is enhanced.

The invention discloses a cardiovascular lesion classification method and system based on data analysis, relates to the technical field of cardiovascular lesion classification, and aims to solve the problem of poor accuracy when cardiovascular lesions of patients are classified. According to the method, multi-dimensional features are extracted for images, time sequence signals and structured data, and fusion is realized through data layer clinical association screening, feature layer composite vector recombination and decision layer weight summation. The method breaks through the limitation of a single data dimension, enables a classification result to be more fit with a pathological mechanism, remarkably improves the recognition precision of complex lesions and complications, guarantees the reliability of hardware through precise configuration of equipment and multi-dimensional detection, customizes a preprocessing process according to the data type, and associates multi-source data through a patient ID and a timestamp, thereby improving the recognition precision of the complex lesions and complications. The problems that equipment is disordered and data formats are different in a traditional process are solved.

The invention relates to the technical field of intelligent medical multi-modal data analysis and nursing aid decision making, in particular to a neurology nursing risk dynamic early warning and intelligent decision support system. Comprising the steps that a data acquisition module acquires a physiological time sequence and nursing logs to obtain a multi-modal original data set; the feature extraction module performs nonlinear dynamic analysis on the physiological data to generate a state manifold vector, and performs semantic embedding on a log to generate an intervention state tensor containing spatio-temporal features; the causal reasoning module constructs a dynamic causal map, maps vectors and tensors as nodes, and adjusts transmission weights by using the tensors to suppress non-pathological fluctuations; the decision-making module calculates dynamic risks and difference values based on atlas anti-fact deduction, and generates graded early warning and an optimal nursing path; the feedback module updates the atlas parameters according to the processing result. According to the system, the problem of high-frequency false alarm caused by daily nursing actions of traditional monitoring equipment is effectively solved, the alarm fatigue of medical staff is relieved, and focusing on real critical events is ensured.

The invention belongs to the technical field of traditional Chinese medicine and software, and particularly relates to a traditional Chinese medicine syndrome differentiation aid decision-making method based on trans-modal attention Transform. The invention provides traditional Chinese medicine syndrome differentiation based on cross-modal attention Transform, and aims to explore a more scientific and systematic traditional Chinese medicine syndrome differentiation method by integrating multi-modal data such as visual sense, auditory sense, language, pulse condition and the like. Specifically, information such as complexion and tongue coating is obtained by inspection diagnosis through an image processing technology; the auscultation and diagnosis obtains sound information such as respiration and cough through a sound recognition technology; the inquiry analyzes the chief complaint and symptom description of the patient through a natural language processing technology; pulse condition data are collected through the sensor technology in the clinics. A multi-modal hierarchical dialectical logic framework is constructed, an exclusive attention mechanism is designed for each dialectical method, the overall view and dialectical thinking of traditional Chinese medicine are fully reflected, and a more reasonable dialectical result is generated.

The invention relates to the technical field of artificial intelligence auxiliary medical treatment, and discloses a child tooth trauma intelligent grading and dynamic processing decision auxiliary system. A trauma identification module; an injury condition grading module; a processing suggestion generation module; a prognosis risk dynamic evaluation module; and a man-machine interaction module. The method comprises the following steps: acquiring an image containing child tooth trauma information and clinical data; utilizing a multi-label classification model to identify a trauma type; calculating a comprehensive injury condition score through a hierarchical multi-modal scoring network and determining an injury condition grade; calling a knowledge graph and a rule engine to generate and dynamically correct a processing suggestion; and continuously updating the prognosis risk assessment result based on the time sequence review data. The invention aims to solve the problems of strong evaluation subjectivity and complex decision in the diagnosis and treatment of the tooth trauma of children, provides standardized and intelligent decision assistance for clinic, and realizes closed-loop prognosis management.

The invention discloses an intensive care treatment scheme intelligent decision-making system based on data driving, and relates to the technical field of medical information, and the system comprises a data collection module which is used for obtaining multi-source clinical data of an intensive care unit in real time; and the decision generation module is connected with the data acquisition module and is used for generating a treatment scheme according to the clinical data. According to the intensive care treatment scheme intelligent decision-making system based on data driving, through four-dimensional snapshot construction of a dynamic decision-making chain and medical interpretable evolution relation labeling, continuous logic of an intensive care decision-making is guaranteed, data abnormal driving change and medical logic jump risks are effectively identified, medical errors caused by decision-making faults are reduced, and the decision-making efficiency is improved. The abstract decision-making process is converted into a concrete evidence chain capable of penetrating auditing, and the clinical responsibility traceability and the doctor-patient credibility are improved.

The invention relates to the technical field of medical image information processing, and discloses a medical image information management system based on wisdom medical treatment, which comprises a scene perception preprocessing module, a cross-modal attention fusion module, a weak supervision annotation module, a comparative analysis module, a collaborative report generation module, a knowledge graph reasoning decision module and a consultation collaborative platform module. The image quality is improved through scene adaptive preprocessing; a hierarchical cross-modal attention mechanism is adopted to realize multi-modal feature alignment and fusion; lesion labeling is carried out based on a cascade weak supervised learning strategy; obtaining a focus evolution trend through time sequence correlation analysis; generating a standardized diagnosis report by using a visual language collaborative network; providing treatment scheme recommendation based on individualized knowledge graph reasoning; and a multidisciplinary consultation cooperation platform is constructed. According to the method, the scene adaptability of image quality evaluation is improved, the multi-modal semantic alignment capability is enhanced, the dependence of focus labeling on a fine sample is reduced, and dynamic disease monitoring and individualized diagnosis and treatment decision support are realized.

