Seminars and Events

Artificial Intelligence Research for Health

Interpretability and Fairness of Deep Learning Models on Health Dataset

Event Details

Abstract: The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV, the largest publicly available healthcare dataset, and conduct comprehensive analyses of interpretability as well as dataset representation bias and prediction fairness of deep learning models for in-hospital mortality prediction.

Host: Michael Pazzani, Principal Scientist

To promote discussion, we have arranged a lunch after our Artificial Intelligence Research for Health Seminar: Monday, June 19, 2023, 11:00am – 12:00pm PDT.

Sign up for in-person attendance and lunch here.

The AI Research for Health seminar features AI and health researchers presenting their research on AI and Data Science with an impact on human health. The goal is to foster new collaborations among researchers in these fields.  The talks will be live at The USC-Information Science Institute in Marina Del Rey (room 1135/1137) and on zoom.

We have also received inquiries about attending in person if you are not affiliated with ISI. This requires checking in with reception on the 10th-floor where you will be directed to the 11th floor conference room. We will also validate parking.

Students and postdocs are welcome. There is a shuttle from campus: Marina del Rey Shuttle – USC Transportation

Register for zoom by clicking here: Webinar Registration – Zoom

 

Speaker Bio

Yan Liu is a Professor in the Computer Science Department and the Director of Machine Learning Center at the University of Southern California. She received her Ph.D. degree from Carnegie Mellon University.  Her research interest is machine learning and its applications to climate science, health care and sustainability. She has received several awards, including NSF CAREER Award, Okawa Foundation Research Award,  New Voices of Academies of Science, Engineering, and Medicine, Biocom Catalyst Award Winner, ACM Dissertation Award Honorable Mention, Best Paper Award in SIAM Data Mining Conference.