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Deep learning models (aka Deep Neural Networks) have revolutionized manyfields including computer vision, natural language processing, speechrecognition, and is being increasingly used in clinical healthcareapplications. However, few works exist which have benchmarked the performanceof the deep learning models with respect to the state-of-the-art machinelearning models and prognostic scoring systems on publicly available healthcaredatasets. In this paper, we present the benchmarking results for severalclinical prediction tasks such as mortality prediction, length of stayprediction, and ICD-9 code group prediction using Deep Learning models,ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFAscores. We used the Medical Information Mart for Intensive Care III (MIMIC-III)(v1.4) publicly available dataset, which includes all patients admitted to anICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for thebenchmarking tasks. Our results show that deep learning models consistentlyoutperform all the other approaches especially when the `raw' clinical timeseries data is used as input features to the models.

Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets