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.
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