  Major postoperative complications are devastating to surgical patients. Some
of these complications are potentially preventable via early predictions based
on intraoperative data. However, intraoperative data comprise long and
fine-grained multivariate time series, prohibiting the effective learning of
accurate models. The large gaps associated with clinical events and protocols
are usually ignored. Moreover, deep models generally lack transparency.
Nevertheless, the interpretability is crucial to assist clinicians in planning
for and delivering postoperative care and timely interventions. Towards this
end, we propose a hierarchical model combining the strength of both attention
and recurrent models for intraoperative time series. We further develop an
explanation module for the hierarchical model to interpret the predictions by
providing contributions of intraoperative data in a fine-grained manner.
Experiments on a large dataset of 111,888 surgeries with multiple outcomes and
an external high-resolution ICU dataset show that our model can achieve strong
predictive performance (i.e., high accuracy) and offer robust interpretations
(i.e., high transparency) for predicted outcomes based on intraoperative time
series.
