%0 Journal Article
%@ 2291-9694
%I JMIR Publications
%V 12
%N 
%P e56893
%T Privacy-Preserving Prediction of Postoperative Mortality in Multi-Institutional Data: Development and Usability Study
%A Suh,Jungyo
%A Lee,Garam
%A Kim,Jung Woo
%A Shin,Junbum
%A Kim,Yi-Jun
%A Lee,Sang-Wook
%A Kim,Sulgi
%+ Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88,  Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea, 82 2 3010 1783, sangwooklee20@gmail.com
%K machine learning
%K privacy
%K in-hospital mortality
%K homomorphic encryption
%K multi-institutional system
%D 2024
%7 5.7.2024
%9 Original Paper
%J JMIR Med Inform
%G English
%X Background: To circumvent regulatory barriers that limit medical data exchange due to personal information security concerns, we use homomorphic encryption (HE) technology, enabling computation on encrypted data and enhancing privacy. Objective: This study explores whether using HE to integrate encrypted multi-institutional data enhances predictive power in research, focusing on the integration feasibility across institutions and determining the optimal size of hospital data sets for improved prediction models. Methods: We used data from 341,007 individuals aged 18 years and older who underwent noncardiac surgeries across 3 medical institutions. The study focused on predicting in-hospital mortality within 30 days postoperatively, using secure logistic regression based on HE as the prediction model. We compared the predictive performance of this model using plaintext data from a single institution against a model using encrypted data from multiple institutions. Results: The predictive model using encrypted data from all 3 institutions exhibited the best performance based on area under the receiver operating characteristic curve (0.941); the model combining Asan Medical Center (AMC) and Seoul National University Hospital (SNUH) data exhibited the best predictive performance based on area under the precision-recall curve (0.132). Both Ewha Womans University Medical Center and SNUH demonstrated improvement in predictive power for their own institutions upon their respective data’s addition to the AMC data. Conclusions: Prediction models using multi-institutional data sets processed with HE outperformed those using single-institution data sets, especially when our model adaptation approach was applied, which was further validated on a smaller host hospital with a limited data set. 
%R 10.2196/56893
%U https://medinform.jmir.org/2024/1/e56893
%U https://doi.org/10.2196/56893