511 results about "Data treatment" patented technology

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 discloses a supply chain risk identification method and system based on a knowledge graph, belongs to the technical field of supply chain management and artificial intelligence crossing, and aims to solve the technical problem of how to realize dynamic monitoring, accurate identification and active early warning of supply chain risks, improve full star, real-time performance and interpretability of supply chain risk identification, and improve the risk identification efficiency. According to the technical scheme, the method comprises the following steps: collecting and treating multi-source data: collecting static background information and dynamic risk information of a supplier, and carrying out highly intelligent data treatment on the collected static background information and dynamic risk information of the supplier through a data treatment engine to ensure data quality and consistency; constructing a dynamic knowledge graph; intelligent risk identification: based on a graph topological structure and dynamic attributes, identifying key risk nodes and communities, tracing in time, marking risks, and performing early warning; decision support and visualization are carried out; and dynamically optimizing and feeding back.

The invention relates to the technical field of clinical experiment data processing, and discloses a clinical experiment data analysis method based on reinforcement learning, and the method comprises the steps: obtaining time sequence physiological parameters and treatment scheme execution state parameters; performing hierarchical feature extraction on the time sequence physiological parameters, constructing a dynamic response feature model, and generating an initial treatment response evaluation model in combination with the execution state parameters; loading risk constraints and curative effect indexes to generate a comprehensive evaluation model, and simulating to obtain strategy regulation and control data; constructing and training a reinforcement learning decision model, and generating a treatment path correction model and initial treatment parameters; obtaining dynamic strategy adjustment information, and constructing a multi-stage optimization model for iterative optimization to obtain optimal treatment path parameters; reasoning a curative effect standard reaching probability based on a real-time model, and adjusting a strategy to realize dynamic curative effect matching. According to the method, the clinical data dynamic analysis and treatment scheme optimization capability is improved, and the method is suitable for precise medical scenes.

The invention relates to the technical field of medical data processing, in particular to an intelligent decision-making system and method for multi-modal medical data dynamic fusion, and the system comprises a data collection module which is used for collecting multi-modal medical data; the data preprocessing module is used for preprocessing the collected multi-modal medical data; the multi-modal fusion and decision generation module is used for constructing a cascade learning framework comprising a plurality of subtask models; the dynamic decision adjustment module is used for setting cascade priorities among different modes in different diagnosis and treatment scenes; and the closed-loop optimization module is used for constructing a closed-loop process in combination with a national diagnosis and treatment guide and real clinical data. According to the method, in different diagnosis and treatment scenes, the system can automatically adjust the cascade priority of each modal data according to the illness state stage, so that the characterization capability of the decision model on the complex illness state is remarkably improved, the limitation of traditional static fusion is broken through by the dynamic fusion mechanism, and the accuracy of diagnosis and treatment suggestions and the scene adaptability are remarkably enhanced.

The invention discloses a geological mapping method based on unmanned aerial vehicle three-dimensional modeling, and relates to the technical field of geological survey and intelligent path planning, and the method comprises the steps: S1, extracting an initial feature data set from geological information through constructing a three-dimensional space data processing frame, decomposing the original data into a plurality of sub-region data sets, obtaining a preliminary classification geological information set, and carrying out the calculation of the preliminary classification geological information set; s2, analyzing abnormal values and boundary information in the sub-region data set according to the preliminarily classified geological information set, and determining a potential key geological feature point position set, and S3, matching the point position set with geological environment parameters according to the key geological feature point position set, recalibrating the point positions if the matching degree is lower than a preset threshold value, and if the matching degree is lower than the preset threshold value, recalibrating the point positions if the matching degree is lower than the preset threshold value. An optimized geologic characteristic distribution diagram is obtained; according to the geological mapping method based on unmanned aerial vehicle three-dimensional modeling, the accuracy and efficiency of geological survey in a complex terrain environment are effectively improved, and comprehensive technical support is provided for field geological survey.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

