数字中医药（英文）2026，Vol.9Issue(1)：68-79,12.DOI:10.1016/j.dcmed.2026.02.006
融合中医神情特征的抑郁症机器学习识别模型
A machine learning-based depression recognition model integrating spirit-expression features from traditional Chinese medicine
摘要
Abstract
Objective To develop a depression recognition model by integrating the spirit-expression di-agnostic framework of traditional Chinese medicine(TCM)with machine learning algo-rithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational ap-proaches. Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3 – 10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagno-sis device,and facial diagnosis features were extracted using the Open CV computer vision li-brary technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagno-sis feature parameters of the two groups,to compare the differences in TCM spirit and expres-sion and facial features.Five machine learning algorithms,including extreme gradient boost-ing(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector ma-chine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depres-sion recognition model based on the fusion of TCM spirit and expression features.The perfor-mance of the model was evaluated using metrics such as accuracy,precision,and the area un-der the receiver operating characteristic(ROC)curve(AUC).The model results were ex-plained using the Shapley Additive exPlanations(SHAP). Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expre-ssions in TCM and facial features between the two groups were shown as follows.(i)Quan-tispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythema-tous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complex-ion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM"spirit-expression"diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualiza-tion results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic fea-tures with machine learning enables the construction of a high-precision depression detec-tion model,offering a novel paradigm for objective depression diagnosis.关键词
中医/神/表情/特征融合/抑郁症/识别模型Key words
Traditional Chinese medicine/Spirit/Expression/Feature fusion/Depression/Recognition model引用本文复制引用
尧明慧,朱蓉蓉,钱鹏,刘慧琳,孙喜蓉,高利民,李福凤..融合中医神情特征的抑郁症机器学习识别模型[J].数字中医药（英文）,2026,9(1):68-79,12.基金项目
General Program of National Natural Science Founda-tion of China(82474390),Construction Project of Pudong New Area Famous TCM Studios(National Pilot Zone for TCM Development,Shanghai)(PDZY-2025-0716),and Shanghai Municipal Science and Technology Program Project Shanghai Key Laboratory of Health Identification and Assessment(21DZ2271000). （82474390）
