21 results about "Outcome variable" patented technology

The invention relates to the technical field of artificial intelligence and nutrition health management, and discloses an intelligent nutrition correction tracking early warning method and system.The intelligent nutrition correction tracking early warning method comprises the steps that a hierarchical heterogeneity causal effect model is constructed, and individual features are mapped to a potential type space; representing a causal relationship between nutrition intervention and result variables by using a structural causal model, and evaluating an intervention effect by using a dual robust estimator; personalized grouping is realized by identifying individual subgroups; a reinforcement learning algorithm based on context awareness is combined with an anti-fact reward function, and a personalized nutrition intervention strategy is optimized; key feature combinations are extracted through a differentiable decision tree and SHAP value analysis, and multi-level causal interpretation is generated. According to the method, anti-factual reasoning, reinforcement learning and causal relationship extraction technologies are fused, accurate prediction, evaluation and early warning of the individual nutrition intervention effect are realized, and the personalized precision of nutrition intervention and the user compliance are remarkably improved.

A fraud call recognition method and system based on causal graph and agent collaboration comprises the steps of obtaining a fraud ASR text, mapping extracted multi-category to-be-mapped objects into semantic concept variable vectors, then learning a causal structure based on a data matrix formed by all semantic concept variable vectors, constructing a fraud causal graph, and recognizing a fraud call in the fraud ASR text. Calculating a causal effect of the reason variable on the result variable, and storing the causal effect as a relation weight in a causal graph; the method comprises the steps of obtaining a to-be-recognized ASR text, mapping multiple categories of to-be-mapped objects extracted from the to-be-recognized ASR text into semantic concept variable vectors, then retrieving matched causal chains in a causal graph to obtain a plurality of candidate causal chains, and then generating fraud judgment and risk degree evaluation values based on a large language model inference engine. And determining whether the to-be-identified call is a fraud call. The invention relates to the field of telecommunication anti-fraud, can deeply fuse a causal knowledge base and a large language model, and effectively improves the effectiveness, adaptability and reliability of communication anti-fraud identification.

The embodiment of the invention discloses a data processing method, in the method, first intervention data of an intervention variable in a decision service can be utilized to perform causal effect estimation on the decision service based on an anti-fact thought, and anti-fact intervention data obtained based on the anti-fact thought can be a continuous variable. The variable can be a discrete variable, so that the causal effect estimation can be carried out on various types of intervention variables in various decision service scenes. Besides, in the method, the causal effect model can perform causal effect estimation on the input intervention variable in a group corresponding to the input confusion variable so as to obtain an output result about the result variable. Thus, the anti-fact intervention data and the first confusion data are input into the causal effect model, the first estimation result can be obtained through the causal effect model, and for a group described by the first confusion data, accurate causal effect estimation is obtained according to the anti-fact intervention data.

The invention discloses a whole blood donation adverse reaction risk prediction method and system, and relates to the technical field of medical informatization and intelligent risk prediction. The prediction method comprises the following steps: performing data import and integration on demographic information, blood donation history and adverse reaction records of blood donors; performing preprocessing and variable definition on the data; the method comprises the following steps: respectively establishing a plurality of machine learning models by taking a serious blood donation adverse reaction as a main outcome variable and an adverse reaction type as a secondary outcome variable, screening an optimal main outcome variable prediction model and an optimal secondary outcome variable prediction model through a plurality of evaluation indexes, and verifying the models by adopting a cross validation method. And finally, realizing risk prediction and hierarchical management of blood donation adverse reactions by using the selected optimal model. According to the method, layered identification and multi-model training of the primary and secondary result variables can be realized, the accuracy and intelligent level of blood donation adverse reaction prediction are effectively improved, and the method is suitable for risk prevention and control and management of large-scale blood donation crowds.

The invention discloses a fault diagnosis system and method based on a production process causal graph model, and the system comprises a data collection and preprocessing module which is used for collecting and processing fault feature data, fault result data and auxiliary data in the operation process of a production process; the agent variable screening module is used for screening fault agent variables by calculating the correlation between auxiliary data and unobserved hybrid variables in combination with domain knowledge; the causal effect estimation module obtains a causal effect by adopting a mode of combining a tool variable method and deep dual machine learning; the causal graph model is constructed according to a causal effect estimation result and a Bayesian network to represent a causal relationship and a conditional dependency relationship among a fault feature variable, a fault result variable and a fault agent variable; the fault diagnosis module; inputting the operation data collected in real time into the constructed causal graph model, and evaluating the influence degree of different factors on the fault diagnosis result; the method effectively overcomes the interference of hybrid variables in the production process of the manufacturing industry, and improves the fault diagnosis precision and reliability.

