Matthias Hüser

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	All	Since 2015
Citations	271	271
h-index	4	4
i10-index	3	3

160

120

201620172018201920202 2 16 94 153

Co-authors

Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
Vincent FortuinPhD student, ETH ZürichVerified email at inf.ethz.ch
Stephanie L HylandPhD Student, Weill Cornell/Memorial Sloan Kettering Cancer CenterVerified email at cornell.edu
Xinrui LyuDoctoral student, Biomedical Informatics Group, Department of Computer Science, ETH ZürichVerified email at inf.ethz.ch
Francesco LocatelloPhD student, ETH Zürich, Max Planck Institute for Intelligent SystemsVerified email at ethz.ch
Heiko StrathmannResearch Scientist at DeepmindVerified email at ucl.ac.uk
Valeria De LucaData Scientist, Novartis Institutes for Biomedical ResearchVerified email at novartis.com
Martin JaggiEPFLVerified email at epfl.ch
George ZerveasBrown University, ETH ZurichVerified email at brown.edu
Emanuela KellerUniversity Hospital ZurichVerified email at usz.ch
Christian BockETH ZurichVerified email at bsse.ethz.ch
Bastian RieckMachine Learning & Computational Biology Lab, D-BSSE, ETH ZürichVerified email at bsse.ethz.ch
Karsten BorgwardtProfessor, ETH ZurichVerified email at bsse.ethz.ch
Michael MoorPhD Student, Machine Learning and Computational Biology Lab, ETH ZurichVerified email at bsse.ethz.ch
Max HornPhD Student in Machine Learning and Computational Biology, ETH ZürichVerified email at bsse.ethz.ch

Matthias Hüser

Biomedical Informatics Group, ETH Zurich

Verified email at inf.ethz.ch - Homepage

Medical Informatics Non-parametric Bayesian inference Deep Learning


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Comprehensive analysis of alternative splicing across tumors from 8,705 patients A Kahles, KV Lehmann, NC Toussaint, M Hüser, SG Stark, ... Cancer cell 34 (2), 211-224. e6, 2018	205	2018
SOM-VAE: Interpretable Discrete Representation Learning on Time Series V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch ICLR 2019, 2018	39	2018
Improving clinical predictions through unsupervised time series representation learning X Lyu, M Hüser, SL Hyland, G Zerveas, G Rätsch Machine Learning for Health (ML4H) Workshop at NeurIPS 2018. arXiv:1812.00490, 2018	12	2018
Early prediction of circulatory failure in the intensive care unit using machine learning SL Hyland, M Faltys, M Hüser, X Lyu, T Gumbsch, C Esteban, C Bock, ... Nature Medicine 26 (3), 364-373, 2020	5	2020
Forecasting intracranial hypertension using time series and waveform features M Hüser MSc. thesis, ETH Zurich, 2015	4	2015
Forecasting intracranial hypertension using waveform and time series features M Hüser, V De Luca, M Jaggi, W Karlen, E Keller Vasospasm, The International Conference on Neurovascular Events after …, 2015	3	2015
Forecasting intracranial hypertension using multi-scale waveform metrics M Hüser, A Kündig, W Karlen, V De Luca, M Jaggi Physiological Measurement 41 (1), 014001, 2020	1	2020
Temporal prediction of cerebral hypoxia in neurointensive care patients: a feasibility study V De Luca, M Hüser, M Jaggi, W Karlen, E Keller 16th International Symposium on Intracranial Pressure and Neuromonitoring, 2016	1	2016
Predicting Circulatory System Deterioration in Intensive Care Unit Patients SL Hyland, M Faltys, M Hüser, X Lyu, C Esteban, T Merz, G Rätsch Proceedings of the 1st Joint Workshop on AI in Health, 0	1*
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps L Manduchi, M Hüser, G Rätsch, V Fortuin arXiv preprint arXiv:1910.01590, 2019		2019

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