Transformation and integration of heterogeneous health data in a privacy-preserving distributed learning infrastructure

Chang Sun; Vincent Emonet; Johan van Soest; Annemarie Koster; Andre Dekker; Michel Dumontier

Transformation and integration of heterogeneous health data in a privacy-preserving distributed learning infrastructure

Chang Sun, Vincent Emonet, Johan van Soest, Annemarie Koster, Andre Dekker, Michel Dumontier

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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Abstract

Problem statement: A growing volume and variety of personal health data are being collected by different entities, such as healthcare providers, insurance companies, and wearable device manufacturers. Combining heterogeneous health data offers unprecedented opportunities to augment our understanding of human health and disease. However, a major challenge to research lies in the difficulty of accessing and analyzing health data that are dispersed in their format (e.g. CSV, XML), sources (e.g., medical records, laboratory data), representation (unstructured, structured), and governance (e.g., data collection and maintenance)[2]. Such considerations are crucial when we link and use personal health data across multiple legal entities with different data governance and privacy concerns.

Original language	English
Title of host publication	Semantic Web Applications and Tools for Health Care and Life Sciences
Pages	141-142
Number of pages	2
Volume	2849
Publication status	Published - 1 Jan 2019
Event	12th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences - Edinburgh, United Kingdom Duration: 10 Dec 2019 → 11 Dec 2019 Conference number: 12

Publication series

Series	CEUR Workshop Proceedings
ISSN	1613-0073

Conference

Conference	12th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences
Abbreviated title	SWAT4HCLS 2019
Country/Territory	United Kingdom
City	Edinburgh
Period	10/12/19 → 11/12/19

Access to Document

Full TextFinal published version, 257 KBLicence: Taverne

https://ceur-ws.org/Vol-2849/paper-21.pdf

Cite this

@inproceedings{fcf2c5c52b3c4c4eacea1e32f8093756,

title = "Transformation and integration of heterogeneous health data in a privacy-preserving distributed learning infrastructure",

abstract = "Problem statement: A growing volume and variety of personal health data are being collected by different entities, such as healthcare providers, insurance companies, and wearable device manufacturers. Combining heterogeneous health data offers unprecedented opportunities to augment our understanding of human health and disease. However, a major challenge to research lies in the difficulty of accessing and analyzing health data that are dispersed in their format (e.g. CSV, XML), sources (e.g., medical records, laboratory data), representation (unstructured, structured), and governance (e.g., data collection and maintenance)[2]. Such considerations are crucial when we link and use personal health data across multiple legal entities with different data governance and privacy concerns.",

author = "Chang Sun and Vincent Emonet and {van Soest}, Johan and Annemarie Koster and Andre Dekker and Michel Dumontier",

note = "Publisher Copyright: Copyright {\textcopyright} 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).; 12th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2019 ; Conference date: 10-12-2019 Through 11-12-2019",

year = "2019",

month = jan,

day = "1",

language = "English",

volume = "2849",

series = "CEUR Workshop Proceedings",

publisher = "Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V",

pages = "141--142",

booktitle = "Semantic Web Applications and Tools for Health Care and Life Sciences",

}

Sun, C , Emonet, V , van Soest, J , Koster, A , Dekker, A & Dumontier, M 2019, Transformation and integration of heterogeneous health data in a privacy-preserving distributed learning infrastructure. in Semantic Web Applications and Tools for Health Care and Life Sciences. vol. 2849, CEUR Workshop Proceedings, pp. 141-142, 12th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, Edinburgh, United Kingdom, 10/12/19. <https://ceur-ws.org/Vol-2849/paper-21.pdf>

Transformation and integration of heterogeneous health data in a privacy-preserving distributed learning infrastructure. / Sun, Chang ; Emonet, Vincent ; van Soest, Johan et al.
Semantic Web Applications and Tools for Health Care and Life Sciences. Vol. 2849 2019. p. 141-142 (CEUR Workshop Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

TY - GEN

T1 - Transformation and integration of heterogeneous health data in a privacy-preserving distributed learning infrastructure

AU - Sun, Chang

AU - Emonet, Vincent

AU - van Soest, Johan

AU - Koster, Annemarie

AU - Dekker, Andre

AU - Dumontier, Michel

N1 - Conference code: 12

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Problem statement: A growing volume and variety of personal health data are being collected by different entities, such as healthcare providers, insurance companies, and wearable device manufacturers. Combining heterogeneous health data offers unprecedented opportunities to augment our understanding of human health and disease. However, a major challenge to research lies in the difficulty of accessing and analyzing health data that are dispersed in their format (e.g. CSV, XML), sources (e.g., medical records, laboratory data), representation (unstructured, structured), and governance (e.g., data collection and maintenance)[2]. Such considerations are crucial when we link and use personal health data across multiple legal entities with different data governance and privacy concerns.

AB - Problem statement: A growing volume and variety of personal health data are being collected by different entities, such as healthcare providers, insurance companies, and wearable device manufacturers. Combining heterogeneous health data offers unprecedented opportunities to augment our understanding of human health and disease. However, a major challenge to research lies in the difficulty of accessing and analyzing health data that are dispersed in their format (e.g. CSV, XML), sources (e.g., medical records, laboratory data), representation (unstructured, structured), and governance (e.g., data collection and maintenance)[2]. Such considerations are crucial when we link and use personal health data across multiple legal entities with different data governance and privacy concerns.

M3 - Conference article in proceeding

VL - 2849

T3 - CEUR Workshop Proceedings

SP - 141

EP - 142

BT - Semantic Web Applications and Tools for Health Care and Life Sciences

T2 - 12th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences

Y2 - 10 December 2019 through 11 December 2019

ER -