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首页|期刊导航|中国针灸|基于真实世界数据的针灸联合常规治疗对PCOS-IR疗效的预测:机器学习与可解释性分析

基于真实世界数据的针灸联合常规治疗对PCOS-IR疗效的预测:机器学习与可解释性分析

郭本婕 孙家豪 杨妍 陈卓琳 李锂 郭静 孙建华 裴丽霞

中国针灸2026,Vol.46Issue(5):669-677,9.
中国针灸2026,Vol.46Issue(5):669-677,9.DOI:10.13703/j.0255-2930.20250530-k0003

基于真实世界数据的针灸联合常规治疗对PCOS-IR疗效的预测:机器学习与可解释性分析

Efficacy prediction of acupuncture and moxibustion combined with conventional treatment for PCOS-IR based on real-world data:machine learning and interpretability analysis

郭本婕 1孙家豪 1杨妍 1陈卓琳 1李锂 1郭静 2孙建华 1裴丽霞1

作者信息

  • 1. 南京中医药大学附属医院/江苏省中医院针灸康复科,南京 210029
  • 2. 南京中医药大学针灸推拿学院
  • 折叠

摘要

Abstract

Objective To construct an efficacy prediction model for polycystic ovary syndrome with insulin resistance(PCOS-IR)treated with acupuncture and moxibustion combined with conventional treatment based on machine learning.Methods Data from two real-world studies were collected for the training set(284 cases)and validation set(132 cases).The training set included the data of PCOS-IR patients visited from January 2023 to September 2024,and the validation set was composed of the patients visited from September 2024 to February 2025.Logistic regression(LR)and random forest(RF)algorithms were combined for predictive feature selection,and 5 representative machine learning models with different principles were built based on the screening results.The predictive effectiveness of the best model was assessed by receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).Finally,the predictive results of the best model were interpreted using the Shapley additive interpretation(SHAP)framework.Results The predictive features used for model construction included fasting insulin(FINS),total cholesterol(TC),the upper limit of the menstrual cycle length cycle(UML),body mass index(BMI),and alanine aminotransferase(ALT).The RF model showed the most balanced performance in terms of accuracy,precision,F1 score,and area under curve(AUC),suggesting that it achieved the overall favorable performance in predicting the efficacy on PCOS-IR.The SHAP analysis further revealed the importance of these 5 predictive features in efficacy prediction;and in particular,FINS,as an indicator of insulin level,was conductive most significantly to the efficacy prediction.Conclusion The efficacy prediction model constructed for PCOS-IR treated with acupuncture and moxibustion,combined with conventional regimens,provides an important empirical evidence for identifying beneficiary population and optimizing treatment strategy.

关键词

多囊卵巢综合征合并胰岛素抵抗/针灸/真实世界/机器学习/疗效预测

Key words

polycystic ovary syndrome with insulin resistance/acupuncture and moxibustion/real world/machine learning/efficacy prediction

引用本文复制引用

郭本婕,孙家豪,杨妍,陈卓琳,李锂,郭静,孙建华,裴丽霞..基于真实世界数据的针灸联合常规治疗对PCOS-IR疗效的预测:机器学习与可解释性分析[J].中国针灸,2026,46(5):669-677,9.

基金项目

国家自然科学基金面上项目:82174489 ()

第七批全国老中医药专家传承工作项目:2023YL024-43 ()

中国针灸

0255-2930

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