Studying the association of diabetes and healthcare cost on distributed data from the Maastricht Study and Statistics Netherlands using a privacy-preserving federated learning infrastructure.

Publisher: Elsevier Country of Publication: United States NLM ID: 100970413 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-0480 (Electronic) Linking ISSN: 15320464 NLM ISO Abbreviation: J Biomed Inform Subsets: MEDLINE

Publication: Orlando : Elsevier
Original Publication: San Diego, CA : Academic Press, c2001-

Diabetes Mellitus, Type 2*/therapy ; Prediabetic State*; Health Care Costs ; Humans ; Netherlands ; Privacy

The mining of personal data collected by multiple organizations remains challenging in the presence of technical barriers, privacy concerns, and legal and/or organizational restrictions. While a number of privacy-preserving and data mining frameworks have recently emerged, much remains to show their practical utility. In this study, we implement and utilize a secure infrastructure using data from Statistics Netherlands and the Maastricht Study to learn the association between Type 2 Diabetes Mellitus (T2DM) and healthcare expenses considering the impact of lifestyle, physical activities, and complications of T2DM. Through experiments using real-world distributed personal data, we present the feasibility and effectiveness of the secure infrastructure for practical use cases of linking and analyzing vertically partitioned data across multiple organizations. We discovered that individuals diagnosed with T2DM had significantly higher expenses than those with prediabetes, while participants with prediabetes spent more than those without T2DM in all the included healthcare categories to different degrees. We further discuss a joint effort from technical, ethical-legal, and domain-specific experts that is highly valued for applying such a secure infrastructure to real-life use cases to protect data privacy.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Keywords: Distributed data; Federated learning; Healthcare cost; Privacy-preserving data mining; Type 2 diabetes; Vertically partitioned data

Date Created: 20220905 Date Completed: 20221013 Latest Revision: 20221114

20240628

10.1016/j.jbi.2022.104194

36064113