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？