Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes. - Oliver Carr; Avelino Javer; Patrick Rockenschaub; Owen Parsons; Robert Dürichen (ML4H@NeurIPS, 2021)

From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU. - Patrick Rockenschaub; Adam Hilbert; Tabea Kossen; Falk von Dincklage; Vince Istvan Madai; Dietmar Frey (CoRR, 2023)

Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML. - Robin Van De Water; Hendrik Schmidt; Paul W. G. Elbers; Patrick Thoral; Bert Arnrich; Patrick Rockenschaub (ICLR, 2024)

DomainLab: A modular Python package for domain generalization in deep learning. - Xudong Sun 002; Carla Feistner; Alexej Gossmann; George Schwarz; Rao Muhammad Umer; Lisa Beer; Patrick Rockenschaub; Rahul Babu Shrestha; Armin Gruber; Nutan Chen; Sayedali Shetab Boushehri; Florian Buettner 001; et al. (CoRR, 2024)

Robust prediction under missingness shifts. - Patrick Rockenschaub; Zhicong Xian; Alireza Zamanian; Marta Piperno; Octavia-Andreea Ciora; Elisabeth Pachl; Narges Ahmidi (CoRR, 2024)

External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis. - Patrick Rockenschaub; Ela Marie Akay; Benjamin Gregory Carlisle; Adam Hilbert; Joshua Wendland; Falk Meyer-Eschenbach; Anatol-Fiete Näher; Dietmar Frey; Vince Istvan Madai (BMC Medical Informatics Decis. Mak., 2025)