3615 results about "Medical information" patented technology

The invention discloses a data analysis platform management method and system, and belongs to the technical field of computer data processing, and the method comprises the steps: obtaining multi-source heterogeneous data from a medical information system; establishing a cross-system data association model, performing dynamic feature extraction and weight distribution on multi-source heterogeneous data, and outputting a resource supply-demand relationship graph; analyzing the resource supply-demand relation graph, and identifying business conflicts in medical resource scheduling; generating a conflict solution of the business conflict based on a multi-objective optimization algorithm, and generating an execution instruction; and issuing an execution instruction to the target medical system, verifying an execution result of the execution instruction, and updating system parameters. According to the method, a cross-system dynamic modeling and multi-objective optimization strategy is adopted, and a closed-loop verification mechanism and a multi-source heterogeneous data fusion technology are combined, so that intelligent prediction, accurate scheduling and self-learning optimization of medical resource conflicts can be realized, full-process traceable execution of high-value consumables is ensured, and the resource utilization efficiency and the emergency response capability are comprehensively improved.

The invention discloses an electronic medical record intelligent evaluation method based on complex quality control indexes, and relates to the field of medical information processing and artificial intelligence. The method comprises the steps that an original electronic medical record text is collected and preprocessed, and structured diagnosis and treatment information and an event sequence diagram are extracted; through prompt word chain construction and a semantic reasoning mechanism, a large language model is guided to intelligently evaluate complex quality control indexes in medical records. The complex quality control indexes comprise diagnosis basis sufficiency, treatment scheme rationality, key result and change record integrity, treatment measure integrity and causal relationship rationality. According to the method, technologies such as a medical knowledge graph, a graph neural network and semantic vector retrieval are utilized to realize external knowledge recall and causal reasoning support; a self-consistency reasoning mechanism, a self-reflection mechanism and a multi-model cross validation mechanism are introduced to improve the accuracy and credibility of an evaluation result; and finally, outputting a structured quality control report and a visual reasoning chain. According to the method, the intelligence and refinement level in a complex medical quality control task can be remarkably improved, high interpretability and practical value are achieved, and the method is suitable for application scenes such as hospital quality management, scientific research evaluation and medical document standardization.

The invention discloses a pediatric abnormal breathing detection method and system based on a physiological signal, and relates to the technical field of medical information processing, and the method comprises the steps: collecting a multi-modal physiological signal, employing wavelet decomposition and a soft threshold function to eliminate noise, and outputting a preprocessing signal group in combination with morphological opening and closing operation; calculating a time domain feature, a frequency domain feature and a nonlinear feature of the preprocessed signal group, organizing the features into a three-layer pyramid structure, analyzing time sequence similarity by adopting an attention mechanism, and outputting a fusion feature sequence; and carrying out preliminary anomaly detection on the fused feature sequence, carrying out classification based on an age grouping threshold, extracting context feature vectors of windows before and after an abnormal point, and carrying out secondary discrimination to realize accurate identification and graded early warning of the respiratory anomaly. The respiration monitoring system with high reliability, high adaptability and intelligent learning ability is constructed, and comprehensive technical support is provided for early recognition, real-time early warning and intelligent diagnosis of children respiratory system diseases.

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 nursing risk intervention decision-making system and method based on a knowledge graph, and relates to the technical field of medical informationization, and the method comprises the steps: building a dynamic medical nursing knowledge graph based on multi-source nursing data and a deep learning model, and carrying out the entity recognition through a bidirectional long-short term memory network and a conditional random field; capturing evolution characteristics of an entity relationship through a marginal attention mechanism and a time sequence diagram convolutional network, and optimizing a knowledge graph structure based on connectivity constraints and a relationship weight threshold; a knowledge structure is dynamically adjusted by using a strengthened learning driven graph optimization mechanism, the relationship weight between entities is optimized in real time, a multi-level risk representation model is constructed, and accurate recognition and early warning of nursing risks are realized through local risk feature extraction, global risk information aggregation and time sequence risk evolution analysis. According to the method, the complex association relationship between the medical entities and the nursing risk decision are deeply fused, and the multi-dimensional medical entity features are constructed into the association network between the entities, so that early risk identification is realized.

