Publications
Detailed Information
HarmoSATE: Harmonized embedding-based self-attentive encoder to improve accuracy of privacy-preserving federated predictive analysis
Cited 0 time in
Web of Science
Cited 0 time in Scopus
- Authors
- Issue Date
- 2024-03
- Publisher
- Elsevier BV
- Citation
- Information Sciences, Vol.662, p. 120265
- Abstract
- Accurate privacy-preserving prediction using electronic health record (EHR) data distributed in multiple hospitals is essential to enable stakeholders related to healthcare services to obtain useful information without privacy leakage. In this paper, we propose harmonized embedding-based self-attentive encoder (HarmoSATE), which is a new method for privacy-preserving federated predictive analysis. We extract contextual embeddings of local institutions using Word2Vec, and then harmonize locally-trained embeddings using a neural network-based harmonization technique. The proposed method uses a deep representative encoder based on self-attention to learn complex and dynamic patterns inherent to harmonized embeddings of medical concepts. To evaluate our method, we implemented experiments using sequential medical codes collected from the Medical Information Mart for Intensive Care-III dataset in a distributed setting. It achieved a significant increase in average AUC, ranging from 3% to 8% depending on the experiments compared to baseline models, demonstrating superior prediction accuracy of a patient's diagnosis in the next admission. HarmoSATE can be a useful alternative to obtain accurate and practical results for various predictive tasks that use sensitive and distributed EHR data while preserving patients' privacy.
- ISSN
- 0020-0255
- Files in This Item:
- There are no files associated with this item.
Related Researcher
- Graduate School of Engineering Practice
- Department of Engineering Practice
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.