Documentation of Complementary and Integrative Health Therapies in the Electronic Health Record: A Scoping Review

Austin, Robin; Lu, Sheng-Chieh; Jantraporn, Ratchada; Park, Suhyun; Geiger-Simpson, Elena; Koithan, Mary; Kreitzer, MaryJo; Delaney, Connie W

doi:10.1089/jicm.2022.0748

Cited by 2 publications

(1 citation statement)

References 49 publications

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“… 8 This delay hampers the development and distribution of standardized electronic medical records (EMRs). 43 , 44 For AI-utilized practices and research, establishing the basic infrastructure for data collection, including standardized terminology and EMRs, must be prioritized. 45 One of the largest barriers to establishing this is the fact that TCIM encompasses a large group of unrelated therapies originating from a wide range of geographic regions and schools of thought, and to date there is still no standardized method that can account for all aspects of TCIM use (e.g., diagnosis, treatment, etc.)…”

Section: Challenges That Exist At the Intersection Of Ai And Tcimmentioning

confidence: 99%

Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare

Ng,

Cramer,

Lee

et al. 2024

Integrative Medicine Research

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Section: Challenges That Exist At the Intersection Of Ai And Tcimmentioning

confidence: 99%

Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare

Ng,

Cramer,

Lee

et al. 2024

Integrative Medicine Research

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Complementary and Integrative Health Information in the literature: its lexicon and named entity recognition

Zhou,

Austin,

et al. 2023

Journal of the American Medical Informatics Association

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Objective To construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to help better represent the often underrepresented physical and psychological CIH approaches in standard terminologies, and to also apply state-of-the-art natural language processing (NLP) techniques to help recognize them in the biomedical literature. Materials and methods We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant sources of knowledge. The Lexicon encompasses 724 unique concepts with 885 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS), and we developed and utilized BERT models comparing their efficiency in CIH named entity recognition to well-established models including MetaMap and CLAMP, as well as the large language model GPT3.5-turbo. Results Of the 724 unique concepts in CIHLex, 27.2% could be matched to at least one term in the UMLS. About 74.9% of the mapped UMLS Concept Unique Identifiers 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 0.91, 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.

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Documentation of Complementary and Integrative Health Therapies in the Electronic Health Record: A Scoping Review

Cited by 2 publications

References 49 publications

Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare

Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare

Complementary and Integrative Health Information in the literature: its lexicon and named entity recognition

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