Not every unusual hospital login is a security incident.
Healthcare professionals may access patient records outside normal working hours because of emergencies, night shifts, or changing patient assignments.
CareShield AI is my research prototype exploring how contextual information could improve electronic health record access monitoring.
Features
- Synthetic healthcare access-event generation
- Contextual behavioural analysis
- Random Forest classification baseline
- Interactive Streamlit dashboard
- Investigation queue and risk indicators
- Chronological model evaluation
Technology
Python, Pandas, NumPy, Scikit-learn, Streamlit, and Plotly.
Important limitation
The project uses synthetic events and labels generated from simulated rules. Model performance therefore does not establish effectiveness against real hospital cyberattacks.
Future research includes independent threat scenarios, model calibration, and comparisons with transparent rule-based detection.
GitHub repository: https://github.com/tasnem-tech/careshield-ai
My goal is to explore explainable, context-aware cybersecurity rather than treating every unusual event as malicious.
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