Track / Overview

The deep learning revolution of the past years promised to deliver hitherto-unprecedented improvements in understanding massive amounts of data. Given the prevalence of such data sets in the clinical practice, ranging from time series of vital parameters of patients to irregularly-sampled information about drugs that are administered, this domain constitutes a prime target for machine learning research. The hope is that such clinical machine learning approaches are capable of (foremost) improving patient welfare, detecting novel biomarkers for complex syndromes such as sepsis or circulatory failure, and may assist doctors in their daily routine.

Clinical data, however, is also fraught with idiosyncratic challenges that need to be overcome in order for machine learning models to perform well. One of these challenges, for example, is that some measurements are sampled at irregular time intervals. This necessitates special choices for the models. Other hurdles include differences in measurement modalities—impeding the transfer of models between different hospital sites, for example—and differences in prevalence (for classification tasks), exacerbating model comparison.

In this track, we will bring together practitioners and researchers to showcase state-of-the-art machine learning models for the clinical practice. Particular emphasis will be placed on discussions about the use of machine learning for prospective studies. Which additional aspects (concerning ethics, legal discussions, and many more) have to be considered? What success stories are already out there? What can we learn from successful, ongoing, or failed implementations? We aim to provide a track with stimulating discussions about all of these aspects, culminating (ideally) in participants authoring a white paper detailing the future of this field.

Track / Speakers

Marcel Salathé

Professor, EPFL

Bastian Rieck

Senior Assistant, ETH Zurich

Matteo Togninalli

COO, Visium

Damian Roqueiro

Senior Researcher, ETH Zurich

Christian Bock

PhD Student, ETH Zurich

Daniel Rueckert

Professor, Imperial College London

Michael Menden

Junior Group Leader, Helmholtz Zentrum München

Stephanie Hyland

Senior Researcher, Microsoft Research

Steve Jiang

Professor, University of Texas Southwestern

Danielle Belgrave

Principal Research Manager, Microsoft Research

Julia Vogt

Professor, ETH Zurich

Tobias Gass

Manager Adaptive RT, Varian

Alistair Johnson

Scientist, SickKids

Assaf Gottlieb

Assistant Professor, UTHealth

Finale Doshi-Velez

Associate Professor, Harvard

Bernice Elger

Professor, Universität Basel

Vanessa Schumacher

Group Head – Tissue Biomarker and Digital Pathology, Roche

Track / Co-organizers

Bastian Rieck

Senior Assistant, ETH Zurich

Damian Roqueiro

Senior Researcher, ETH Zurich

Felix Hensel

Postdoctoral researcher, ETH Zurich

Juliane Klatt

Post-doctoral Researcher, ETH Zurich

Sarah Brüningk

Postdoctoral Researcher, ETH Zurich

Michael Moor

MD, PhD Student, ETH Zurich

Karsten Borgwardt

Professor, ETH Zurich

Christian Bock

PhD Student, ETH Zurich

Max Horn

PhD Student, ETH Zurich

AMLD EPFL 2021 / Tracks & talks

AI & Physics

Alexey Melnikov, Giuseppe Carleo, Balaji Lakshminarayanan, Marylou Gabrié, Agnes Valenti, Bruno Loureiro, Gregor Kasieczka, Anna Dawid, Paolo Molignini, Roman Worschech, Diego Tapias, Aishik Ghosh, Frank Schäfer, Andrew Saxe

09:00-18:15 September 30Online

AI & Resilience in dynamic environments

Grégoire Caro, Ilya Feige, Lisa R. Goldberg, Jeffrey R. Bohn, Olga Fink

09:00-17:00 October 25Online

AI & Cities

09:00-17:00 December 02Forum Rolex, EPFL and online

AMLD / Global partners