The invention discloses a real-time health risk prediction method and system based on a dynamic knowledge graph, and relates to the technical field of medical information. The method comprises the following steps: carrying out multi-modal fusion and privacy protection preprocessing on medical and nursing heterogeneous data, and realizing semantic consistency of cross-mechanism data based on an entity alignment method of a cross-modal graph neural network; based on a hierarchical federated learning framework, local model parameters are subjected to hierarchical encryption and aggregation through a secure multi-party computing protocol to generate an initial global model, and prediction distribution of the global model is optimized in combination with knowledge distillation of differential privacy constraints; designing a gradient difference dynamic updating trigger mechanism of noise robustness, smoothing noise interference through a sliding window mean value, and realizing adaptive threshold calibration through linkage model performance verification; and light-weight deployment real-time reasoning is realized based on redundant edge pruning of confidence and 8-bit symmetric quantization. On the premise of protecting data privacy, the real-time performance and accuracy of health risk prediction are remarkably improved, and the method is suitable for a cross-institution medical care collaborative decision-making scene.

The invention discloses a neurology nursing potential safety hazard monitoring system and method, and relates to the technical field of medical information safety monitoring, and the system comprises a first collection processing unit which is used for collecting monitoring data of a target patient, inputting the monitoring data into a time sequence deep network for processing, and obtaining risk feature data; the second collecting and processing unit is used for collecting physiological state data of the target patient, constructing a risk assessment rule base based on a medical knowledge graph to analyze the physiological state data, and generating risk probability data; and the risk early warning unit is used for inputting the risk feature data and the risk probability data into a Bayesian fusion network, generating risk early warning information and sending the risk early warning information to the medical care terminal. According to the invention, collaborative analysis of multi-source information is realized through the Bayesian fusion network, the problem of low early warning accuracy of a traditional single monitoring method is solved, and the reliability of potential safety hazard monitoring is improved.

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.

The invention discloses a medical record quality management system and method, and relates to the technical field of medical information processing, and the method comprises the steps: collecting medical record data, and carrying out the preprocessing of the collected medical record data; analyzing the preprocessed medical record data by adopting an NLP, and extracting a medical record core field through an NER; constructing a dynamic medical knowledge graph, and matching the dynamic medical knowledge graph with the medical record core field; after matching is completed, a graph reasoning algorithm is used for conducting logic consistency verification on the medical record, logic contradiction points are marked, and a verification result is generated; and generating a medical record quality auditing report according to the verification result. According to the method, the dynamic medical knowledge graph is constructed and matched with the core field of the medical record, and the graph reasoning algorithm is adopted to perform logic consistency verification on the medical record, so that the intelligence and precision of medical record quality management are realized.

The invention relates to the field of medical informatization. The invention discloses a thyroid cancer electronic medical record system based on multi-modal data fusion. The thyroid cancer electronic medical record system comprises a multi-modal data acquisition module which acquires patient texts, ultrasonic images, genes, biochemical indexes and clinical data and performs standardized calibration to generate standard data; the multi-modal feature extraction module extracts semantic, structure, mutation, change and fluctuation features of each standard data through multiple technologies; the single-mode prediction model construction module constructs single-mode prediction models of texts, images and the like based on the features and outputs results; and the multi-modal fusion prediction module fuses the single-modal model based on the deep learning framework to output a multi-modal fusion prediction result. According to the invention, multi-modal data are integrated, and the accuracy and comprehensiveness of thyroid cancer diagnosis are improved. The system ensures consistency through standardized data processing, and assists doctors to accurately judge pathological types, recommend therapeutic schedules and evaluate prognosis by means of a multi-modal feature extraction and fusion mechanism.

