Analyzing and mining Electronic Health Records (EHR) data is essential for improving patient care and reducing healthcare costs. Sequential deep learning (DL) methods have become increasingly popular for analyzing temporal EHR data in various healthcare applications. Despite their promising performance, DL methods face significant challenges in being adopted in real-world healthcare settings. These challenges include handling irregular temporal scales and asynchronous multi-variable EHR inputs, ensuring model interpretability, and addressing fairness and performance disparities. In this talk, we will introduce DL algorithms for learning data representation in temporal EHR data and propose solutions to overcome these challenges.
Dr. Liu is an artificial intelligence (AI) researcher at Fred Hutch. He joined Dr. Etzioni's lab at Hutch in July 2023 to develop innovative AI algorithms that can support cancer diagnosis, treatment, and recurrence monitoring. Dr. Liu received his Ph.D. in Computer Science from University of Kentucky. His research interests lie in developing unbiased, interpretable, and robust AI models to provide precise and equitable predictions for patient outcomes.