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Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model

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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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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (Nos. NRF- 2014R1A1A1038082, 2015R1D1A1A01058033, and 2015M3A9E 3052338).

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Authors and Affiliations

  1. Department of Science in Korean Medicine, Graduate School, Kyung Hee University, Seoul, 130-701, Republic of Korea

    Won-Mo Jung, Ye-Seul Lee, Hyangsook Lee, Hi-Joon Park & Younbyoung Chae

  2. Acupuncture & Meridian Science Research Center, College of Korean Medicine, Kyung Hee University, Seoul, 130-701, Republic of Korea

    In-Soo Park, Ye-Seul Lee, Hyangsook Lee, Dae-Hyun Hahm, Hi-Joon Park & Younbyoung Chae

  3. Department of Physiology, College of Korean Medicine, Gachon University, Seoul, 131-120, Republic of Korea

    Chang-Eop Kim

  4. Department of Physiology, School of Medicine, Kyung Hee University, Seoul, 130-701, Republic of Korea

    Dae-Hyun Hahm

  5. Department of Preventive Medicine, College of Korean Medicine, Kyung Hee University, Seoul, 130-701, Republic of Korea

    Bo-Hyoung Jang

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  1. Won-Mo Jung
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Correspondence to Younbyoung Chae.

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Jung, WM., Park, IS., Lee, YS. et al. Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model. Front. Med. 13, 112–120 (2019). https://doi.org/10.1007/s11684-017-0582-z

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  • DOI: https://doi.org/10.1007/s11684-017-0582-z

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