IJCAI-ECAI 2022

Medicine has the potential to be transformed by machine learning (ML) by addressing core challenges such as time-series forecasts, clustering (phenotyping), and heterogeneous treatment effect estimation. However, to be embraced by clinicians and patients, ML approaches need to be interpretable. So far though, ML interpretability has been largely confined to explaining static predictions. 

In this keynote, I describe an extensive new framework for ML interpretability. This framework allows us to 1) interpret ML methods for time-series forecasting, clustering (phenotyping), and heterogeneous treatment effect estimation using feature and example-based explanations, 2) provide personalized explanations of ML methods with reference to a set of examples freely selected by the user, and 3) unravel the underlying governing equations of medicine from data, enabling scientists to make new discoveries.