The challenges of assembling, maintaining and making available large data sets of clinical data for research

Emily R. Jefferson; Emanuele Trucco

doi:10.1016/b978-0-08-102816-2.00021-6

The challenges of assembling, maintaining and making available large data sets of clinical data for research

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Clinical images are needed to train algorithms however obtaining such images can present a range of challenges not necessarily obvious to those with a pure computational science background. This chapter provides a summary of the data governance constraints around access to patient records and what controls should be considered for secure access to anonymized health data.

Original language	English
Title of host publication	Computational Retinal Image Analysis
Subtitle of host publication	Tools, Applications and Perspectives
Editors	Emanuele Trucco, Tom MacGillivray, Yanwu Xu
Publisher	Academic Press
Chapter	20
Pages	429-444
Number of pages	16
ISBN (Print)	9780081028162
DOIs	https://doi.org/10.1016/b978-0-08-102816-2.00021-6
Publication status	Published - 2019

Publication series

Name	The Elsevier and MICCAI Society Book Series

Keywords

Clinical images
Data governance
Safe Havens
Trusted research environments
GDPR
Public trust

Access to Document

10.1016/b978-0-08-102816-2.00021-6Licence: Elsevier User Licence

MICA: InterdisciPlInary Collaboration for EfficienT and Effective Use of Clinical Images in Big Data Health Care RESearch: PICTURES (Programme Grant) (Joint with Universities of Edinburgh and Abertay)
Doney, A., Jefferson, E., Palmer, C., Steele, D., Trucco, M. & Wang, H.
Medical Research Council
1/08/19 → 31/07/24
Project: Research

Health Informatics Centre
Health and Clinical Services
Facility/equipment: Facility

Cite this

Jefferson, E. R., & Trucco, E. (2019). The challenges of assembling, maintaining and making available large data sets of clinical data for research. In E. Trucco, T. MacGillivray, & Y. Xu (Eds.), Computational Retinal Image Analysis: Tools, Applications and Perspectives (pp. 429-444). (The Elsevier and MICCAI Society Book Series). Academic Press. https://doi.org/10.1016/b978-0-08-102816-2.00021-6

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title = "The challenges of assembling, maintaining and making available large data sets of clinical data for research",

abstract = "Clinical images are needed to train algorithms however obtaining such images can present a range of challenges not necessarily obvious to those with a pure computational science background. This chapter provides a summary of the data governance constraints around access to patient records and what controls should be considered for secure access to anonymized health data.",

keywords = "Clinical images, Data governance, Safe Havens, Trusted research environments, GDPR, Public trust",

author = "Jefferson, {Emily R.} and Emanuele Trucco",

note = "Funding: MRC grant MR/S010351/1",

year = "2019",

doi = "10.1016/b978-0-08-102816-2.00021-6",

language = "English",

isbn = "9780081028162",

series = "The Elsevier and MICCAI Society Book Series",

publisher = "Academic Press",

pages = "429--444",

editor = "Emanuele Trucco and Tom MacGillivray and Yanwu Xu",

booktitle = "Computational Retinal Image Analysis",

}

Jefferson, ER & Trucco, E 2019, The challenges of assembling, maintaining and making available large data sets of clinical data for research. in E Trucco, T MacGillivray & Y Xu (eds), Computational Retinal Image Analysis: Tools, Applications and Perspectives. The Elsevier and MICCAI Society Book Series, Academic Press, pp. 429-444. https://doi.org/10.1016/b978-0-08-102816-2.00021-6

The challenges of assembling, maintaining and making available large data sets of clinical data for research. / Jefferson, Emily R.; Trucco, Emanuele.

Computational Retinal Image Analysis: Tools, Applications and Perspectives. ed. / Emanuele Trucco; Tom MacGillivray; Yanwu Xu. Academic Press, 2019. p. 429-444 (The Elsevier and MICCAI Society Book Series).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - The challenges of assembling, maintaining and making available large data sets of clinical data for research

AU - Jefferson, Emily R.

AU - Trucco, Emanuele

N1 - Funding: MRC grant MR/S010351/1

PY - 2019

Y1 - 2019

N2 - Clinical images are needed to train algorithms however obtaining such images can present a range of challenges not necessarily obvious to those with a pure computational science background. This chapter provides a summary of the data governance constraints around access to patient records and what controls should be considered for secure access to anonymized health data.

AB - Clinical images are needed to train algorithms however obtaining such images can present a range of challenges not necessarily obvious to those with a pure computational science background. This chapter provides a summary of the data governance constraints around access to patient records and what controls should be considered for secure access to anonymized health data.

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KW - Data governance

KW - Safe Havens

KW - Trusted research environments

KW - GDPR

KW - Public trust

U2 - 10.1016/b978-0-08-102816-2.00021-6

DO - 10.1016/b978-0-08-102816-2.00021-6

M3 - Chapter

SN - 9780081028162

T3 - The Elsevier and MICCAI Society Book Series

SP - 429

EP - 444

BT - Computational Retinal Image Analysis

A2 - Trucco, Emanuele

A2 - MacGillivray, Tom

A2 - Xu, Yanwu

PB - Academic Press

ER -

Jefferson ER , Trucco E. The challenges of assembling, maintaining and making available large data sets of clinical data for research. In Trucco E, MacGillivray T, Xu Y, editors, Computational Retinal Image Analysis: Tools, Applications and Perspectives. Academic Press. 2019. p. 429-444. (The Elsevier and MICCAI Society Book Series). Epub 2019 Nov 22. doi: 10.1016/b978-0-08-102816-2.00021-6

The challenges of assembling, maintaining and making available large data sets of clinical data for research

Abstract

Publication series

Keywords

Access to Document

Fingerprint

Projects

MICA: InterdisciPlInary Collaboration for EfficienT and Effective Use of Clinical Images in Big Data Health Care RESearch: PICTURES (Programme Grant) (Joint with Universities of Edinburgh and Abertay)

Equipment

Health Informatics Centre

Cite this