Computer-assisted analysis of a data record from observations is provided. The data record contains, for each observation, a data vector that includes values of input variables and a value of a target variable. A neuron network structure is learned from differently initialized neuron networks based on the data record. The neuron networks respectively include an input layer, one or more hidden layers, and an output layer. The input layer includes at least a portion of the input variables, and the output layer includes the target variable. The neuron network structure outputs the mean value of the target variables of the output layers of the neuron networks. Sensitivity values are determined by the neuron network structure and stored. Each sensitivity value is assigned an observation and an input variable. The sensitivity value includes the derivative of the target variable of the assigned observation with respect to the assigned input variable.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for computer-assisted analysis of a data record from observations, the data record containing for each observation a data vector that comprises values of a plurality of input variables and a value of a target variable, the method comprising: learning a neuron network structure from a plurality of differently initialized neuron networks based on the data record, the plurality of differently initialized neuron networks respectively comprising an input layer, one or more hidden layers, and an output layer, the input layer of a respective neuron network of the plurality of differently initialized neuron networks comprising at least a portion of input variables of the plurality of input variables, and the output layer of a respective neuron network of the plurality of differently initialized neuron networks comprising the target variable, the neuron network structure outputting a mean value of the target variables of the output layers of the plurality of differently initialized neuron networks; and determining sensitivity values using the learned neuron network structure and storing the determined sensitivity values, each of the sensitivity values being assigned an observation and an input variable, and the respective sensitivity value including a derivative of the target variable of the assigned observation with respect to the assigned input variable. 2. The method of claim 1, wherein the determining comprises determining the sensitivity values via a modified error back propagation of respective input variables in the neuron network structure, in the modified error back propagation, weightings between the layers of the neuron network structure not being adjusted, and the derivative, included in a residual error, of the target variable of the assigned observation with respect to the assigned input variable being output instead of the residual error. 3. The method of claim 1, wherein the plurality of differently initialized neuron networks of the neuron network structure are feed-forward networks. 4. The method of claim 1, wherein an input layer of a respective neuron network of the plurality of differently initialized neuron networks comprises a randomly selected portion of the input variables. 5. The method of claim 1, further comprising visualizing the stored sensitivity values on a graphical user interface. 6. The method of claim 5, wherein the visualization is configured such that a matrix composed of a plurality of rows and columns is reproduced on the graphical user interface, a respective row representing an input variable, and a respective column representing an observation, or vice versa, a respective entry, relevant to a row and column, of the matrix visually coding a sensitivity value that belongs to the observation and input variable in accordance with the row and column of the entry. 7. The method of claim 6, wherein the sign of the derivative that is included in the sensitivity value of the respective entry of the matrix is coded. 8. The method of claim 7, wherein the sign of the derivative that is included in the sensitivity value of the respective entry of the matrix is color coded. 9. The method of claim 7, wherein a positive derivative represents a different color than a negative derivative, and the color intensity of the corresponding color is greater the larger an absolute value of the derivative. 10. The method of claim 5, further comprising visualizing a sum of absolute values of the sensitivity values on the graphical user interface over all observations for a respective input variable. 11. The method of claim 10, wherein the sum of absolute values of the sensitivity values is visualized on the graphical user interface over all observations for a respective input variable in the form of a bar diagram, a bar with a largest sum of the absolute values of the sensitivity values being normalized to 1, and all other bars being represented relative to the bar. 12. The method of claim 1, wherein the observations are assigned to consecutive instants, the observation for a respective instant comprising input variables that were determined at the respective instant, and a target variable that was determined at the respective instant, at a later instant, or at an earlier instant. 13. The method of claim 1, wherein the learning and the determining are repeated iteratively, a number of input variables, for which a sum of absolute values of the sensitivity values is greatest over all observations, being stored after the determining, when the learning is next carried out, no longer being considered as input variables in the input layers of the plurality of differently initialized neuron networks (of the neuron network structure. 14. The method of claim 13, wherein additionally considered in the neuron network structure as input variables are one or more pseudo input variables having values that are represented in the data vectors by random numbers, an iterative repetition of the learning and the determining being aborted when in the determining the number of input variables for which the sum of the absolute values of the sensitivity values over all observations is greatest includes a pseudo input variable. 15. The method of claim 1, wherein the data record composed of observations includes data vectors comprising state variables of a technical system at consecutive operating instants, a data vector corresponding to a respective operating instant and comprising the values of the input variables at the respective operating instant, and the value of the target variable at the respective operating instant, a past operating instant, or a future operating instant. 16. The method of claim 15, wherein the technical system is a gas turbine. 17. The method of claim 16, wherein the target variable comprises an emission of nitrogen oxides or combustion chamber humming. 18. The method of claim 1, wherein the data record composed of observations comprises data vectors for different production charges of a product, a respective data vector including as values of the input variables parameters of the production of the corresponding production charge, and as a value of the target variable a quality measure of the corresponding production charge. 19. The method of claim 1, wherein the data record composed of observations comprises data vectors that include a raw material price as a value of the target variable, and factors affecting the raw material price as values of the input variables. 20. A computer program product comprising a non-transitory computer-readable storage medium having program code including instructions executable by a computer for computer-assisted analysis of a data record from observations, the data record containing for each observation a data vector that comprises values of a plurality of input variables and a value of a target variable, the instructions comprising: learning a neuron network structure from a plurality of differently initialized neuron networks based on the data record, the plurality of differently initialized neuron networks respectively comprising an input layer, one or more hidden layers, and an output layer, the input layer of a respective neuron network of the plurality of differently initialized neuron networks comprising at least a portion of input variables of the plurality of input variables, and the output layer of a respective neuron network of the plurality of differently initialized neuron networks comprising the target variable, the neuron network structure outputting a mean value of the target variables of the output layers of the plurality of differently initialized neuron networks; and determining sensitivity values using the learned neuron network structure and storing the determined sensitivity values, each of the sensitivity values being assigned an observation and an input variable, and the respective sensitivity value including a derivative of the target variable of the assigned observation with respect to the assigned input variable. 21. In a non-transitory computer-readable storage medium having instructions executable by a computer for computer-assisted analysis of a data record from observations, the data record containing for each observation a data vector that comprises values of a plurality of input variables and a value of a target variable, the instructions comprising: learning a neuron network structure from a plurality of differently initialized neuron networks based on the data record, the plurality of differently initialized neuron networks respectively comprising an input layer, one or more hidden layers, and an output layer, the input layer of a respective neuron network of the plurality of differently initialized neuron networks comprising at least a portion of input variables of the plurality of input variables, and the output layer of a respective neuron network of the plurality of differently initialized neuron networks comprising the target variable, the neuron network structure outputting a mean value of the target variables of the output layers of the plurality of differently initialized neuron networks; and determining sensitivity values using the learned neuron network structure and storing the determined sensitivity values, each of the sensitivity values being assigned an observation and an input variable, and the respective sensitivity value including a derivative of the target variable of the assigned observation with respect to the assigned input variable.