Objective: Our study aimed to construct an exhaustive Complementary and
Integrative Health (CIH) Lexicon (CIHLex) to better represent the often
underrepresented physical and psychological CIH approaches in standard
terminologies. We also intended to apply advanced Natural Language Processing
(NLP) models such as Bidirectional Encoder Representations from Transformers
(BERT) and GPT-3.5 Turbo for CIH named entity recognition, evaluating their
performance against established models like MetaMap and CLAMP. Materials and
Methods: We constructed the CIHLex by integrating various resources, compiling
and integrating data from biomedical literature and relevant knowledge bases.
The Lexicon encompasses 198 unique concepts with 1090 corresponding unique
terms. We matched these concepts to the Unified Medical Language System (UMLS).
Additionally, we developed and utilized BERT models and compared their
efficiency in CIH named entity recognition to that of other models such as
MetaMap, CLAMP, and GPT3.5-turbo. Results: From the 198 unique concepts in
CIHLex, 62.1% could be matched to at least one term in the UMLS. Moreover,
75.7% of the mapped UMLS Concept Unique Identifiers (CUIs) were categorized as
"Therapeutic or Preventive Procedure." Among the models applied to CIH named
entity recognition, BLUEBERT delivered the highest macro average F1-score of
0.90, surpassing other models. Conclusion: Our CIHLex significantly augments
representation of CIH approaches in biomedical literature. Demonstrating the
utility of advanced NLP models, BERT notably excelled in CIH entity
recognition. These results highlight promising strategies for enhancing
standardization and recognition of CIH terminology in biomedical contexts.

本文旨在构建一个综合的 CIH 词汇表并应用自然语言处理技术，特别是 BERT 和 GPT-3.5 Turbo 模型，对 CIH 命名实体进行识别和评估，结果表明 BERT 模型的性能优于其他模型，这为在生物医学语境中增强 CIH 术语的标准化和认知提供了有希望的策略。