Abstract
Extensive bedside monitoring in Intensive Care Units (ICUs) has resulted in complex temporal data regarding patient physiology, which presents an upscale context for clinical data analysis. In the other hand, identifying the time-series patterns within these data may provide a high aptitude to predict clinical events. Hence, we investigate, during this work, the implementation of an automatic data-driven system, which analyzes large amounts of multivariate temporal data derived from Electronic Health Records (EHRs), and extracts high-level information so as to predict in-hospital mortality and Length of Stay (LOS) early. Practically, we investigate the applicability of LSTM network by reducing the time-frame to 6-hour so as to enhance clinical tasks. The experimental results highlight the efficiency of LSTM model with rigorous multivariate time-series measurements for building real-world prediction engines.
Abstract (translated)
在重症监护室(ICU)中进行广泛的床边监测,产生了关于病人生理学的复杂的时序数据,为临床数据分析提供了一个向上扩展的背景。另一方面,在这些数据中发现时序模式可能提供高预测能力,从而有助于预测临床事件。因此,在这次工作中,我们研究了一种自动数据驱动系统,该系统从电子健康记录(EHR)中分析大量的多变量时序数据,并提取高级信息,以预测在医院内的死亡率和住院天数(LOS)。实际上,我们将该网络的适用性降低到6小时,以增强临床任务。实验结果显示,LSTM模型用严格的多变量时序测量方法构建的实际预测引擎的效率。
URL
https://arxiv.org/abs/2308.12800
