118 results about "Outcome analysis" patented technology

The invention discloses a medical adverse event risk prediction, prevention and control method based on causal inference. The method comprises the following steps: S1, collecting and preprocessing multi-modal medical data; s2, constructing a causal network through a time slicing technology; s3, introducing ethical factors to form an ethical causal network; s4, generating a causal enhancement vector; s5, constructing a dynamic risk prediction model, and outputting an evaluation result; s6, analyzing a path contribution degree, and screening high-risk paths and nodes; s7, generating a personalized prevention and control scheme; and S8, generating a transparent report, and displaying the transparent report to the medical personnel and the patient. According to the method, the dynamic causal relationship among the medical variables is accurately modeled through the dynamic causal network, the multi-modal data fusion and the ethical correction technology, the personalized prevention and control scheme is generated in combination with the optimization algorithm, comprehensive support is provided for medical decision making, and implementation of precise medical treatment is promoted.

The invention discloses a health degree assessment management system and method based on equipment state monitoring, and relates to the technical field of industrial equipment monitoring and intelligent health assessment, and the system comprises a temperature rise data acquisition module which is used for obtaining real-time temperature rise data in an equipment operation process and comparing the real-time temperature rise data with a pre-stored standard temperature rise curve, and a data analysis module which is used for analyzing the real-time temperature rise data and comparing the real-time temperature rise data with the pre-stored standard temperature rise curve. The system comprises a temperature rise curve comparison module for analyzing the change trend of a temperature rise curve according to a comparison result and identifying abnormal temperature rise characteristics, an early warning adjustment module for dynamically adjusting an early warning threshold according to the identified abnormal temperature rise characteristics so as to early warn risks in advance, and an early warning trigger module for carrying out equipment real-time early warning based on the dynamically adjusted early warning threshold. According to the health degree assessment management system and method based on equipment state monitoring, the change trend of a temperature rise curve during equipment operation is dynamically identified so as to solve the problem of overheating early warning delay.

The invention discloses a health monitoring system based on pulse signal multi-domain feature extraction and deep learning, and the system is characterized in that a multi-dimensional feature extraction module employs an FMCW radar and a sensor device to collect and obtain pulse signals, carries out the multi-level processing and feature extraction of the obtained pulse signals, and extracts features related to a health state; the feature association mapping module matches and associates the pulse signal features of different frequency bands with the health state; the state recognition module automatically recognizes and classifies pulse signal features and health states based on a deep learning model; and the health information feedback module analyzes the health state of the user according to the health state recognition result of the state recognition module, provides health feedback and generates corresponding personalized health guidance information. By using the system, comprehensive analysis of health conditions can be realized, continuous and highly-targeted health tracking and evaluation services can be provided, and modern personalized and intelligent traditional Chinese medicine health monitoring and management requirements can be met.

The invention discloses a runoff prediction method based on multi-source data fusion, and relates to the technical field of model prediction, and the method comprises the following steps: S1, data source selection and collection; s2, data preprocessing; s3, designing a data fusion strategy; s4, feature extraction and selection; s5, model selection and construction; s6, model training and verification; s7, optimizing and improving the model; and S8, runoff prediction and result analysis. According to the runoff prediction method based on multi-source data fusion, through fusion of multi-source data and introduction of multiple deep learning models, the capture ability of the model to a complex non-linear relationship is enhanced, and the physical consistency and the coping ability to an extreme event are also ensured; a more comprehensive runoff prediction result is provided, the prediction robustness and the reliability of an analysis result can be improved, and the defects that a traditional model is single in data source, poor in physical consistency, insufficient in prediction uncertainty analysis and the like are overcome.

A large model agent generation content quality evaluation method based on double baselines comprises the steps that data generated by human beings is acquired, preprocessing and semantic segmentation are carried out, and data samples after semantic segmentation are formed; performing theme extraction on the data samples after data segmentation to form a theme list; the content of the data forming the theme list is generated through an intelligent agent workflow and the content of the data forming the theme list is directly generated through a large model, and an evaluated sample and a contrast sample are obtained; comparing and scoring the data sample after semantic segmentation, the evaluated sample and the contrast sample; selecting a sorting algorithm to carry out score conversion on the graded and compared samples; and performing statistical verification and result analysis. According to the method, a comprehensive, reliable and efficient solution is provided for quality evaluation of the generation content of the large model agent. The method not only can accurately evaluate the content quality, but also provides powerful support for continuous optimization of an intelligent agent system.

