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Predictive Multi-level Patient Representations from Electronic Health Records

机译：电子健康记录中的预测性多级患者代表

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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 Multilevel 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.
机译：互联网时代的到来在过去几十年中导致电子健康记录（EHR）的爆炸性增长。 EHR数据可视为临床事件的集合，包括实验室结果，用药记录，生理指标等，可用于临床结果预测任务，以支持智能卫生系统的构建。从这些临床事件中学习患者代表以进行临床结果预测是一个重要但具有挑战性的步骤。大多数相关研究将患者的EHR数据按时间顺序转换为一系列临床事件，然后使用顺序模型来学习患者代表以进行结果预测。但是，临床事件序列包含数千个事件类型和时间依赖性。我们进一步观察到，短期内发生的临床事件不受任何时间顺序的限制，而长期内发生的事件则受时间依赖性的影响。多尺度时间属性使传统的顺序模型难以捕获临床事件序列中的短期共现和长期时间依赖性。为了应对上述挑战，本文提出了一种多级表示模型（MRM）。 MRM首先使用稀疏注意力机制对短期共现进行建模，然后使用基于间隔的事件池来删除冗余信息并减少序列长度，最后通过长短期记忆（LSTM）预测临床结果。对真实数据集的实验表明，我们提出的模型使用EHR数据极大地提高了临床结果预测任务的性能。

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