Inpatient2Vec: Medical Representation Learning for Inpatients Representation learning (RL) plays an important role in extracting proper representations from complex medical data for various analyzing tasks, such as patient grouping, clinical endpoint prediction and medication recommendation. Medical data can be divided into two typical categories, outpatient and inpatient, that have different data characteristics. However, few existing RL methods are specially designed for inpatients data, which have strong temporal relations and consistent diagnosis. In addition, for unordered medical activity set, existing medical RL methods utilize a simple pooling strategy, which would result in indistinguishable contributions among the activities for learning. In this work, we propose Inpatient2Vec, a novel model for learning three kinds of representations for inpatient, including medical activity, hospital day and diagnosis. A multilayer selfattention mechanism with two training tasks is designed to capture the inpatient data characteristics and process the unordered set. Using a realworld dataset, we demonstrate that the proposed approach outperforms the competitive baselines on semantic similarity measurement and clinical events prediction tasks.

Predictive Multilevel Patient Representations from Electronic Health Records 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 multiscale temporal property makes it difficult for traditional sequential models to capture the shortterm cooccurrence and the longterm temporal dependencies in clinical event sequences. In response to the above challenges, this paper proposes a Multilevel Representation Model (MRM). MRM first uses a sparse attention mechanism to model the shortterm cooccurrence, then uses intervalbased event pooling to remove redundant information and reduce sequence length and finally predicts clinical outcomes through Long ShortTerm Memory (LSTM). Experiments on realworld datasets indicate that our proposed model largely improves the performance of clinical outcome prediction tasks using EHR data.