Research

Noel’s research interests focus on the intersection of machine learning and digital health. Some research highlights include¹:

In his bachelor thesis at Charité Berlin, 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.

In Kronenberg et al. (2024), Noel co-led a study that identified multimorbidity clusters in intensive care unit (ICU) patients. 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).

Noel has also contributed to several projects exploring the use of 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.

Additional work includes outcomes research in ICU patients. For instance, von Wedel et al. (2025) examined mortality differences between female and male patients at comparable thresholds of mechanical power.

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