Clinical information extraction for Low-resource languages with Few-shot
  learning using Pre-trained language models and Prompting
Phillip Richter-Pechanski Philipp Wiesenbach Dominic M. Schwab Christina Kiriakou Nicolas Geis Christoph Dieterich Anette Frank
Automatic extraction of medical information from clinical documents poses
several challenges: high costs of required clinical expertise, limited
interpretability of model predictions, restricted computational resources and
privacy regulations. Recent advances in domain-adaptation and prompting methods
showed promising results with minimal training data using lightweight masked
language models, which are suited for well-established interpretability
methods. We are first to present a systematic evaluation of these methods in a
low-resource setting, by performing multi-class section classification on
German doctor's letters. We conduct extensive class-wise evaluations supported
by Shapley values, to validate the quality of our small training data set and
to ensure the interpretability of model predictions. We demonstrate that a
lightweight, domain-adapted pretrained model, prompted with just 20 shots,
outperforms a traditional classification model by 30.5% accuracy. Our results
serve as a process-oriented guideline for clinical information extraction
projects working with low-resource.