Zs Associates, Inc.

Srinivas Sainaga Chilukuri

Provided herein are methods and systems for selecting featured within high dimensional datasets to predict an event. In one embodiment, a method comprises determining, by a processor, a probability of occurrence of one or more features within a high dimensional patient dataset; profiling, by the processor, the one or more features in accordance with their respective probability of occurrence; executing, by the processor, a feature generation model to select at least one feature from the profiled features and a corresponding time window for the at least one feature; executing, by the processor, a time search model to select at least one time interval from a set of time intervals that includes time intervals associated with the profiled features or the at least one feature; executing, by the processor, a meta-learning model to calculate a fitness score based on the at least one feature and the at least one time interval; and using, by the processor, responsive to the at least one feature having the fitness score that satisfies a threshold, the at least one feature to predict an event associated with a patient.

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Buffalo Grove, IL, United States of America

Srinivas Sainaga Chilukuri