Enhancing traditional Chinese medicine diagnostics: Integrating ontological knowledge for multi-label symptom entity classification

Autor: Hangle Hu, Chunlei Cheng, Qing Ye, Lin Peng, Youzhi Shen
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Mathematical Biosciences and Engineering, Vol 21, Iss 1, Pp 369-391 (2024)
Druh dokumentu: article
ISSN: 1551-0018
DOI: 10.3934/mbe.2024017?viewType=HTML
Popis: In traditional Chinese medicine (TCM), artificial intelligence (AI)-assisted syndrome differentiation and disease diagnoses primarily confront the challenges of accurate symptom identification and classification. This study introduces a multi-label entity extraction model grounded in TCM symptom ontology, specifically designed to address the limitations of existing entity recognition models characterized by limited label spaces and an insufficient integration of domain knowledge. This model synergizes a knowledge graph with the TCM symptom ontology framework to facilitate a standardized symptom classification system and enrich it with domain-specific knowledge. It innovatively merges the conventional bidirectional encoder representations from transformers (BERT) + bidirectional long short-term memory (Bi-LSTM) + conditional random fields (CRF) entity recognition methodology with a multi-label classification strategy, thereby adeptly navigating the intricate label interdependencies in the textual data. Introducing a multi-associative feature fusion module is a significant advancement, thereby enabling the extraction of pivotal entity features while discerning the interrelations among diverse categorical labels. The experimental outcomes affirm the model's superior performance in multi-label symptom extraction and substantially elevates the efficiency and accuracy. This advancement robustly underpins research in TCM syndrome differentiation and disease diagnoses.
Databáze: Directory of Open Access Journals
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