Welcome

We work on developing and extending new machine learning techniques for precision medicine, the life sciences and clinical data analysis. This field is exciting and challenging because new methods for a better understanding of diseases are enormous important. The field of action comprises many areas such as prediction of response to treatment in personalized medicine, (sparse) biomarker detection, tumor classification or the understanding of interactions between genes or groups of genes. The challenge lies not only in developing fast, robust and reliable systems but also in systems that are easy to interpret and usable in clinical practice.

News


Julia Vogt interviews with NZZ, Tagesschau and SRF Radio 1

Read, watch and listen to Julia Vogt's latest interviews


ETH Podcast: How Machine Learning can help in medicine

Julia Vogt and Fanny Yang appear on the ETH Podcast


Predictors of 5-year mortality in young dialysis patients

Latest paper mentioned on EurekAlert!


Publications


Abstract

Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research. However, existing self-supervised generative models approximating an ELBO are not able to fulfill all desired requirements of multimodal models: their posterior approximation functions lead to a trade-off between the semantic coherence and the ability to learn the joint data distribution. We propose a new, generalized ELBO formulation for multimodal data that overcomes these limitations. The new objective encompasses two previous methods as special cases and combines their benefits without compromises. In extensive experiments, we demonstrate the advantage of the proposed method compared to state-of-the-art models in self-supervised, generative learning tasks.

Authors

Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt

Submitted

Ninth International Conference on Learning Representations, ICLR 2021

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Abstract

[to appear]

Authors

Ricards Marcinkevics, Patricia Reis Wolfertstetter, Sven Wellmann, Christian Knorr, Julia E Vogt

Submitted

Frontiers in Pediatrics [to appear]

Abstract

Survival analysis has gained significant attention in the medical domain with many far-reaching applications. Although a variety of machine learning methods have been introduced for tackling time-to-event prediction in unstructured data with complex dependencies, clustering of survival data remains an under-explored problem. The latter is particularly helpful in discovering patient subpopulations whose survival is regulated by different generative mechanisms, a critical problem in precision medicine. To this end, we introduce a novel probabilistic approach to cluster survival data in a variational deep clustering setting. Our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and the potentially censored survival times. We compare our model to the related work on survival clustering in comprehensive experiments on a range of synthetic, semi-synthetic, and real-world datasets. Our proposed method performs better at identifying clusters and is competitive at predicting survival times in terms of the concordance index and relative absolute error.

Authors

Laura Manduchi, Ricards Marcinkevics, Julia E. Vogt

Submitted

Contributed talk at AI for Public Health Workshop at ICLR 2021

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Abstract

Generating interpretable visualizations of multivariate time series in the intensive care unit is of great practical importance. Clinicians seek to condense complex clinical observations into intuitively understandable critical illness patterns, like failures of different organ systems. They would greatly benefit from a low-dimensional representation in which the trajectories of the patients’ pathology become apparent and relevant health features are highlighted. To this end, we propose to use the latent topological structure of Self-Organizing Maps (SOMs) to achieve an interpretable latent representation of ICU time series and combine it with recent advances in deep clustering. Specifically, we (a) present a novel way to fit SOMs with probabilistic cluster assignments (PSOM), (b) propose a new deep architecture for probabilistic clustering (DPSOM) using a VAE, and (c) extend our architecture to cluster and forecastclinical states in time series (T-DPSOM). We show that our model achieves superior clustering performance compared to state-of-the-art SOM-based clustering methods while maintaining the favorable visualization properties of SOMs. On the eICU data-set, we demonstrate that T-DPSOM provides interpretable visualizations ofpatient state trajectories and uncertainty estimation. We show that our method rediscovers well-known clinical patient characteristics, such as a dynamic variant of the Acute Physiology And Chronic Health Evaluation (APACHE) score. Moreover, we illustrate how itcan disentangle individual organ dysfunctions on disjoint regions of the two-dimensional SOM map.

Authors

Laura Manduchi, Matthias Hüser, Martin Faltys, Julia Vogt, Gunnar Rätsch, Vincent Fortuin

Submitted

ACM CHIL 2021

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Abstract

In the quest for efficient and robust learning methods, combining unsupervised state representation learning and reinforcement learning (RL) could offer advantages for scaling RL algorithms by providing the models with a useful inductive bias. For achieving this, an encoder is trained in an unsupervised manner with two state representation methods, a variational autoencoder and a contrastive estimator. The learned features are then fed to the actor-critic RL algorithm Proximal Policy Optimization (PPO) to learn a policy for playing Open AI’s car racing environment. Hence, such procedure permits to decouple state representations from RL-controllers. For the integration of RL with unsupervised learning, we explore various designs for variational autoencoders and contrastive learning. The proposed method is compared to a deep network trained directly on pixel inputs with PPO. The results show that the proposed method performs slightly worse than directly learning from pixel inputs; however, it has a more stable learning curve, a substantial reduction of the buffer size, and requires optimizing 88% fewer parameters. These results indicate that the use of pre-trained state representations hasseveral benefits for solving RL tasks.

Authors

Juan M. Montoya, Imant Daunhawer, Julia E. Vogt, Marco Wiering

Submitted

ICAART 2021

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