The invention provides a potential relation reasoning-based medical knowledge graph retrieval system and a potential relation reasoning-based medical knowledge graph retrieval method. The system comprises a medical data preprocessing module, a knowledge extraction and medical graph generation module, a medical knowledge graph potential relation reasoning complementation module, a keyword network and semantic analysis retrieval module and a semantic mapping and query response module. The system can pre-process and standardize the medical data to generate the knowledge graph containing abundant medical information, not only can complement the knowledge graph through graph structure analysis and semantic reasoning technologies to ensure the integrity and accuracy of the knowledge graph, but also can carry out efficient retrieval based on user query to improve the user experience. And the user query intention and the related entity relationship are accurately matched, and finally an accurate and comprehensive medical information query result is provided.

The invention relates to a medical information processing and intelligent data management technology, and discloses a hospital multi-source heterogeneous data integration management system and method based on an EMPI, and the system carries out the real-time subscription and batch extraction of the data of an HIS, an EMR, an LIS, a PACS, a DRG and a pre-hospital first-aid system through an interface adapter, and generates a unified main index identifier through the matching of the EMPI, and carries out the real-time subscription and batch extraction of the data of the HIS, the EMR, the LIS, the PACS, the DRG and the pre-hospital first-aid system. Field-level cleaning, standardization and semantic mapping are carried out on the free text and the structured data, an intermediate standardized record set is formed, an event graph is constructed, event cluster recognition is carried out in combination with time, clinical entities, doctor seeing scenes and department similarity, consistency judgment and EM iterative fusion are carried out on the basis of data source reliability, and event cluster recognition is carried out. And outputting a unified patient file set and a downstream service interface. According to the invention, high-precision integration, standardized management and traceable application of multi-source data are realized, and a reliable data basis is provided for clinical decision support, medical insurance management and scientific research.

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 discloses a medical information multi-agent expert thinking chain collaborative reasoning method and system, and the method comprises the steps: constructing a sub-expert agent and a summarizer agent based on a large language model, processing the medical information, and outputting a processing conclusion of the medical information; and secondly, constructing an evaluator agent, performing medical information conflict evaluation on the output of the sub-expert agent and the summarizer agent, activating a multi-agent debate mechanism when the conclusion output of the summarizer agent exceeds a medical conflict threshold, and outputting whether a processing conclusion is credible or not. Then constructing a coordinator agent, selecting called sub-expert agents and summarizer agents, and generating a plurality of forward reasoning thinking chains; and finally, the evaluator agent evaluates whether the output of the forward reasoning thinking chain exceeds a medical conflict threshold, and iteratively updates an output processing conclusion. According to the method, complex problems in medical information processing problems can be analyzed, and reasoning accuracy and interpretability are remarkably improved.

The invention discloses an electronic medical record automatic quality control system and method based on a large language model, and the method comprises the steps: extracting medical record data from a medical information system, and carrying out the data preprocessing; and performing deep semantic analysis on the medical text by utilizing a large language model, and extracting key diagnosis and operation information. And performing semantic representation on the medical terms by fine tuning the pre-training language model and combining with a multi-layer attention mechanism. And constructing an ICD coding knowledge graph, and calculating the semantic similarity between the diagnosis terms and the ICD coding terms in the electronic medical record by using an embedded vector technology to realize automatic matching. And according to a predefined quality control rule, automatically checking the electronic medical record through the logic expression, and generating a quality control report and an improvement suggestion. And finally, visually displaying quality control and coding results, and outputting a quality control report. The electronic medical record quality control efficiency and accuracy are improved, and standardization and automation of quality control work are achieved.

The invention discloses a whole course follow-up visit management system and method for gynaecology and obstetrics patients, and relates to the field of medical information management.The method comprises the steps that when a patient sees a doctor for the first time, basic information, medical related information, medical image data, gene detection data, sensor data and patient self-report data of the patient are input; preprocessing the input patient data, and integrating the preprocessed patient data into a personal health record; establishing a health assessment model of the patient based on the integrated personal health record, outputting a current health assessment score of the patient, and generating a follow-up visit plan according to the current health assessment score of the patient; and establishing an obstetrical complication prediction model based on the personal health record, and outputting a patient complication prediction value. Potential obstetrical complication risks are recognized in advance, especially when the health condition of the pregnant and lying-in woman changes, the prediction result can be dynamically updated, real-time monitoring and early warning of complication are achieved, and safety management of the lying-in woman is optimized.

