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Extracting Information from Clinical Text for Secondary Use Applications
January 8, 2020 @ 11:00 am - 12:00 pm
SPEAKER: Meliha Yetisgen, PhD.
There is a great amount of information captured in physicians’ comments made during health care. Increasingly, researchers are finding valuable uses by mining and aggregating this data in clinical and translational studies which lead to improved patient care and clinical research. However, most patient information that describes patient state, diagnostic procedures, and disease progress is represented in free-text form in electronic medical records. For meaningful use, one of the challenges is to capture the rich semantics surrounding the medical concepts in partially structured clinical text. In this talk, I will summarize the on-going research in my lab on building generalizable machine learning based Natural Language Processing (NLP) approaches to process clinical text for secondary use applications in the domains of cancer and substance abuse. One major obstacle in building high performance NLP models is creating high quality gold standard annotations. In addition to extraction methods, I will describe our most recent work on building an active learning framework to identify text samples for annotation that maximizes model learning and human annotation efficiency.
Meliha Yetisgen is an Associate Professor in the Department of Biomedical Informatics and Medical Education and Adjunct Associate Professor in the Department of Linguistics at the University of Washington (UW). She leads the UW-BioNLP research group. Before joining to UW, she worked as a researcher in industry and served on the advisory boards of text mining startups. Her current research interests include natural language processing and machine learning. Dr. Yetisgen received her BS degree on Computer Engineering from Bilkent University (Ankara, Turkey) and MS degree on Computer Engineering from Middle East Technical University (Ankara, Turkey). She received her PhD from University of Washington with a thesis on automated hypothesis generation from biomedical literature.