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

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.

Keywords

acupuncture / indication / neural network / pattern identification / prediction

References

[1] Dayhoff JE, DeLeo JM. Artificial neural networks: opening the black box. Cancer 2001; 91(8 Suppl): 1615–1635

[2] Mantzaris D, Anastassopoulos G, Adamopoulos A. Genetic algorithm pruning of probabilistic neural networks in medical disease estimation. Neural Netw 2011; 24(8): 831–835

[3] Hua B, Abbas E, Hayes A, Ryan P, Nelson L, O’Brien K. Reliability of Chinese medicine diagnostic variables in the examination of patients with osteoarthritis of the knee. J Altern Complement Med 2012; 18(11): 1028–1037

[4] Chang CM, Chu HT, Wei YH, Chen FP, Wang S, Wu PC, Yen HR, Chen TJ, Chang HH. The core pattern analysis on Chinese herbal medicine for Sjögren’s syndrome: a nationwide population-based study. Sci Rep 2015; 5(1): 9541

[5] Kang H, Zhao Y, Li C, Chen Y, Tang K, Yang L, Ma C, Peng J, Zhu R, Liu Q, Hu Y, Cao Z. Integrating clinical indexes into four-diagnostic information contributes to the traditional Chinese medicine (TCM) syndrome diagnosis of chronic hepatitis B. Sci Rep 2015; 5(1): 9395

[6] O’Brien KA, Abbas E, Zhang J, Guo ZX, Luo R, Bensoussan A, Komesaroff PA. An investigation into the reliability of Chinese medicine diagnosis according to Eight Guiding Principles and Zang-Fu Theory in Australians with hypercholesterolemia. J Altern Complement Med 2009; 15(3): 259–266

[7] Farquhar J. Knowing Practice: the Clinical Encounter of Chinese Medicine. Boulder, Colo.: Westview, 1994

[8] Kaptchuk TJ. The Web That Has No Weaver. Chicago, IL: Comtemporary Books, 2000

[9] Sherman KJ, Cherkin DC, Hogeboom CJ. The diagnosis and treatment of patients with chronic low-back pain by traditional Chinese medical acupuncturists. J Altern Complement Med 2001; 7(6): 641–650

[10] Jung WM, Lee T, Lee IS, Kim S, Jang H, Kim SY, Park HJ, Chae Y. Spatial patterns of the indications of acupoints using data mining in classic medical text: a possible visualization of the meridian system. Evid Based Complement Alternat Med 2015; 2015: 457071

[11] Ha L, Li T, Wang F. Exploration and analysis on the “similar-indication acupoints”. Chin Acupunct Mox (Zhongguo Zhen Jiu) 2015; 35(12): 1263–1265 (in Chinese)

[12] Napadow V, Liu J, Kaptchuk TJ. A systematic study of acupuncture practice: acupoint usage in an outpatient setting in Beijing, China. Complement Ther Med 2004; 12(4): 209–216

[13] Wang YY, Lin F, Jiang ZL. Pattern of acupoint selection based on complex network analysis technique. Chin Acupunct Mox (Zhongguo Zhen Jiu) 2011; 31(1): 85–88 (in Chinese)

[14] Ramesh AN, Kambhampati C, Monson JRT, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl 2004; 86(5): 334–338

[15] Yang CC, Veltri P. Intelligent healthcare informatics in big data era. Artif Intell Med 2015; 65(2): 75–77

[16] Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 1958; 65(6): 386–408

[17] Dutta R, Aryal J, Das A, Kirkpatrick JB. Deep cognitive imaging systems enable estimation of continental-scale fire incidence from climate data. Sci Rep 2013; 3(1): 3188

[18] Rezaei-Darzi E, Farzadfar F, Hashemi-Meshkini A, Navidi I, Mahmoudi M, Varmaghani M, Mehdipour P, Soudi Alamdari M, Tayefi B, Naderimagham S, Soleymani F, Mesdaghinia A, Delavari A, Mohammad K. Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model. Arch Iran Med 2014; 17(12): 837–843

[19] Chang YJ, Yeh ML, Li YC, Hsu CY, Lin CC, Hsu MS, Chiu WT. Predicting hospital-acquired infections by scoring system with simple parameters. PLoS One 2011; 6(8): e23137

