Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting
<|reference_start|>Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting: 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.<|reference_end|>
@article{richter-pechanski2024clinical,
    title={Clinical information extraction for Low-resource languages with Few-shot
  learning using Pre-trained language models and Prompting},
    author={Phillip Richter-Pechanski, Philipp Wiesenbach, Dominic M. Schwab,
  Christina Kiriakou, Nicolas Geis, Christoph Dieterich, Anette Frank},
    journal={arXiv preprint arXiv:2403.13369},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13369},
    primaryClass={cs.CL cs.AI cs.LG}
}