Abstract: A computer-implemented method of decision optimization in a multi-record environment is disclosed. The method includes receiving a request to make a recommendation in relation to a data record and defining the recommendation in terms of an optimization problem including decision objectives including objective contribution functions and constraints including constraint contribution functions. The method also includes extracting input data from a data source, the input data including individual instances of data and attributes describing the individual instances of data. The method also includes identifying a context of the optimization problem based upon the individual instances of data. The context relates to a behavior of the input data given the decision objectives and the constraints. The method further includes solving the optimization problem by satisfying the decision objectives and the constraints, in the context, to generate a solution and providing the recommendation based on the solution.

Abstract: A model-assisted system for processing data to extract a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of dates represented in at least one document included in the medical record; identify a plurality of snippets of information included in the at least one document, each snippet of the plurality of snippets being associated with a date of the plurality of dates; inputting the plurality of snippets into a machine learning model, the machine learning model having been trained to determine associations between dates and patient events based on a training set of snippet data; and determine whether each date of the plurality of dates is associated with a patient event based on an output of the machine learning model.