10 results about "Outcome variable" patented technology

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 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 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.

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.

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.