  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 multi-scale temporal property makes it difficult for
traditional sequential models to capture the short-term co-occurrence and the
long-term temporal dependencies in clinical event sequences. In response to the
above challenges, this paper proposes a Multi-level Representation Model (MRM).
MRM first uses a sparse attention mechanism to model the short-term
co-occurrence, then uses interval-based event pooling to remove redundant
information and reduce sequence length and finally predicts clinical outcomes
through Long Short-Term Memory (LSTM). Experiments on real-world datasets
indicate that our proposed model largely improves the performance of clinical
outcome prediction tasks using EHR data.
