Robin P. van de Water

🧠 AI in Health Researcher
🧑‍🎓 PhD in Machine Learning
🏛️ Hasso Plattner Institute
🏠 Berlin, Germany

See my Google Scholar profile for an automatically updating list of publications.

  • Matthew McDermott, Ethan Steinberg, Jason Fries, Robin P. van de Water, Chao Pang, Patrick Rockenschaub, Pawel Renc, Jungwoo Oh, Kamilė Stankevičiūtė, Justin Xu, Tom Joseph Pollard, Nassim Oufattole, Michael Wornow, Teya Bergamaschi, Hyewon Jeong, Simon Lee, Vincent Jeanselme, Kiril Klein, Mikkel Odgaard, Maria Elkjær Montgomery, Arkadiusz Sitek, Mads Nielsen, Jeffrey Chiang, Noa Dagan, Isaac Kohane, Shalmali Joshi, Edward Choi, and Nigam Shah, MEDS: A Simple, Interoperable Data Standard and Ecosystem for Health AI Research, Accepted to NEJM AI, to appear in vol:3, iss:6 (2026).
  • Robin P. van de Water, Hendrik Schmidt, and Patrick Rockenschaub, ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML, Journal of Open Source Software (JOSS) (2026)
  • Matthias Kirchler, Matteo Ferro, Veronica Lorenzini, Robin P. van de Water, Christoph Lippert, and Andrea Ganna, Large Language Models Improve Transferability of Electronic Health Record-Based Predictions across Countries and Coding Systems, Published at NPJ Digital Medicine (2026)
  • Katharina Alefs, Susanne Ibing, Pia Francesca Rissom, Jan Carlo Schmid, Arkadiusz Kwasigroch, Robin van de Water, Bernhard Y. Renard, and Eugenia Alleva, Towards Foundation Model-Based Propensity Score Matching from Electronic Health Records, Machine Learning for Health Symposium 2025
  • Matthew B. A. McDermott, Justin Xu, Teya S. Bergamaschi, Hyewon Jeong, Simon A. Lee, Nassim Oufattole, Patrick Rockenschaub, Kamilė Stankevičiūtė, Ethan Steinberg, Jimeng Sun,Robin P. van de Water, Michael Wornow, John Wu, and Zhenbang Wu, MEDS: Building Models and Tools in a Reproducible Health AI Ecosystem, Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, Toronto ON Canada: ACM, Aug. 2025
  • Robin P. van de Water, Scaling Up Clinical ML from Datasets to Entire Health Systems through the MEDS Ecosystem, SBHD 2025 International Conference on Systems Biology of Human Diseases, Berlin, June 2025
  • Max M. Maurer, Bjarne Pfitzner, Robin P. van de Water, Lara Faraj, Christoph Riepe, Daniela Zuluaga, Felix Krenzien, Nathanael Raschzok, Robert Siegel, Christian Schineis, Bert Arnrich, Katharina Beyer, Johann Pratschke, Igor M. Sauer, and Axel Winter, Privacy Preserving Federated Learning for 90-Day Mortality Prediction in Colorectal Surgery: A Multicenter Retrospective Development and Comparison Study, International Journal of Surgery (London, England), Aug. 2025
  • Axel Winter, Bjarne Pfitzner, Robin P. van de Water, Lara Faraj, Christoph Riepe, Wolf-Heinrich Hahn, Felix Krenzien, Christian Schineis, Thomas Malinka, and Wenzel Schöning, Overcoming the Data Barrier: Transfer Learning for 90-Day Mortality Prediction in General Surgery–a Retrospective Multicenter Development and Comparison Study, International Journal of Surgery, 2025
  • Bjarne Pfitzner, Max M. Maurer, Axel Winter, Christoph Riepe, Igor M. Sauer, Robin van de Water, Christian Denecke, Johann Pratschke, and Bert Arnrich, Differentially-Private Federated Learning with Non-IID Data For Surgical Risk Prediction, International Journal of Semantic Computing 19.3, 2025
  • Christoph Riepe, Robin van de Water, Axel Winter, Bjarne Pfitzner, Lara Faraj, Robert Ahlborn, Maximilian Schulze, Daniela Zuluaga, Christian Schineis, Katharina Beyer, Johann Pratschke, Bert Arnrich, Igor M Sauer, Max M Maurer, 90-Day mortality prediction in elective visceral surgery using machine learning: a retrospective multicenter development, validation and comparison study
  • M. B. A. McDermott et al. (MEDS-DEV Working Group), “MEDS Decentralized, Extensible Validation (MEDS-DEV) Benchmark: Establishing Reproducibility and Comparability in ML for Health,” ML4H Demo Track, Nov. 2024, Accessed: Dec. 24, 2024.
