Advancing named entity recognition in interprofessional collaboration and education
IntroductionNamed Entity Recognition (NER) plays a critical role in interprofessional collaboration (IPC) and education, providing a means to identify and classify domain-specific entities essential for efficient interdisciplinary communication and knowledge sharing. While traditional methods, such...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1578769/full |
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| author | Rui Zhang Yifeng Shan MengZhe Zhen |
| author_facet | Rui Zhang Yifeng Shan MengZhe Zhen |
| author_sort | Rui Zhang |
| collection | DOAJ |
| description | IntroductionNamed Entity Recognition (NER) plays a critical role in interprofessional collaboration (IPC) and education, providing a means to identify and classify domain-specific entities essential for efficient interdisciplinary communication and knowledge sharing. While traditional methods, such as rule-based systems and machine learning models, have achieved moderate success in various domains, they often struggle with the dynamic, context-sensitive nature of IPC scenarios. Existing approaches lack adaptability to evolving terminologies and insufficiently address the complex interaction dynamics inherent in multi-disciplinary frameworks.MethodsTo address these limitations, we propose a Synergistic Collaboration Framework (SCF) integrated with an Adaptive Synergy Optimization Strategy (ASOS). SCF models IPC as a dynamic multi-agent system, where disciplines are represented as intelligent agents interacting within a weighted graph structure. Each agent contributes dynamically to the collaborative process, adapting its knowledge, skills, and resources to optimize global utility while minimizing conflicts and enhancing synergy. ASOS complements this by employing real-time feedback loops, conflict resolution algorithms, and resource reallocation strategies to iteratively refine contributions and interactions.ResultsExperimental evaluations demonstrate significant improvements in entity recognition accuracy, conflict mitigation, and overall collaboration efficiency compared to baseline methods.DiscussionThis study advances the theoretical and practical applications of NER in IPC, ensuring scalability and adaptability to complex, real-world scenarios. |
| format | Article |
| id | doaj-art-1359c8d38bb249a0bebcc7c66e23c904 |
| institution | Matheson Library |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-1359c8d38bb249a0bebcc7c66e23c9042025-06-26T05:28:07ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-06-011210.3389/fmed.2025.15787691578769Advancing named entity recognition in interprofessional collaboration and educationRui Zhang0Yifeng Shan1MengZhe Zhen2Business School, Shandong Xiehe University, Jinan, Shandong, ChinaSchool of Basic Education, Ningbo University of Finance and Economics, Ningbo, Zhejiang, ChinaSchool of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo, Zhejiang, ChinaIntroductionNamed Entity Recognition (NER) plays a critical role in interprofessional collaboration (IPC) and education, providing a means to identify and classify domain-specific entities essential for efficient interdisciplinary communication and knowledge sharing. While traditional methods, such as rule-based systems and machine learning models, have achieved moderate success in various domains, they often struggle with the dynamic, context-sensitive nature of IPC scenarios. Existing approaches lack adaptability to evolving terminologies and insufficiently address the complex interaction dynamics inherent in multi-disciplinary frameworks.MethodsTo address these limitations, we propose a Synergistic Collaboration Framework (SCF) integrated with an Adaptive Synergy Optimization Strategy (ASOS). SCF models IPC as a dynamic multi-agent system, where disciplines are represented as intelligent agents interacting within a weighted graph structure. Each agent contributes dynamically to the collaborative process, adapting its knowledge, skills, and resources to optimize global utility while minimizing conflicts and enhancing synergy. ASOS complements this by employing real-time feedback loops, conflict resolution algorithms, and resource reallocation strategies to iteratively refine contributions and interactions.ResultsExperimental evaluations demonstrate significant improvements in entity recognition accuracy, conflict mitigation, and overall collaboration efficiency compared to baseline methods.DiscussionThis study advances the theoretical and practical applications of NER in IPC, ensuring scalability and adaptability to complex, real-world scenarios.https://www.frontiersin.org/articles/10.3389/fmed.2025.1578769/fullnamed entity recognitioninterprofessional collaborationsynergy optimizationadaptive frameworkdynamic multi-agent systems |
| spellingShingle | Rui Zhang Yifeng Shan MengZhe Zhen Advancing named entity recognition in interprofessional collaboration and education Frontiers in Medicine named entity recognition interprofessional collaboration synergy optimization adaptive framework dynamic multi-agent systems |
| title | Advancing named entity recognition in interprofessional collaboration and education |
| title_full | Advancing named entity recognition in interprofessional collaboration and education |
| title_fullStr | Advancing named entity recognition in interprofessional collaboration and education |
| title_full_unstemmed | Advancing named entity recognition in interprofessional collaboration and education |
| title_short | Advancing named entity recognition in interprofessional collaboration and education |
| title_sort | advancing named entity recognition in interprofessional collaboration and education |
| topic | named entity recognition interprofessional collaboration synergy optimization adaptive framework dynamic multi-agent systems |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1578769/full |
| work_keys_str_mv | AT ruizhang advancingnamedentityrecognitionininterprofessionalcollaborationandeducation AT yifengshan advancingnamedentityrecognitionininterprofessionalcollaborationandeducation AT mengzhezhen advancingnamedentityrecognitionininterprofessionalcollaborationandeducation |