Abstract
Hospitals have a keen interest to root out unauthorized access of Electronic Health Records (EHRs). The retrospective analysis of suspicious EHR views may help to take preventive measures against such access. However, investigating EHR access manually is labor-intensive and only allows for checking a small sample of cases. We explore
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