The way we analyse clinical texts has undergone major changes over the last
years. The introduction of language models such as BERT led to adaptations for
the (bio)medical domain like PubMedBERT and ClinicalBERT. These models rely on
large databases of archived medical documents. While performing well in terms
of accuracy, both the lack of interpretability and limitations to transfer
across languages limit their use in clinical setting. We introduce a novel
light-weight graph-based embedding method specifically catering radiology
reports. It takes into account the structure and composition of the report,
while also connecting medical terms in the report through the multi-lingual
SNOMED Clinical Terms knowledge base. The resulting graph embedding uncovers
the underlying relationships among clinical terms, achieving a representation
that is better understandable for clinicians and clinically more accurate,
without reliance on large pre-training datasets. We show the use of this
embedding on two tasks namely disease classification of X-ray reports and image
classification. For disease classification our model is competitive with its
BERT-based counterparts, while being magnitudes smaller in size and training
data requirements. For image classification, we show the effectiveness of the
graph embedding leveraging cross-modal knowledge transfer and show how this
method is usable across different languages.

我们引入了一种新颖的轻量级基于图的嵌入方法，特别适用于放射学报告，通过多语言 SNOMED 临床术语知识库连接报告中的医学术语，揭示了临床术语之间的潜在关系，实现了更好地理解和临床准确性的表示，而不依赖于大规模预训练数据集。我们展示了这种嵌入方法在 X 光报告的疾病分类和图像分类两个任务上的应用，并且相较于基于 BERT 的模型，在体积和训练数据需求方面要小得多。同时，我们展示了图嵌入方法如何利用跨模态知识传递，在不同语言之间具有可用性。