Privacy-Aware Deep Learning Architecture for Secure Electronic Health Record Analysis

Abstract

The accelerated process of healthcare system digitalization has greatly augmented the use of Electronic Health Records (EHRs) that can support the further development of data-driven clinical decision-making. Nonetheless, the privacy and security issues that arise in EHR data are so sensitive that they restrict the feasibility of the deep learning models application to real-life medical settings. This paper has developed a privacy conscious deep learning framework that can combine federated learning with differential privacy in order to achieve decentralized EHR analytics that is secure. The suggested model allows joint model training among various hospitals without the need to disseminate raw patient data, which also protects the confidentiality of the data. The researchers have used a hybrid deep learning model which is a combination of Long Short-Term Memory (LSTM) networks and attention mechanism that is able to capture temporal dependencies alongside feature relevance in longitudinal clinical data. Differential privacy is implemented as a controlled noise injection to updates in a model, which offers formal privacy guarantees in addition to model utility. The experimental analysis of the MIMIC-III data shows that the proposed method obtains better predictive performance, with an accuracy rate of 90% and AUC of 0.92 as well as a high level of privacy protection. Its findings indicate that there is a trade-off of acceptable accuracy on both model accuracy and preservation of privacy, thus validating the appropriateness of the suggested architecture in regard to secure, scalable, and real-world clinical deployment.