The invention relates to the technical field of medical data processing and prediction, and discloses a chemotherapy adverse reaction prediction and intervention system based on big data. The system integrates an unstructured disease course text and structured inspection data of a patient, generates a time-series symptom event, and performs time window alignment and fusion on the time-series symptom event, a medication record and a physical sign monitoring stream to form a multi-dimensional time-series data block. The system is combined with an external medical knowledge base to construct a dynamic association network among symptoms, medicines and physiological indexes, and dynamically calculates the confidence coefficient of an adverse reaction mode according to real-time data. By using a predictive model of the timing attention mechanism, the system can output a continuous curve of patient risk over time. The system automatically matches and generates a personalized intervention instruction sequence containing specific measures and execution time windows according to key time points and modes when the risk curve exceeds a threshold value. According to the invention, dynamic and advanced early warning and accurate intervention of adverse reaction risks of chemotherapy are realized.

The invention relates to the technical field of soil monitoring and data analysis, and discloses a soil carbon and nitrogen content dynamic prediction method based on deep learning. The method comprises the following steps: acquiring soil monitoring data from an environment monitoring platform, performing dimension reduction by using a multi-layer perceptron model to obtain core features, and dividing a dynamic monitoring data set according to the core features; taking the data set as input, and constructing an initial prediction model by using a time convolutional network; and constructing a meteorological factor library and an influence map, replacing an initial model time node, and obtaining a climatic factor node prediction model through cross validation. And performing regression fitting and cross validation verification by using a Gaussian process, and constructing a soil dynamic prediction model. According to the method, through multi-step data processing and model construction, the influence of soil data characteristics and meteorological factors is effectively mined, the dynamic change of the soil carbon and nitrogen content can be accurately predicted, and powerful support is provided for the fields of precision agriculture, environmental protection and the like.

The invention relates to the field of data management, in particular to a VOCs pollution working condition data treatment method based on artificial intelligence. The method comprises the following steps: firstly, constructing a three-dimensional model of a factory, a pipeline and treatment equipment based on a BIM tool and a GIS technology, and deploying an Internet of Things sensor to collect VOCs pollution data; collecting original infrared absorption spectrum data of VOCs, and performing component identification by using a non-negative matrix factorization algorithm; a machine learning model is trained in combination with historical data, and short-term emission prediction is carried out; cooperative scheduling is carried out on the governance equipment through a multi-agent game optimization algorithm, and an optimal operation scheme is generated; constructing volume cloud modeling, and dynamically displaying an optimization effect; and if the predicted emission exceeds the standard, performing emission abnormity traceability analysis by using a graph neural network, and outputting a fault diagnosis report. According to the method, the VOCs emission prediction precision and the treatment efficiency are effectively improved.

The invention provides a tumor patient clinical test matching system and method based on a large language model and an OCR technology, and is applied to the field of medical data processing. The method comprises the following steps: analyzing clinical data and test information, processing an unstructured text, and generating structured clinical feature data through context association analysis; key data is extracted and subjected to double verification correction, and structured data supplementary information is generated; enhancing the structured clinical feature data and supplementary information based on a multi-modal processing assembly line module, extracting an image quantitative index, analyzing an immunohistochemical result, and generating a comprehensive matching score; through a rule engine and semantic similarity calculation, item-by-item comparison of patient features and entry and exhaust conditions is realized, and a preliminary matching result is generated; edge case misjudgment is corrected through context-aware multi-round reasoning, sorting is adjusted in combination with clinical test priority weights, and an optimized clinical test matching list is generated; and generating a clinical test matching report based on the data.

The invention provides a sewage treatment detection method and system with a diagnosis function, and the method comprises the steps: firstly receiving multi-source detection data which is uploaded by each treatment unit in a sewage treatment process and comprises sewage pollutant component data, treatment equipment operation parameter data and flora activity data in a reaction tank; performing cross-unit association mapping processing on the multi-source detection data to generate a substance transformation association network among processing units, constructing a dynamic balance model of the sewage treatment system based on the network, inputting the multi-source detection data acquired in real time into the model, analyzing abnormal association nodes deviating from a balance state and corresponding abnormal influence factors, and determining the abnormal association nodes and the corresponding abnormal influence factors; and finally, generating a diagnosis report containing an abnormal processing path and an equipment regulation and control instruction according to an analysis result, thereby accurately positioning a problem source of the sewage treatment system, timely adjusting equipment parameters to recover system balance, and improving the processing efficiency and stability.

