| Real-valued (medical) time series generation with recurrent conditional gans C Esteban, SL Hyland, G Rätsch arXiv preprint arXiv:1706.02633, 2017 | 785 | 2017 |
| 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 | 245 | 2020 |
| Identification of active transcriptional regulatory elements from GRO-seq data CG Danko, SL Hyland, LJ Core, AL Martins, CT Waters, HW Lee, ... Nature methods 12 (5), 433-438, 2015 | 189 | 2015 |
| A global metagenomic map of urban microbiomes and antimicrobial resistance D Danko, D Bezdan, EE Afshin, S Ahsanuddin, C Bhattacharya, DJ Butler, ... Cell 184 (13), 3376-3393. e17, 2021 | 179 | 2021 |
| Making the most of text semantics to improve biomedical vision–language processing B Boecking, N Usuyama, S Bannur, DC Castro, A Schwaighofer, S Hyland, ... European conference on computer vision, 1-21, 2022 | 68 | 2022 |
| Neural document embeddings for intensive care patient mortality prediction P Grnarova, F Schmidt, SL Hyland, C Eickhoff arXiv preprint arXiv:1612.00467, 2016 | 64 | 2016 |
| Improving clinical predictions through unsupervised time series representation learning X Lyu, M Hueser, SL Hyland, G Zerveas, G Raetsch arXiv preprint arXiv:1812.00490, 2018 | 50 | 2018 |
| Learning Unitary Operators with Help From u (n) SL Hyland, G Rätsch AAAI 2017, 2016 | 49 | 2016 |
| Temporal pointwise convolutional networks for length of stay prediction in the intensive care unit E Rocheteau, P Liò, S Hyland Proceedings of the conference on health, inference, and learning, 58-68, 2021 | 42 | 2021 |
| Leveraging electronic health records for data science: common pitfalls and how to avoid them CM Sauer, LC Chen, SL Hyland, A Girbes, P Elbers, LA Celi The Lancet Digital Health 4 (12), e893-e898, 2022 | 24 | 2022 |
| 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 | 24 | 2021 |
| Missing data was handled inconsistently in UK prediction models: a review of method used A Tsvetanova, M Sperrin, N Peek, I Buchan, S Hyland, GP Martin Journal of Clinical Epidemiology 140, 149-158, 2021 | 22 | 2021 |
| Learning to exploit temporal structure for biomedical vision-language processing S Bannur, S Hyland, Q Liu, F Perez-Garcia, M Ilse, DC Castro, B Boecking, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 20 | 2023 |
| Predicting the impact of treatments over time with uncertainty aware neural differential equations. E De Brouwer, J Gonzalez, S Hyland International Conference on Artificial Intelligence and Statistics, 4705-4722, 2022 | 14 | 2022 |
| Machine learning for health (ML4H) 2020: Advancing healthcare for all SK Sarkar, S Roy, E Alsentzer, MBA McDermott, F Falck, I Bica, G Adams, ... Machine Learning for Health, 1-11, 2020 | 14 | 2020 |
| 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 | 11 | 2019 |
| A generative model of words and relationships from multiple sources SL Hyland, T Karaletsos, G Rätsch Association for the Advancement of Artificial Intelligence, 2016 | 9 | 2016 |
| Intraoperative prediction of postanaesthesia care unit hypotension K Palla, SL Hyland, K Posner, P Ghosh, B Nair, M Bristow, Y Paleva, ... British Journal of Anaesthesia 128 (4), 623-635, 2022 | 8 | 2022 |
| On the intrinsic privacy of stochastic gradient descent SL Hyland, S Tople Preprint at https://arxiv. org/pdf/1912.02919. pdf, 2019 | 8 | 2019 |
| An empirical study on the intrinsic privacy of SGD SL Hyland, S Tople arXiv preprint arXiv:1912.02919, 2019 | 6 | 2019 |
Gunnar RätschProfessor, ETH ZürichGeverifieerd e-mailadres voor inf.ethz.ch
