摘要
Objective Accurately identifying the key influencing factors of psychological birth trauma in primiparous women is crucial for implementing effective preventive and intervention measures.This study aimed to develop and validate an interpretable machine learning prediction model for identifying the key influencing factors of psychological birth trauma in primiparous women.Methods A multicenter cross-sectional study was conducted on primiparous women in four tertiary hospitals in Sichuan Province,southwestern China,from December 2023 to March 2024.The Childbirth Trauma Index was used in assessing psychological birth trauma in primiparous women.Data were collected and randomly divided into a training set(80%,n=289)and a testing set(20%,n=73).Six different machine learning models were trained and tested.Training and prediction were conducted using six machine learning models included Linear Regression,Support Vector Regression,Multilayer Perceptron Regression,eXtreme Gradient Boosting Regression,Random Forest Regression,and Adaptive Boosting Regression.The optimal model was selected based on various performance metrics,and its predictive results were interpreted using SHapley Additive exPlanations(SHAP)and accumulated local effects(ALE).Results Among the six machine learning models,the Multilayer Perceptron Regression model exhibited the best overall performance in the testing set(MAE=3.977,MSE=24.832,R2=0.507,EVS=0.524,RMSE=4.983).In the testing set,the R2 and EVS of the Multilayer Perceptron Regression model increased by 8.3%and 1.2%,respectively,compared to the traditional linear regression model.Meanwhile,the MAE,MSE,and RMSE decreased by 0.4%,7.3%,and 3.7%,respectively,compared to the traditional linear regression model.The SHAP analysis indicated that intrapartum pain,anxiety,postpartum pain,resilience,and planned pregnancy are the most critical influencing factors of psychological birth trauma in primiparous women.The ALE analysis indicated that higher intrapartum pain,anxiety,and postpartum pain scores are risk factors,while higher resilience scores are protective factors.Conclusions Interpretable machine learning prediction models can identify the key influencing factors of psychological birth trauma in primiparous women.SHAP and ALE analyses based on the Multilayer Perceptron Regression model can help healthcare providers understand the complex decision-making logic within a prediction model.This study provides a scientific basis for the early prevention and personalized intervention of psychological birth trauma in primiparous women.
目的准确识别初产妇分娩心理创伤的关键影响因素对于实施有效的预防和干预措施至关重要。该研究开发并验证一个可解释的机器学习预测模型,用于识别初产妇的分娩心理创伤的关键影响因素。方法于2023年12月至2024年3月在中国四川省的4所三级甲等医院选取362名分娩的初产妇作为研究对象,并采用一般资料问卷、分娩创伤指数、广泛性焦虑障碍量表-2、患者健康问卷、家庭关怀度指数问卷、简式韧性量表、视觉模拟量表对其进行调查。数据收集后随机分为训练集(80%,n=289)和测试集(20%,n=73)。使用线性回归、支持向量回归、多层感知机回归、极限梯度提升、随机森林回归和自适应增强回归6种不同的机器学习模型进行训练和测试。基于多种性能指标选择最优模型,并通过沙普利加性解释和局部累积效应分析对该模型的预测结果进行解释。结果在6种机器学习模型中,多层感知机回归模型在测试集中的表现最佳(MAE=3.977,MSE=24.832,R2=0.507,EVS=0.524,RMSE=4.983)。在测试集中,与传统的线性回归模型相比,多层感知机回归模型的R2和EVS分别提高了8.3%和1.2%;MAE、MSE和RMSE分别降低了0.4%、7.3%和3.7%。沙普利加性解释表明,产时疼痛、焦虑、产后疼痛、心理弹性以及计划妊娠是初产妇分娩心理创伤的关键影响因素。局部累积效应分析表明,较强的产时疼痛、较高的焦虑和产后疼痛评分是风险因素;而较高的心理弹性评分是保护因素。结论可解释性机器学习预测模型能够识别初产妇分娩心理创伤的关键影响因素。基于多层感知机回归模型的沙普利加性解释和局部累积效应分析可以帮助医护人员理解预测模型内部复杂的决策逻辑,为初产妇分娩心理创伤的早期预防和个性化干预提供科学依据。
基金
supported by the Sichuan Province Nursing Scientific Research Project Plan(H23022)
the 2022 Municipal-University Science and Technology Strategic Cooperation Special Fund of Nanchong Science and Technology Bureau(22SXQT0222)。