The invention relates to a medical health big data exploration system, and the system comprises a demand understanding module which is used for converting a data exploration demand into a structured exploration task; the compliance review module is used for performing compliance review on the probing task; the code generation module is used for generating a query code according to the probing task and the target database characteristics; the query execution module is used for querying query result data corresponding to the query code from a target database according to the query code; the query result analysis module is used for performing multi-dimensional analysis on the query result data to obtain a multi-dimensional analysis result; a report generation module; and the result presentation module is used for presenting the exploration report in an interactive mode in a user interface and supporting iterative improvement of the report by providing a feedback mechanism, so that the problems of data isomerism, difficulty in quality evaluation, low exploration efficiency, high professional threshold, insufficient privacy protection and the like in the existing medical data exploration process are solved.

The invention discloses an RWS-based post-market drug effect tracking platform, which comprises a data acquisition layer, a data processing layer and a result analysis layer, and is characterized in that the data acquisition layer is used for acquiring RWS data from different sources, the data processing layer is used for preprocessing the RWS data from different sources, constructing an original data chain based on a block chain technology, and analyzing the result of the original data chain; writing the preprocessed RWS data of different sources into an original data chain for storage; and the result analysis layer is used for acquiring the preprocessed RWS data through the original data chain, analyzing the preprocessed RWS data, constructing a drug tracking knowledge graph, and comprehensively analyzing the drug tracking knowledge graph to obtain an indication distribution result, an adverse reaction monitoring result, a treatment cost distribution result and a curative effect analysis result.

The invention provides a crack-containing pipeline failure analysis software system, and belongs to the field of crack-containing pipeline failure evaluation. The system comprises a parameter import module, a random sampling module, a penetration crack length module, a leakage rate module, a failure analysis module and a display module. The parameter import module imports pipeline material parameters, operation environment parameters and the like into the computer; the random sampling module is used for carrying out random sampling according to a common distribution function on the basis of importing parameters; the fatigue crack propagation module is used for calculating a stress intensity factor and fatigue crack propagation; the leakage rate module is used for calculating a detectable leakage length; and the result analysis module is used for integrating all calculation results and input parameters into a failure probability analysis chart. According to the method, the probability fracture mechanics method is adopted, the detection accuracy is improved, and the failure probability of equipment can be evaluated more quickly and visually.

A system receives feature parameters, each identifying possible values for one of a set of features. The system receives outcomes corresponding to the feature parameters. The system generates a simulated patient population dataset with multiple simulated patient datasets, each simulated patient dataset associated with the outcomes and including feature values falling within the possible values identified by the feature parameters. The system may train a machine learning engine based on the simulated patient population dataset and optionally additional simulated patient population datasets. The machine learning engine generates predicted outcomes based on the training in response to queries identifying feature values.

The invention discloses a driving risk prediction system, equipment and device based on a physical examination report, and belongs to the technical field of medicine and driving safety. The system comprises a data acquisition module used for acquiring a driver health examination report and traffic accident data; a data processing module; the accident occurrence probability calculation module adopts a generalized linear model based on Logistic regression to calculate a p value of the model; the p value represents the conditional probability of an accident calculated under a given characteristic condition; the accident occurrence frequency calculation module is used for calculating a regression coefficient of each physical examination index by adopting a generalized linear model based on Poisson regression; and the result analysis module is used for analyzing calculation results of the accident occurrence probability calculation module and the accident occurrence frequency calculation module, and is used for guiding actual driving behaviors. According to the invention, the health examination information of the driver can be associated with the driving risk, and the probability and number of the driving risk can be predicted, thereby helping to improve the driving safety.