  • R. van de Water, H. Schmidt, P. Elbers, P. Thoral, B. Arnrich, and P. Rockenschaub, ‘Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML’. arXiv, Jun. 08, 2023. Available: http://arxiv.org/abs/2306.05109, accepted at the 12th International Conference on Learning Representations (ICLR) 2024.
  • A. Winter, R. van de Water, et al., “Enhancing Preoperative Outcome Prediction: A Comparative Retrospective Case–Control Study on Machine Learning versus the International Esodata Study Group Risk Model for Predicting 90-Day Mortality in Oncologic Esophagectomy,” Cancers, vol. 16, no. 17, Art. no. 17, Jan. 2024, doi: 10.3390/cancers16173000.
  • B. Arnrich, E. Choi, J.A. Fries, M.B.A. McDermott, J.Oh, T.J. Pollard, N. Shah, E. Steinberg, M. Wornow, R. van de Water, (MEDS working group, alpabethized authors) An ML-oriented Interface for Medical Record Datasets, https://github.com/Medical-Event-Data-Standard, accepted at Time Series for Health (TS4H) at ICLR 2024
  • R.van de Water, A. Winter, M. Maurer, F. Treykorn, I. Sauer, B Pfitzner, B. Arnrich, Combining Time Series Modalities to Create Endpoint-driven Patient Records, Accepted at Workshop for Data Centric ML at ICLR 2024
  • R.van de Water, A. Winter, M. Maurer, F. Treykorn, I. Sauer, B Pfitzner, B. Arnrich, Combining Hospital-grade Clinical Data and Wearable Vital Sign Monitoring to Predict Surgical Complications, Accepted at Workshop for Timeseries For Health (TS4H) at ICLR 2024
  • B. Pfitzner, M. M. Maurer, A. Winter, C. Riepe, I. M. Sauer, R. van de Water, Bert Arnrich, Differentially-Private Federated Learning with Non-IID Data For Surgical Risk Prediction, First IEEE International Conference on AI for Medicine, Health, and Care (2024)
  • A. Winter, R. van de Water et al., Advancing Preoperative Outcome Prediction: A Comparative Analysis of Machine Learning and ISEG Risk Score for Predicting 90-Day Mortality after Esophagectomy, 2023, accepted at the 141st Congress of the German Society of Surgery (2024)
  • O. Konak, R. van de Water et al., ‘HARE: Unifying the Human Activity Recognition Engineering Workflow’, accepted at MDPI Sensors 2023
  • O. Konak, A. Wischmann, R. van de Water, and B. Arnrich, ‘A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition’. arXiv, Jul. 06, 2023. doi: 10.48550/arXiv.2307.02906. accepted at iWOAR 2023
  • R. van de Water, F. Ventura, Z. Kaoudi, J.-A. Quiané-Ruiz, and V. Markl, ‘Farming your ML-based query optimizer’s food’, in 2022 IEEE 38th international conference on data engineering (ICDE), 2022, pp. 3186–3189. (Best demo award 2022)
  • R. van de Water, F. Ventura, Z. Kaoudi, J. Quiane-Ruiz, and V. Markl, ‘Farm your ML-based query optimizer’s Food!–Human-Guided training data generation–’, presented at the Conference on Innovative Data Systems Research (CIDR), 2022.
  • L. Maas, M. Geurtsen, F. Nouwt, S. Schouten, R. van de Water, S. van Dulmen, F. Dalpiaz, K. van Deemter, S. Brinkkemper ‘The Care2Report system: automated medical reporting as an integrated solution to reduce administrative burden in healthcare’, in Information technology in healthcare: IT architectures and implementations in healthcare environments, Hawaii International Conference on System Sciences (HICSS), 2020.

Preprints

  • Robin P. van de Water, Axel Winter, Daniela Zuluaga Lotero, Bjarne Pfitzner, Lara Faraj, Bert Arnrich, Patrick Rockenschaub, Wenzel Schoning, Thomas Malinka, Christian Denecke, Johann Pratschke, Igor M. Sauer, and Max M. Maurer, Continuous Multimodal AI with Wearable Vital Signs Predicts Postoperative Complications in the General Ward, Nov. 2025, doi: 10.1101/2025.11.25.25340950, medRxiv: 2025.11.25.25340950 under review at The Lancet Digital Health (2026)
  • Alisher Turubayev, Anna Shopova, Fabian Lange, Mahmut Kamalak, Paul Mattes, Victoria Ayvasky, Bert Arnrich, Bjarne Pfitzner, and Robin P. van de Water, Closing Gaps: An Imputation Analysis of ICU Vital Signs, Oct. 2025, doi: 10.48550/arXiv.2510.24217, arXiv: 2510.24217 [cs]