Medicine stands apart from other areas where machine learning (ML) can be applied. Where we have seen advances in other fields driven by lots of data, it is the complexity of medicine, not the volume of data, that makes the challenge so hard. But at the same time this makes medicine the most exciting area for anyone who is really interested in exploring the boundaries of ML, because we are given real-world problems to formalize and solve. And the solutions are ones that are societally important, and they potentially impact us all (just think COVID-19!).

ML has of course already achieved very impressive results in numerous areas. Standout examples include computer vision and image recognition, playing games or in teaching robots. AI empowered by ML is so good at mastering these things because they are easily-stated problems where the solutions are well-defined and easily verifiable. “Easily-stated problems” have a clear challenge to solve and clear rules to play by; “well-defined solutions,” fall into a easily recognizable class of answers; while a “verifiable solution” is one that we as humans can understand in terms of judging whether the model has succeeded or not. Unfortunately, in medicine the problems are not well-posed, the solutions are often not well-defined, and they aren’t easy to verify.

This tutorial will present new methods to build clinical decision support systems at scale, forecast disease trajectories, estimate individualized treatment effects, personalize active monitoring and screening, and transfer knowledge across clinical environments. It will also discuss how to make ML interpretable, explainable and trustworthy so that clinicians, patients and policy makers can use it to derive actionable intelligence. Finally, it will discuss how all of these technologies can be integrated to build learning machines for healthcare, transforming electronic health records that are simply capturing data into engines of personalized decision support, cooperation, effective health management and discovery.