The invention discloses a physical examination quality control system. The quality control system is in interactive connection with a mobile terminal through the Internet; the quality control system comprises a quality control module and a data management and analysis module. The quality control module comprises a first-level quality control module, a second-level quality control module and a third-level quality control module. The primary quality control module comprises a problem unit, an analysis unit and a marking unit; the secondary quality control module comprises a quality control spot check unit, a quality control table unit and a rectification unit; the third-level quality control module comprises a quality control summary conference management unit, a conference content unit and a tracking evaluation unit; and the data management and analysis module provides a mobile terminal with query, statistics and analysis permissions to perform query, quantity statistics and query result analysis according to the screening conditions. According to the invention, comprehensive quality control of multiple inventions is realized, omissions and errors in the service process are reduced, and high quality and safety of medical services are ensured.

The invention discloses a chronic disease risk prediction and dynamic management system based on multi-modal data fusion, and relates to the technical field of chronic disease management, the chronic disease risk prediction and dynamic management system comprises a data acquisition layer, a data fusion processing layer, a risk prediction management layer and an application service layer, the data fusion processing layer performs preprocessing, feature extraction and fusion analysis, the risk prediction management layer constructs a risk prediction model according to result analysis, and the application service layer provides a risk prediction result, a personalized management scheme and an interactive interface service for a user. Through the arrangement of the data acquisition layer, the data fusion processing layer and the risk prediction management layer, 'symptom-physiology-image 'full-dimensional data is covered, more comprehensive health state evaluation is supported, dynamic intelligent prediction can be carried out, diet, exercise and medication suggestions are automatically adjusted according to patient execution feedback and the latest prediction result, and the health state evaluation efficiency is improved. And a self-adaptive management cycle is formed.

The invention discloses a natural resource environment quality comprehensive evaluation method and system based on spatial distribution, and relates to the technical field of environment evaluation. The evaluation method comprises the steps of S1, determining an evaluation target and an evaluation range, S2, collecting and processing data, S3, constructing an evaluation index system, S4, implementing comprehensive evaluation, S5, establishing a dynamic weight adjustment mechanism, and S6, analyzing a result and formulating a strategy. The method is technically characterized in that a related mechanism of weight adjustment triggered by abnormal indexes is established, on one hand, the recognition and feedback capability of key problems is enhanced, and on the other hand, conversion of an evaluation system from static evaluation to dynamic response is promoted, so that the problems that traditional evaluation results are lagged and ecological environment changes cannot be reflected in time are solved, and the evaluation efficiency is improved. According to the method, the closed-loop regulation and control capability of dynamic adjustment and optimization is embodied, after a primary weight correction action is carried out, when a certain index deviates from a normal threshold value, a secondary weight correction program is automatically started, and the system weight of a criterion layer where the index is located is improved.

The invention relates to the technical field of diagnosis and treatment data processing, and particularly discloses a clinical examination result analysis method based on a medical knowledge graph, and the method comprises the steps: collecting historical clinical examination data of patients with different target diseases in an electronic medical record system; screening and extracting a disease keyword text sequence of the target disease according to the historical clinical examination data; constructing an ontology model based on the medical knowledge mapping relationship in the database, inputting the disease keyword text sequence into the ontology model to construct a medical knowledge graph model of the target disease, and outputting a plurality of disease keyword parameters; acquiring clinical manifestation information of target diseases of different patients in real time, extracting a knowledge graph of current clinical examination data of the patients and illness state trend values corresponding to the illness state keyword parameters according to the illness state keyword parameters, judging whether the illness states of the patients are abnormal or not, generating illness state evaluation parameters and outputting clinical examination scheme results of the patients; the accuracy of clinical examination results is improved; and the medical detection investment cost is reduced.

The invention relates to the technical field of medical data analysis, and discloses a clinical examination result analysis method and system based on a medical knowledge graph. The method comprises the following steps: discretizing continuous test data of a patient into data segments with change modes, mapping a time sequence mode of the data segments to corresponding entity nodes of a knowledge graph, and constructing knowledge graph segments carrying dynamic information; and performing heuristic extension by identifying key mode paths in the analysis paths to generate a candidate analysis path set. And performing synchronous iteration comparison on the core analysis path and real-time inspection data, dynamically correcting the confidence coefficient weight of each path, and finally outputting a structured report according to the optimized main analysis link. According to the method, fusion of inspection data time sequence features and the knowledge graph is realized, and the depth of result analysis and the adaptive capacity to illness state evolution are improved through dynamic interaction optimization of paths and data.

