| CXPlain: Causal Explanations for Model Interpretation under Uncertainty P Schwab, W Karlen Advances in Neural Information Processing Systems (NeurIPS), 2019 | 255 | 2019 |
| Learning Counterfactual Representations for Estimating Individual Dose-Response Curves P Schwab, L Linhardt, S Bauer, JM Buhmann, W Karlen AAAI Conference on Artificial Intelligence, 2020 | 155 | 2020 |
| Clinical Predictive Models for COVID-19: Systematic Study P Schwab, ADM Schütte, B Dietz, S Bauer Journal of Medical Internet Research 22 (10), e21439, 2020 | 142* | 2020 |
| Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks P Schwab, L Linhardt, W Karlen arXiv preprint arXiv:1810.00656, 2018 | 126 | 2018 |
| Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks P Schwab, GC Scebba, J Zhang, M Delai, W Karlen Computing in Cardiology, 2017 | 110 | 2017 |
| Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation A DuMont Schütte, J Hetzel, S Gatidis, T Hepp, B Dietz, S Bauer, ... npj Digital Medicine 4 (1), 1-14, 2021 | 77 | 2021 |
| Global Healthcare Fairness: We Should be Sharing More, not Less, Data KP Seastedt, P Schwab, Z O’Brien, E Wakida, K Herrera, PGF Marcelo, ... PLOS Digital Health 1 (10), e0000102, 2022 | 76* | 2022 |
| Real-time Prediction of COVID-19 related Mortality using Electronic Health Records P Schwab, A Mehrjou, S Parbhoo, LA Celi, J Hetzel, M Hofer, B Schölkopf, ... Nature Communications 12 (1), 1-16, 2021 | 69 | 2021 |
| PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data P Schwab, W Karlen AAAI Conference on Artificial Intelligence, 2019 | 64 | 2019 |
| Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks P Schwab, D Miladinovic, W Karlen AAAI Conference on Artificial Intelligence, 2019 | 61 | 2019 |
| Crowdsourcing Digital Health Measures to Predict Parkinson’s Disease Severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge SK Sieberts, J Schaff, M Duda, BÁ Pataki, M Sun, P Snyder, JF Daneault, ... npj Digital Medicine 4 (1), 1-12, 2021 | 50 | 2021 |
| Learning Neural Causal Models with Active Interventions N Scherrer, O Bilaniuk, Y Annadani, A Goyal, P Schwab, B Schölkopf, ... arXiv preprint arXiv:2109.02429, 2021 | 47 | 2021 |
| CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data M Chevalley, Y Roohani, A Mehrjou, J Leskovec, P Schwab arXiv preprint arXiv:2210.17283, 2022 | 31 | 2022 |
| A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data P Schwab, W Karlen IEEE Journal for Biomedical Health Informatics, 2020 | 30 | 2020 |
| Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care P Schwab, E Keller, C Muroi, DJ Mack, C Strässle, W Karlen International Conference on Machine Learning (ICML), 2018 | 30 | 2018 |
| Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection C Muroi, S Meier, V De Luca, DJ Mack, C Strässle, P Schwab, W Karlen, ... Neurocritical Care, 1-8, 2019 | 24 | 2019 |
| GeneDisco: A Benchmark for Experimental Design in Drug Discovery A Mehrjou, A Soleymani, A Jesson, P Notin, Y Gal, S Bauer, P Schwab International Conference on Learning Representations (ICLR-22), 2022 | 21 | 2022 |
| Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations S Bing, A Dittadi, S Bauer, P Schwab PloS Digital Health, 2022 | 16 | 2022 |
| Diabetes detection from whole-body magnetic resonance imaging using deep learning B Dietz, J Machann, V Agrawal, M Heni, P Schwab, J Dienes, S Reichert, ... JCI Insight, 2021 | 14* | 2021 |
| Capturing the Essence: Towards the Automated Generation of Transparent Behavior Models P Schwab, H Hlavacs AAAI Conference on Artificial Intelligence and Interactive Digital …, 2015 | 14 | 2015 |
Walter KarlenBiomedical engineering & AI systems for medicine, University of UlmE-mailová adresa ověřena na: ieee.org