Explainable Artificial Intelligence in Precision Medicine
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 briefly describe my group’s efforts to develop interpretable ML 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. My talk will focus in greater detail on: MERGE, which uses ML to identify molecular markers for chemotherapy drugs for acute myeloid leukemia in collaboration with UW Medicine, published in Nature Communications (Jan 2018) and our follow-up projects in collaboration with Harvard Medical School; our explainable artificial intelligence system, Prescience, for preventing hypoxemia in patients under anesthesia, recently featured on the cover of Nature Biomedical Engineering (Oct 2018); and SHAP, our general ML framework on model interpretability, published as a full oral presentation at Neural Information Processing Systems (Dec 2017; cited 250).
Prof. Su-In Lee is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering and an Adjunct Associate Professor in the Departments of Genome Sciences, Electrical Engineering, and Biomedical Informatics and Medical Education 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.