Analyze Decentralized Personal Health Data using Solid, Digital Consent, and Federated Learning

Chang Sun; Johan van Soest; Michel Dumontier

Analyze Decentralized Personal Health Data using Solid, Digital Consent, and Federated Learning

Chang Sun^*, Johan van Soest, Michel Dumontier

^*Corresponding author for this work

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

78 Downloads (Pure)

Abstract

TIDAL is a Solid (SOcial Linked Data)-based, citizen-centric data platform that facilitates interactions by citizens and researchers for health research. In this demonstration, we will show how TIDAL 1) can store personal data in Solid pods as RDF with well-known health-related vocabularies (e.g., SNOMED CT), 2) controls access to query fine-grained subsets of personal data, 3) enables researchers to post a human- and machine-readable digital consent using Data Privacy Vocabulary, and 4) uses federated learning to analyze personal health data from multiple individuals using a Personal Health Train framework. TIDAL offers a new data paradigm of sharing and using personal data for research and ultimately increase the availability of personal data for societal relevant uses.

Original language	English
Title of host publication	Semantic Web Applications and Tools for Health Care and Life Sciences 2023
Pages	169-170
Number of pages	2
Volume	3415
Publication status	Published - 1 Jan 2023
Event	14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences - Basel, Switzerland Duration: 13 Feb 2023 → 16 Feb 2023 Conference number: 14

Publication series

Series	CEUR Workshop Proceedings
ISSN	1613-0073

Conference

Conference	14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences
Abbreviated title	SWAT4HCLS 2023
Country/Territory	Switzerland
City	Basel
Period	13/02/23 → 16/02/23

Access to Document

Full TextFinal published version, 3.08 MBLicence: Taverne

https://bbuelens.solidcommunity.net/public/html/SolidSymposium2023_ChangSun.pdf

Cite this

@inproceedings{c0f459dd029344a0a1749e1ced9d240e,

title = "Analyze Decentralized Personal Health Data using Solid, Digital Consent, and Federated Learning",

abstract = "TIDAL is a Solid (SOcial Linked Data)-based, citizen-centric data platform that facilitates interactions by citizens and researchers for health research. In this demonstration, we will show how TIDAL 1) can store personal data in Solid pods as RDF with well-known health-related vocabularies (e.g., SNOMED CT), 2) controls access to query fine-grained subsets of personal data, 3) enables researchers to post a human- and machine-readable digital consent using Data Privacy Vocabulary, and 4) uses federated learning to analyze personal health data from multiple individuals using a Personal Health Train framework. TIDAL offers a new data paradigm of sharing and using personal data for research and ultimately increase the availability of personal data for societal relevant uses.",

author = "Chang Sun and {van Soest}, Johan and Michel Dumontier",

note = "Publisher Copyright: {\textcopyright} 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).; 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2023 ; Conference date: 13-02-2023 Through 16-02-2023",

year = "2023",

month = jan,

day = "1",

language = "English",

volume = "3415",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS",

pages = "169--170",

booktitle = "Semantic Web Applications and Tools for Health Care and Life Sciences 2023",

}

Sun, C , van Soest, J & Dumontier, M 2023, Analyze Decentralized Personal Health Data using Solid, Digital Consent, and Federated Learning. in Semantic Web Applications and Tools for Health Care and Life Sciences 2023. vol. 3415, CEUR Workshop Proceedings, pp. 169-170, 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, Basel, Switzerland, 13/02/23. <https://bbuelens.solidcommunity.net/public/html/SolidSymposium2023_ChangSun.pdf>

Analyze Decentralized Personal Health Data using Solid, Digital Consent, and Federated Learning. / Sun, Chang ; van Soest, Johan ; Dumontier, Michel.
Semantic Web Applications and Tools for Health Care and Life Sciences 2023. Vol. 3415 2023. p. 169-170 (CEUR Workshop Proceedings).

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

TY - GEN

T1 - Analyze Decentralized Personal Health Data using Solid, Digital Consent, and Federated Learning

AU - Sun, Chang

AU - van Soest, Johan

AU - Dumontier, Michel

N1 - Conference code: 14

PY - 2023/1/1

Y1 - 2023/1/1

N2 - TIDAL is a Solid (SOcial Linked Data)-based, citizen-centric data platform that facilitates interactions by citizens and researchers for health research. In this demonstration, we will show how TIDAL 1) can store personal data in Solid pods as RDF with well-known health-related vocabularies (e.g., SNOMED CT), 2) controls access to query fine-grained subsets of personal data, 3) enables researchers to post a human- and machine-readable digital consent using Data Privacy Vocabulary, and 4) uses federated learning to analyze personal health data from multiple individuals using a Personal Health Train framework. TIDAL offers a new data paradigm of sharing and using personal data for research and ultimately increase the availability of personal data for societal relevant uses.

AB - TIDAL is a Solid (SOcial Linked Data)-based, citizen-centric data platform that facilitates interactions by citizens and researchers for health research. In this demonstration, we will show how TIDAL 1) can store personal data in Solid pods as RDF with well-known health-related vocabularies (e.g., SNOMED CT), 2) controls access to query fine-grained subsets of personal data, 3) enables researchers to post a human- and machine-readable digital consent using Data Privacy Vocabulary, and 4) uses federated learning to analyze personal health data from multiple individuals using a Personal Health Train framework. TIDAL offers a new data paradigm of sharing and using personal data for research and ultimately increase the availability of personal data for societal relevant uses.

M3 - Conference article in proceeding

VL - 3415

T3 - CEUR Workshop Proceedings

SP - 169

EP - 170

BT - Semantic Web Applications and Tools for Health Care and Life Sciences 2023

T2 - 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences

Y2 - 13 February 2023 through 16 February 2023

ER -

Analyze Decentralized Personal Health Data using Solid, Digital Consent, and Federated Learning

Abstract

Publication series

Conference

Access to Document

Fingerprint

Cite this