The majority of clinical information useful for patient care and research is locked in clinical notes and only accessible with great pain and effort. Natural Language Processing has the potential to unlock the information in the notes to support phenotyping for precision medicine, quality improvement, and health services research. This talk will illustrate the potential of NLP through existing applications, will describe the challenges of making NLP a real and scalable solution, and will provide concrete suggestions for how the audience can help NLP reach its potential in health care and discovery.
Dr. Chapman earned her Bachelor's degree in Linguistics and her PhD in Medical Informatics from the University of Utah in 2000. From 2000-2010 she was a National Library of Medicine postdoctoral fellow and then a faculty member at the University of Pittsburgh. She joined the Division of Biomedical Informatics at the University of California, San Diego in 2010. In 2013, Dr. Chapman became the chair of the University of Utah, Department of Biomedical Informatics where she continues her research on natural language processing in the context of informatics solutions to problems that vex health care.