Building models for health prediction based on Electronic Health Records
(EHR) has become an active research area. EHR patient journey data consists of
patient time-ordered clinical events/visits from patients. Most existing
studies focus on modeling long-term dependencies between visits, without
explicitly taking short-term correlations between consecutive visits into
account, where irregular time intervals, incorporated as auxiliary information,
are fed into health prediction models to capture latent progressive patterns of
patient journeys. We present a novel deep neural network with four modules to
take into account the contributions of various variables for health prediction:
i) the Stacked Attention module strengthens the deep semantics in clinical
events within each patient journey and generates visit embeddings, ii) the
Short-Term Temporal Attention module models short-term correlations between
consecutive visit embeddings while capturing the impact of time intervals
within those visit embeddings, iii) the Long-Term Temporal Attention module
models long-term dependencies between visit embeddings while capturing the
impact of time intervals within those visit embeddings, iv) and finally, the
Coupled Attention module adaptively aggregates the outputs of Short-Term
Temporal Attention and Long-Term Temporal Attention modules to make health
predictions. Experimental results on MIMIC-III demonstrate superior predictive
accuracy of our model compared to existing state-of-the-art methods, as well as
the interpretability and robustness of this approach. Furthermore, we found
that modeling short-term correlations contributes to local priors generation,
leading to improved predictive modeling of patient journeys.Comment: 10 pages, 4 figures, accepted at ACM BCB 202