Clinical Text Analysis and Mining using Artificial Intelligence

Feb 17

Monday, February 17, 2020 - 12:00pm to 1:00pm

Gross Hall, Ahmadieh Family Grand Hall, Room 330

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Presenter

Jie Yang, Harvard Medical School, Harvard University

Clinical text, such as progress reports, safety reports, includes large amounts of detailed patient and disease information. In this talk, I will focus on learning and case identification problems on clinical text, and present how we can develop artificial intelligence-based approaches that extract knowledge and support the clinical decisions. First, I will introduce the pathological feature assessment for melanoma (skin cancer) patients using natural language processing techniques. Then I will present an attentive deep neural network model that automatically identifies the allergic events from hospital safety reports. I will show the generalizability and interpretability of the proposed model and demonstrate how does the model extract the clinical knowledge which is complementary with human knowledge

Contact

Currin, Ellen
660-5252
ecurrin@ee.duke.edu