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This paper will explore methods for effectively extracting information from clinical narratives. The proposed research will investigate the application of state of the art natural language processing techniques to clinical narratives, such as medical admission notes, discharge summaries, progress notes, and pathology reports to extract information of interest. The objective of this knowledge discovery task is the ability to generate a chronology of events, for a given patient, ultimately leading to patient cohort discovery. This in turn facilitates efficient information retrieval and enables patient specific question answering. The paper details the proposed research problem which spans across areas of information extraction, temporal relation extraction and reasoning, and machine learning with the help of two use case scenarios: 1) Automatic patient accrual for clinical trials and 2) Information retrieval over a biorepository.