| 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 | 440 | 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 | 125 | 2020 |
| SOM-VAE: Interpretable Discrete Representation Learning on Time Series V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch ICLR 2019, 2018 | 93 | 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 | 32 | 2018 |
| 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 | 9 | 2020 |
| DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps L Manduchi, M Hüser, G Rätsch, V Fortuin arXiv preprint arXiv:1910.01590, 2019 | 9 | 2019 |
| Deep self-organization: Interpretable discrete representation learning on time series V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch | 8* | 2018 |
| T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states L Manduchi, M Hüser, M Faltys, J Vogt, G Rätsch, V Fortuin Proceedings of the Conference on Health, Inference, and Learning, 236-245, 2021 | 7 | 2021 |
| Neighborhood contrastive learning applied to online patient monitoring H Yèche, G Dresdner, F Locatello, M Hüser, G Rätsch International Conference on Machine Learning, 11964-11974, 2021 | 4 | 2021 |
| 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 |
| HiRID-ICU-Benchmark--A Comprehensive Machine Learning Benchmark on High-resolution ICU Data H Yèche, R Kuznetsova, M Zimmermann, M Hüser, X Lyu, M Faltys, ... arXiv preprint arXiv:2111.08536, 2021 | 2 | 2021 |
| Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps L Manduchi, M Hüser, G Rätsch, V Fortuin | 2 | 2019 |
| WRSE-a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU J Heitz, J Ficek, M Faltys, TM Merz, G Rätsch, M Hüser Survival Prediction-Algorithms, Challenges and Applications, 54-69, 2021 | 1 | 2021 |
| 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* | |
| Early prediction of respiratory failure in the intensive care unit M Hüser, M Faltys, X Lyu, C Barber, SL Hyland, TM Merz, G Rätsch arXiv preprint arXiv:2105.05728, 2021 | | 2021 |
| Machine Learning Approaches for Patient Monitoring in the Intensive Care Unit M Hüser ETH Zurich, 2021 | | 2021 |
| A Machine Learning-based Early Warning System for Circulatory System Deterioration in Intensive Care Unit Patients SL Hyland, M Faltys, M Hüser, X Lyu, C Esteban, G Rätsch, T Merz AMIA, 2018 | | 2018 |
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
