| Foundation models for generalist medical artificial intelligence M Moor, O Banerjee, ZSH Abad, HM Krumholz, J Leskovec, EJ Topol, ... Nature 616 (7956), 259-265, 2023 | 934 | 2023 |
| 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 | 381 | 2020 |
| A survey of topological machine learning methods F Hensel, M Moor, B Rieck Frontiers in Artificial Intelligence 4, 681108, 2021 | 226 | 2021 |
| Topological Autoencoders M Moor, M Horn, B Rieck, K Borgwardt International Conference on Machine Learning (ICML), 7045--7054, 2020 | 203 | 2020 |
| Med-Flamingo: a Multimodal Medical Few-shot Learner M Moor, Q Huang, S Wu, M Yasunaga, C Zakka, Y Dalmia, EP Reis, ... Accepted to ML4H 2023, 2023 | 184 | 2023 |
| Accelerating detection of lung pathologies with explainable ultrasound image analysis J Born, N Wiedemann, M Cossio, C Buhre, G Brändle, K Leidermann, ... Applied Sciences 11 (2), 672, 2021 | 165 | 2021 |
| Almanac: Retrieval-Augmented Language Models for Clinical Medicine C Zakka, A Chaurasia, R Shad, A Dalal, J Kim, M Moor, K Alexander, ... NEJM AI, 2023 | 153 | 2023 |
| Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology B Rieck, M Togninalli, C Bock, M Moor, M Horn, T Gumbsch, K Borgwardt International Conference on Learning Representations (ICLR), 2019., 2018 | 152 | 2018 |
| Set Functions for Time Series M Horn, M Moor, C Bock, B Rieck, K Borgwardt International Conference on Machine Learning (ICML), 4353-4363, 2020 | 149 | 2020 |
| Early prediction of sepsis in the ICU using machine learning: a systematic review M Moor, B Rieck, M Horn, CR Jutzeler, K Borgwardt Frontiers in medicine 8, 348, 2021 | 146 | 2021 |
| Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping M Moor, M Horn, B Rieck, D Roqueiro, K Borgwardt Proceedings of the 4th Machine Learning for Healthcare Conference, 2019., 2019 | 114* | 2019 |
| Topological Graph Neural Networks M Horn, E De Brouwer, M Moor, Y Moreau, B Rieck, K Borgwardt International Conference on Learning Representations (ICLR), 2022, 2022 | 106 | 2022 |
| Predicting sepsis using deep learning across international sites: a retrospective development and validation study M Moor, N Bennett, D Plečko, M Horn, B Rieck, N Meinshausen, ... The Lancet's eClinicalMedicine 62, 102124, 2023 | 31* | 2023 |
| Association mapping in biomedical time series via statistically significant shapelet mining C Bock, T Gumbsch, M Moor, B Rieck, D Roqueiro, K Borgwardt Bioinformatics 34 (13), i438-i446, 2018 | 30 | 2018 |
| Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning C Bock, M Moor, CR Jutzeler, K Borgwardt Artificial Neural Networks, 33-71, 2020 | 29 | 2020 |
| Quantification of liver, subcutaneous, and visceral adipose tissues by MRI before and after bariatric surgery AC Meyer-Gerspach, R Peterli, M Moor, P Madörin, A Schötzau, D Nabers, ... Obesity surgery 29, 2795-2805, 2019 | 25 | 2019 |
| AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments S Schmidgall, R Ziaei, C Harris, E Reis, J Jopling, M Moor arXiv preprint arXiv:2405.07960, 2024 | 24 | 2024 |
| Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting B Yan, R Liu, DE Kuo, S Adithan, EP Reis, S Kwak, VK Venugopal, ... Accepted at Findings of EMNLP 2023., 2023 | 16 | 2023 |
| Zero-shot causal learning H Nilforoshan, M Moor, Y Roohani, Y Chen, A Šurina, M Yasunaga, ... Accepted at Neurips 2023 (Spotlight), 2023 | 11 | 2023 |
| Path Imputation Strategies for Signature Models M Moor, M Horn, C Bock, K Borgwardt, B Rieck ICML 2020 Workshop on the Art of Learning with Missing Values (Artemiss), 2020 | 11* | 2020 |
Bastian RieckProfessor, AIDOS Lab, University of FribourgVerified email at unifr.ch
