An HPC-Driven Data Science Platform to Speed-up Time Series Data Analysis of Patients with the Acute Respiratory Distress Syndrome

An increasing number of data science approaches that take advantage of deep learning in computational medicine and biomedical engineering require parallel and scalable algorithms using High-Performance Computing systems. Especially computational methods for analysing clinical datasets that consist of multivariate time series data can benefit from High-Performance Computing when applying computing-intensive Recurrent Neural Networks. This paper proposes a dynamic data science platform consisting of modular High-Performance Computing systems using accelerators for innovative Deep Learning algorithms to speed-up medical applications that take advantage of large biomedical scientific databases. This platform's core idea is to train a set of Deep Learning models very fast to easily combine and compare the different Deep Learning models' forecast (out-of-sample) performance to their past (in-sample) performance. Considering that this enables a better understanding of what Deep Learning models can be useful to apply to specific medical datasets, our case study leverages the three data science methods Gated Recurrent Units, one-dimensional convolutional layers, and their combination. We validate our approach using the open MIMIC-III database in a case study that assists in understanding, diagnosing, and treating a specific condition that affects Intensive Care Unit patients, namely Acute Respiratory Distress Syndrome.