实用临床医药杂志2026，Vol.30Issue(3)：41-48,8.DOI:10.7619/jcmp.20255957
慢性便秘患者肠道准备不合格的机器学习预测模型的构建与验证
Construction and validation of machine learning based predictive models for inadequate bowel preparation in patients with chronic constipation
摘要
Abstract
Objective To explore the influencing factors of inadequate bowel preparation before colonoscopy in patients with chronic constipation(CC),and to construct and validate a predictive model for inadequate bowel preparation tailored to this high-risk group based on machine learning al-gorithms.Methods A total of 700 CC patients with colonoscopy in Nanjing Hospital of Traditional Chinese Medicine from May 2022 to May 2024 were selected by consecutive sampling method,and they were divided into qualified group(560 cases)and unqualified group(140 cases)according to the quality of bowel preparation.Univariate and multivariate Logistic regression analyses were em-ployed to identify the influencing factors of inadequate bowel preparation before colonoscopy in CC pa-tients.Using SPSS software,machine learning algorithms including Logistic regression,decision tree(CRT),and back propagation neural network(BPNN)were applied to construct predictive models for inadequate bowel preparation before colonoscopy in CC patients.A total of 250 CC patients with colonoscopy in Nanjing Hospital of Traditional Chinese Medicine from June to October 2024 were se-lected as an independent external validation cohort for assessment of the generalization ability of the models.The predictive value of the three models was comprehensively compared using metrics such as the area under the curve(AUC)of receiver operating characteristic(ROC)curve,sensitivity,and specificity.Results Univariate and multivariate Logistic regression analyses revealed that age,duration of constipation,diabetes,history of abdominal surgery,waiting timefor colonoscopy,and whether simethicone was taken were independent factors contributing to inadequate bowel preparation before colonoscopy in CC patients(P＜0.05).The model constructed based on the CRT module in-dicated that the duration of constipation,age,and diabetes were classification factors for inadequate bowel preparation in CC patients.According to the standardized importance results of independent variables in the BPNN model,the top five factors influencing inadequate bowel preparation in CC patients were the duration of constipation,age,waiting time for colonoscopy,whether simethicone was taken,and diabetes.The AUC of the models constructed using the three machine learning algo-rithms were all larger than 0.800,with the Logistic regression model demonstrating the best predic-tive performance,with an AUC of 0.889(95％CI,0.857 to 0.922),a sensitivity of 0.843,and a specificity of 0.821.Conclusion The three machine learning predictive models constructed in this study can effectively identify high-risk individuals with inadequate bowel preparation among CC pa-tients.Logistic regression model exhibits the best overall performance,providing a reliable tool for clinical risk stratification and precise intervention.关键词
慢性便秘/结肠镜检查/肠道准备/机器学习算法/Logistic回归模型/决策树/神经网络计算机/预后Key words
chronic constipation/colonoscopy/bowel preparation/machine learning algo-rithms/Logistic regression model/decision tree/neural network computer/prognosis分类
医药卫生引用本文复制引用
闻艳,穆蔚然,高雪丽,陈婉珍..慢性便秘患者肠道准备不合格的机器学习预测模型的构建与验证[J].实用临床医药杂志,2026,30(3):41-48,8.基金项目
国家自然科学基金资助项目(82205014) （82205014）
