What symptoms are associated with the patient's genotype? Did patients treated with medication fare better than patients treated surgically? Which patients are more likely to be readmitted to the hospital? Many of the pressing problems in health care today require access to detailed information locked in narrative reports. Natural language processing offers better access to symptoms, risk factors, diagnoses, and treatment outcomes described in text; however, accurately identifying this information requires a plethora of resources and tools. In this talk I will describe the work our research lab has done to help NLP developers, clinicians and researchers, informaticists, and patients better access the rich data contained in narrative reports.
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.