The application discloses a regional scale process model simulation precision optimization method, system and device, relates to the field of model optimization, and comprises the following steps: inputting a crop parameter combination to be optimized into a crop-water productivity model, determining result variables yield and canopy coverage; performing cross-validation on the result variables yield and canopy coverage to determine evaluation indexes; and constructing a multi-objective index evaluation optimization model for optimization of key parameters of a regional scale PCMs; wherein the multi-objective index evaluation optimization model takes the sum of Nash efficiency coefficients NSE of the canopy coverage and the result variable yield in the training set and the test set as an objective function, and takes a root mean square error RMSE as a constraint condition; the evaluation indexes are input into the multi-objective index evaluation optimization model, a parallel algorithm group composed of multiple meta-heuristic optimization methods is adopted to solve the multi-objective index evaluation optimization model, and the key parameters of the regional scale PCMs are optimized to obtain an optimal crop parameter combination.

The application discloses a method for determining a pipeline leakage point based on dimension analysis and CFD simulation, relates to the technical field of pipeline detection, and can solve the problems of high pipeline detection cost and long time consumption. The specific scheme comprises the following steps: obtaining a plurality of physical variables affecting pipeline leakage behavior, the physical variables comprising influence factor variables and result variables, performing dimensionless processing on the plurality of physical variables to obtain a dimensionless relationship group; obtaining a historical data set through CFD simulation, the historical data set storing a mapping relationship between parameter values corresponding to the influence factor variables and parameter values corresponding to the result variables; generating a prediction model by using a training data set and the dimensionless relationship group; obtaining acquisition data of a current pipeline, the acquisition data comprising a pipeline leakage rate value, a pipeline fluid density value, a pipeline fluid flow rate value, a pipeline diameter value, and a pressure change value between a pipeline inlet and an outlet; and inputting the acquisition data into the prediction model to predict a pipeline leakage position.

The invention discloses a power supply service risk early warning method, system, equipment and medium, and particularly relates to the technical field of risk early warning, which is characterized by comprising the following steps: constructing a reason variable containing a plurality of risk factors and a result variable containing a plurality of risk indexes, and obtaining a plurality of risk factors and a plurality of risk indexes according to the Bayesian causal effect; generating a risk causal effect between the risk factor and the risk index, and obtaining a risk causal effect parameter; splicing the text feature matrix and the risk causal effect parameters, and inputting the spliced joint risk features into a multi-head self-attention mechanism to obtain risk features perceived by the multi-head attention mechanism; carrying out normalization processing on the risk features sensed by the multi-head attention mechanism and the spliced joint risk features to obtain feature coding vectors of risk factors, and processing the feature coding vectors of the risk factors by adopting a feedforward neural network and a normalization layer to obtain risk factors; and mapping the processed feature vectors into category probabilities of different risk grading early warning by using an activation function.

The invention relates to a method for augmenting training data for training machine learning models (25), which method is used for suggestions (16) for recommending process parameters and / or for predicting a quality of produced components and / or products, wherein the suggestions are forwarded to an environment (14) in which input variables (22) for the machine learning model (25) are detected, said input variables are interpreted in a space (26) of the input variables (22) as original points, wherein, on the basis of a predefined distribution of a measurement uncertainty, hyper-cuboids (21) are determined for each original point, and in each hyper-cuboid (21) a set of further new artificial points are derived for further artificial input variables (24) and have a respective relationship to their corresponding original point; and the original points are applied with their set of corresponding new artificial points for the training of the machine learning model (25), whereby respective result variables are derived by means of the model (25). The invention further relates to a system (1).

The disclosure provides a multi-agent data joint method, system, server and terminal device, original data is distributed in K data owners, the original data includes instrumental variables, exposure factors and outcome variables, the method comprises the following steps: the K data owners encrypt the instrumental variables and the exposure factors by using a first encryption key; receiving the encrypted instrumental variables and the exposure factors and obtaining a first regression coefficient; the K data owners obtain the predicted value of the exposure factors by using the first regression coefficient and the instrumental variables, and encrypt the predicted value and the outcome variables by using a second encryption key; receiving the encrypted predicted value and the outcome variables and obtaining a second regression coefficient; the K data owners decrypt the second regression coefficient to obtain the regression coefficient between the predicted value of the exposure factors and the outcome variables in the original data. Thus, under the premise of ensuring data privacy, cross-institutional sharing and data fusion are realized, and Mendelian randomization analysis is completed.

