A prediction model of qi stagnation: A prospective observational study referring to two existing models

Ayako Maeda-Minami, Keiko Ihara, Tetsuhiro Yoshino, Yuko Horiba, Masaru Mimura, Kenji Watanabe

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: To establish a prediction model of qi stagnation referring to two existing models. Design: Prospective observational study. Setting: We recruited patients who visited the Kampo Clinic at Keio University from February 2011 to March 2013. Methods: We constructed a random forest algorithm with 202 items as independent variables to predict qi stagnation patterns using full agreement data of the physicians’ diagnosis and the result of two existing scores as a reference standard. To compare the new model with the two existing models, we calculated the discriminant ratio (prediction accuracy), precision, sensitivity (recall), specificity, and F-measure of these models. Results: The number of eligible participants was 1,194, and 29.1% of them were diagnosed with qi stagnation by Kampo physicians. The discriminant ratio, precision, sensitivity, specificity, and F-measure in our new model were 0.960, 0.672, 0.911, 0.964, and 0.774, respectively. Our new model had a significantly higher discriminant ratio than the two existing models. Conclusions: We constructed a better qi stagnation prediction model than the previously established ones. Our results can be utilized to reach an international agreement on qi stagnation pattern diagnosis in traditional East Asian medicine.

Original languageEnglish
Article number105619
JournalComputers in Biology and Medicine
Volume146
DOIs
Publication statusPublished - 2022 Jul

Keywords

  • Decision support system
  • International classification of diseases
  • Machine learning
  • Traditional medicine pattern

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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