上海中医药杂志2026，Vol.60Issue(6)：1-7,7.DOI:10.16305/j.1007-1334.2026.z20250908005
基于机器学习构建化学疗法期间结直肠癌肝郁脾虚证诊断模型
Development of diagnostic model for liver depression and spleen deficiency syndrome in colorectal cancer patients during chemotherapy based on machine learning
摘要
Abstract
Objective To develop a quantitative diagnostic model based on standardized traditional Chinese medicine(TCM)syndrome elements(symptoms and physical signs)for liver depression and spleen deficiency(LDSD)syndrome in colorectal cancer patients undergoing chemotherapy using the LASSO-logistic regression algorithm.Methods Relevant clinical data of 400 colorectal cancer patients undergoing chemotherapy were collected,and their TCM syndromes were identified.A diagnostic model for LDSD syndrome was established via LASSO-logistic regression analysis and subsequently evaluated.Results ①Among the 400 patients,the proportion of LDSD syndrome is relatively higher(27.75%),followed by spleen qi deficiency syndrome(19.75%),spleen-kidney yang deficiency syndrome(14.25%),and damp-heat accumulation syndrome(11.25%).② LASSO regression analysis identified 12 variables with non-zero regression coefficients,and further univariate logistic regression analysis indicated that 11 of these variables were influencing factors for the outcome.These 11 variables were included in a multivariate logistic regression analysis,and 6 variables(knee weakness,hypochondriac distension and pain,poor emotional status,belching with acid regurgitation,rapid pulse,and wiry pulse)were finally identified and used to develop the diagnostic model,with a nomogram generated accordingly.③The results of performance evaluations(receiver operating characteristic curve,calibration curve and decision curve analysis)showed that the predictive model for LDSD syndrome had certain accuracy and reliability across different datasets.Conclusions The diagnostic model for LDSD syndrome developed in this study enables a data-driven"experience-data"dual-track diagnostic mode for TCM syndrome differentiation.It not only provides a practical tool for the precise TCM diagnosis and treatment of colorectal cancer during chemotherapy,but also demonstrates the methodological value of machine learning in the modernization of traditional Chinese medicine.In the future,multi-modal data integration and external validation can be adopted to promote its translation into clinical practice.关键词
结直肠癌/中医证候/机器学习/人工智能/诊断模型/肝郁脾虚证Key words
colorectal cancer/traditional Chinese medicine syndrome/machine learning/artificial intelligence/diagnostic model/liver depression and spleen deficiency syndrome引用本文复制引用
王紫薇,王朝伟,孙云川,何新颖,毕凌,王炎..基于机器学习构建化学疗法期间结直肠癌肝郁脾虚证诊断模型[J].上海中医药杂志,2026,60(6):1-7,7.基金项目
国家自然科学基金项目(82474230) （82474230）
