109 citations · 175 across the 5 of their papers we have counts for
6 papers
CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models
Paul Grundmann, Dennis Fast, Jan Frick +4
With their growing capabilities, generative large language models (LLMs) are being increasingly investigated for complex medical tasks. However, their effectiveness in real-world c…
MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data
Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou +6
As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fi…
MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain
Keno K. Bressem, Jens-Michalis Papaioannou, Paul Grundmann +12
This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million Germ…
Cross-Lingual Knowledge Transfer for Clinical Phenotyping
Jens-Michalis Papaioannou, Paul Grundmann, Betty van Aken +5
Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide. However, current state-…
Self-supervised Answer Retrieval on Clinical Notes
Paul Grundmann, Sebastian Arnold, Alexander Löser
Retrieving answer passages from long documents is a complex task requiring semantic understanding of both discourse and document context. We approach this challenge specifically in…
Learning Contextualized Document Representations for Healthcare Answer Retrieval
Sebastian Arnold, Betty van Aken, Paul Grundmann +2
We present Contextual Discourse Vectors (CDV), a distributed document representation for efficient answer retrieval from long healthcare documents. Our approach is based on structu…