Computer-implemented systems and methods are disclosed for automatically generating predictive models using data driven featurization. The systems and methods provide for obtaining data associated with a target event, annotating the data to identify a target event and establishing one or more limits on the data, censoring the data based on the annotations, determining features of the censored data, and analyzing the features to determine a predictive model. In some embodiments, the systems and methods further provide for converting the features into a binary representation and analyzing the binary representation to produce the predictive model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An electronic device comprising: a data storage configured to store one or more data sets associated with a target event; a data input engine configured to obtain the one or more data sets associated with the target event from one or more data sources; a featurization engine comprising: an annotator configured to annotate the one or more data sets, wherein the annotations include an identification of the target event, an identification of records of the one or more data sets that are associated with the target event, and an establishment of one or more limits on the one or more data sets; a data censor configured to censor the one or more data sets based on the annotations; a summarizer configured to determine one or more features of the censored data, wherein the one or more features are representative of data in the one or more data sets; an analysis engine configured to: analyze the one or more features, wherein the analysis identifies a subset of the one or more features that are indicative of the target event; and produce a predictive model based on the analysis. 2. The electronic device of claim 1, wherein the featurization engine is further configured to convert the features into a binary representation of the features and the analysis engine is further configured to analyze the binary representation. 3. The electronic device of claim 2, wherein conversion of features into the binary representation uses quantile binning. 4. The electronic device of claim 2, wherein the analysis of the binary representation includes chi-squared modeling. 5. The electronic device of claim 1, further comprising a feedback engine configured to obtain feedback associated with the predictive model. 6. The electronic device of claim 5, wherein the feedback engine obtains feedback from domain experts. 7. A method performed by one or more processors and comprising: obtaining one or more data sets associated with a target event from one or more data sources; annotating the one or more data sets, wherein the annotations include identifying the target event within the one or more data sets, identifying records of the one or more data sets that are associated with the target event, and establishing one or more limits on the one or more data sets; censoring the one or more data sets based on the annotations; determining one or more features of the censored data, wherein the one or more features are representative of data in the one or more data sets; analyzing the one or more features, wherein the analysis identifies a subset of the one or more features that are indicative of the target event; and producing a predictive model based on the analysis. 8. The method of claim 7, further comprising converting the one or more features into a binary representation and analyzing the binary representation. 9. The method of claim 8, wherein converting the one or more features into a binary representation uses quantile binning. 10. The method of claim 8, wherein analyzing the binary representation uses chi-squared modeling. 11. The method of claim 7, further comprising obtaining feedback associated with the predictive model. 12. The method of claim 11, wherein the feedback is obtained from domain experts. 13. A non-transitory computer readable storage medium storing a set of instructions that are executable by a first computing device that includes one or more processors to cause the first computing device to perform a method for evaluating costs associated with a first event, the method comprising: obtaining one or more data sets associated with a target event from one or more data sources; annotating the one or more data sets, wherein the annotations include identifying the target event within the one or more data sets, identifying records of the one or more data sets that are associated with the target event, and establishing one or more limits on the one or more data sets; censoring the one or more data sets based on the annotations; determining one or more features of the censored data, wherein the one or more features are representative of data in the one or more data sets; analyzing the one or more features, wherein the analysis identifies a subset of the one or more features that are indicative of the target event; and producing a predictive model based on the analysis. 14. The non-transitory computer-readable storage medium of claim 13, wherein the set of instructions that are executable by the at least one processor of the first computing device cause the first computing device to further perform: converting the one or more features into a binary representation and analyzing the binary representation. 15. The non-transitory computer-readable storage medium of claim 14, wherein converting the one or more features into a binary representation uses quantile binning. 16. The non-transitory computer-readable storage medium of claim 14, wherein analyzing the binary representation uses chi-squared modeling. 17. The non-transitory computer-readable storage medium of claim 13, wherein the set of instructions that are executable by the at least one processor of the first computing device cause the first computing device to further perform: obtaining feedback associated with the predictive model. 18. The non-transitory computer-readable storage medium of claim 17, wherein the feedback is obtained from domain experts.