There is a growing interest in identifying, weighing and accounting for the impact of health determinants that lie outside of the traditional healthcare system, yet there is a remarkable paucity of data and sources to sustain these efforts. Decision support systems would greatly benefit from leveraging models which are able to extend and use such cross-domain knowledge. This paper describes an approach to identify and explore related social and clinical terms based on large corpora of unstructured data. Using word embedding techniques on relevant sources of knowledge, we have identified terms that appear close together in the high-dimensional space. In particular, having created a model with cross-domain knowledge on the social determinants of health, we have been able to demonstrate that it is possible to surface terms in this domain when querying for related clinical terms, thereby creating a bridge between the social and clinical determinants of health. This is a promising approach with significant applicability in decision support efforts in healthcare.