Personal Health Train on FHIR: A Privacy Preserving Federated Approach for Analyzing FAIR Data in Healthcare

Ananya Choudhury; Johan van Soest; Stuti Nayak; Andre Dekker

doi:10.1007/978-981-15-6315-7_7

Personal Health Train on FHIR: A Privacy Preserving Federated Approach for Analyzing FAIR Data in Healthcare

Ananya Choudhury^*, Johan van Soest, Stuti Nayak, Andre Dekker

^*Corresponding author for this work

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

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Abstract

Big data and machine learning applications focus on retrieving data on a central location for analysis. However, healthcare data can be sensitive in nature and as such difficult to share and make use for secondary purposes. Healthcare vendors are restricted to share data without proper consent from the patient. There is a rising awareness among individual patients as well regarding sharing their personal information due to ethical, legal and societal problems. The current data-sharing platforms in healthcare do not sufficiently handle these issues. The rationale of the Personal Health Train (PHT) approach shifts the focus from sharing data to sharing processing/analysis applications and their respective results. A prerequisite of the PHT-infrastructure is that the data is FAIR (findable, accessible, interoperable, reusable). The aim of the paper is to describe a methodology of finding the number of patients diagnosed with hypertension and calculate cohort statistics in a privacy-preserving federated manner. The whole process completes without individual patient data leaving the source. For this, we rely on the Fast Healthcare Interoperability Resources (FHIR) standard.

Original language	English
Title of host publication	Machine Learning, Image Processing, Network Security and Data Sciences - 2nd International Conference, MIND 2020, Proceedings
Editors	Arup Bhattacharjee, Samir Kr. Borgohain, Badal Soni, Gyanendra Verma, Xiao-Zhi Gao
Publisher	Springer
Pages	85-95
Number of pages	11
Volume	1240 CCIS
ISBN (Print)	9789811563140
DOIs	https://doi.org/10.1007/978-981-15-6315-7_7
Publication status	Published - 1 Jan 2020
Event	2nd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences - Silchar, India Duration: 30 Jul 2020 → 31 Jul 2020 Conference number: 2

Publication series

Series	Communications in Computer and Information Science
Volume	1240 CCIS
ISSN	1865-0929

Conference

Conference	2nd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences
Abbreviated title	MIND 2020
Country/Territory	India
City	Silchar
Period	30/07/20 → 31/07/20

Keywords

FAIR
FHIR
Personal health train

Access to Document

10.1007/978-981-15-6315-7_7

Full TextFinal published version, 1.26 MBLicence: Taverne

Cite this

Choudhury, A., van Soest, J., Nayak, S., & Dekker, A. (2020). Personal Health Train on FHIR: A Privacy Preserving Federated Approach for Analyzing FAIR Data in Healthcare. In A. Bhattacharjee, S. K. Borgohain, B. Soni, G. Verma, & X.-Z. Gao (Eds.), Machine Learning, Image Processing, Network Security and Data Sciences - 2nd International Conference, MIND 2020, Proceedings (Vol. 1240 CCIS, pp. 85-95). Springer. https://doi.org/10.1007/978-981-15-6315-7_7

Choudhury, Ananya ; van Soest, Johan ; Nayak, Stuti et al. / Personal Health Train on FHIR : A Privacy Preserving Federated Approach for Analyzing FAIR Data in Healthcare. Machine Learning, Image Processing, Network Security and Data Sciences - 2nd International Conference, MIND 2020, Proceedings. editor / Arup Bhattacharjee ; Samir Kr. Borgohain ; Badal Soni ; Gyanendra Verma ; Xiao-Zhi Gao. Vol. 1240 CCIS Springer, 2020. pp. 85-95 (Communications in Computer and Information Science, Vol. 1240 CCIS).

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title = "Personal Health Train on FHIR: A Privacy Preserving Federated Approach for Analyzing FAIR Data in Healthcare",

abstract = "Big data and machine learning applications focus on retrieving data on a central location for analysis. However, healthcare data can be sensitive in nature and as such difficult to share and make use for secondary purposes. Healthcare vendors are restricted to share data without proper consent from the patient. There is a rising awareness among individual patients as well regarding sharing their personal information due to ethical, legal and societal problems. The current data-sharing platforms in healthcare do not sufficiently handle these issues. The rationale of the Personal Health Train (PHT) approach shifts the focus from sharing data to sharing processing/analysis applications and their respective results. A prerequisite of the PHT-infrastructure is that the data is FAIR (findable, accessible, interoperable, reusable). The aim of the paper is to describe a methodology of finding the number of patients diagnosed with hypertension and calculate cohort statistics in a privacy-preserving federated manner. The whole process completes without individual patient data leaving the source. For this, we rely on the Fast Healthcare Interoperability Resources (FHIR) standard.",

keywords = "FAIR, FHIR, Personal health train",

author = "Ananya Choudhury and {van Soest}, Johan and Stuti Nayak and Andre Dekker",