The invention belongs to the technical field of farmland tail water pollution load analysis and evaluation, particularly relates to a farmland tail water pollution load analysis and evaluation method, and aims to overcome the obvious defects of an existing monitoring and evaluation method in the aspects of monitoring point layout, index coverage, data processing, analysis depth and the like. In order to solve the problems that farmland tail water pollution load analysis and evaluation cannot meet accurate and dynamic farmland tail water pollution load analysis and evaluation requirements and pollution prevention and control decisions are difficult to effectively support in the prior art, the invention provides the following scheme: the method comprises the following steps: S1, carrying out multi-source acquisition on farmland information data, and carrying out fusion processing; s2, setting a fixed sampling unit and a movable sampling unit according to farmland information setting; according to the method, multi-source data such as farmland basic information, agricultural production activity data, meteorological data and water quality monitoring data are comprehensively considered, and comprehensive and accurate quantification and evaluation of farmland tail water pollution load are realized through a data preprocessing and fusion technology.

The invention discloses a data quality treatment method and system based on an AI Agent, and belongs to the technical field of artificial intelligence and data treatment. An initial data semantic distribution map is constructed, cross-dimension correlation feature factors are extracted, a multi-scale quality anomaly sensitive factor matrix is constructed, time sequence evolution weights are embedded, and a dynamic feature evolution trajectory is formed; performing perception modeling on the evolution trajectory by using an AI Agent, generating a multi-level quality risk thermodynamic diagram, extracting a deviation dense region and constructing an anomaly propagation path set; in combination with upstream and downstream data links and task flow information, calculating a potential impact factor weight, constructing a causal traceability map, and injecting a correction strategy label for a key field node; the AI Agent autonomously selects an adaptive strategy combination according to the target data segment, and performs online intervention on the target data segment; according to the method, closed-loop treatment of the data quality problem from perception and judgment to intervention and feedback is realized, and the method has the advantages of self-adaption, high interpretability and the like.

The invention belongs to the technical field of data processing, and discloses a construction method and system based on a medical examination medical knowledge base and a medium. Comprising the steps that a basic medical data set is collected, data cleaning is conducted, and a structured medical data set is output; constructing a multistage medical dictionary and establishing semantic mapping with the structured medical data set to generate a standard medical data set; carrying out medical related entity extraction on the standard medical data set and establishing a semantic relationship of each medical entity to obtain a medical entity set; performing semantic alignment on the medical entity set to obtain a semantic alignment entity set; constructing a medical examination knowledge rule base and constructing an initial medical examination knowledge graph in combination with the semantic alignment entity set; performing entity association extension on the initial medical examination knowledge graph, and outputting an enhanced medical examination knowledge graph; real-time clinical data are collected, and the medical examination knowledge graph is updated and enhanced based on the real-time clinical data to obtain the medical examination knowledge base, so that convenience is provided for medical decision making and intelligent reasoning diagnosis.

The invention relates to the technical field of water quality data analysis, and discloses a river water quality pollution assessment method and system based on big data analysis, and the method comprises the steps: building a water quality parameter set; obtaining a preprocessed water quality data set; obtaining a global statistical analysis result through descriptive statistical analysis; carrying out hierarchical multi-time evaluation analysis to obtain a hierarchical evaluation result; obtaining a deviation value between the current water quality data and standard water quality data of a laboratory; reliable water quality characteristic data are obtained; a difference trend analysis result is obtained; performing relevance verification on the river water quality distribution map and the time change map of the target area according to the difference trend analysis result to form a comprehensive analysis result; and finally generating a comprehensive water quality pollution assessment report containing water quality traceability information and a comprehensive analysis result. According to the invention, a multi-parameter sensing technology and intelligent data processing are organically combined, so that the water quality of the river is efficiently, accurately and carefully analyzed, and the pollution degree is evaluated.

