  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.
