Irregular sampling of time series in electronic health records (EHRs) is one
of the main challenges for developing machine learning models. Additionally,
the pattern of missing data in certain clinical variables is not at random but
depends on the decisions of clinicians and the state of the patient. Point
process is a mathematical framework for analyzing event sequence data that is
consistent with irregular sampling patterns. Our model, TEE4EHR, is a
transformer event encoder (TEE) with point process loss that encodes the
pattern of laboratory tests in EHRs. The utility of our TEE has been
investigated in a variety of benchmark event sequence datasets. Additionally,
we conduct experiments on two real-world EHR databases to provide a more
comprehensive evaluation of our model. Firstly, in a self-supervised learning
approach, the TEE is jointly learned with an existing attention-based deep
neural network which gives superior performance in negative log-likelihood and
future event prediction. Besides, we propose an algorithm for aggregating
attention weights that can reveal the interaction between the events. Secondly,
we transfer and freeze the learned TEE to the downstream task for the outcome
prediction, where it outperforms state-of-the-art models for handling
irregularly sampled time series. Furthermore, our results demonstrate that our
approach can improve representation learning in EHRs and can be useful for
clinical prediction tasks.

TEE4EHR 是一个具有点过程损失的 Transformer 事件编码器模型，用于分析电子健康记录中实验室检验模式，通过在各种基准的事件序列数据集上验证其实用性，并在两个真实的电子健康记录数据库上进行实验证明了其能够改善电子健康记录中的表示学习并在临床预测任务中提供帮助。

TEE4EHR：电子健康记录中更好的表示学习的 Transformer 事件编码器

TEE4EHR: Transformer Event Encoder for Better Representation Learning in  Electronic Health Records

Irregular sampling occurs in many time series modeling applications where it
presents a significant challenge to standard deep learning models. This work is
motivated by the analysis of physiological time series data in electronic
health records, which are sparse, irregularly sampled, and multivariate. In
this paper, we propose a new deep learning framework for this setting that we
call Multi-Time Attention Networks. Multi-Time Attention Networks learn an
embedding of continuous-time values and use an attention mechanism to produce a
fixed-length representation of a time series containing a variable number of
observations. We investigate the performance of this framework on interpolation
and classification tasks using multiple datasets. Our results show that the
proposed approach performs as well or better than a range of baseline and
recently proposed models while offering significantly faster training times
than current state-of-the-art methods.

本篇论文针对电子健康档案中所涉及的稀疏、不规则采样、多变量的生理时序数据提出一种名为 Multi-Time Attention Networks 的多时间注意网络的深度学习框架，并使用多个数据集进行内插和分类任务的性能研究，结果表明该框架的性能优于许多基线和最近提出的模型，同时提供了比当前最先进方法更快的训练速度。

不规则采样时间序列的多时间注意力网络

Multi-Time Attention Networks for Irregularly Sampled Time Series

In this paper we propose a data augmentation method for time series with
irregular sampling, Time-Conditional Generative Adversarial Network (T-CGAN).
Our approach is based on Conditional Generative Adversarial Networks (CGAN),
where the generative step is implemented by a deconvolutional NN and the
discriminative step by a convolutional NN. Both the generator and the
discriminator are conditioned on the sampling timestamps, to learn the hidden
relationship between data and timestamps, and consequently to generate new time
series. We evaluate our model with synthetic and real-world datasets. For the
synthetic data, we compare the performance of a classifier trained with
T-CGAN-generated data, against the performance of the same classifier trained
on the original data. Results show that classifiers trained on T-CGAN-generated
data perform the same as classifiers trained on real data, even with very short
time series and small training sets. For the real world datasets, we compare
our method with other techniques of data augmentation for time series, such as
time slicing and time warping, over a classification problem with unbalanced
datasets. Results show that our method always outperforms the other approaches,
both in case of regularly sampled and irregularly sampled time series. We
achieve particularly good performance in case with a small training set and
short, noisy, irregularly-sampled time series.

本文提出了一种数据增强方法 Time-Conditional Generative Adversarial Network (T-CGAN)，通过条件生成对抗网络生成新的时间序列，对比其他数据增强技术，得到了更好的分类效果。