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Advancing named entity recognition in interprofessional collaboration and education.

Rui Zhang1, Yifeng Shan2, MengZhe Zhen3

  • 1Business School, Shandong Xiehe University, Jinan, Shandong, China.

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|July 11, 2025
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Summary

This study introduces a novel framework for Named Entity Recognition (NER) in interprofessional collaboration (IPC), enhancing accuracy and efficiency in dynamic, multi-disciplinary settings. The approach improves communication and knowledge sharing across diverse professional groups.

Keywords:
adaptive frameworkdynamic multi-agent systemsinterprofessional collaborationnamed entity recognitionsynergy optimization

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Area of Science:

  • Computer Science
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Named Entity Recognition (NER) is crucial for interprofessional collaboration (IPC) and education, aiding communication and knowledge sharing.
  • Traditional NER methods struggle with the dynamic and context-sensitive nature of IPC, lacking adaptability to evolving terminologies and complex interactions.
  • Existing approaches fail to adequately address the intricate dynamics inherent in multi-disciplinary frameworks.

Purpose of the Study:

  • To propose a novel Synergistic Collaboration Framework (SCF) integrated with an Adaptive Synergy Optimization Strategy (ASOS) to overcome limitations in current NER for IPC.
  • To model IPC as a dynamic multi-agent system where disciplines interact as intelligent agents.
  • To enhance adaptability, conflict mitigation, and synergy in interprofessional communication.

Main Methods:

  • The Synergistic Collaboration Framework (SCF) models interprofessional collaboration (IPC) as a dynamic multi-agent system.
  • Disciplines are represented as intelligent agents within a weighted graph, dynamically adapting knowledge, skills, and resources.
  • The Adaptive Synergy Optimization Strategy (ASOS) uses real-time feedback, conflict resolution, and resource reallocation for iterative refinement.

Main Results:

  • Experimental evaluations show significant improvements in entity recognition accuracy compared to baseline methods.
  • The proposed framework demonstrates enhanced conflict mitigation in interprofessional scenarios.
  • Overall collaboration efficiency in dynamic, multi-disciplinary settings is notably improved.

Conclusions:

  • This research advances the theoretical and practical applications of NER in IPC.
  • The Synergistic Collaboration Framework (SCF) and Adaptive Synergy Optimization Strategy (ASOS) ensure scalability and adaptability for complex, real-world scenarios.
  • The study provides a robust solution for efficient interdisciplinary communication and knowledge sharing.