The invention discloses an AI-driven disease diagnosis and curative effect monitoring analysis system, which comprises a data processing module used for collecting multi-source diagnosis and treatment data and constructing a multi-modal sample set; the diagnosis prediction module is used for generating a diagnosis label, a diagnosis confidence value and a prediction curative effect trend through a multi-task learning model; the curative effect comparison module is used for collecting actual curative effect data, constructing a curative effect observation sequence and generating a curative effect deviation sequence based on the curative effect observation sequence and the predicted curative effect trend; the credibility evaluation module is used for executing credibility review based on the curative effect deviation sequence and the diagnosis confidence value; and the path evolution module is used for recording continuous multi-round diagnosis correction results, generating a jump type diagnosis path and updating a diagnosis and treatment data chain. The dynamic closed-loop verification mechanism between the multi-mode diagnosis and treatment data and the predicted curative effect trend improves the accuracy of disease diagnosis, the reliability of curative effect prediction and the adaptability of the diagnosis and treatment process.

The invention provides a large-model-driven traditional Chinese medicine curative effect multi-dimensional intelligent evaluation system and device, and relates to the technical field of traditional Chinese medicine curative effect intelligent evaluation. The system mainly comprises a data layer, a knowledge layer, a model layer and an application layer which are arranged in sequence; the data layer is used for collecting tongue images, pulse condition data, cardiovascular data, structured data, unstructured data and the like, and carrying out data treatment operations such as structured transformation, block chain evidence storage and abnormal value correction; the knowledge layer comprises a traditional Chinese medicine diagnosis and treatment knowledge map, a traditional Chinese medicine prescription compatibility relationship library, a curative effect evaluation standard library, a component-target network, a semantic mapping engine and a syndrome-treatment association map; the model layer comprises a multi-modal feature extraction module and a curative effect prediction module; the application layer comprises a doctor end and a patient end. According to the scheme, quantitative analysis of traditional Chinese medicine syndrome differentiation rules is realized through a large model, and the syndrome classification consistency of patients is improved.

The invention provides a snail bioactive peptide quality tracing system and method based on big data, and belongs to the technical field of snail bioactive peptides. Comprising a data acquisition module used for acquiring various data in the production process of the snail bioactive peptide, a big data processing module used for integrating and cleaning the data, and an intelligent quality analysis module used for analyzing the quality of the snail bioactive peptide. The intelligent quality analysis module is used for carrying out quality prediction and abnormal early warning on the snail bioactive peptide, and the block chain tracing module is used for carrying out quality tracing on the snail bioactive peptide. According to the method, data islands of cultivation, production and detection links can be broken, cross-link association analysis is realized, manual sampling inspection is upgraded to AI-driven real-time early warning and process optimization, data authenticity is guaranteed through a block chain technology, international compliance requirements are met, and consumer trust and brand price are improved by adopting three-dimensional visualization and scientific data.

The invention relates to the technical field of hydrological data processing, in particular to a water conservancy intelligent internet-of-things sensing method and system, and the method comprises the following steps: combining statistical data into a feature set based on hydrological monitoring data including flow, water level and rainfall information, judging whether the data accords with normal distribution, and generating a data distribution feature vector set. According to the method, the change trend of the data is captured through multi-level data distribution characteristic analysis, and the monitoring precision of the data exception is enhanced. In the prediction process, new data and weight adjustment are gradually fused, so that the model can adapt to environmental changes in real time, and the hydrological state in a future short period is accurately predicted. Besides, abnormal data can be identified in real time based on analysis of quantiles, potential problems can be quickly found, and state division of different levels can be carried out according to abnormal degrees. In sampling scheduling, the priority of data acquisition is adjusted according to a probability model of state transition, so that monitoring resources are efficiently configured.

