Abstract: The availability of comprehensive electronic medical records that include narrative reports provides an opportunity for natural language processing (NLP) technologies to play a major role in clinical research. One of the main advantages of employing these technologies is the automatic extraction of relevant clinical information to identify critical illness phenotypes and to facilitate clinical and translational studies of large cohorts of critically ill patients. In this talk, I will present an NLP system for the task of pneumonia identification. Based on the information extracted from the narrative reports associated with a patient admitted in the intensive care unit, the task is to identify whether or not the patient is positive for pneumonia. I will show that, an approach using statistical feature selection, in which only a small subset of informative features from the initial feature space is considered, achieves significantly better results than a baseline, which uses all the features from the same feature space. The addition of a feature that extracts the assertion value of all pneumonia expressions from the clinical dataset considered further improves the performance of the NLP system for this task. Speaker Bio: Cosmin Adrian Bejan is a senior fellow in the Division of Biomedical and Health Informatics at the University of Washington. Prior to his current Technologies at the University of Southern California. Cosmin received his M.S. and B.S. degrees in computer science from the University of Iasi, Romania. He holds a Ph.D. degree in computer science from the University of Texas at Dallas, where he investigated natural language processing and machine learning methodologies in order to capture the semantics of the event structures that are encoded in text. His research interests are in the areas of natural language processing, biomedical informatics, and machine learning with a focus on event semantics, open-domain and clinical information extraction, and commonsense causal reasoning.