The invention relates to the technical field of model data processing, and discloses a data processing method based on artificial intelligence modeling, and the method comprises the steps: data preprocessing: removing low-information-content features, and standardizing input data; feature screening and optimization: constructing a feature association network by using mutual information, removing redundant features through a spectrum segmentation algorithm, and optimizing feature distribution; modeling and evaluation: completing model construction, training and performance evaluation based on a sparse neural network, and generating feature contribution rate analysis; performing task scheduling and feedback, realizing automatic scheduling of data processing tasks through a time expression, and performing closed-loop management on processing results; and performing result analysis and optimization decision, performing deep analysis on the evaluation result and the feature contribution rate, and generating an optimization suggestion. According to the method, full-process automation of data processing is realized, the efficiency, the performance and the interpretability are improved, the problems of insufficient feature optimization, high modeling complexity, shallow result analysis and the like are solved, and the method is suitable for various data analysis scenes.

The invention discloses a method for unifying an InSAR reference framework by using a GNSS and decomposing a velocity field, which comprises the following steps: (1) modeling a GNSS north-south velocity field based on a multi-scale spherical wavelet method, and deducting the contribution of the modeling velocity field in the sight line direction from an InSAR deformation field; (2) directly resolving east-west direction and vertical direction deformation fields by using an InSAR lifting rail sight direction velocity field; (3) establishing a quadratic polynomial function to carry out difference fitting so as to remove the influence of orbit errors and a reference frame; and (4) carrying out result analysis and verification in combination with cross comparison of ground measurement data. According to the method, the InSAR sight direction velocity field is decomposed in the east-west direction and the vertical direction by considering the contribution of north-south deformation, the reference framework of the calculated velocity field is unified by using GNSS data, so that more real deformation information is obtained, the data accuracy is ensured through multi-source data cross validation, and reliable data support can be provided.

The invention relates to the technical field of medical image processing and radiotherapy, in particular to a cervical cancer internal and external irradiation dose superposition system based on deep learning, which comprises a data input module, a deformation registration module, a dose calculation module, a dose superposition module and a result analysis module. The data input module verifies and preprocesses internal and external irradiation CT image data; the deformation registration module adopts a V-Net framework to be combined with an algorithm based on Transform to accurately align images; the dose calculation module calculates dose distribution by using a convolutional neural network; the dose superposition module performs weighted superposition on the internal and external irradiation doses to generate a comprehensive dose volume histogram; and the result analysis module evaluates the dose conditions of the target region and the normal tissue by using statistical and visual tools. According to the method, the accuracy of dose-related operation is improved, the working efficiency is improved, personalized precise radiotherapy is assisted, and the method has positive significance in promoting development and clinical application of the radiotherapy technology.

The invention discloses an evaluation method for a multi-modal large model, and the method comprises the following steps: S1, data collection: collecting multi-modal data from a multi-modal data source, including images, voices and / or texts, the data of each modal including features related to the performance of the model; s2, model training: training a multi-modal large model by using the collected multi-modal data; s3, performance evaluation: performing performance evaluation on the trained multi-modal large model; s4, model optimization: optimizing the multi-modal large model according to a performance evaluation result; and S5, result analysis and suggestion: carrying out result analysis on the optimized multi-modal large model, and providing a corresponding optimization suggestion. The scheme has the advantages of less manual participation, high efficiency and high accuracy, and is suitable for the evaluation requirements of various multi-modal large models.

The invention relates to the technical field of hospital integrated management, in particular to a hospital integrated service platform system for performance appraisal, which comprises a data quality control module, a simulation appraisal module and an appraisal result analysis module, the data quality control module is used for performing format verification and logic consistency verification on the data uploaded by the hospital, performing abnormal data detection and data integrity check on the uploaded data, correcting the data and then feeding back a data quality control result; the simulation assessment module is used for carrying out simulation assessment according to the formulated performance assessment indexes and scoring rules after the data quality meets the requirements, carrying out assessment scoring based on assessment index data and outputting an assessment result; and the assessment result analysis module is used for carrying out index and historical trend analysis on the assessment result of the hospital after the simulation assessment is completed, and finding out problems in hospital operation. Rapid and accurate simulation examination is carried out on the hospital, and the examination result is output, so that the examination efficiency is improved.

