BMC Medical Informatics and Decision Making (Feb 2021)

Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population

  • Yulin Shi,
  • Xiaojuan Hu,
  • Ji Cui,
  • Longtao Cui,
  • Jingbin Huang,
  • Xuxiang Ma,
  • Tao Jiang,
  • Xinghua Yao,
  • Fang Lan,
  • Jun Li,
  • Zijuan Bi,
  • Jiacai Li,
  • Yu Wang,
  • Hongyuan Fu,
  • Jue Wang,
  • Yanting Lin,
  • Jingxuan Bai,
  • Xiaojing Guo,
  • Liping Tu,
  • Jiatuo Xu

DOI
https://doi.org/10.1186/s12911-021-01410-3
Journal volume & issue
Vol. 21, no. 1
pp. 1 – 14

Abstract

Read online

Abstract Background Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnostic criteria, it is often neglected in clinical diagnosis, especially in the early stage of disease. Many clinical practices and researches have shown that tongue and pulse conditions reflect the body's overall state. Establishing an objective evaluation method for diagnosing disease fatigue and non-disease fatigue by combining clinical symptom, index, and tongue and pulse data is of great significance for clinical treatment timely and effectively. Methods In this study, 2632 physical examination population were divided into healthy controls, sub-health fatigue group, and disease fatigue group. Complex network technology was used to screen out core symptoms and Western medicine indexes of sub-health fatigue and disease fatigue population. Pajek software was used to construct core symptom/index network and core symptom-index combined network. Simultaneously, canonical correlation analysis was used to analyze the objective tongue and pulse data between the two groups of fatigue population and analyze the distribution of tongue and pulse data. Results Some similarities were found in the core symptoms of sub-health fatigue and disease fatigue population, but with different node importance. The node-importance difference indicated that the diagnostic contribution rate of the same symptom to the two groups was different. The canonical correlation coefficient of tongue and pulse data in the disease fatigue group was 0.42 (P < 0.05), on the contrast, correlation analysis of tongue and pulse in the sub-health fatigue group showed no statistical significance. Conclusions The complex network technology was suitable for correlation analysis of symptoms and indexes in fatigue population, and tongue and pulse data had a certain diagnostic contribution to the classification of fatigue population.

Keywords