Deep learning models (aka Deep Neural Networks) have revolutionized many
fields including computer vision, natural language processing, speech
recognition, and is being increasingly used in clinical healthcare
applications. However, few works exist which have benchmarked the performance
of the deep learning models with respect to the state-of-the-art machine
learning models and prognostic scoring systems on publicly available healthcare
datasets. In this paper, we present the benchmarking results for several
clinical prediction tasks such as mortality prediction, length of stay
prediction, and ICD-9 code group prediction using Deep Learning models,
ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA
scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III)
(v1.4) publicly available dataset, which includes all patients admitted to an
ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the
benchmarking tasks. Our results show that deep learning models consistently
outperform all the other approaches especially when the `raw' clinical time
series data is used as input features to the models.

本篇论文通过 MIMIC-III 数据集进行临床预测任务（如死亡率预测、住院时间预测以及 ICD-9 编码分组预测）的基准测试，结果表明，相较于基于机器学习模型和预测评分系统，深度学习模型在使用原始临床时间序列数据作为输入特征时有更好的表现。