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 provides an analysis processing method and system for a multi-source medical image, is applied to the technical field of medical image processing, and provides a basis for subsequent processing by acquiring medical image data. All the images are resampled to a unified spatial resolution, the images of the same patient at different time points are registered and aligned to the same spatial coordinate system, and after spatial unification and registration processing, anatomical structures in the images are aligned at different time points. At the moment, feature vectors capable of describing textures and shapes are extracted from the registered images, the feature vectors are subjected to standardization processing and then fused, and finally a quantitative analysis result with cross-time-point comparability is obtained and used for disease analysis, curative effect evaluation and other procedures. Therefore, the method has the advantages of processing multi-source heterogeneous medical image data, processing long-term follow-up visit medical image data and generating a cross-time-point comparable quantitative analysis result.

The invention discloses an intelligent coagulant adding method for a waterworks. The method comprises the following steps: acquiring test data and production statistical data of the waterworks in a historical time period; performing data processing on the test data and the production statistical data to obtain a target data set for training; training the initial addition model by using the target data set to obtain a target addition model; obtaining current monitoring data of the water quality of the water plant, and inputting the current monitoring data into the target adding model to obtain a predicted adding amount of the coagulant; on the basis of the factory water turbidity monitored in real time, the feedback regulating quantity of the predicted adding quantity is determined; and performing feedback adjustment on the predicted dosage based on the feedback adjustment quantity to obtain a target dosage corresponding to the current monitoring data, and adding the target dosage to the waterworks. The condition that the outgoing water quality does not reach the standard when the incoming water quality abnormally fluctuates is avoided, and stable water quality control of a water plant is ensured.

The invention relates to the technical field of plant phenotype data processing, in particular to a plant phenotype automatic acquisition and feature processing method oriented to a multi-element environment. According to the method, the plant multi-modal phenotypic data are synchronously collected through the Internet of Things nodes, the coupling strength is calculated in combination with the environmental factors, the phenotypic parameters are divided into an environment sensitive type and a steady type, and the data classification accuracy is improved. A growth trend field is constructed by means of phenological phase decoupling analysis and geographic space superposition, abnormal segments are dynamically eliminated by combining physiological synergy degree and a mutation detection algorithm, and trend analysis stability is enhanced. A phenotype-environment response map is generated through standardized processing and an environment equivalent value model, a key environment sensitive window is recognized, a regional and staged cross-environment regulation scheme is output, intelligent and accurate management of the crop growth environment is achieved, and the method is suitable for agricultural yield increase and breeding optimization.

The invention relates to the technical field of data processing, and discloses a temperature effect modeling method and system for a concrete tower drum. The method comprises the steps of establishing a finite element model considering joint grading thermal resistance and prestress correction material parameters, adaptively adjusting a time step length according to a solar altitude change rate to calculate a radiation load, obtaining a convergence temperature field through bidirectional coupling iteration of a temperature field and a stress field, and extracting characteristic temperature to train a neural network to realize rapid prediction. According to the invention, the precision and efficiency of temperature effect analysis of the fabricated concrete tower drum are improved.

The invention relates to the technical field of hepatitis diagnosis and treatment management, and discloses a block chain-based hepatitis patient full-period management method. The method comprises the following steps: creating a patient identity chain in a block chain network, and storing patient registration data and initial diagnosis information; full-cycle diagnosis and treatment data of a patient are obtained and divided into clinical examination, iconography, medication records and life sign monitoring data. Performing multi-dimensional cleaning on the clinical examination data to generate a standardized data set; inputting the iconography data into a pre-trained hepatitis feature extraction model, and outputting a liver injury feature set; verifying the integrity of the medication record, and generating a non-tampering medication time sequence chain; comparing the life sign monitoring data with preset health reference parameters to generate a sign deviation coefficient set; according to the method, data safety and traceability are guaranteed through the block chain, diagnosis and treatment data quality is improved through multi-dimensional data processing, patient privacy is protected, and reliable support is provided for full-period management of hepatitis patients.

