The breadth, scale, and temporal granularity of modern electronic health records (EHR) systems offers great potential for estimating personalized and contextual patient health trajectories using sequential deep learning. However, learning useful representations of EHR data is challenging due to its high dimensionality, sparsity, multimodality, irregular and variable-specific recording frequency, and timestamp duplication when multiple measurements are recorded simultaneously. Although recent efforts to fuse structured EHR and unstructured clinical notes suggest the potential for more accurate prediction of clinical outcomes, less focus has been placed on EHR embedding approaches that directly address temporal EHR challenges by learning time-aware representations from multimodal patient time series. In this paper, we introduce a dynamic embedding and tokenization framework for precise representation of multimodal clinical time series that combines novel methods for encoding time and sequential position with temporal cross-attention. Our embedding and tokenization framework, when integrated into a multitask transformer classifier with sliding window attention, outperformed baseline approaches on the exemplar task of predicting the occurrence of nine postoperative complications of more than 120,000 major inpatient surgeries using multimodal data from three hospitals and two academic health centers in the United States.

我们提出了一个动态嵌入和分词框架，通过学习多模态临床时间序列的时间感知表示，并结合时间交叉注意力，将其整合到滑动窗口关注的多任务Transformer分类器中，在预测超过120,000例住院手术的九种术后并发症的示例任务中，使用来自美国三家医院和两个学术医疗中心的多模态数据，我们的嵌入和分词框架优于基线方法。

多模态电子健康记录的动态嵌入和标记化的时间交叉注意力