School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne 3086, Victoria, Australia.
School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne 3086, Victoria, Australia.
Neural Netw. 2023 Oct;167:741-750. doi: 10.1016/j.neunet.2023.07.033. Epub 2023 Aug 12.
Accurate estimation of in-hospital mortality based on patients' physiological time series data improves the performance of the clinical decision support systems and assists hospital providers in allocating resources. In practice, the data quality issues of missing values are ubiquitous in electronic health records (EHRs). Since the vital signs are usually observed with irregular temporal intervals and different sampling rates, it is challenging to predict clinical outcomes with sparse and incomplete multivariate time series. We propose an auto-regressive recurrent neural network (RNN) based model, dubbed the bi-directional recursive encoder-decoder network (BiRED), to jointly perform data imputation and mortality prediction. To capture complex patterns of medical time sequences, a 2D cross-regression with an RNN unit (2DCR-RNN) and an imputation block with an RNN unit (IB-RNN) are designed as the recurrent component of the encoder and decoder, respectively. Furthermore, a state initialization method is proposed to alleviate errors accumulated in the generated sequence. The experimental results on two real EHR datasets show that our proposed method can predict hospital mortality with high AUC scores.
基于患者生理时间序列数据的精确住院死亡率估计可以提高临床决策支持系统的性能，并帮助医院资源分配者。在实践中，电子健康记录（EHRs）中普遍存在数据质量问题，即缺失值问题。由于生命体征通常以不规则的时间间隔和不同的采样率进行观察，因此稀疏和不完整的多变量时间序列预测临床结果具有挑战性。我们提出了一种基于自回归递归神经网络（RNN）的模型，称为双向递归编码器-解码器网络（BiRED），以联合执行数据插补和死亡率预测。为了捕获医疗时间序列的复杂模式，我们设计了一个带有 RNN 单元的 2D 交叉回归（2DCR-RNN）和一个带有 RNN 单元的插补块（IB-RNN），分别作为编码器和解码器的递归组件。此外，还提出了一种状态初始化方法，以减轻生成序列中累积的错误。在两个真实的 EHR 数据集上的实验结果表明，我们提出的方法可以用高 AUC 分数预测医院死亡率。
