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ORCIDMatthias Hüser
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2020 – today
- 2021
[b1]Matthias Hüser:
Machine Learning Approaches for Patient Monitoring in the Intensive Care Unit. ETH Zurich, Zürich, Switzerland, 2021- [c7]Laura Manduchi, Matthias Hüser, Martin Faltys
, Julia E. Vogt, Gunnar Rätsch, Vincent Fortuin:
T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. CHIL 2021: 236-245 - [c6]Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch:
Neighborhood Contrastive Learning Applied to Online Patient Monitoring. ICML 2021: 11964-11974 - [c5]Hugo Yèche, Rita Kuznetsova, Marc Zimmermann, Matthias Hüser, Xinrui Lyu, Martin Faltys, Gunnar Rätsch:
HiRID-ICU-Benchmark - A Comprehensive Machine Learning Benchmark on High-resolution ICU Data. NeurIPS Datasets and Benchmarks 2021 - [c4]Jonathan Heitz, Joanna Ficek, Martin Faltys, Tobias M. Merz, Gunnar Rätsch, Matthias Hüser:
WRSE - a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU. SPACA 2021: 54-69 - [i9]Matthias Hüser, Martin Faltys, Xinrui Lyu, Chris Barber, Stephanie L. Hyland, Tobias M. Merz, Gunnar Rätsch:
Early prediction of respiratory failure in the intensive care unit. CoRR abs/2105.05728 (2021) - [i8]Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch:
Neighborhood Contrastive Learning Applied to Online Patient Monitoring. CoRR abs/2106.05142 (2021) - [i7]Hugo Yèche, Rita Kuznetsova, Marc Zimmermann, Matthias Hüser, Xinrui Lyu, Martin Faltys, Gunnar Rätsch:
HiRID-ICU-Benchmark - A Comprehensive Machine Learning Benchmark on High-resolution ICU Data. CoRR abs/2111.08536 (2021) - 2020
- [i6]Jonathan Heitz, Joanna Ficek, Martin Faltys, Tobias M. Merz, Gunnar Rätsch, Matthias Hüser:
WRSE - a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU. CoRR abs/2011.00865 (2020)
2010 – 2019
- 2019
- [c3]Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch:
SOM-VAE: Interpretable Discrete Representation Learning on Time Series. ICLR (Poster) 2019 - [i5]Matthias Hüser, Adrian Kündig, Walter Karlen, Valeria De Luca, Martin Jaggi:
Forecasting intracranial hypertension using multi-scale waveform metrics. CoRR abs/1902.09499 (2019) - [i4]Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean A. Bodenham, Karsten M. Borgwardt, Gunnar Rätsch, Tobias M. Merz:
Machine learning for early prediction of circulatory failure in the intensive care unit. CoRR abs/1904.07990 (2019) - [i3]Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin:
Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps. CoRR abs/1910.01590 (2019) - 2018
- [c2]Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Cristóbal Esteban, Gunnar Rätsch, Tobias Merz:
A Machine Learning-based Early Warning System for Circulatory System Deterioration in Intensive Care Unit Patients. AMIA 2018 - [c1]Stephanie L. Hyland, Matthias Hüser, Xinrui Lyu, Martin Faltys, Tobias Merz, Gunnar Rätsch:
Predicting circulatory system deterioration in intensive care unit patients. AIH@IJCAI 2018: 87-92 - [i2]Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch:
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. CoRR abs/1806.02199 (2018) - [i1]Xinrui Lyu, Matthias Hüser, Stephanie L. Hyland, George Zerveas, Gunnar Rätsch:
Improving Clinical Predictions through Unsupervised Time Series Representation Learning. CoRR abs/1812.00490 (2018)
Coauthor Index
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