The invention discloses a general surgery department medical information sharing method and system based on medical big data, and relates to the technical field of medical information sharing, and the system comprises an authority management module which is used for dynamically distributing medical data access authority according to the identity attribute of a user, establishing a multi-level access control model, and storing the access control model; the model is based on user identities and roles, and is combined with sensitivity levels of data. According to the general surgery department medical information sharing method and system based on the medical big data, through dynamic permission adjustment, the permission of the user is dynamically adjusted according to an emergency demand scene and a special situation in an actual medical environment, and medical demands in different scenes are met; the flexibility and adaptability of the system are improved, unauthorized access possibly caused by too loose permission and interference on medical operation efficiency caused by too strict permission in the permission distribution and management process are avoided, and meanwhile it is guaranteed that personal privacy of a patient is not invaded or leaked.

The invention discloses a blood tumor patient clinical data analysis method and system based on a knowledge graph, and relates to the technical field of medical information processing. The method comprises the following steps: analyzing an unstructured text by utilizing a natural language processing model, and constructing a standardized entity set; aggregating laboratory indexes through a dynamic window, and constructing a time axis data set for marking a treatment stage; a standardized entity is integrated based on a blood tumor ontology model, a dynamic global knowledge graph is constructed in combination with a clinical causal relationship and a time sequence path, and chain reaction early warning is realized; based on the real-time patient sub-atlas, successful cases with the same molecular features are retrieved in the knowledge atlas to generate a candidate treatment scheme set, a graph neural network trained through an optimization algorithm is utilized to predict the complication risk of each scheme, and finally the optimal treatment scheme is evaluated and selected; according to the method, a clinical data barrier is broken through, dynamic reasoning, advanced risk early warning and personalized scheme optimization of the whole treatment cycle are realized, and the diagnosis and treatment accuracy, safety and medical resource utilization efficiency are remarkably improved.

The application includes systems and methods for phenotyping. A request is received to identify a target population having a phenotype. Predefined characteristics associated with the phenotype are obtained. Using a retriever model, a set of subjects is identified as potential members of the target population by searching databases. Medical information is obtained by searching a second set of one or more databases and providing corresponding information to an artificial intelligence (AI) component. Natural language instructions are provided to the AI component to provide context to the AI component to determine whether a respective subject in the set of subjects has at least one of the one or more predefined characteristics. A subset of subjects is identified by the AI system to be, or to have a high likelihood of being, a member of the target population.

The invention relates to the field of medical informatization, in particular to a nursing risk assessment system and method based on multi-modal data. According to the nursing risk assessment system, data acquisition, preprocessing, multi-modal fusion, risk assessment and early warning are integrated, and comprehensive, accurate, timely and personalized nursing assessment is realized. The system collects physiological signals, nursing behaviors and environment data, the data are preprocessed and fused to generate feature vectors, a risk assessment matrix is constructed, and risk indexes are calculated. The risk early warning module sends early warning information to the nurse work station in real time, and nursing safety is ensured. Innovative technologies such as multi-modal data fusion, real-time dynamic evaluation, personalized adjustment and the like greatly improve the nursing quality, reduce the occurrence rate of adverse events, relieve the burden of medical staff and improve the utilization efficiency of medical resources. Long-term application is hopeful to significantly improve patient prognosis and reduce medical cost, and makes an important contribution to development of intelligent medical treatment and precise nursing.

