Data at Scale

Alberto Traverso; Frank J. W. M. Dankers; Leonard Wee; Sander M. J. van Kuijk

doi:10.1007/978-3-319-99713-1_2

Data at Scale

Review

In: Fundamentals of Clinical Data Science [Internet]. Cham (CH): Springer; 2019. Chapter 2.

2018 Dec 22.

Authors

Alberto Traverso¹, Frank J. W. M. Dankers^{1

2}, Leonard Wee¹, Sander M. J. van Kuijk³

Book Editors

Pieter Kubben¹, Michel Dumontier², Andre Dekker³

Affiliations

¹ Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
² Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
³ Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, Maastricht, The Netherlands

Book Affiliations

¹ Department of Neurosurgery, Maastricht University, Maastricht, Limburg, The Netherlands
² Institute of Data Science, Maastricht University, Maastricht, Limburg, The Netherlands
³ Maastro Clinic, Maastricht, Limburg, The Netherlands

Excerpt

Pre-requisites to better understand the chapter: basic knowledge of major sources of clinical data.

Logical position of the chapter with respect to the previous chapter: in the previous chapter, you have learned what the major sources of clinical data are. In this chapter, we will dive into the main characteristics of presented data sources. In particular, we will learn how to distinguish and classify data according to its scale.

Learning objectives: you will learn the major differences between data sources presented in previous chapters; how clinical data can be classified according to its scale. You will get familiar with the concept of ‘big’ clinical data; you will learn which are the major concerns limiting ‘big’ data exchange.

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Review