April 10, 2023 Monday
Abstract:
Complex systems like healthcare continually produce large amounts of irregularly spaced discrete events. Understanding the generating process of these event data has long been an interesting problem. Temporal point process models provide an elegant tool for modeling these event data in continuous time. The learned model can be used to predict the time-to-event and event types. Recent advances in neural-based temporal point process models have exhibited superior ability in event prediction. However, the lack of interpretability of these black-box models hinders their applications in high-stakes systems like healthcare. Recently, we proposed an interpretable temporal point process modeling and learning framework, where the intensity functions (i.e., occurrence rate) of events are informed by a collection of human-readable temporal logic rules. Our framework enables the extraction of medical knowledge or clinical experiences from noisy raw event data as a compact set of temporal logic rules. The discovered rules can contribute to the sharing of clinical experiences and aid in improving treatment strategies.
Speaker:
Prof. Shuang LI
Tenure-track Assistant Professor, School of Data Science, The Chinese University of Hong Kong, Shenzhen
Shuang Li is currently a tenure-track Assistant Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. She received her Ph.D. in Industrial Engineering (specification in Statistics, minor in Operations Research) from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology in 2019. After that, she was a postdoctoral fellow working with Dr. Susan Murphy in the Department of Statistics at Harvard University. She has published in top-tier machine learning conferences and journals, including ICML, NeurIPs, and JMLR. Her works have been selected as an oral presentation and a spotlight presentation at NeurIPS. She was also a finalist in the INFORMS Quality, Statistics, and Reliability (QSR) Best Student Paper Competition and Social Media Analytics Best Student Paper Competition.