Abstract: A mechanism is provided in a data processing system to implement a feature extraction tool for graphical user interface based feature extraction. The feature extraction tool receives selection by a user of a dataset from which features are to be extracted. The feature extraction tool loads a plurality of feature definitions. The feature extraction tool generates a graphical user interface that allows the user to add features from the plurality of features to a feature file. The feature extraction tool presents the graphical user interface to the user and receives user selection of at least one feature to be added to the feature file. The feature extraction tool generates the feature file based on the user selection of the at least one feature.

Abstract: A mechanism is provided in a data processing system to implement a feature extraction tool for graphical user interface based feature extraction. The feature extraction tool receives selection by a user of a dataset from which features are to be extracted. The feature extraction tool loads a plurality of feature definitions. The feature extraction tool generates a graphical user interface that allows the user to add features from the plurality of features to a feature file. The feature extraction tool presents the graphical user interface to the user and receives user selection of at least one feature to be added to the feature file. The feature extraction tool generates the feature file based on the user selection of the at least one feature.

Abstract: Embodiments of the present systems and methods may provide techniques for measuring similarity between two datasets using classification error as a measure of the similarity between the two datasets and for improving the similarity between the two datasets. For example, in an embodiment, a computer-implemented method for determining treatment effects may comprise receiving data relating to observations of treatments outcomes of at least one treatment in a plurality of treatment groups, wherein the data for each treatment group forms a dataset, reweighting at least some of the datasets to balance biases in the data among the datasets by: determining bias between at least two datasets using a classification error; and generating balancing weights for at least one of the datasets to reduce the bias between the at least two dataset, and determining treatment effects using at least one reweighted dataset.

Abstract: There is provided, in accordance with some embodiments, a method for receiving electronic documents representing a dependency graph comprising feature extractors at each graph node and directed edges corresponding to computational dependencies of the feature extractors. For at least some feature extractors, extractor defining data, comprising extractor data and computational dependencies of the graph node in the dependency graph are determined, and a node lookup key based on the extractor defining data is computed. When the node lookup key is associated with a stored set of output feature values, the stored set is assigned as output values of the feature extractor. When node lookup key is not associated with a stored set of output feature values, a new set of output feature values is computed, stored, and associated the node lookup key. The one set of output feature values are sent as an output feature set.

Abstract: Embodiments of the present systems and methods may provide generation of user and non-user cohorts for the estimation of drug's effect from longitudinal observational data that provide reduction of biases, account for progression of disease over time, and improve statistical significance. For example, in an embodiment, a computer-implemented method for conducting an observational trial of a drug under study may comprise receiving data relating to a plurality of patients, the data including drug prescription information for each patient, assigning each of the plurality of patients to a trial cohort based on the drug prescription information, setting an index date for each of the plurality of patients, and conducting an observational drug trial using the generated cohorts and index dates.

Abstract: There is provided, in accordance with some embodiments, a method for receiving electronic documents representing a dependency graph comprising feature extractors at each graph node and directed edges corresponding to computational dependencies of the feature extractors. For at least some feature extractors, extractor defining data, comprising extractor data and computational dependencies of the graph node in the dependency graph are determined, and a node lookup key based on the extractor defining data is computed. When the node lookup key is associated with a stored set of output feature values, the stored set is assigned as output values of the feature extractor. When node lookup key is not associated with a stored set of output feature values, a new set of output feature values is computed, stored, and associated the node lookup key. The one set of output feature values are sent as an output feature set.

Abstract: A computer-implemented method and apparatus for assessing treatment adherence by patients, the method comprising: receiving a model providing statistical significance of patients' response to treatment, the model based on treatment assigned to the patients, wherein the patients are diagnosed with a disease; computing by the computerized device a p-value for a result received for a patient diagnosed with the disease and being treated by the treatment, by applying the model to at least one patient; and issuing an alert responsive to the p-value being indicative of the result being unexpected beyond a threshold.