Context-Based, Predictive Access Control to Electronic Health Records

Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where well-being is jeopardized, different actors associated with cases should be granted permission to Electronic Health Records (EHRs) of patients. The research objective our study develop machine learning based on patients’ time sequential metrics and integrate them an Attribute Based Access Control (ABAC) mechanism. We propose ABAC mechanism that can yield EHRs systems by applying prognostic context handlers contextual information, used identify conditions permit medical Specifically, we use recent history predict for next two hours leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted values evaluated personalized fuzzy handlers, criticality status. developed method provides secure clinicians information simultaneously safeguards well-being. Integrating this predictive proved a robust tool enhance performance modern System.