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 harnessing 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 a affiliated postdoctoral researcher at the Eils lab focussing on aritficial 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 visited MIT as a visiting PhD student.

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