Efficacy of Acupuncture/Acupressure in the Prevention and Treatment of Nausea and Vomiting Across Multiple Patient Populations: Implications for Practice

Acupoint massage: a comprehensive descriptive review of its forms, applications, and underlying mechanisms

Acupoint massage is a non-invasive traditional therapy that has demonstrated reliable clinical outcomes in pain management, mental health relief, sleep disorder regulation, gastrointestinal treatment, and as an adjunct therapy for cancer. Its convenience and cost-effectiveness further enhance its appeal. However, the existing English literature lacks a systematic review that encompasses the various forms of acupoint massage. The acupoint massage forms adaptability is particularly notable when considering the diverse conditions, it addresses, as well as its applicability across different age groups and gender differences. Providing a comprehensive understanding, it is crucial to outline common practices and explore specific applications in key areas. The comprehensive understanding can create opportunities for effective collaboration between preclinical and clinical studies. Defining and categorizing different forms of acupoint massage is essential, alongside investigating the neural circuits involved in touch sensation. Future efforts should enhance collaboration with modern biology, facilitating the transition from empirical to evidence-based practice. This review summarizes forms, applications, and mechanisms of mainstream acupoint massage and provides insights for future research and applications, promoting deeper integration into healthcare.

Relation extraction using large language models: a case study on acupuncture point locations

OBJECTIVE

In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like Generative Pre-trained Transformers (GPTs) and Llama present a significant opportunity for extracting relations related to acupoint locations from textual knowledge sources. This study aims to explore the performance of LLMs in extracting acupoint-related location relations and assess the impact of fine-tuning on GPT's performance.

MATERIALS AND METHODS

We utilized the World Health Organization Standard Acupuncture Point Locations in the Western Pacific Region (WHO Standard) as our corpus, which consists of descriptions of 361 acupoints. Five types of relations ("direction_of", "distance_of", "part_of", "near_acupoint", and "located_near") (n = 3174) between acupoints were annotated. Four models were compared: pre-trained GPT-3.5, fine-tuned GPT-3.5, pre-trained GPT-4, as well as pretrained Llama 3. Performance metrics included micro-average exact match precision, recall, and F1 scores.

RESULTS

Our results demonstrate that fine-tuned GPT-3.5 consistently outperformed other models in F1 scores across all relation types. Overall, it achieved the highest micro-average F1 score of 0.92.

DISCUSSION

The superior performance of the fine-tuned GPT-3.5 model, as shown by its F1 scores, underscores the importance of domain-specific fine-tuning in enhancing relation extraction capabilities for acupuncture-related tasks. In light of the findings from this study, it offers valuable insights into leveraging LLMs for developing clinical decision support and creating educational modules in acupuncture.

CONCLUSION

This study underscores the effectiveness of LLMs like GPT and Llama in extracting relations related to acupoint locations, with implications for accurately modeling acupuncture knowledge and promoting standard implementation in acupuncture training and practice. The findings also contribute to advancing informatics applications in traditional and complementary medicine, showcasing the potential of LLMs in natural language processing.