Explainable Artificial Intelligence for Biology and Health Care
Modern machine learning (ML) models can accurately predict patient progress, an individual's phenotype, or molecular events such as transcription factor binding. However, they do not explain why selected features make sense or why a particular prediction was made. For example, a model may predict that a patient will get chronic kidney disease, which can lead to kidney failure. The lack of explanations about which features drove the prediction – e.g., high systolic blood pressure, high BMI, or others – hinders medical professionals in making diagnoses and decisions on appropriate clinical actions. I will talk about my group’s efforts to develop explainable AI techniques for varied biological and medical applications, including treating cancer based on a patient’s own molecular profile, identifying therapeutic targets for Alzheimer’s, predicting kidney diseases, preventing complications during surgery, enabling pre-hospital diagnoses for trauma patients, and improving our understanding of pan-cancer biology and genome biology.
Prof. Su-In Lee is a Paul G. Allen Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She completed her PhD in 2009 at Stanford University with Prof. Daphne Koller in the Stanford Artificial Intelligence Laboratory. Before joining the UW in 2010, Lee was a Visiting Assistant Professor in the Computational Biology Department at Carnegie Mellon University School of Computer Science. She has received the National Science Foundation CAREER Award and been named an American Cancer Society Research Scholar. She has received generous grants from the National Institutes of Health, the National Science Foundation, and the American Cancer Society.
Her research has conceptually and fundamentally advanced how AI can be integrated with biomedicine by addressing novel, forward-looking, and stimulating questions, enabled by AI's potential. For example, although the primary focus of AI applications in the field of medicine had been on accurately predicting a patient’s phenotype or outcome, she focused on the question of why. This line of work has led to highly cited seminal publications in the field of foundational AI, clinical medicine, and computational molecular biology. Her research aims to push the boundaries of both foundational AI and molecular biomedicine, to address new questions and make novel discoveries from high-throughput molecular data or patient's medical record data.