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An Inquiry-Based Teaching Model for Nursing Professional Courses Based on Data Mining and Few-Shot Learning

Fuling Fan1

  • 1Nursing Department, Luohe Medical College, Luohe 462000, China.

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This study enhances hospital management by applying data mining (DM) and few-shot learning to analyze nursing data. These methods improve decision-making and support strategic planning in healthcare.

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Area of Science:

  • Educational Technology
  • Health Informatics
  • Data Science

Background:

  • Teaching is a structured process linking theory and practice.
  • The Inquiry-Based Teaching Model (ITM) is widely adopted.
  • Data mining (DM) uncovers patterns in large datasets.

Purpose of the Study:

  • To apply DM techniques, specifically decision trees, to hospital management.
  • To enhance data analysis capabilities for hospital managers.
  • To introduce few-shot learning to address data scarcity in nursing research.

Main Methods:

  • Decision tree algorithm from data mining.
  • Few-shot learning for improved model analysis.
  • Application of these techniques to hospital management data.

Main Results:

  • Provides hospital managers with advanced data analysis tools.
  • Offers technical support for developing hospital management strategies.
  • Improves analytical model performance despite limited nursing research data.

Conclusions:

  • DM and few-shot learning can significantly aid hospital management.
  • These technologies offer robust support for data-driven decision-making in healthcare.
  • Addressing data scarcity is crucial for advancing nursing informatics.