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Choudhury, A , van Soest, J, Nayak, S & Dekker, A 2020, Personal Health Train on FHIR: A Privacy Preserving Federated Approach for Analyzing FAIR Data in Healthcare. in A Bhattacharjee, SK Borgohain, B Soni, G Verma & X-Z Gao (eds), Machine Learning, Image Processing, Network Security and Data Sciences - 2nd International Conference, MIND 2020, Proceedings. vol. 1240 CCIS, Springer, Communications in Computer and Information Science, vol. 1240 CCIS, pp. 85-95, 2nd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, Silchar, India, 30/07/20. https://doi.org/10.1007/978-981-15-6315-7_7

Personal Health Train on FHIR: A Privacy Preserving Federated Approach for Analyzing FAIR Data in Healthcare. / Choudhury, Ananya ; van Soest, Johan; Nayak, Stuti et al.
Machine Learning, Image Processing, Network Security and Data Sciences - 2nd International Conference, MIND 2020, Proceedings. ed. / Arup Bhattacharjee; Samir Kr. Borgohain; Badal Soni; Gyanendra Verma; Xiao-Zhi Gao. Vol. 1240 CCIS Springer, 2020. p. 85-95 (Communications in Computer and Information Science, Vol. 1240 CCIS).

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

TY - GEN

T1 - Personal Health Train on FHIR

T2 - 2nd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences

AU - Choudhury, Ananya

AU - van Soest, Johan

AU - Nayak, Stuti

AU - Dekker, Andre

N1 - Conference code: 2

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Big data and machine learning applications focus on retrieving data on a central location for analysis. However, healthcare data can be sensitive in nature and as such difficult to share and make use for secondary purposes. Healthcare vendors are restricted to share data without proper consent from the patient. There is a rising awareness among individual patients as well regarding sharing their personal information due to ethical, legal and societal problems. The current data-sharing platforms in healthcare do not sufficiently handle these issues. The rationale of the Personal Health Train (PHT) approach shifts the focus from sharing data to sharing processing/analysis applications and their respective results. A prerequisite of the PHT-infrastructure is that the data is FAIR (findable, accessible, interoperable, reusable). The aim of the paper is to describe a methodology of finding the number of patients diagnosed with hypertension and calculate cohort statistics in a privacy-preserving federated manner. The whole process completes without individual patient data leaving the source. For this, we rely on the Fast Healthcare Interoperability Resources (FHIR) standard.

AB - Big data and machine learning applications focus on retrieving data on a central location for analysis. However, healthcare data can be sensitive in nature and as such difficult to share and make use for secondary purposes. Healthcare vendors are restricted to share data without proper consent from the patient. There is a rising awareness among individual patients as well regarding sharing their personal information due to ethical, legal and societal problems. The current data-sharing platforms in healthcare do not sufficiently handle these issues. The rationale of the Personal Health Train (PHT) approach shifts the focus from sharing data to sharing processing/analysis applications and their respective results. A prerequisite of the PHT-infrastructure is that the data is FAIR (findable, accessible, interoperable, reusable). The aim of the paper is to describe a methodology of finding the number of patients diagnosed with hypertension and calculate cohort statistics in a privacy-preserving federated manner. The whole process completes without individual patient data leaving the source. For this, we rely on the Fast Healthcare Interoperability Resources (FHIR) standard.

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U2 - 10.1007/978-981-15-6315-7_7

DO - 10.1007/978-981-15-6315-7_7

M3 - Conference article in proceeding

SN - 9789811563140

VL - 1240 CCIS

T3 - Communications in Computer and Information Science

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BT - Machine Learning, Image Processing, Network Security and Data Sciences - 2nd International Conference, MIND 2020, Proceedings

A2 - Bhattacharjee, Arup

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A2 - Verma, Gyanendra

A2 - Gao, Xiao-Zhi

PB - Springer

Y2 - 30 July 2020 through 31 July 2020

ER -

Choudhury A , van Soest J, Nayak S, Dekker A. Personal Health Train on FHIR: A Privacy Preserving Federated Approach for Analyzing FAIR Data in Healthcare. In Bhattacharjee A, Borgohain SK, Soni B, Verma G, Gao XZ, editors, Machine Learning, Image Processing, Network Security and Data Sciences - 2nd International Conference, MIND 2020, Proceedings. Vol. 1240 CCIS. Springer. 2020. p. 85-95. (Communications in Computer and Information Science, Vol. 1240 CCIS). doi: 10.1007/978-981-15-6315-7_7