Systems and techniques are described for determining at least one of a translational force or a rotational force applied by a dental appliance to a tooth or teeth of a patient. A system includes model teeth configured to represent a portion of a dental arch and receive a dental appliance, a post extending from a first end coupled to a respective tooth to a second end coupled to a base, at least one strain gauge integrally formed on a surface of the post between the first end and the second end, and processor communicatively coupled to the at least one strain gauge and configured to determine at least one of a translational force or a rotational force applied by the dental appliance to the respective tooth.

An AI-based system and method for supporting differential diagnosis and standard of care in healthcare. The method involves receiving patient information from various sources, including patient-reported symptoms, physician notes, and sensor data from medical devices. The patient information is preprocessed and analyzed using deep learning models to generate a ranked list of potential diagnoses, each associated with likelihood scores and key contributing factors. The potential diagnoses are provided to physicians via an interactive interface, and physician feedback is collected to fine-tune the AI models using reinforcement learning. The method aims to enhance physician decision-making, improve diagnostic efficiency, and ensure adherence to the standard of care by leveraging AI's ability to analyze vast amounts of data more effectively than human physicians.

The present invention relates to the technical field of dental medical treatment, and specifically discloses a method for using a CBCT (Cone Beam Computer Tomography) for automatically positioning a tooth, which comprises the following steps: S01, archiving CT data; S02, calculating AI and generating AI results; S03, entering CT reading by a client; S04, downloading and loading the CT data; S05, opening a function of a tooth lens; S06, selecting a corresponding tooth position in a tooth position list; and S07, selecting a 3D tooth rendering mode. The function of the ‘tooth lens’ is added into traditional CT reading, which is suitable for clinical disease diagnoses such as ‘tooth extraction, root canal therapy, tooth repair’ and the like in oral treatment, and doctors can precisely and quickly position the single tooth through selection for the tooth position, which facilitates more comprehensive analysis.

The invention discloses a knowledge graph-based cerebral hemorrhage clinical pathway intelligent recommendation method and system, and the method comprises the steps: constructing a pathway knowledge graph and an illness state grading system through analyzing a diagnosis and treatment guide, recognizing a key node of pathway execution and an illness state grade switching boundary, and configuring a differentiation decision rule for a single illness state region and a mixed illness state region; the method comprises the following steps of: performing dimensionalization acquisition on multi-source illness state data of a patient, extracting illness state characteristics, monitoring a dynamic index change trend, generating illness state description through weighted characteristic extraction and dynamic early warning analysis, and calculating a matching degree between a current illness state and a candidate path; when path execution deviation is detected, a deviation source node is positioned through cross-stage transitivity analysis and reverse tracing, and a targeted path correction instruction is generated; normativity of an adjustment scheme is ensured through node integrity verification and forced constraint correction, a clinical pathway recommendation result matched with the actual illness state of the patient is output, and adaptability and accuracy of clinical pathway recommendation are improved.

The invention relates to the technical field of medical image diagnosis, and discloses a tooth and fracture line recognition treatment method based on the combination of an AI technology and a CBCT image, and the method comprises the steps: obtaining and preprocessing the CBCT image, and carrying out the multi-scale analysis and recognition of a tooth structure, a microcrack and a fracture line through a first AI model. And the second AI model combines the identification result and the patient characteristics, and generates a personalized treatment scheme through multi-objective optimization. Clinical feedback is used for continuously iteratively optimizing double models, and the diagnosis and treatment precision and effect are improved. The system comprises an image data acquisition unit, an image data preprocessing unit, a tooth and fracture line identification unit, a personalized treatment scheme generation unit and a feedback and optimization unit. Through AI and CBCT image fusion, accurate identification of teeth and fracture lines is realized, a personalized treatment scheme is recommended in combination with individual features of a patient and a multi-objective optimization algorithm, rapid response is realized, a closed-loop feedback mechanism continuous optimization model is established, and diagnosis and treatment precision, efficiency and individualization level are remarkably improved.

The invention relates to the technical field of medical monitoring, in particular to an intelligent monitoring and health management system for liver, gall and pancreas postoperative drainage fluid, which comprises a data acquisition module for acquiring core specific indexes of liver, gall and pancreas special drainage fluid, visual images of the drainage fluid, dynamic flow and physiological data of a patient; the intelligent analysis module constructs a hepatobiliary pancreatic postoperative exclusive multi-modal model, deeply couples postoperative disease course time sequence characteristics to identify risks at different stages after the operation, and triggers risk assessment when the operation is abnormal; the early warning module is provided with a light, medium and heavy three-level mechanism, and carries out resource adaptation type grading accurate pushing to a responsible physician and a nurse station terminal in combination with a medical care real-time load and a spatial distance. The health management module fuses individual features of the patient and real-time monitoring data, and constructs a personalized rehabilitation scheme in combination with rehabilitation feedback of the patient; and the data synchronization module transmits model update parameters through an encryption channel, and perfects the complete-cycle health archive of the patient. Therefore, the problems that in the prior art, an early warning mechanism is rigid, and data security and full-period management are insufficient are solved.

