Reliable generation of privacy-preserving synthetic electronic health record time series via diffusion models

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 31; no. 11; pp. 2529 - 2539
• Main Authors: Tian, Muhang, Chen, Bernie, Guo, Allan, Jiang, Shiyi, Zhang, Anru R
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-11-2024
• Subjects: diffusion models; time series generation; electronic health records
• ISSN: 1067-5027; 1527-974X; 1527-974X
• DOI: 10.1093/jamia/ocae229

Abstract
Objective
Electronic health records (EHRs) are rich sources of patient-level data, offering valuable resources for medical data analysis. However, privacy concerns often restrict access to EHRs, hindering downstream analysis. Current EHR deidentification methods are flawed and can lead to potential privacy leakage. Additionally, existing publicly available EHR databases are limited, preventing the advancement of medical research using EHR. This study aims to overcome these challenges by generating realistic and privacy-preserving synthetic EHRs time series efficiently.
Materials and Methods
We introduce a new method for generating diverse and realistic synthetic EHR time series data using denoizing diffusion probabilistic models. We conducted experiments on 6 databases: Medical Information Mart for Intensive Care III and IV, the eICU Collaborative Research Database (eICU), and non-EHR datasets on Stocks and Energy. We compared our proposed method with 8 existing methods.
Results
Our results demonstrate that our approach significantly outperforms all existing methods in terms of data fidelity while requiring less training effort. Additionally, data generated by our method yield a lower discriminative accuracy compared to other baseline methods, indicating the proposed method can generate data with less privacy risk.
Discussion
The proposed model utilizes a mixed diffusion process to generate realistic synthetic EHR samples that protect patient privacy. This method could be useful in tackling data availability issues in the field of healthcare by reducing barrier to EHR access and supporting research in machine learning for health.
Conclusion
The proposed diffusion model-based method can reliably and efficiently generate synthetic EHR time series, which facilitates the downstream medical data analysis. Our numerical results show the superiority of the proposed method over all other existing methods.