The invention discloses a traditional Chinese medicine intelligent diagnosis and treatment system and method. The diagnosis and treatment system comprises a medical thinking layer used for clinical information reasoning decision; the expert mixed framework layer is used for refined data processing, analysis and reasoning; the clinical tool layer is used for collecting and analyzing clinical information; the interaction layer is used for providing digital human, voice, image-text and other interaction modes for patients and doctors, and the medical thinking layer and the expert mixed framework layer are in communication interaction with the interaction layer and the clinical tool layer. The diagnosis and treatment system has the advantages that expert models in different fields are combined through the expert mixed framework layer to achieve refined and intelligent support and optimization of the traditional Chinese medicine diagnosis and treatment process, the traditional Chinese medicine clinical real diagnosis and treatment process is simulated, the medical thinking layer is applied to conduct clinical information reasoning and decision making, and the diagnosis and treatment efficiency is improved. And the reliability and interpretability of the traditional Chinese medicine intelligent diagnosis and treatment paradigm are improved, so that a grassroots traditional Chinese medicine doctor can be assisted to improve the differentiation accuracy, and the popularity of traditional Chinese medicine diagnosis and treatment services is promoted.

The invention relates to the technical field of data visualization, in particular to an industrial internet-based data comprehensive treatment visualization application system, which comprises a node process extraction module, a data link organization module, a communication relation analysis module, an abnormal node treatment module and a comprehensive treatment display module. According to the method, the node sequence is set, the progressive trend is judged based on the time difference, the reverse nodes are screened out, the time sequence consistency is guaranteed, the node time interval continuity is checked in sequence, the node dependency relationship is dynamically modeled, the connection distortion risk is reduced, and the connection weak sections are screened; the method is advantaged in that connectivity abnormity identification efficiency is improved, node timestamp offset and dependence missing frequency are extracted, abnormity classification management is completed according to a standard production takt and a threshold value, abnormal node attribution is refined, traceability and treatment precision is improved, and industrial process data treatment depth, transparency and decision support capability are enhanced.

The invention provides an urban internal lake water pollution treatment method and system. Real-time water quality data are collected from a lake; processing the collected data; using the prediction model to predict a water quality trend and identify a pollution risk; according to the analysis result of the machine learning algorithm and the water quality model, the decision support module generates a targeted treatment strategy; according to the treatment strategy provided by the decision support module, specific treatment measures are implemented; and the intelligent management and control module is used for monitoring and managing the whole treatment process in real time. All the modules are linked to construct a whole-process and intelligent treatment system, the treatment efficiency of urban internal lakes is effectively improved, and continuous improvement of water quality and long-acting restoration of an ecological system are achieved.

The invention discloses a cancer recurrence probability deep learning prediction system based on multi-modal time sequence characteristics, and relates to the technical field of medical data processing. The method comprises the following steps: constructing a multi-modal time sequence data set by collecting clinical static data, dynamic treatment data and time sequence monitoring data of a patient; missing value filling and abnormal value elimination based on sliding Z-score are carried out on the data to form an effective data set; screening key clinical static features by using a random forest model; constructing a radiotherapy cumulative toxicity model to quantify residual toxicity, and generating dynamic characteristics by combining the dose change rate three-state characteristics; converting the features into a four-dimensional time sequence tensor through a dynamic sliding window; optimizing LSTM hyper-parameters by adopting random search, and training a high-precision prediction model to output a recurrence probability; finally, differentiated clinical intervention is triggered based on risk grading, a'prediction-decision 'closed-loop system is formed, and the initiative and accuracy of cancer recurrence diagnosis and treatment are remarkably improved.

The invention discloses a nursing health education method and system based on a rule engine, and a medium, belongs to the technical field of medical data processing and analysis, and is used for solving the technical problems that an existing health education scheme is not formulated as a personalized education scheme, and is lack of interactivity with a patient and long-term education effect evaluation and follow-up. The method comprises the steps of determining a health propaganda and education material set of each specialized disease under a corresponding nursing path based on medical health information; extracting a propaganda and education inference rule from the health propaganda and education material set, and building a propaganda and education rule engine according to the propaganda and education inference rule; obtaining full-time health data of a patient to be propagandized and educated, and segmenting the full-time health data through the sliding window to obtain target health data; performing propaganda and education reasoning rule matching on the target health data through a propaganda and education rule engine, and determining a health propaganda and education scheme of the patient to be propagandized and educated; and pushing the health propaganda and education scheme and the corresponding test scheme to the patient to be propagandized and educated.