… security, and collaborative model training while preserving patient confidentiality. In FL, EHRs
… collaboratively train the machine learning models without sharing the raw EHRs data. Only …

… is still a need for experimentation with machine learning for EHR security application. Thus,
we … Novel security applications are paramount for safeguarding EHR and, in this article, the …

… The cost to the healthcare organization of the loss of information during security … electronic
health records (EHRs) secure and private. Among common security mechanisms are secure …

… classification methods using simulated logs of EHR. The simulated data of EHR logs in this
study was quite useful since the different types of machine learning algorithms needed to be …

… detect suspicious accesses against Electronic Health Records (EHRs). The main … machine
learning provides a more robust security mechanism for sustainable management of the EHR …

… data-sharing framework within the EHR sector that considers all … of electronic health record
(EHR) policies, integrating ontologies and machine learning to enhance privacy and security …

… can manipulate the machine learning predictions with EHRs easily and selectively at …
machine learning models without any knowledge of the models. With less than 5% of the raw EHR …

… to pick out capacity security threats or weak spots in their EHR security structures. The …
the algorithm can perceive security risks or ability threats within the EHR records. Precision …

… in this process of integrating ML models into EHRs and clinical workflows. 1, 4-6 Based on
… in the EHR (Table). An essential first consideration is to specify the targeted EHR workflow, 5 …

… a theoretical Machine Learning framework that will detect suspicious user access to an EHR
… approach is an ideal option for maintaining healthcare data security as compared to the …