Yue Guo Presentation
Yue Guo, a physician-scientist and doctoral candidate at the University of Washington, will present "Making Health Knowledge Accessible Through Personalized Language Processing."
Abstract:
The pandemic exposed the difficulties the general public faces when attempting to use scientific information to guide health-related decisions. Though widely available in scientific papers, the information required to guide these decisions is often not accessible; medical jargon, scientific writing styles, and insufficient background explanations make this information opaque to non-experts. Consequently, there is a pressing need to deliver scientific knowledge in lay language, which has motivated researches on automated plain language summary generation to make the health information more accessible.
In this talk, I will discuss my efforts in this direction, including building a novel dataset, identifying unique challenges within this task, and developing new methods to address those challenges. A key part of this process has been evaluating existing metrics to see if they effectively measure performance for this task, and considering if there might be better options. Finally, I will broaden the discussion beyond just health information, exploring how we can personalize and improve communication across different domains.
Bio:
Yue Guo is a physician-scientist and doctoral candidate at the University of Washington, Seattle, where she is pursuing a PhD in biomedical and health informatics. Her unique background, which includes an M.B.B.S (equivalent to an MD) from Capital Medical University in China, a master's in epidemiology from Johns Hopkins University, and postdoctoral research experience in radiation oncology and molecular radiation sciences at the Johns Hopkins University School of Medicine, allows her to bridge the gap between clinical medicine and informatics research.
Guo's research is driven by a passion for improving healthcare delivery through the innovative application of artificial intelligence techniques. By leveraging her expertise in natural language processing and her deep understanding of clinical medicine, her doctoral work focuses on harnessing the power of cutting-edge AI technologies, such as large language models, to make health information more accessible, understandable, and actionable for patients and the general public.