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Artificial Intelligence-Based Framework for Analyzing Health Care Staff Security Practice: Mapping Review and

Prosper Kandabongee Yeng1, Livinus Obiora Nweke1, Bian Yang1

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Summary
This summary is machine-generated.

Analyzing health care staff security practices is crucial. Artificial intelligence methods, including random forest, decision tree, and support vector machine, can effectively model and analyze these practices using electronic health record logs.

Keywords:
analysisartificial intelligenceframeworkhealth caremachine learningmodelingsecuritysecurity practice

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Area of Science:

  • Health Informatics
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Healthcare security practices face challenges balancing access control with preventing legitimate therapeutic access.
  • Broad access mechanisms like "breaking-the-glass" risk undermining data confidentiality and integrity.
  • Inadvertent access prevention can lead to severe patient health consequences.

Purpose of the Study:

  • To identify suitable artificial intelligence (AI) methods and data sources for analyzing healthcare staff security practices.
  • To develop and implement a framework for modeling and analyzing security practices using real access logs.

Main Methods:

  • A mapping review identified AI methods, data sources, and attributes for framework development.
  • Simulated electronic health record (EHR) log data were used to assess framework implementation and compare algorithm performance.

Main Results:

  • Eighteen articles were included, revealing predominant use of K-nearest neighbor, Bayesian network, and decision tree algorithms on EHR and network logs.
  • The decision tree achieved the highest precision (0.655), SVM the highest recall (0.977), and random forest the highest F1-score (0.775).
  • A two-class approach using random forest, decision tree, and SVM achieved a precision of 0.998.

Conclusions:

  • A two-class approach effectively analyzes healthcare staff security practices to detect malicious and nonmalicious activities.
  • Random forest, decision tree, and SVM are recommended algorithms for analyzing security practices.
  • Analyzing real access logs can help improve security behaviors in the context of big data.