MLFPM 2020 lecture
Mihaela van der Schaar will be giving a keynote at the second Machine Learning Frontiers in Precision Medicine (MLFPM) summer school.
AutoML and interpretability: powering the machine learning revolution in healthcare
AutoML and interpretability are both fundamental to the successful uptake of machine learning by non-expert end users. The former will lower barriers to entry and unlock potent new capabilities that are out of reach when working with ad-hoc models, while the latter will ensure that outputs are transparent, trustworthy, and meaningful. In healthcare, AutoML and interpretability are already beginning to empower the clinical community by enabling the crafting of actionable analytics that can inform and improve decision-making by clinicians, administrators, researchers, policymakers, and beyond.
This keynote presents state-of-the-art AutoML and interpretability methods for healthcare developed in our lab and how they have been applied in various clinical settings (including cancer, cardiovascular disease, cystic fibrosis, and recently Covid-19), and then explains how these approaches form part of a broader vision for the future of machine learning in healthcare.
The Marie Curie Innovative Training Network entitled “Machine Learning Frontiers in Precision Medicine” brings together leading European research institutes in machine learning and statistical genetics, both from the private and public sector, to train 14 early stage researchers. These scientists will apply machine learning methods to health data. The goal is to reveal new insights into disease mechanisms and therapy outcomes and to exploit the findings for precision medicine, which hopes to offer personalized preventive care and therapy selection for each patient.
Mihaela’s lecture will take place on September 22 at 16:00 CEST.
Full list of time zones:
Liège, Belgium 2020-09-22 at 16:00 CEST
London, United Kingdom 2020-09-22 at 15:00 BST
Paris, France 2020-09-22 at 16:00 CEST
Beijing, China 2020-09-22 at 22:00 CST
New York, USA 2020-09-22 at 10:00 EDT
San Francisco, USA 2020-09-22 at 07:00 PDT
Click here for more time zones.