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  • NL Seminar-Machine Reading for Precision Medicine

    Thu, Nov 21, 2019 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars

    Speaker: Hoifung Poon, MSR/UW

    Talk Title: Machine Reading for Precision Medicine

    Series: Natural Language Seminar

    Abstract: The advent of big data promises to revolutionize medicine by making it more personalized and effective, but big data also presents a grand challenge of information overload. For example, tumor sequencing has become routine in cancer treatment, yet interpreting the genomic data requires painstakingly curating knowledge from a vast biomedical literature, which grows by thousands of papers every day. Electronic medical records contain valuable information to speed up clinical trial recruitment and drug development, but curating such real world evidence from clinical notes can take hours for a single patient. NLP can play a key role in interpreting big data for precision medicine. In particular, machine reading can help unlock knowledge from text by substantially improving curation efficiency. However, standard supervised methods require labeled examples, which are expensive and time-consuming to produce at scale. In this talk, I'll present Project Hanover, where we overcome the annotation bottleneck by combining deep learning with probabilistic logic, and by exploiting self supervision from readily available resources such as ontologies and databases. This enables us to extract knowledge from millions of publications, reason efficiently with the resulting knowledge graph by learning neural embeddings of biomedical entities and relations, and apply the extracted knowledge and learned embeddings to supporting precision oncology.

    Biography: Hoifung Poon is the Director of Precision Health NLP at Microsoft Research and an affiliated professor at the University of Washington Medical School. He leads Project Hanover, with the overarching goal of advancing machine reading for precision health, by combining probabilistic logic with deep learning. He has given tutorials on this topic at top conferences such as the Association for Computational Linguistics ACL and the Association for the Advancement of Artificial Intelligence AAAI. His research spans a wide range of problems in machine learning and natural language processing NLP, and his prior work has been recognized with Best Paper Awards from premier venues such as the North American Chapter of the Association for Computational Linguistics NAACL, Empirical Methods in Natural Language Processing EMNLP, and Uncertainty in AI UAI. He received his PhD in Computer Science and Engineering from University of Washington, specializing in machine learning and NLP.

    Host: Emily Sheng

    More Info: https://nlg.isi.edu/nl-seminar

    Webcast: https://bluejeans.com/165124022

    Location: Information Science Institute (ISI) - Conf Rm #1014

    WebCast Link: https://bluejeans.com/165124022

    Audiences: Everyone Is Invited

    Contact: Peter Zamar

    Event Link: https://nlg.isi.edu/nl-seminar


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