This paper is dedicated to the design and evaluation of the first AMR parser
tailored for clinical notes. Our objective was to facilitate the precise
transformation of the clinical notes into structured AMR expressions, thereby
enhancing the interpretability and usability of clinical text data at scale.
Leveraging the colon cancer dataset from the Temporal Histories of Your Medical
Events (THYME) corpus, we adapted a state-of-the-art AMR parser utilizing
continuous training. Our approach incorporates data augmentation techniques to
enhance the accuracy of AMR structure predictions. Notably, through this
learning strategy, our parser achieved an impressive F1 score of 88% on the
THYME corpus's colon cancer dataset. Moreover, our research delved into the
efficacy of data required for domain adaptation within the realm of clinical
notes, presenting domain adaptation data requirements for AMR parsing. This
exploration not only underscores the parser's robust performance but also
highlights its potential in facilitating a deeper understanding of clinical
narratives through structured semantic representations.

本文旨在设计和评估首个针对临床笔记定制的 AMR 解析器，以促进将临床笔记精确转化为结构化的 AMR 表达式，从而增强大规模临床文本数据的可解释性和可用性。我们通过使用连续训练的先进 AMR 解析器以及数据增强技术，实现了对 THYME 语料库的结直肠癌数据集的高精度解析，并深入研究了在临床笔记领域中进行领域适应所需的数据。本研究不仅突显了解析器的稳健性能，也凸显了它通过结构化语义表示促进对临床叙述的深入理解的潜力。