Machine learning has been widely used in healthcare applications to
approximate complex models, for clinical diagnosis, prognosis, and treatment.
As deep learning has the outstanding ability to extract information from time
series, its true capabilities on sparse, irregularly sampled, multivariate, and
imbalanced physiological data are not yet fully explored. In this paper, we
systematically examine the performance of machine learning models for the
clinical prediction task based on the EHR, especially physiological time
series. We choose Physionet 2019 challenge public dataset to predict Sepsis
outcomes in ICU units. Ten baseline machine learning models are compared,
including 3 deep learning methods and 7 non-deep learning methods, commonly
used in the clinical prediction domain. Nine evaluation metrics with specific
clinical implications are used to assess the performance of models. Besides, we
sub-sample training dataset sizes and use learning curve fit to investigate the
impact of the training dataset size on the performance of the machine learning
models. We also propose the general pre-processing method for the physiology
time-series data and use Dice Loss to deal with the dataset imbalanced problem.
The results show that deep learning indeed outperforms non-deep learning, but
with certain conditions: firstly, evaluating with some particular evaluation
metrics (AUROC, AUPRC, Sensitivity, and FNR), but not others; secondly, the
training dataset size is large enough (with an estimation of a magnitude of
thousands).

本文系统地检验了机器学习模型在临床预测任务方面的表现，尤其是在生理时间序列方面，并选择 Physionet 2019 挑战公共数据集来预测 ICU 单位脓毒症结局。比较了十个基准机器学习模型，包括三种深度学习方法和七种临床预测领域常用的非深度学习方法，使用了九个具有特定临床意义的评估指标来评估模型的表现。结果表明，深度学习的确优于非深度学习，但必须满足特定的条件才能达到更好的表现，这些条件包括使用特定的评估指标以及拥有足够规模的训练数据集。

深度学习在生理时间序列临床预测中是否真的优于非深度机器学习？

Does Deep Learning REALLY Outperform Non-deep Machine Learning for Clinical Prediction on Physiological Time Series?

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。