Faculty of Pharmaceutical Sciences, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, 278-8510, Japan; Center for Kampo Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
Comput Biol Med. 2022 Jul;146:105619. doi: 10.1016/j.compbiomed.2022.105619. Epub 2022 May 16.
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.
建立一种基于两种现有模型的气滞预测模型。
前瞻性观察性研究。
我们招募了 2011 年 2 月至 2013 年 3 月期间在庆应义塾大学接受汉方诊所治疗的患者。
我们使用 202 项独立变量构建了一个随机森林算法，以使用医生诊断的全一致数据和两个现有评分的结果作为参考标准来预测气滞模式。为了将新模型与两种现有模型进行比较，我们计算了这些模型的判别比（预测准确性）、精度、灵敏度（召回率）、特异性和 F 度量。
符合条件的参与者人数为 1194 人，其中 29.1%的人被汉方医生诊断为气滞。我们的新模型的判别比、精度、灵敏度、特异性和 F 度量分别为 0.960、0.672、0.911、0.964 和 0.774。我们的新模型的判别比明显高于两种现有模型。
我们构建了一种比以前建立的模型更好的气滞预测模型。我们的结果可用于在传统东亚医学中就气滞模式诊断达成国际共识。
