In research areas with scarce data, representation learning plays a
significant role. This work aims to enhance representation learning for
clinical time series by deriving universal embeddings for clinical features,
such as heart rate and blood pressure. We use self-supervised training
paradigms for language models to learn high-quality clinical feature
embeddings, achieving a finer granularity than existing time-step and
patient-level representation learning. We visualize the learnt embeddings via
unsupervised dimension reduction techniques and observe a high degree of
consistency with prior clinical knowledge. We also evaluate the model
performance on the MIMIC-III benchmark and demonstrate the effectiveness of
using clinical feature embeddings. We publish our code online for replication.

通过自我监督训练模型来学习高质量的临床特征嵌入表示，以增强临床时间序列的表示学习能力，并通过无监督降维技术可视化学习到的嵌入表示，发现其与先前的临床知识具有高度一致性；同时在 MIMIC-III 基准数据上评估模型性能，并证明了使用临床特征嵌入的有效性。