The invention discloses a method for checking distributed supporting stand columns between a cylindrical FPSO main deck and a process deck. The method for checking the distributed supporting stand columns between the cylindrical FPSO main deck and the process deck comprises the following checking methods that S1, a finite element calculation software model is established; s2, applying a load on the finite element model; s3, module supports are established and arranged, and stand columns are arranged between the main deck and the process deck to serve as supports; s4, pre-analyzing the finite element model; s5, performing finite element model optimization analysis; s6, the screened stand columns are subjected to refining treatment; and S7, performing result analysis on the model, and returning to S6 for adjustment if the overall structural strength meets the requirements of operation, extreme and cabin breaking working conditions, the structural fatigue exceeds the specified service life and the local part does not meet the requirements. According to the checking method for the distributed supporting stand columns between the cylindrical FPSO main deck and the process deck, the calculation result is more accurate, the professional iteration workload of upstream and downstream is reduced, the design period is shortened, and the efficiency is improved.

The invention provides a GRU-attention mechanism-based metering terminal meter fault rate prediction method, relates to the technical field of smart power grid and power equipment health management, and aims to solve the problems of insufficient feature capture and low prediction precision of complex time sequence data in the prior art. The metering terminal meter fault rate prediction method based on the GRU-attention mechanism comprises the following steps: constructing a multi-dimensional fault prediction index system based on equipment operation parameters, age limit parameters and environmental parameters, and preprocessing original data by adopting a median filtering method and Min-Max normalization; an attention mechanism is embedded into a GRU layer to form an end-to-end GRU-attention mixed prediction model, and the steps of model training and optimization, failure rate prediction, result analysis and the like are carried out. According to the method, through fusing the time sequence modeling capability of the GRU and the feature focusing capability of the attention mechanism, the accuracy of metering terminal meter fault rate prediction is remarkably improved, the early warning of the metering equipment fault rate is effectively realized, and an auxiliary decision basis is provided for operation and maintenance work.

The invention relates to the technical field of bridge maintenance management, in particular to a bridge health maintenance intelligent decision platform system based on deep learning, which comprises a data acquisition module used for acquiring real-time monitoring data of a target bridge and a current detection report and a maintenance strategy; the deep learning analysis module is used for analyzing the collected data based on a pre-trained deep learning model to obtain a damage mode and a future damage trend of the target bridge; the intelligent decision support module is used for analyzing the current health state of the target bridge based on the deep learning analysis result and generating targeted maintenance suggestions and priority ranking; and the quality control and feedback optimization module is used for auditing the deep learning analysis result, checking whether detection omission or detection abnormal data exists or not, and feeding back the detection omission or detection abnormal data to the data acquisition module and the intelligent decision support module for optimization. According to the invention, comprehensive monitoring, intelligent evaluation and maintenance strategy recommendation of the steel cable bridge can be realized, and the efficiency and accuracy of bridge maintenance are improved.

The invention discloses an operation duration prediction and scheduling method based on machine learning, and belongs to the technical field of medical care informatics, and the method comprises the following steps: obtaining platform historical processing data of which the operation type is the same as a predicted operation type, and carrying out the analysis to obtain a historical processing difficulty index; a resource consumption index is obtained through analysis, and a duration prediction emergency judgment result is obtained through comprehensive analysis; analyzing to obtain a data transmission channel effect index, analyzing to obtain a final data segmentation judgment result based on the data segmentation preliminary judgment result and the data transmission channel effect index, and performing corresponding data processing to obtain a feature data packet; a corresponding data transmission protocol is selected based on the duration prediction emergency judgment result, so that the feature data packet is transmitted to an operation duration prediction module; and the operation duration prediction module receives the feature data packet, analyzes the feature data packet by using a machine learning algorithm to obtain an operation duration prediction value, and performs automatic scheduling prompt, so that the problem of slow operation duration prediction response in the prior art is solved.

Described herein are examples of material testing systems configured to verify whether two testing methods are similar, and / or to verify whether a software application that implements setup, execution, and / or analysis of the testing method (s) is properly installed and / or operated. These systems are further configured to provide support documents for similar / dissimilar and / or correct installation / operation. Further, these systems are configured to reference data recorded during setup, execution and / or outcome analysis of test methods as evidence of correct installation / operation and / or similarity / dissimilarity.

