Extracting Information and Identifying Data Structures in Pharmacological Big Data using Gawk

Reinhard Schuster, Timo Emcke, Martin Schuster, Thomas Ostermann

2018

Abstract

In the past decades, health related data saw an increase in capture, storage and analysis of very large data sets, referred to as big data. In health services research, big data analysis is used by health policy makers to identify and forecast potential risk factors, causalities or hazards. However, big data due to its volume, its high variety of data types and its high velocity of data flow is often difficult to process. Moreover, big data also shows a high complexity of data structures. In such cases, gawk programming language is a powerful tool to work with by using structural elements such as associative arrays. This article aims at describing the use and interaction of gawk to extract information and identify data structures in pharmacological big data sets. In particular we aimed at showing its strength in combining it with Mathematica based on two examples of the prescription data for potentially inadequate medications for elderly patients and the creation of networks of physicians and drug related neighbourhood relations.

Paper Citation

in Harvard Style

Schuster R., Emcke T., Schuster M. and Ostermann T. (2018). Extracting Information and Identifying Data Structures in Pharmacological Big Data using Gawk. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF; ISBN 978-989-758-281-3, SciTePress, pages 387-394. DOI: 10.5220/0006571603870394

in Bibtex Style

@conference{healthinf18,
author={Reinhard Schuster and Timo Emcke and Martin Schuster and Thomas Ostermann},
title={Extracting Information and Identifying Data Structures in Pharmacological Big Data using Gawk},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF},
year={2018},
pages={387-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006571603870394},
isbn={978-989-758-281-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF
TI - Extracting Information and Identifying Data Structures in Pharmacological Big Data using Gawk
SN - 978-989-758-281-3
AU - Schuster R.
AU - Emcke T.
AU - Schuster M.
AU - Ostermann T.
PY - 2018
SP - 387
EP - 394
DO - 10.5220/0006571603870394
PB - SciTePress