  Online health communities such as the online breast cancer forum enable
patients (i.e., users) to interact and help each other within various
subforums, which are subsections of the main forum devoted to specific health
topics. The changing nature of the users' activities in different subforums can
be strong indicators of their health status changes. This additional
information could allow health-care organizations to respond promptly and
provide additional help for the patient. However, modeling complex transitions
of an individual user's activities among different subforums over time and
learning how these correspond to his/her health stage are extremely
challenging. In this paper, we first formulate the transition of user
activities as a dynamic graph with multi-attributed nodes, then formalize the
health stage inference task as a dynamic graph-to-sequence learning problem,
and hence propose a novel dynamic graph-to-sequence neural networks
architecture (DynGraph2Seq) to address all the challenges. Our proposed
DynGraph2Seq model consists of a novel dynamic graph encoder and an
interpretable sequence decoder that learn the mapping between a sequence of
time-evolving user activity graphs and a sequence of target health stages. We
go on to propose dynamic graph hierarchical attention mechanisms to facilitate
the necessary multi-level interpretability. A comprehensive experimental
analysis of its use for a health stage prediction task demonstrates both the
effectiveness and the interpretability of the proposed models.
