  Predicting (1) when the next hospital admission occurs and (2) what will
happen in the next admission about a patient by mining electronic health record
(EHR) data can provide granular readmission predictions to assist clinical
decision making. Recurrent neural network (RNN) and point process models are
usually employed in modelling temporal sequential data. Simple RNN models
assume that sequences of hospital visits follow strict causal dependencies
between consecutive visits. However, in the real-world, a patient may have
multiple co-existing chronic medical conditions, i.e., multimorbidity, which
results in a cascade of visits where a non-immediate historical visit can be
most influential to the next visit. Although a point process (e.g., Hawkes
process) is able to model a cascade temporal relationship, it strongly relies
on a prior generative process assumption. We propose a novel model, MEDCAS, to
address these challenges. MEDCAS combines the strengths of RNN-based models and
point processes by integrating point processes in modelling visit types and
time gaps into an attention-based sequence-to-sequence learning model, which is
able to capture the temporal cascade relationships. To supplement the patients
with short visit sequences, a structural modelling technique with graph-based
methods is used to construct the markers of the point process in MEDCAS.
Extensive experiments on three real-world EHR datasets have been performed and
the results demonstrate that \texttt{MEDCAS} outperforms state-of-the-art
models in both tasks.
