Abstract: A predictive modeling method may include obtaining a fitted, first-order predictive model configured to predict values of output variables based on values of first input variables; and performing a second-order modeling procedure on the fitted, first-order model, which may include: generating input data including observations including observed values of second input variables and predicted values of the output variables; generating training data and testing data from the input data; generating a fitted second-order model of the fitted first-order model by fitting a second-order model to the training data; and testing the fitted, second-order model of the first-order model on the testing data. Each observation of the input data may be generated by (1) obtaining observed values of the second input variables, and (2) applying the first-order predictive model to corresponding observed values of the first input variables to generate the predicted values of the output variables.

Abstract: Automated data analytics techniques for non-tabular data sets may include methods and systems for (1) automatically developing models that perform tasks in the domains of computer vision, audio processing, speech processing, text processing, or natural language processing; (2) automatically developing models that analyze heterogeneous data sets containing image data and non-image data, and/or heterogeneous data sets containing tabular data and non-tabular data; (3) determining the importance of an image feature with respect to a modeling task, (4) explaining the value of a modeling target based at least in part on an image feature, and (5) detecting drift in image data. In some cases, multi-stage models may be developed, wherein a pre-trained feature extraction model extracts low-, mid-, high-, and/or highest-level features of non-tabular data, and a data analytics models uses those features (or features derived therefrom) to perform a data analytics task.

Abstract: Systems and methods of epidemiological modeling using machine learning are provided, and can include receiving values for an occurrence of the infectious disease during a first time period, generating, from a model trained by a machine learning system, predictions for the occurrence of the infectious disease over a second time period, performing, by a simulator using the predictions, one or more simulations of the occurrence of the infectious disease in one or more geographic regions during one or more time periods subsequent to the second time period, and providing, to a user interface, a first simulation of the one or more simulations performed by the simulator for a first geographic region of the one or more geographic regions during a time period of the one or more time periods.

Abstract: Training computer models by generating time-aware training datasets is provided. A system receives a secondary dataset to be combined with a primary dataset for generation of a training dataset. The primary dataset includes a plurality of data records where at least one data record corresponds to a time-of-prediction value corresponding to a timestamp at which at least one data record was used to generate a prediction. The secondary dataset includes a plurality of features where at least one feature corresponds to a timestamp value. The system selects a feature within the secondary dataset with a timestamp that precedes or matches a time-of-prediction value for a corresponding data record within the primary dataset. The system generates the training dataset that includes the primary dataset and the selected feature. The system trains a model using the generated training dataset.

Abstract: A predictive modeling method may include obtaining a fitted, first-order predictive model configured to predict values of output variables based on values of first input variables; and performing a second-order modeling procedure on the fitted, first-order model, which may include: generating input data including observations including observed values of second input variables and predicted values of the output variables; generating training data and testing data from the input data; generating a fitted second-order model of the fitted first-order model by fitting a second-order model to the training data; and testing the fitted, second-order model of the first-order model on the testing data. Each observation of the input data may be generated by (1) obtaining observed values of the second input variables, and (2) applying the first-order predictive model to corresponding observed values of the first input variables to generate the predicted values of the output variables.

Abstract: A predictive modeling method may include obtaining a fitted, first-order predictive model configured to predict values of output variables based on values of first input variables; and performing a second-order modeling procedure on the fitted, first-order model, which may include: generating input data including observations including observed values of second input variables and predicted values of the output variables; generating training data and testing data from the input data; generating a fitted second-order model of the fitted first-order model by fitting a second-order model to the training data; and testing the fitted, second-order model of the first-order model on the testing data. Each observation of the input data may be generated by (1) obtaining observed values of the second input variables, and (2) applying the first-order predictive model to corresponding observed values of the first input variables to generate the predicted values of the output variables.

Abstract: Combinations of compounds are provided that produce a synergistic effect when administered.

Abstract: Combinations of compounds are provided that produce a synergistic effect when administered.