Data Reconstruction Based on Temporal Expressions in Clinical Notes Learning representations of clinical notes poses challenges in handling complex content that necessitates preprocessing steps to make the data more suitable for data mining. An important issue, addressed here, is that of temporal expressions, where cues indicate the time when clinical events occur. We present a threestep data reconstruction algorithm for transforming similar clinical entities (e.g., symptoms, complications) into sequential data through unsupervised annotation of temporal expressions. First, the data reconstruction algorithm detects if an expression has temporal intent. Second, it decomposes and rewrites the expression into nontemporal subexpression and temporal constraints. Finally, it clusters similar nontemporal subexpressions by using unsupervised sentence embedding under the modified  K medoids paradigm. We experimented with our proposed algorithm on clinical notes associated with chronic obstructive pulmonary disease (COPD). Visualizing reconstruction results of cardiology reports for a longitudinal cohort of patients with COPD demonstrated that this algorithm is feasible.

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.