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

• Published in: Computers in biology and medicine 2022-07, Vol.146, p.105619-105619, Article 105619
• Main Authors: Maeda-Minami, Ayako, Ihara, Keiko, Yoshino, Tetsuhiro, Horiba, Yuko, Mimura, Masaru, Watanabe, Kenji
• Format: Article
• Language: English
• Subjects: Abdomen; Algorithms; Blood pressure; Cold; Decision support system; Diagnosis; Disease; Independent variables; International classification of diseases; Machine learning; Medicine; Observational studies; Patients; Physicians; Prediction models; Questionnaires; Sensitivity; Stagnation; Tacit knowledge; Traditional medicine pattern; Variables
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.105619

To establish a prediction model of qi stagnation referring to two existing models. Prospective observational study. We recruited patients who visited the Kampo Clinic at Keio University from February 2011 to March 2013. 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. 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. 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. •We externally validated two existing prediction models of qi stagnation.•We constructed a random forest algorithm with 202 items to predict qi stagnation.•Our new model worked better than the previous qi stagnation prediction models.•Important items for our new model included items that matched the existing models.