Diagnostic Prediction with Sequence-of-sets Representation Learning for Clinical Events

Abstract

Electronic health records (EHRs) contain both ordered and unordered chronologies of clinical events that occur during a patient encounter. However, during data preprocessing steps, many predictive models impose a predefined order on unordered clinical events sets (e.g., alphabetical, natural order from the chart, etc.), which is potentially incompatible with the temporal nature of the sequence and predictive task. To address this issue, we proposeDPSS, which seeks to capture each patient's clinical event records as sequences of event sets. Foreach clinical event set, we assume that the predictive model should be invariant to the order of concurrent events and thus employ a novel permutation sampling mechanism. This paper evaluates the use of this permuted sampling method given different data-driven models for predicting a heart failure (HF) diagnosis in sub-sequent patient visits. Experimental results using the MIMIC-III dataset show that the permutation sampling mechanism offers improved discriminative power based on the area under the receiver operating curve (AUROC) and precision-recall curve (pr-AUC) metrics as HF diagnosis prediction becomes more robust to different data ordering schemes.

Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance

“…Words in red are non-argument clue words meaningful for argument extraction. and dialogue systems (Zhang et al, 2020a) for presenting unstructured text containing event information in structured form. Compared with previous works (Liu et al, 2018;Wadden et al, 2019;Tong et al, 2020) focusing on sentence-level EAE, more and more recent works tend to explore documentlevel EAE Yang et al, 2021;, which needs to solve long-distance dependency (Ebner et al, 2020) and cross-sentence inference problems.…”