Research

Noel’s research interests focus on developing and applying machine learning methods that yield models with meaningful capacity for data-driven medical insight. He has already contributed to a diversity of both medical and machine learning research projects, including poster presentations, conference abstracts, and peer-reviewed journal publications. Notably, he also leads an acclaimed nonprofit initiative, empowering the next generation of researchers and establishing a culture of open, fair, and critical scientific practice.

Supervisors

Noel has contributed to various research projects, working with

• Prof. Dr. Dr. Felix Balzer (Director, Institute of Medical Informatics, Charité Berlin),
• Prof. Dr. Stefan Haufe (Professor, Research Group for Uncertainty, Inverse Modeling and Machine Learning, Technical University of Berlin),
• Prof. Dr. Roland Eils (Founding Director, Center for Digital Health, Berlin Institute of Health, Charité Berlin),
• and Dr. Benjamin Wild (Group Leader, Research Group for Artificial Intelligence in the Life Sciences, Center for Digital Health, Berlin Institute of Health, Charité Berlin).

Highlights¹

In his bachelor thesis at the Charité Berlin and Technical University of Berlin (2024), Noel developed a proof-of-concept electronic health record (EHR) data processing pipeline. The objective was to streamline the use of quality-assessed EHR data for real world evidence studies and to build the foundation for the Charité Outcomes Research Repository (CORR). As a demonstration, established clinical risk scores were derived and extensively validated. This work has been awarded the Rolf Niedermeier Faculty Prize for outstanding theses.

A main line of work includes medical outcomes research in intensive care unit (ICU) patients. Noel co-led a study (Kronenberg et al., 2024) that identified multimorbidity clusters in ICU patients using latent class analysis (LCA). These clusters were independently associated with mortality and enhanced the predictive performance of a widely used clinical risk score. He presented the work as a poster at the Congress of the German Interdisciplinary Association for Intensive Care and Emergency Medicine (DIVI). In von Wedel et al. (2025), mortality differences were examined between female and male patients at comparable thresholds of mechanical power.

Noel has also contributed to several machine learning projects exploring the use of deep learning models, such as large language models (LLMs), in medicine. In Hegselmann et al. (2025), general-purpose LLMs were evaluated for encoding EHR data in clinical prediction tasks, demonstrating performance on par with or superior to specialized models. In a separate study (von Wedel et al., 2024), the team investigated affiliation bias in LLM-assisted peer review of medical abstracts.

For a complete list of publications, please see Noel’s Google Scholar profile.