Search Results for author: Matthias Hüser

Found 9 papers, 4 papers with code

HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data

1 code implementation NeurIPS Datasets and Benchmarks 2021 Hugo Yèche, Rita Kuznetsova, Marc Zimmermann, Matthias Hüser, Xinrui Lyu, Martin Faltys, Gunnar Rätsch

The recent success of machine learning methods applied to time series collected from Intensive Care Units (ICU) exposes the lack of standardized machine learning benchmarks for developing and comparing such methods.

Circulatory Failure ICU Mortality +5

Neighborhood Contrastive Learning Applied to Online Patient Monitoring

1 code implementation9 Jun 2021 Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch

Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients.

Contrastive Learning Data Augmentation +1

WRSE -- a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU

no code implementations2 Nov 2020 Jonathan Heitz, Joanna Ficek, Martin Faltys, Tobias M. Merz, Gunnar Rätsch, Matthias Hüser

Dynamic assessment of mortality risk in the intensive care unit (ICU) can be used to stratify patients, inform about treatment effectiveness or serve as part of an early-warning system.

DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

2 code implementations3 Oct 2019 Laura Manduchi, Matthias Hüser, Julia Vogt, Gunnar Rätsch, Vincent Fortuin

We show that DPSOM achieves superior clustering performance compared to current deep clustering methods on MNIST/Fashion-MNIST, while maintaining the favourable visualization properties of SOMs.

Deep Clustering Representation Learning +3

Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps

no code implementations25 Sep 2019 Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin

There are very performant deep clustering models on the one hand and interpretable representation learning techniques, often relying on latent topological structures such as self-organizing maps, on the other hand.

Deep Clustering Representation Learning +1

Forecasting intracranial hypertension using multi-scale waveform metrics

no code implementations25 Feb 2019 Matthias Hüser, Adrian Kündig, Walter Karlen, Valeria De Luca, Martin Jaggi

Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours.

Time Series

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

6 code implementations ICLR 2019 Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch

We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.

Dimensionality Reduction Representation Learning +2

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