The invention relates to the technical field of medical artificial intelligence and chronic kidney disease management crossing, in particular to a nonlinear algorithm system for precise chronic disease management of chronic kidney diseases. Through deep coupling of algorithms and in combination with two exclusive quantification formulas, transformation of CKD chronic disease management from standardized follow-up visit to precise and dynamic algorithm driving is realized, and the core pain points of large staging evaluation deviation, progress pre-judgment lag, poor intervention scheme adaptability, follow-up visit strategy homogenization and the like in traditional CKD management are solved. The method provides full-cycle decision support for nephrology doctors in the three-A hospital, customizes an individualized chronic disease management path for CKD patients, is suitable for CKD 1-5 period full-staging patients, and can be widely applied to CKD special management scenes of chronic disease diagnosis and treatment centers in the nephrology department of the three-A hospital, chronic disease management outpatient clinics in special nephropathy hospitals and high-end health management institutions.

The invention discloses an intelligent rehabilitation monitoring method and device for a heart failure patient, and relates to the technical field of rehabilitation data processing.The method comprises the steps that on the basis of multiple collection time scales, monitoring item time sequence data sets are obtained, and the monitoring item time sequence data sets comprise the time sequence data set, corresponding to the multiple collection time scales, of each monitoring item; health feature value extraction is carried out on the time sequence data set of the monitoring items, and feature value sequences corresponding to different collection time scales are generated; constructing a multi-time-scale multivariate sequential sequence, and quantifying a physiological system elasticity coefficient value of the heart failure patient by using a preset event elasticity extraction mechanism; and inputting the multi-time-scale multivariate sequential sequence and the elastic coefficient value as monitoring features into a heart failure rehabilitation trajectory prediction model, outputting a health index sequence in a future intervention window, and performing elastic loss critical point identification as an early intervention point. Therefore, the accuracy and foresight of monitoring the rehabilitation health trend of the heart failure patient are improved.

Systems and methods are provided for monitoring health. An exemplary method includes: collecting a first data regarding a patient during an in-office visit; providing a remote monitoring service for remotely monitoring the patient's health; remotely collecting, using the remote monitoring service, a second data of the patient; providing a probabilistic network for assigning metric-based information to the plurality of data using a plurality of conditional probabilities; processing, using the probabilistic network, the first data and the second data using the probabilistic network; generating, using the processed plurality of data, one or more machine learning models for producing a knowledge base trained to recognize pattern types in the data; generating, using the knowledge base, one or more artificial intelligent features for recommending treatment options based on the data regarding the patient; and providing, using the one or more artificial intelligent features, one or more treatment recommendations for improving the patient's health.

The invention discloses a medical knowledge graph construction method and device for multi-source heterogeneous data fusion and incremental updating, and relates to the technical field of medical knowledge graphs. The method comprises the steps that multi-source medical data such as outpatient records, hospitalization medical records, inspection reports and image data of a patient in the whole life cycle are collected; performing time sequence alignment by adopting a space-time alignment algorithm to obtain standardized medical data, calculating a data fusion weight and constructing an incidence matrix; performing singular value decomposition on the incidence matrix to extract a feature vector, and obtaining a dynamic update coefficient through standardization, cosine similarity calculation, convolutional neural network processing and exponential weighted moving average; adjusting the reference map according to the dynamic updating coefficient increment to generate an updated medical knowledge map, and outputting diagnosis and treatment decision support information; and multi-dimensional medical data space monitoring abnormity can be constructed, a joint optimization model is established to generate a personalized diagnosis and treatment scheme, and edge deployment is realized through a knowledge distillation compression map.

Systems and methods for training predictive models for generating clinical predictions from multimodal medical data include receiving multimodal medical data of one or more medical subjects, and preprocessing and aggregating one or more features of the multimodal medical data. Further, for each cohort of medical subjects from the medical subjects, the method includes training one or more predictive models to generate a clinical prediction for each of the diseases based on the features of the multimodal medical data and deploying the predictive models to a model bank. The predictive models are used for making clinical predictions based on multimodal medical data of individual medical subjects. Use of multimodal medical data improves accuracy of the clinical predictions. Further, deploying predictive models on the model bank improves accessibility and useability of the predictive models.

We provide the system. [Solution] A means for monitoring the posture of users within a living space and generating and notifying them of an exercise reminder when an abnormal posture is detected, A method for monitoring the operating status of home appliances and automatically turning off the power when not in use, A means for recording the location of an item and notifying the user of the location of the item, A means of generating and notifying advice on tidying up based on the state of the living space, A system that includes means for detecting suspicious persons using cameras and generating an alarm when a suspicious person is detected.