The invention discloses an anti-tumor treatment adverse reaction risk early warning method and system, and relates to the technical field of medical information processing, and the method comprises the steps: obtaining the physiological index data of a patient, and obtaining a short-term risk prediction result based on the time sequence change characteristics of the physiological index data of a deep neural network; obtaining clinical feature data of the patient, constructing a susceptibility assessment model, and analyzing correlation features of the clinical feature data through the susceptibility assessment model to obtain a baseline risk assessment result; and according to the short-term risk prediction result and the baseline risk assessment result, calculating a comprehensive risk score, and when the comprehensive risk score exceeds an early warning threshold of the corresponding adverse reaction type, triggering an early warning signal. According to the method, a double-layer risk assessment system is constructed by combining instant physiological index changes and clinical features of patients, the technical problems that an existing early warning method is insufficient in consideration of individual differences of the patients and poor in early warning timeliness are solved, and the accuracy and timeliness of early warning of adverse reactions of anti-tumor treatment are improved.

The invention discloses an elderly disease health question and answer method based on multiple agents and a knowledge graph, and belongs to the crossing field of artificial intelligence and medical information technologies. Structured representation of medical knowledge is realized by constructing a multi-modal medical knowledge graph and through multiple entities and association relationships thereof. In combination with a domain customized large language model and a multi-agent dynamic routing mechanism, a two-stage training strategy is adopted to optimize a base model, including all-parameter pre-training and parameter efficient fine tuning based on a low-rank decomposition technology, so that the professionality and reliability of the model are effectively improved. In a multi-agent collaborative architecture, the system dynamically selects an optimal processing path according to input characteristics, including knowledge graph query, professional answer generation or external knowledge retrieval, and maintains context continuity of multiple rounds of conversations through a dynamic abstract compression algorithm. According to the method, the multi-agent collaborative architecture is combined with the knowledge graph reasoning ability, and the advantages of relation query and semantic matching are integrated through the mixed retrieval strategy.

The invention discloses a knowledge graph-based nursing corpus automatic creation method, and relates to the field of artificial intelligence technology and medical information processing, and the method comprises a multi-dimensional data acquisition stage, a knowledge graph construction stage, a corpus automatic generation and updating stage, and a corpus quality control and evaluation stage. According to the automatic corpus generation method driven by the knowledge graph, dynamic generation and real-time updating of corpora are achieved by constructing an ontology structure, a corpus generation template and an updating algorithm in the nursing field; through a deep learning model and a transfer learning technology, new nursing knowledge and a diversified information structure are continuously introduced into the corpus, and the diversity of corpus content is increased.

The invention relates to the technical field of medical information, in particular to an intelligent prediction model and method for postoperative complications of anesthetized patients, and the method comprises the steps: collecting preoperative to postoperative complete-cycle clinical data of a patient through a medical data interface; analyzing operation codes to generate risk features, extracting vital sign dynamic features, and establishing a complication probability mapping relation through a multi-modal fusion network; combining the complication probability and pharmacokinetic parameters to construct an optimization model, and solving an individualized anesthetic dosage interval by using a gradient descent algorithm; vital signs are dynamically monitored in the operation, a dose re-optimization mechanism is triggered, the infusion rate is adjusted, and a closed-loop control link is formed; and generating a visual decision report. According to the method, through deep integration of complete-cycle clinical data and multi-modal feature modeling, preoperative physiological parameters, operation coding semantic information and intraoperative vital sign dynamic modes are subjected to fusion analysis, a nonlinear mapping relation between dosage and complication probability is constructed, and the risk prediction precision and individualized adaptability are remarkably improved.

The invention relates to the technical field of medical information, in particular to a medical data conjoint analysis system based on medical knowledge graph driving. The system comprises a medical knowledge graph construction module, a medical data quality evaluation module, a medical feature engineering module and a medical knowledge driven analysis module, and heterogeneous knowledge graph modeling can be performed by integrating medical clinical guidelines, biomedical literatures, a drug database and historical medical data so as to generate a medical heterogeneous knowledge graph; obtaining new medical clinical test information and carrying out delayed contradictory learning update to generate a medical dynamic update knowledge graph; the method comprises the following steps: obtaining multi-modal medical data, carrying out quality verification evaluation and medical feature engineering analysis, carrying out knowledge path joint driving analysis at the same time, generating a decision support reasoning path corresponding to medical clinical knowledge, and outputting a corresponding medical knowledge path confidence coefficient. According to the method, fusion reasoning among cross-source data can be realized by constructing the multi-modal medical knowledge graph.