[20] Shi HY, Lee KT, Lee HH, Ho WH, Sun DP, Wang JJ, Chiu CC. Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery. PLoS One 2012; 7(4): e35781

[21] Féraud R, Clérot F. A methodology to explain neural network classification. Neural Netw 2002; 15(2): 237–246

[22] Jung WM, Chae Y, Jang BH. Development of markup language for medical record charting: a Charting Language. Stud Health Technol Inform 2015; 216: 879

[23] Schaul T, Bayer J, Wierstra D, Sun Y, Felder M, Sehnke F, Ruckstieß T, Schmidhuber J. PyBrain. J Mach Learn Res 2010; 11: 743–746

[24] Touretzky D, Pomerleau D. What’s hidden in the hidden layers. Byte 1989; 14(8): 227–233

[25] Pandey B, Mishra RB. Knowledge and intelligent computing system in medicine. Comput Biol Med 2009; 39(3): 215–230

[26] Unschuld P. Medicine in China: a History of Ideas. Berkeley, Los Angeles and London: University of California Press, 1985

[27] Lee T, Jung WM, Lee IS, Lee YS, Lee H, Park HJ, Kim N, Chae Y. Data mining of acupoint characteristics from the classical medical text: DongUiBoGam of Korean Medicine. Evid Based Complement Alternat Med 2014; 2014: 329563

[28] Zhang NL, Yuan S, Chen T, Wang Y. Statistical validation of traditional Chinese medicine theories. J Altern Complement Med 2008; 14(5): 583–587

[29] Zhang NL, Yuan S, Chen T, Wang Y. Latent tree models and diagnosis in traditional Chinese medicine. Artif Intell Med 2008; 42(3): 229–245

[30] Yeung WF, Chung KF, Zhang NLW, Zhang SP, Yung KP, Chen PX, Ho YY. Identification of Chinese medicine syndromes in persistent insomnia associated with major depressive disorder: a latent tree analysis. Chin Med 2016; 11(1): 4

[31] Liu B, Zhou X, Wang Y, Hu J, He L, Zhang R, Chen S, Guo Y. Data processing and analysis in real-world traditional Chinese medicine clinical data: challenges and approaches. Stat Med 2012; 31(7): 653–660

[32] Zhou X, Chen S, Liu B, Zhang R, Wang Y, Li P, Guo Y, Zhang H, Gao Z, Yan X. Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artif Intell Med 2010; 48(2-3): 139–152

[33] Zhang H, Tian CH, Liu BY, Zhou XZ, Wang YH, Liu ZS, . Study of the idea of clinical acupuncture point combination of TCM physician Tian. J Clin Acup Mox (Zhen Jiu Lin Chuang Za Zhi) 2007; 23(2): 36–38 (in Chinese)

[34] Jung WM, Lee SH, Lee YS, Chae Y. Exploring spatial patterns of acupoint indications from clinical data: A STROBE-compliant article. Medicine (Baltimore) 2017; 96(17): e6768

[35] Mamoshina P, Vieira A, Putin E, Zhavoronkov A. Applications of deep learning in biomedicine. Mol Pharm 2016; 13(5): 1445– 1454

[36] Liang, M, Li Z, Chen T, Zeng J. Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Trans Comput Biol Bioinform, 2015. 12(4): 928–937

[37] Shi CH, Wang XJ, Chen JX, Liu RQ, Zhao YH, Yang HJ. Study on the drug selection law for treatment of chronic gastritis with spleen deficiency and stomach dryness by complex system entropy cluster. J Tradit Chin Med 2010; 30(4): 294–298

[38] Ferreira AS, Lopes AJ. Chinese medicine pattern differentiation and its implications for clinical practice. Chin J Integr Med 2011; 17(11): 818–823

[39] Berle CA, Cobbin D, Smith N, Zaslawski C. A novel approach to evaluate traditional Chinese medicine treatment outcomes using pattern identification. J Altern Complement Med 2010; 16(4): 357–367

Acknowledgements

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 2015M3A9E3052338).

Compliance with ethics guidelines

Won-Mo Jung, In-Soo Park, Ye-Seul Lee, Chang-Eop Kim, Hyangsook Lee, Dae-Hyun Hahm, Hi-Joon Park, Bo-Hyoung Jang, and Younbyoung Chae declare that no competing interests exist. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Additional informed consent was obtained from all patients for whom identifying information is included in this article.

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature