DynGraph2Seq: DynamicGraphtoSequence Interpretable Learning for Health Stage Prediction in Online Health Forums 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 usersu0027 activities in different subforums can be strong indicators of their health status changes. This additional information could allow healthcare organizations to respond promptly and provide additional help for the patient. However, modeling complex transitions of an individual useru0027s activities among different subforums over time and learning how these correspond to hisher health stage are extremely challenging. In this paper, we first formulate the transition of user activities as a dynamic graph with multiattributed nodes, then formalize the health stage inference task as a dynamic graphtosequence learning problem, and hence propose a novel dynamic graphtosequence 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 timeevolving user activity graphs and a sequence of target health stages. We go on to propose dynamic graph hierarchical attention mechanisms to facilitate the necessary multilevel 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.

Predictive Multilevel Patient Representations from Electronic Health Records The advent of the Internet era has led to an explosive growth in the Electronic Health Records (EHR) in the past decades. The EHR data can be regarded as a collection of clinical events, including laboratory results, medication records, physiological indicators, etc, which can be used for clinical outcome prediction tasks to support constructions of intelligent health systems. Learning patient representation from these clinical events for the clinical outcome prediction is an important but challenging step. Most related studies transform EHR data of a patient into a sequence of clinical events in temporal order and then use sequential models to learn patient representations for outcome prediction. However, clinical event sequence contains thousands of event types and temporal dependencies. We further make an observation that clinical events occurring in a short period are not constrained by any temporal order but events in a long term are influenced by temporal dependencies. The multiscale temporal property makes it difficult for traditional sequential models to capture the shortterm cooccurrence and the longterm temporal dependencies in clinical event sequences. In response to the above challenges, this paper proposes a Multilevel Representation Model (MRM). MRM first uses a sparse attention mechanism to model the shortterm cooccurrence, then uses intervalbased event pooling to remove redundant information and reduce sequence length and finally predicts clinical outcomes through Long ShortTerm Memory (LSTM). Experiments on realworld datasets indicate that our proposed model largely improves the performance of clinical outcome prediction tasks using EHR data.