The invention relates to an illness condition outcome prediction method and device based on a gating adaptive mechanism. The method comprises the following steps: acquiring chronic disease related data, performing feature correlation analysis according to the chronic disease related data, generating graph data, and performing feature enhancement on the graph data; based on the graph data, utilizing a multi-layer GAT hybrid model to carry out prediction, and obtaining a time sequence of a transition state; wherein the multi-layer GAT hybrid model comprises a plurality of GATConv layers, a plurality of LSTMAggregation layers and a feature fusion layer based on gating self-adaption; and outputting the time sequence of the conversion state as a conversion matrix by using a Grubm angle field. According to the method, graph-level global feature fusion is carried out through a gating mechanism and an attention mechanism, so that the multi-layer GAT hybrid model has a strong graph feature extraction capability, and the precision and accuracy of power prediction can be effectively improved. Meanwhile, the multi-layer GAT hybrid model is used for carrying out outcome prediction on each data point of each patient, and then a Gream angle field is adopted to generate an outcome matrix, so that further outcome analysis is facilitated.

The invention relates to the technical field of mechanical route adjustment, in particular to an intelligent medical instrument production monitoring system based on big data management analysis. The system comprises a disinfection state analysis module, a disinfection route adjustment module and a matching adaptation mode matching module. The disinfection state analysis module is used for carrying out detection result analysis on the initialized disinfection state, the disinfection state of the current batch of medical instruments is obtained, a sensitive disinfection area is positioned to serve as an object of later adjustment, and the disinfection route adjustment module is used for matching the same type of medical instruments; a disinfection interval is divided according to an initial disinfection state and a disinfection state of a sensitive disinfection area of the same type, finally, an adaptive mode matching module is matched and combined with a stage disinfection effect analysis result, and a disinfection state suitable for a current batch of medical instruments is selected through actual data simulation comparison, so that stage adjustment of the disinfection state can be carried out orderly, and the medical instruments can be disinfected. The disinfection state adjusting efficiency is improved.

The invention relates to the technical field of measurement economics analysis models, and discloses a measurement economics analysis model and a measurement analysis method, and the system comprises a data collection and management module which is used for collecting economic data from a plurality of data sources; the model construction and selection module is used for providing various models; the parameter estimation and optimization module estimates model parameters by using an advanced algorithm; the result analysis and visualization module is used for carrying out detailed statistical analysis on an estimation result of the model; and the risk assessment and early warning module is used for assessing data and model risks in measurement analysis. According to the invention, the data acquisition and management module provides a high-quality data basis for other modules and improves the accuracy and reliability of the model, and the model structure and type determined by the model construction and selection module and optimized parameters can present more valuable results in the result analysis and visualization module.

The present application relates to the technical field of natural language processing, and discloses a large language model clinical diagnosis capability fine-grained evaluation method and system. The method comprises: obtaining a patient medical record data set; synchronously performing the following tasks: scoring a question-standard answer-model answer triple based on a predetermined scoring dimension, calculating the medical knowledge score of the evaluated model; the evaluated model performs a recall task and a differential task, confirms or excludes a differential diagnosis list, and obtains a final main diagnosis; linking the differential diagnosis list and the final main diagnosis output by the evaluated model in the patient medical record; and analyzing factors affecting the clinical diagnosis capability of the evaluated model based on the linking result. The present application aims to deeply and synchronously evaluate the medical knowledge and reasoning capability of a large language model based on the same clinical diagnosis situation.

The invention relates to the technical field of artificial intelligence, and discloses a task result analysis method and device, equipment and a medium, and the method comprises the steps: obtaining task result data, and carrying out the preprocessing operation of the task result data, and obtaining structured data; performing feature extraction on the structured data through a deep learning model to obtain a high-dimensional feature matrix; analyzing the high-dimensional feature matrix through an exception detection model to obtain a verification data set containing exception annotations; and determining a field type and data distribution information in the verification data set, and inputting the verification data set into an analysis model, so that the analysis model determines an abnormal distribution result corresponding to the task result data based on the field type and the data distribution information. The method can be applied to business program systems in the fields of financial science and technology and medical pension, and the accuracy and the intelligent degree of task result analysis can be improved.