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A graph based recommender system for managing Covid-19 Crisis.

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This study introduces a graph-based recommender system to optimize medical staff allocation during the COVID-19 crisis. It efficiently matches patients with suitable healthcare professionals based on medical data and staff availability.

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Covid-19 crisisgraph modelrecommender system

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Area of Science:

  • Health Informatics
  • Artificial Intelligence
  • Graph Theory

Background:

  • Managing healthcare resources during crises like COVID-19 is challenging due to limited medical staff.
  • Efficient allocation of medical personnel is crucial for optimal patient care.
  • Patient medical files contain vital information for matching with appropriate specialists.

Purpose of the Study:

  • To develop a graph-based recommender system for optimizing medical staff allocation during the COVID-19 crisis.
  • To enhance patient care by efficiently matching patients with suitable medical staff.
  • To address the challenge of limited healthcare professionals by leveraging data-driven recommendations.

Main Methods:

  • Utilizing graph-based algorithms to model patient-staff relationships.
  • Analyzing patient medical files to extract disease and symptom information.
  • Incorporating medical staff availability and competency data into the recommendation engine.

Main Results:

  • The recommender system effectively analyzes patient data to identify optimal medical staff profiles.
  • It proposes suitable medical staff based on patient needs, staff availability, and skill proximity.
  • Demonstrates potential for improving resource management in healthcare crisis scenarios.

Conclusions:

  • A graph-based recommender system can significantly aid in managing healthcare resources during crises.
  • The system offers an efficient method for matching patients with the most appropriate medical staff.
  • This approach can lead to improved patient outcomes and optimized utilization of medical expertise.