| 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 | 806 | 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 | 366 | 2020 |
| SOM-VAE: Interpretable Discrete Representation Learning on Time Series V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch ICLR 2019, 2018 | 194 | 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 | 64 | 2018 |
| 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 | 40 | 2021 |
| HiRID, a high time-resolution ICU dataset (version 1.1. 1) M Faltys, M Zimmermann, X Lyu, M Hüser, S Hyland, G Rätsch, T Merz Physio. Net 10, 2021 | 36 | 2021 |
| 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 | 33 | 2021 |
| 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 | 24 | 2021 |
| 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 | 21 | 2020 |
| DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps L Manduchi, M Hüser, G Rätsch, V Fortuin arXiv preprint arXiv:1910.01590, 2019 | 21 | 2019 |
| Machine learning for early prediction of circulatory failure in the intensive care unit SL Hyland, M Faltys, M Hüser, X Lyu, T Gumbsch, C Esteban, C Bock, ... arXiv preprint arXiv:1904.07990, 2019 | 12 | 2019 |
| 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 | 4 | 2021 |
| 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 | 4 | 2015 |
| A comprehensive ml-based respiratory monitoring system for physiological monitoring & resource planning in the icu M Hüser, X Lyu, M Faltys, A Pace, M Hoche, SL Hyland, H Yèche, ... medRxiv, 2024.01. 23.24301516, 2024 | 3 | 2024 |
| 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 | 3 | 2021 |
| Forecasting intracranial hypertension using time series and waveform features M Hüser MSc. thesis, ETH Zurich, 2015 | 3 | 2015 |
| Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps L Manduchi, M Hüser, G Rätsch, V Fortuin | 2 | 2019 |
| An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit X Lyu, B Fan, M Hüser, P Hartout, T Gumbsch, M Faltys, TM Merz, ... Bioinformatics 40 (Supplement_1), i247-i256, 2024 | 1 | 2024 |
| Machine Learning Approaches for Patient Monitoring in the intensive care unit M Hüser ETH Zurich, 2021 | 1 | 2021 |
| Predicting circulatory system deterioration in intensive care unit patients. SL Hyland, M Hüser, X Lyu, M Faltys, T Merz, G Rätsch AIH@ IJCAI, 87-92, 2018 | 1 | 2018 |
Gunnar RätschProfessor, ETH ZürichPreverjeni e-poštni naslov na inf.ethz.ch
