ArticleFrontiers of medicine2019
Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model.
Article in Frontiers of medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Artificial intelligence guided acupuncture decision making and treatment: a review of research.Frontiers in medicine · 2026Review
- Prediction of the acupuncture effects on pregnancy outcomes with personalized, embryonic, endometrial characteristics in women undergoing frozen-thawed embryo transfer.Journal of assisted reproduction and genetics · 2025Article
- Discovering the key symptoms for identifying patterns in functional dyspepsia patients: Doctor's decision and machine learning.Integrative medicine research · 2025Article
- Performance of GPT-4 for planning acupuncture treatment: comparison with human clinician performance.Frontiers in medicine · 2025Article
- A practical guide to implementing artificial intelligence in traditional East Asian medicine research.Integrative medicine research · 2024Article
- Acupuncture indication knowledge bases: meridian entity recognition and classification based on ACUBERT.Database : the journal of biological databases and curation · 2024Article
- Deep autoencoder-powered pattern identification of sleep disturbance using multi-site cross-sectional survey data.Frontiers in medicine · 2022Article
- The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review.Frontiers in pharmacology · 2022Article
- Special Issue: State of the Art in Research on Acupuncture Treatment.Journal of clinical medicine · 2021Article
- Acupoint selection based on pattern identification results or disease state.Integrative medicine research · 2020Article
- Statistical inference of acupoint specificity: forward and reverse inference.Integrative medicine research · 2020Article
- Commonality and Specificity of Acupuncture Point Selections.Evidence-based complementary and alternative medicine : eCAM · 2020Article
- A Key Node Mining Method Based on Acupoint-Disease Network (ADN): A New Perspective for Exploring Acupoint Specificity.Evidence-based complementary and alternative medicine : eCAM · 2020Article
- Revealing Associations between Diagnosis Patterns and Acupoint Prescriptions Using Medical Data Extracted from Case Reports.Journal of clinical medicine · 2019Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Comprehension of the medical diagnoses of doctors and treatment of diseases is important to understand the underlying principle in selecting appropriate acupoints. The pattern recognition process that pertains to symptoms and diseases and informs acupuncture treatment in a clinical setting was explored. A total of 232 clinical records were collected using a Charting Language program. The relationship between symptom information and selected acupoints was trained using an artificial neural network (ANN). A total of 11 hidden nodes with the highest average precision score were selected through a tenfold cross-validation. Our ANN model could predict the selected acupoints based on symptom and disease information with an average precision score of 0.865 (precision, 0.911; recall, 0.811). This model is a useful tool for diagnostic classification or pattern recognition and for the prediction and modeling of acupuncture treatment based on clinical data obtained in a real-world setting. The relationship between symptoms and selected acupoints could be systematically characterized through knowledge discovery processes, such as pattern identification.
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