Recent advances in deep learning architectures for sequence modeling have not fully transferred to tasks handling time-series from electronic health records. In particular, in problems related to the Intensive Care Unit (ICU), the state-of-the-art remains to tackle sequence classification in a tabular manner with tree-based methods. Recent findings in deep learning for tabular data are now surpassing these classical methods by better handling the severe heterogeneity of data input features. Given the similar level of feature heterogeneity exhibited by ICU time-series and motivated by these findings, we explore these novel methods' impact on clinical sequence modeling tasks. By jointly using such advances in deep learning for tabular data, our primary objective is to underscore the importance of step-wise embeddings in time-series modeling, which remain unexplored in machine learning methods for clinical data. On a variety of clinically relevant tasks from two large-scale ICU datasets, MIMIC-III and HiRID, our work provides an exhaustive analysis of state-of-the-art methods for tabular time-series as time-step embedding models, showing overall performance improvement. In particular, we evidence the importance of feature grouping in clinical time-series, with significant performance gains when considering features within predefined semantic groups in the step-wise embedding module.

翻译：近期序列建模深度学习的进展尚未完全迁移至电子健康记录时间序列处理任务。具体而言，在重症监护室相关问题上，当前最先进方法仍是通过基于树的表格化方法处理序列分类。针对表格数据的深度学习最新成果通过更有效地处理输入特征的严重异质性，已开始超越这些传统方法。鉴于重症监护室时间序列呈现类似的异质性特征，受这些发现启发，我们探索了这些新方法对临床序列建模任务的影响。通过联合运用表格数据深度学习的进步，我们的主要目标是强调逐步嵌入在时间序列建模中的重要性——这在临床数据机器学习方法中尚未被探索。基于MIMIC-III和HiRID两个大规模重症监护室数据集的多种临床相关任务，本研究对作为时间步嵌入模型的表格时间序列最先进方法进行了全面分析，展示了整体性能的提升。我们特别证实了特征分组在临床时间序列中的重要性——当在逐步嵌入模块中考虑预定义语义组内的特征时，性能提升尤为显著。