  Sepsis is a life-threatening condition that seriously endangers millions of
people over the world. Hopefully, with the widespread availability of
electronic health records (EHR), predictive models that can effectively deal
with clinical sequential data increase the possibility to predict sepsis and
take early preventive treatment. However, the early prediction is challenging
because patients' sequential data in EHR contains temporal interactions of
multiple clinical events. And capturing temporal interactions in the long event
sequence is hard for traditional LSTM. Rather than directly applying the LSTM
model to the event sequences, our proposed model firstly aggregates
heterogeneous clinical events in a short period and then captures temporal
interactions of the aggregated representations with LSTM. Our proposed
Heterogeneous Event Aggregation can not only shorten the length of clinical
event sequence but also help to retain temporal interactions of both
categorical and numerical features of clinical events in the multiple heads of
the aggregation representations. In the PhysioNet/Computing in Cardiology
Challenge 2019, with the team named PKU_DLIB, our proposed model, in high
efficiency, achieved utility score (0.321) in the full test set.