A method and system for analyzing urban green development efficiency, belonging to the field of smart city construction, is presented. It addresses the limitations of existing green productivity measurement methods in handling high-dimensional control variables and potential nonlinear relationships, as well as the inaccurate identification of causal effects. The method includes: acquiring a panel dataset of the target city; calculating a green efficiency value, stripped of environmental factors and random noise, using a data envelopment analysis model adjusted by stochastic frontier analysis; constructing and executing a dual machine learning model using the green efficiency value as the outcome variable, a pre-defined treatment variable as the treatment variable, and a high-dimensional control variable as a covariate; obtaining an estimate of the treatment effect of the treatment variable on the outcome variable through cross-fitting and residual regression; and generating analytical results on urban green development efficiency based on the green efficiency value and the estimated treatment effect. It is primarily used in the field of green productivity measurement.

Computer-implemented method for generating a process model (601) for use in the analysis of the operation of a process (300) which can be operated in a number of different process stages as defined by a stage variable associated with the process (300), comprising: Acquiring training data from the process (300) during the operation of the process (300), wherein the training data include a value for each of a set of process parameters, a value for the stage variable, and a value of an outcome variable associated with each of a variety of different process measurement times; Dividing the training data into time fractions of the data using a computer processing device (102, 13) to produce a set of time fractional data for each time fraction of the data, wherein each set of time fractional data includes a value for each of the set of process parameters, a value for the stage variable and a value for the result variable; Storing sets of time-proportional data in a computer memory; using a computer processing device (102, 13) determine a set of process stage means from the training data, wherein the set of process stage means includes a stage variable mean for each of the process stages and one or more process parameter means for each of the process stages; Storing the sets of process stage resources in a computer memory; using a computer processing device (102, 13) determining a set of time-part means for each of the time parts of the data using the stored process stage means, wherein each set of time-part means includes a time-part mean for each of the process parameters; using a computer processing device (102, 13) developing a set of deviations from the mean for each time fraction of the data, wherein the set of deviations from the mean for a given time fraction of the data for each process parameter within the given time fraction of the data includes the use of the process parameter value of the given time fraction of the data and the time fraction mean for the process parameter for the given time fraction of the data to develop the deviation from the mean for the process parameter for the given time fraction of the data; and using a computer processing device (102, 13) generating a process model (601) using the sets of deviations from the mean for the time components of the data and the result variable values ​​for the time components of the data.

An estimation device includes an input unit that generates supervised data including causal variables, process types, and outcome variable for each of multiple processes, and a training unit that uses the supervised data to generate a learning model by learning the outcome variables from the causal variables and the process types for each of the processes.

The application discloses a method and system for predicting the risk of adverse reactions of whole blood donation, and relates to the technical field of medical informatization and intelligent risk prediction. The method comprises the following steps: data import and integration are performed on the demographic information, blood donation history and adverse reaction records of blood donors; data preprocessing and variable definition are performed; a plurality of machine learning models are established by taking serious blood donation adverse reactions as main outcome variables and adverse reaction types as secondary outcome variables; the best main outcome variable prediction model and the secondary outcome variable prediction model are screened through a plurality of evaluation indexes; and the models are verified by using a cross-validation method. Finally, the selected best model is used to realize risk prediction and hierarchical management of blood donation adverse reactions. The method can realize hierarchical identification and multi-model training of the main and secondary outcome variables, effectively improve the accuracy and intelligent level of blood donation adverse reaction prediction, and is suitable for risk prevention and control and management of a large-scale blood donation population.

The application relates to the technical field of data processing, in particular to a charging place safety risk dynamic scoring system based on knowledge graph and causal reasoning. Multi-source sensing data of a charging place is acquired to construct a four-dimensional space-time evolution knowledge graph, nodes of which represent equipment and environment entities, and edges of which represent time-stamped time sequence interaction relations; a structural causal model is constructed, safety risk scoring is set as a result variable, equipment operation state is set as a processing variable, and environment monitoring data is set as a mixed variable; entity space-time feature representation is extracted through a space-time graph convolution network; based on the feature representation, potential risk probability of the processing variable in an anti-fact state is calculated, a difference in causal effect is obtained by comparing an observed fact risk probability to serve as a correction factor; and the correction factor is injected into a probability prediction model to output a dynamic risk score. The application can strip pseudo-correlation caused by environment mixed factors and overcome risk misjudgment and score drift caused by environment co-occurrence factors.

