Contribution to a conference proceedings

FZJ-2021-04353

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png An HPC-Driven Data Science Platform to Speed-up Time Series Data Analysis of Patients with the Acute Respiratory Distress Syndrome

Barakat, C. (Corresponding author)FZJ* ; Fritsch, S.FZJ* ; Riedel, M.FZJ* ; Brynjolfsson, S.Extern*

2021

This record in other databases:

Please use a persistent id in citations: http://hdl.handle.net/2128/29059  doi:10.23919/MIPRO52101.2021.9596840

Abstract: 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.

---

Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. SMITH - Medizininformatik-Konsortium - Beitrag Forschungszentrum Jülich (01ZZ1803M) (01ZZ1803M)
3. DEEP-EST - DEEP - Extreme Scale Technologies (754304) (754304)
4. EUROCC - National Competence Centres in the framework of EuroHPC (951732) (951732)

Appears in the scientific report 2021

---

Database coverage:

---