Podrobná bibliografie
| Název: |
基于机器学习算法的无痛分娩初产妇产后尿潴留 风险预警模型构建. (Chinese) |
| Alternate Title: |
Construction of Early Warning Model of Postpartum Urinary Retention Risk after Painless Delivery of Primipara Based on Machine Learning Algorithm. (English) |
| Autoři: |
廖水秀, 范秋华, 戴书荣 |
| Zdroj: |
Progress in Modern Biomedicine; 2025, Vol. 25 Issue 20, p3292-3298, 7p |
| Témata: |
MACHINE learning, RANDOM forest algorithms, DISEASE risk factors, PRIMIPARAS, RETENTION of urine, PREDICTION models, LOGISTIC regression analysis |
| Abstract (English): |
Objective: To construct an early warning model of postpartum urinary retention risk after painless delivery of primipara by using machine learning algorithm, and to find the best effective early warning model, so as to provide scientific basis for early and accurate identification of high-risk groups in clinical practice. Methods: This study was a single-center retrospective study, 80 primipara who delivered painlessly in Tingzhou Hospital of Fujian Province from July 2021 to June 2024 were included, they were divided into urinary retention group (18 cases) and non urinary retention group (62 cases) according to whether there was urinary retention after delivery. General data between two groups were compared, Univariate and Multivariate logistic regression were used to screen for influencing factors, three machine learning algorithms: Random Forest, Support Vector Machine, and Logistic Regression were used to construct an early warning model, the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, and specificity were used as performance evaluation indicators to evaluate the predictive performance of the model. Results: Univariate analysis showed that age, body mass index (BMI), gestational week, length of the second stage of labor, anesthetic dose, lateral episiotomy were associated with postpartum urinary retention (P<0.05); Multivariate logistic regression identified BMI ≥28 kg/m² (OR=3.210, 95%CI: 1.450-7.090), length of the second stage of labor ≥ 2 hours (OR=2.890, 95%CI: 1.230-6.810) anesthetic dose ≥15 mL ( OR = 3.56, 95% CI: 1.670-7.620), and lateral episiotomy (OR=2.540, 95%CI: 1.120-5.780) as independent risk factors. After comprehensive evaluation of various indicators, the random forest model has the best predictive performance. Conclusion: The risk warning model constructed based on machine learning has good predictive performance, and the random forest model performs the best, which can provide effective support for early clinical intervention. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
目的: 运用机器学习算法构建无痛分娩初产妇产后尿潴留风险预警模型, 寻找最佳效能预警模型, 为临 床早期精准识别高危人群提供科学依据。方法: 本研究为单中心回顾性研究, 纳入 2021 年 7 月至 2024 年 6 月 于福建省汀州医院行无痛分娩的 80 例初产妇, 依据产后是否发生尿潴留分为尿潴留组 (18 例) 与非尿潴留组 (62 例)。对比两组一般资料, 借助单因素及多因素 Logistic 回归筛选影响因素, 采用随机森林、支持向量机、 Logistic 回归三种机器学习算法构建预警模型, 以受试者工作特征曲线下面积 (area under the receiver operating characteristic curve, ROC-AUC) 、准确度、灵敏度、特异度作为模型性能指标评估其预测性能。结果: 单因素分析 显示, 年龄、身体质量指数 (body mass index, BMI) 、孕周、第二产程时长、麻醉药物剂量、会阴侧切与产后尿潴 留相关 (P<0.05); 多因素 Logistic 回归确定 BMI 逸 28 kg/m² (OR=3.210, 95% CI: 1.450-7.090) 、第二产程时 长逸 ≥2h (OR=2.890, 95%CI: 1.230-6.810) 、麻醉药物剂量 逸 ≥15mL (OR=3.560, 95%CI: 1.670-7.620) 、会阴侧切 (OR=2.540, 95%CI: 1.120-5.780) 为独立危险因素。综合评估各项指标, 随机森林模型预测性能最优。结论: 基 于机器学习构建的风险预警模型预测性能良好, 随机森林模型表现最佳, 可为临床早期干预提供有效支持。 [ABSTRACT FROM AUTHOR] |
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| Databáze: |
Biomedical Index |
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