Clinical Text Analysis and Mining using Artificial Intelligence
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.
Jie Yang is a postdoctoral research fellow at Harvard University (Harvard Medical School). Prior to Harvard, he received his Ph.D. in computer science from Singapore University of Technology and Design in 2018. He also holds a Master's degree in Microelectronics and double bachelor degrees in both Electronics and Physics. He was a visiting graduate student at the University of Oxford in 2018. His research interest lies in artificial intelligence and healthcare, with a particular focus on natural language processing and clinical decision support. He won the COLING 2018 best paper award, ACL 2018 best demo paper nomination, and ISTD Best Dissertation Award. He is the developer of several popular open-source packages, which have attracted more than 3000 stars on GitHub.