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The advent of the Internet era has led to an explosive growth in theElectronic Health Records (EHR) in the past decades. The EHR data can beregarded as a collection of clinical events, including laboratory results,medication records, physiological indicators, etc, which can be used forclinical outcome prediction tasks to support constructions of intelligenthealth systems. Learning patient representation from these clinical events forthe clinical outcome prediction is an important but challenging step. Mostrelated studies transform EHR data of a patient into a sequence of clinicalevents in temporal order and then use sequential models to learn patientrepresentations for outcome prediction. However, clinical event sequencecontains thousands of event types and temporal dependencies. We further make anobservation that clinical events occurring in a short period are notconstrained by any temporal order but events in a long term are influenced bytemporal dependencies. The multi-scale temporal property makes it difficult fortraditional sequential models to capture the short-term co-occurrence and thelong-term temporal dependencies in clinical event sequences. In response to theabove challenges, this paper proposes a Multi-level Representation Model (MRM).MRM first uses a sparse attention mechanism to model the short-termco-occurrence, then uses interval-based event pooling to remove redundantinformation and reduce sequence length and finally predicts clinical outcomesthrough Long Short-Term Memory (LSTM). Experiments on real-world datasetsindicate that our proposed model largely improves the performance of clinicaloutcome prediction tasks using EHR data.

Predictive Multi-level Patient Representations from Electronic Health Records