This invention belongs to the field of educational data analysis and machine learning technology, specifically disclosing a method for analyzing performance influencing factors and causal effects that integrates Direct Machine Learning (DML) and Conformal Prediction (CP). This method introduces the orthogonalized causal estimation mechanism of dual machine learning into the analysis of performance influencing factors, and combines it with the interval construction idea of ​​conformal prediction to achieve the identification of performance influencing factors and the evaluation of causal effect intervals. By constructing outcome variable models and treatment variable models, the influence of high-dimensional covariates is eliminated through residualization, and orthogonal scores are used to achieve robust estimation of the causal effects of candidate factors on performance variables. Furthermore, the orthogonal scores are used as a measure of inconsistency in conformal prediction to determine the acceptance region of candidate causal effect parameters, thereby generating causal effect intervals. This method has good interpretability and practicality in identifying performance influencing factors, estimating causal effects, and quantifying intervals.

The invention discloses a directed acyclic graph construction method for medical observation data causal inference. The directed acyclic graph construction method comprises the following steps: obtaining an exposure variable and an outcome variable under a PECO framework; in the medical literature database, retrieving a literature set comprising an exposure variable and an outcome variable; in the literature set, determining respective cause and consequence variable sets of an exposure variable and an outcome variable; in the variable set, cause and consequence variables which belong to the exposure variable and the result variable at the same time are determined, a common cause, a common result and an intermediary variable are generated according to the effect direction in the corresponding literature, and an initial directed acyclic graph is formed. The relation between variables in the directed acyclic graph is supplemented through a knowledge query method, directed acyclic graph optimization is carried out based on a causal criterion, and finally a minimum full adjustment set is generated. The directed acyclic graph construction based on the evidence-based method is realized, the transparency and repeatability of the directed acyclic graph are improved, and statistical analysis strategy support is provided for medical observation data causal inference research.

A computer-implemented method includes receiving input data items, each input data item comprising: first attributes representative of characteristics of the input data item, a treatment variable associated with the data item and an outcome variable representative of an outcome associated with the input data item. Second attributes of the input data items are generated from the first attributes, the second plurality of attributes having smaller dimensions than the first attributes. A first input data item having a first value for the treatment variable is selected; and a matching second input data item is selected based on a distance along a manifold between the first input data item and the second input data item, the second input data item having a second value for the treatment variable. The method provides a means of estimating the treatment effect of the treatment.

Disclosed is an estimation method for joint causal effects of multiple exposures based on high-dimensional independent variables, including the following steps: reducing a dimension by using a modified adaptive least absolute shrinkage and selection operator (LASSO); calculating balance weights by using a nonparametric multiple treatments covariate balancing generalized propensity score (npmtCBGPS) method, and determining an optimal value of a tuning parameter by taking a minimum multiple treatment dual-weighted coefficient (mtDWC) as a criterion; and estimating joint causal effects of multiple continuous exposure factors on an outcome variable by using an inverse probability weighting (IPW) method. According to the present invention, in a framework of a GOAL method, a multiple treatments GOAL (mtGOAL) method by combining the npmtCBGPS method with the adaptive LASSO, and a method capable of estimating joint causal effects of multiple continuous exposure factors on an outcome variable in the presence of high-dimensional covariates are proposed.

The estimation device (100) is characterized by having: an input unit (110) that generates supervisory data containing causal variables, treatment types and outcome variables for each of the multiple treatments; and a learning unit (120) that uses the supervisory data to learn outcome variables based on causal variables and treatment types for each of the multiple treatments, thereby generating a learning model.

The invention relates to the technical field of financial science and technology, can be applied to the agricultural insurance industry, and discloses an insurance claim risk assessment method, device and equipment and a storage medium. The method comprises the following steps: acquiring historical accident data with a time sequence, and determining accident factors according to the historical accident data; taking the accident factor as a dependent variable, taking whether the accident occurs as a result variable, fitting a function relationship between the dependent variable and the result variable, and establishing a first time sequence model according to the function relationship; based on a Granger causality test method, executing Granger causality test according to the first time sequence model and the historical accident data, and executing result test according to a Granger causality test result to obtain a feature relation graph; and performing claim settlement risk assessment according to the characteristic relation graph. According to the method, the application scene barrier of a traditional algorithm model can be broken through, the risk factors are mined from the global perspective, the time sequence is introduced to associate the risk factors, and the evaluation accuracy of the agricultural insurance claim payment condition is improved.