Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 8 (10)

Revealing Associations Between Diagnosis Patterns and Acupoint Prescriptions Using Medical Data Extracted From Case Reports

Affiliations

Revealing Associations Between Diagnosis Patterns and Acupoint Prescriptions Using Medical Data Extracted From Case Reports

Cheol-Han Kim et al. J Clin Med.

Abstract

Objective: The optimal acupoints for a particular disease can be determined by analysis of diagnosis patterns. The objective of this study was to reveal the association between such patterns and the acupoints prescribed in clinical practice using medical data extracted from case reports.

Methods: This study evaluated online virtual diagnoses made by currently practicing Korean medical doctors (N = 80). The doctors were presented with 10 case reports published in Korean medical journals and were asked to diagnose the patients and prescribe acupoints accordingly. A network analysis and the term frequency-inverse document frequency (tf-idf) method were used to analyse and quantify the relationship between diagnosis patterns and prescribed acupoints.

Results: The network analysis showed that ST36, LI4, LR3, and SP6 were the most frequently used acupoints across all diagnoses. The tf-idf values showed the acupoints used for specific diseases, such as BL40 for bladder disease and LU9 for lung disease.

Conclusions: The associations between diagnosis patterns and prescribed acupoints were identified using an online virtual diagnosis modality. Network and text mining analyses revealed commonly applied and disease-specific acupoints in both qualitative and quantitative terms.

Keywords: acupuncture; data mining; network analysis; pattern identification.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Study protocol: (A). Ten previously published case reports were presented in this online study. Patient and disease data were provided in a single slide. The doctors were asked to diagnose the patient and prescribe acupoints accordingly. (B). Data collection and pre-processing. The diagnosis pattern and acupoints of the doctors were obtained and pre-processed according to International Statistical Classification of Diseases and Related Health Problems (ICD)-10 codes and World Health Organization (WHO)-standardised acupoints. (C). Data analysis. Network analysis was performed on the diagnosis patterns and prescribed acupoints, with nodes (acupoints and diagnosis patterns) and edges (correlated diagnosis patterns and acupoints). Furthermore, correlated acupoints and diagnosis patterns were extracted and subjected to term frequency-inverse document frequency (tf-idf) weighting.
Figure 2
Figure 2
Network analysis of diagnosis patterns and prescribed acupoints. A diagnosis pattern -acupoint network was generated featuring nodes (acupoints) and edges (pairs of correlated acupoints). R42 (vertigo), U30 (diseases of the musculoskeletal system and connective tissue), U60 (qi deficiency), U61 (blood disorder), U62 (qi-blood-yin-yang deficiency), U63 (pattern of fluid and humour), U65 (liver excess), U66 (heart deficiency), U67 (heart excess pattern), U68 (spleen disease), U71 (kidney disease), and U73 (stomach disease) were included. Red and grey nodes represent diseases and acupoints, respectively. Nodes with higher eigenvector centrality are located in the centre of the network. The thickness of the edge is proportional to the frequency of correlations between linked nodes.
Figure 3
Figure 3
Associations between diagnosis patterns and acupoints. The significantly associated diagnosis patterns and acupoint codes were as follows: BL40 with U76; BL60 with U52; GB30 with R10; HT3 with U24; LU9 with U51, U57, and U69; and ST35 with U61. Acupoints are on the x-axis. On the y-axis, 38 diagnosis patterns are represented; the corresponding ICD-10 codes are shown as different-coloured boxes (green: symptom-based codes; blue: diseases defined in Korean medicine; orange: pattern of six meridians and external contractions; purple: qi-blood-yin-yang deficiency -Fluid-Humour; yellow: visceral system and bowel-related; and grey: Sasang constitution).

Similar articles

See all similar articles

References

    1. Sherman K.J., Cherkin D.C., Hogeboom C.J. The Diagnosis and Treatment of Patients with Chronic Low-Back Pain by Traditional Chinese Medical Acupuncturists. J. Altern. Complement. Med. 2001;7:641–650. doi: 10.1089/10755530152755199. - DOI - PubMed
    1. Alraek T. Designing clinical studies that take into account traditional East Asian medicine’s systems and methods—with focus on pattern identification. Chin. J. Integr. Med. 2014;20:332–335. doi: 10.1007/s11655-014-1807-5. - DOI - PubMed
    1. Napadow V., Liu J., Kaptchuk T.J. A systematic study of acupuncture practice: Acupoint usage in an outpatient setting in Beijing, China. Complement. Ther. Med. 2004;12:209–216. doi: 10.1016/j.ctim.2004.10.001. - DOI - PubMed
    1. Jung W.-M., Lee I.-S., Wallraven C., Ryu Y.-H., Park H.-J., Chae Y. Cortical Activation Patterns of Bodily Attention triggered by Acupuncture Stimulation. Sci. Rep. 2015;5:12455. doi: 10.1038/srep12455. - DOI - PMC - PubMed
    1. Wang Y.-Y., Lin F., Jiang Z.-L. Pattern of acupoint selection based on complex network analysis technique. Zhongguo Zhen Jiu Chin. Acupunct. Moxibustion. 2011;31:85–88. - PubMed
Feedback