Stefan Hegselmann

Researcher at BIH and resident physician at Charité in Berlin.

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I am interested in machine learning for healthcare. My research focuses on using large generative models to enable efficient, high-performing predictions, and on applying natural language processing techniques to clinical text. I am particularly interested in using state-of-the-art machine learning methods to address meaningful medical problems and improve healthcare delivery.

I am currently an affiliated postdoctoral researcher at the Eils lab focussing on artificial intelligence in healthcare. I am also a resident physician at the Department of Cardiology, Angiology and Intensive Care Medicine at the Charité in Berlin, where I am involved in clinical practice. My goal is to bridge the gap between modern machine learning and clinical practice.

Previously, I have obtained a Master’s degree in computer science from RWTH Aachen University with stays at the University of Gothenburg and UC Berkeley. After that I obtained a medical degree (MD) and a PhD in computer science from the University of Münster and worked with the Clinical ML group at MIT.

You can find all my papers on my Google Scholar profile.

news

Apr 01, 2025 Transferred to this new website.

selected publications

  1. Large language models are few-shot clinical information extractors
    Monica Agrawal, Stefan Hegselmann, Hunter Lang, and 2 more authors
    In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Dec 2022
  2. Tabllm: Few-shot classification of tabular data with large language models
    Stefan Hegselmann, Alejandro Buendia, Hunter Lang, and 3 more authors
    In International Conference on Artificial Intelligence and Statistics, Dec 2023
  3. A data-centric approach to generate faithful and high quality patient summaries with large language models
    Stefan Hegselmann, Shannon Zejiang Shen, Florian Gierse, and 3 more authors
    Conference on Health, Inference, and Learning 2024, Dec 2024