TY - JOUR
T1 - Privacy-preserving dataset combination and Lasso regression for healthcare predictions
AU - van Egmond, Marie Beth
AU - Spini, Gabriele
AU - van der Galien, Onno
AU - IJpma, Arne
AU - Veugen, Thijs
AU - Kraaij, Wessel
AU - Sangers, Alex
AU - Rooijakkers, Thomas
AU - Langenkamp, Peter
AU - Kamphorst, Bart
AU - van de L’Isle, Natasja
AU - Kooij-Janic, Milena
N1 - Funding Information:
The BigMedilytics project has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 780495.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/9/16
Y1 - 2021/9/16
N2 - Background: Recent developments in machine learning have shown its potential impact for clinical use such as risk prediction, prognosis, and treatment selection. However, relevant data are often scattered across different stakeholders and their use is regulated, e.g. by GDPR or HIPAA. As a concrete use-case, hospital Erasmus MC and health insurance company Achmea have data on individuals in the city of Rotterdam, which would in theory enable them to train a regression model in order to identify high-impact lifestyle factors for heart failure. However, privacy and confidentiality concerns make it unfeasible to exchange these data. Methods: This article describes a solution where vertically-partitioned synthetic data of Achmea and of Erasmus MC are combined using Secure Multi-Party Computation. First, a secure inner join protocol takes place to securely determine the identifiers of the patients that are represented in both datasets. Then, a secure Lasso Regression model is trained on the securely combined data. The involved parties thus obtain the prediction model but no further information on the input data of the other parties. Results: We implement our secure solution and describe its performance and scalability: we can train a prediction model on two datasets with 5000 records each and a total of 30 features in less than one hour, with a minimal difference from the results of standard (non-secure) methods. Conclusions: This article shows that it is possible to combine datasets and train a Lasso regression model on this combination in a secure way. Such a solution thus further expands the potential of privacy-preserving data analysis in the medical domain.
AB - Background: Recent developments in machine learning have shown its potential impact for clinical use such as risk prediction, prognosis, and treatment selection. However, relevant data are often scattered across different stakeholders and their use is regulated, e.g. by GDPR or HIPAA. As a concrete use-case, hospital Erasmus MC and health insurance company Achmea have data on individuals in the city of Rotterdam, which would in theory enable them to train a regression model in order to identify high-impact lifestyle factors for heart failure. However, privacy and confidentiality concerns make it unfeasible to exchange these data. Methods: This article describes a solution where vertically-partitioned synthetic data of Achmea and of Erasmus MC are combined using Secure Multi-Party Computation. First, a secure inner join protocol takes place to securely determine the identifiers of the patients that are represented in both datasets. Then, a secure Lasso Regression model is trained on the securely combined data. The involved parties thus obtain the prediction model but no further information on the input data of the other parties. Results: We implement our secure solution and describe its performance and scalability: we can train a prediction model on two datasets with 5000 records each and a total of 30 features in less than one hour, with a minimal difference from the results of standard (non-secure) methods. Conclusions: This article shows that it is possible to combine datasets and train a Lasso regression model on this combination in a secure way. Such a solution thus further expands the potential of privacy-preserving data analysis in the medical domain.
UR - http://www.scopus.com/inward/record.url?scp=85115127131&partnerID=8YFLogxK
U2 - 10.1186/s12911-021-01582-y
DO - 10.1186/s12911-021-01582-y
M3 - Article
C2 - 34530824
AN - SCOPUS:85115127131
SN - 1472-6947
VL - 21
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
IS - 1
M1 - 266
ER -