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Authors: Reinhard Schuster 1 ; Timo Emcke 2 ; Martin Schuster 3 and Thomas Ostermann 4

Affiliations: 1 Statutory Health Insurance of North Germany, Germany ; 2 Association of Statutory Health Insurance Physicians, Germany ; 3 Lübeck University and Institut of Theorectical Informatics, Germany ; 4 Witten/Herdecke University, Germany

Keyword(s): Big Data, Gawk, Mathematica, Associative Arrays, Priscus Drugs, Drug Related Neighbourhood Relations.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Data Mining ; Databases and Datawarehousing ; Databases and Information Systems Integration ; Electronic Health Records and Standards ; Enterprise Information Systems ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

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 physi cians and drug related neighbourhood relations. (More)

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Paper citation in several formats:
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) - HEALTHINF; ISBN 978-989-758-281-3; ISSN 2184-4305, SciTePress, pages 387-394. DOI: 10.5220/0006571603870394

@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) - HEALTHINF},
year={2018},
pages={387-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006571603870394},
isbn={978-989-758-281-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF
TI - Extracting Information and Identifying Data Structures in Pharmacological Big Data using Gawk
SN - 978-989-758-281-3
IS - 2184-4305
AU - Schuster, R.
AU - Emcke, T.
AU - Schuster, M.
AU - Ostermann, T.
PY - 2018
SP - 387
EP - 394
DO - 10.5220/0006571603870394
PB - SciTePress