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Online health communities such as the online breast cancer forum enablepatients (i.e., users) to interact and help each other within varioussubforums, which are subsections of the main forum devoted to specific healthtopics. The changing nature of the users' activities in different subforums canbe strong indicators of their health status changes. This additionalinformation could allow health-care organizations to respond promptly andprovide additional help for the patient. However, modeling complex transitionsof an individual user's activities among different subforums over time andlearning how these correspond to his/her health stage are extremelychallenging. In this paper, we first formulate the transition of useractivities as a dynamic graph with multi-attributed nodes, then formalize thehealth stage inference task as a dynamic graph-to-sequence learning problem,and hence propose a novel dynamic graph-to-sequence neural networksarchitecture (DynGraph2Seq) to address all the challenges. Our proposedDynGraph2Seq model consists of a novel dynamic graph encoder and aninterpretable sequence decoder that learn the mapping between a sequence oftime-evolving user activity graphs and a sequence of target health stages. Wego on to propose dynamic graph hierarchical attention mechanisms to facilitatethe necessary multi-level interpretability. A comprehensive experimentalanalysis of its use for a health stage prediction task demonstrates both theeffectiveness and the interpretability of the proposed models.

DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums