  Pain is a complex concept that can interconnect with other concepts such as a
disorder that might cause pain, a medication that might relieve pain, and so
on. To fully understand the context of pain experienced by either an individual
or across a population, we may need to examine all concepts related to pain and
the relationships between them. This is especially useful when modeling pain
that has been recorded in electronic health records. Knowledge graphs represent
concepts and their relations by an interlinked network, enabling semantic and
context-based reasoning in a computationally tractable form. These graphs can,
however, be too large for efficient computation. Knowledge graph embeddings
help to resolve this by representing the graphs in a low-dimensional vector
space. These embeddings can then be used in various downstream tasks such as
classification and link prediction. The various relations associated with pain
which are required to construct such a knowledge graph can be obtained from
external medical knowledge bases such as SNOMED CT, a hierarchical systematic
nomenclature of medical terms. A knowledge graph built in this way could be
further enriched with real-world examples of pain and its relations extracted
from electronic health records. This paper describes the construction of such
knowledge graph embedding models of pain concepts, extracted from the
unstructured text of mental health electronic health records, combined with
external knowledge created from relations described in SNOMED CT, and their
evaluation on a subject-object link prediction task. The performance of the
models was compared with other baseline models.
