针对不规则采样的临床数据，提出动态时间嵌入方法提升模型处理能力。
Temporal Dynamic Embedding for Irregularly Sampled Time Series
- 将每个变量视为随时间演化的嵌入向量，动态选择观测到的变量子集
- 在三个临床数据集上表现优于或媲美主流方法，训练更快
- 适合处理患者记录不规则、变量数量变化的医疗时序数据
在医疗等实际应用中，每位患者的临床数据以不规则间隔记录，形成稀疏且不规则的时间序列，难以作为神经网络所需的结构化输入。为此，我们提出时间动态嵌入（TDE），使神经网络能够处理变量数量随时间变化的数据。TDE将每个时间序列变量视为随时间演化的嵌入向量，而非固定结构表示，从而避免关键缺失问题。在每个时间步，TDE仅选择并聚合当前观测到的变量子集，基于最新观测表征患者状态。实验在PhysioNet 2012、MIMIC-III和PhysioNet 2019三个临床数据集上进行，TDE模型表现优于或媲美基于插补的基线及若干近期先进方法，且训练时间更短。
原文摘要 · Abstract (English)
In several practical applications, particularly healthcare, clinical data of each patient is individually recorded in a database at irregular intervals as required. This causes a sparse and irregularly sampled time series, which makes it difficult to handle as a structured representation of the prerequisites of neural network models. We therefore propose temporal dynamic embedding (TDE), which enables neural network models to receive data that change the number of variables over time. TDE regards each time series variable as an embedding vector evolving over time, instead of a conventional fixed structured representation, which causes a critical missing problem. For each time step, TDE allows for the selective adoption and aggregation of only observed variable subsets and represents the current status of patient based on current observations. The experiment was conducted on three clinical datasets: PhysioNet 2012, MIMIC-III, and PhysioNet 2019. The TDE model performed competitively or better than the imputation-based baseline and several recent state-of-the-art methods with reduced training runtime.
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