The invention discloses a postoperative physical sign monitoring system based on medical information processing. The postoperative physical sign monitoring system comprises a data acquisition module, a physical sign feature extraction module, a postoperative physical sign evaluation model establishment module and a postoperative physical sign monitoring module. The invention belongs to the field of medical information processing, and particularly relates to a postoperative physical sign monitoring system based on medical information processing. Quantifying the total change trend of the heart rate fluctuation in the monitoring time; the quantitative basis is provided for the postoperative recovery process by integrating the blood pressure and heart rate changes; the sign monitoring effect is improved; focusing on dynamic adjustment through a time weight function, a recovery stage weight and an environment correction factor in a postoperative key time period; deviation weights are introduced for fluctuations of heart rates and blood pressures, loss coefficients are dynamically adjusted, segmented loss functions are designed according to physical sign values, conditions of different risk intervals are processed respectively, and the method adapts to uncertainty of individual differences and physical sign fluctuations of postoperative patients; and thus, the accuracy of post-operation sign monitoring is improved.

The invention relates to the technical field of medical information processing, in particular to a health data processing method and system based on a distributed ledger library, and the method comprises the steps: a node firstly links a health data abstract to obtain a differential privacy budget token and a reversible tensor hash key; then, carrying out encryption training on local model parameters by using the key, injecting Gaussian noise according to budget to generate a differential protection gradient, and carrying out uplink together with zero-knowledge proof; the account book end decrypts the threshold value, uses a structural equation model to deduce a causal correction matrix according to the reputation weight aggregation gradient, and automatically adds a budget and turns the key when the concept drifts and the budget is insufficient; the operation mechanism splices the aggregation parameters and local parameters, generates a gating vector in combination with a causal correction matrix and a reputation weight, outputs disease risk prediction, and only uploads prediction hash and error information; chain-level audible privacy protection, dynamic budget management and hybrid deviation suppression are realized, and the safety and accuracy of a cross-institution medical model are improved.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

The invention relates to the field of medical informatization, and discloses an intelligent medical record generation method and system for realizing non-sensitive experience, and the method comprises the steps: carrying out the real-time dynamic capturing of a whole patient reception process, and extracting a patient physiological parameter boundary, a doctor seeing interaction node and a diagnosis and treatment environment noise region based on a medical scene semantic segmentation algorithm; acquiring a diagnosis and treatment state multi-source data stream sequence; performing cross-modal time sequence deep association matching on the diagnosis and treatment state multi-source data stream sequence; based on the multi-dimensional dynamic feature matrix, utilizing an adaptive semantic evolution clustering algorithm to extract potential semantic offset paths in a medical record generation process, and identifying high-risk information distortion candidate nodes by detecting distribution abnormity of semantic evolution trajectories; dividing the diagnosis and treatment semantic units and the influence intervals corresponding to the high-risk information distortion candidate nodes as candidate correction areas; and generating a map based on the local medical record, and performing intervention decision on the information evolution link. The method has the advantage of improving the experience feeling of the patient.

The invention relates to the technical field of medical information processing, in particular to an information sharing method of a pediatric clinical nursing system. And obtaining complete medical record sample data including age, symptom results, treatment cycles and drug doses. Comprehensively analyzing the similarity between the age and the disease result, determining a difference characteristic value, and carrying out initial clustering to form a cluster; disease distribution and influence characteristics are analyzed from disease results in view of differences of disease response and treatment requirements of child patients of different age groups. Selecting a to-be-analyzed disease, determining a prominence index by combining the distribution density and the treatment cycle of the to-be-analyzed disease in the cluster, and reflecting distribution characteristics; meanwhile, the occurrence frequency, the difference characteristic value, the treatment cycle and the medication dosage are analyzed, and the influence degree index is judged. Clinical indexes are determined by combining the influence degree and the outburst index, and individual features of the child patient are accurately represented. Clustering analysis is carried out based on clinical indexes, a more accurate clustering result is obtained, and the information sharing accuracy is improved.