We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the intensive care unit (ICU) of a major urban medical center, our data consists of multivariate time series of observations. The data is irregularly sampled, leading to missingness patterns in re-sampled sequences. In this work, we show the remarkable ability of RNNs to make effective use of binary indicators to directly model missing data, improving AUC and F1 significantly. However, while RNNs can learn arbitrary functions of the missing data and observations, linear models can only learn substitution values. For linear models and MLPs, we show an alternative strategy to capture this signal. Additionally, we evaluate LSTMs, MLPs, and linear models trained on missingness patterns only, showing that for several diseases, what tests are run can be more predictive than the results themselves.

本文采用简单的策略解决临床时间序列中缺失数据的问题，通过将缺失数据视为特征，并利用简单的二进制指示器处理，实现了儿科重症监护病房（PICU）临床时间序列的多标签诊断分类预测。同时，我们也展示了基于缺失数据模式的训练模型不仅能刻画缺失数据信号，同时显示该信号的预测性可以超过部分疾病的测试结果本身。

使用递归神经网络对临床时间序列中的缺失数据进行建模