【摘要】 目的 系统评价基于机器学习方法构建的小于胎龄儿预测模型，为模型的构建和优化提供参考。方法 计算机检索PubMed、EMbase、Web of Science、CBM、WanFang Data、VIP和CNKI数据库，搜集关于小于胎龄儿预测模型的研究，检索时限均从建库至2022年8月10日。由2位评价员独立筛选文献、提取资料并评价纳入研究的偏倚风险后进行系统评价。结果 共纳入14个研究，样本量范围347～215 865例。使用逻辑回归、随机森林等19种方法构建了40个预测模型，13个研究的偏倚风险评价结果为高风险，预测模型的曲线下面积（AUC）为0.561～0.953。结论 小于胎龄儿预测模型的整体偏倚风险较高，预测性能一般，XGBoost方法构建的模型在不同研究中都获得了最好的预测性能，stacking方法将不同模型融合可以提升预测性能。母亲血压、胎儿腹围、头围与估计胎儿体重是小于胎龄儿的重要预测因子。更多还原

【Abstract】 Objective To systematically review prediction models of small for gestational age(SGA) based on machine learning and provide references for the construction and optimization of such a prediction model.Methods The PubMed, EMbase, Web of Science, CBM, WanFang Data, VIP and CNKI databases were electronically searched to collect studies on SGA prediction models from database inception to August 10, 2022. Two researchers independently screened the literature, extracted data, evaluated the risk of bias of the included studies, and conducted a systematic review. Results A total of 14 studies, comprising 40 prediction models constructed using 19 methods, such as logical regression and random forest, were included. The results of the risk of bias assessment from 13 studies were high;the area under the curve of the prediction models ranged from 0.561 to 0.953. Conclusion The overall risk of bias in the prediction models for SGA was high, and the predictive performance was average. Models built using extreme gradient boosting(XGBoost) demonstrated the best predictive performance across different studies. The stacking method can improve predictive performance by integrating different models. Finally, maternal blood pressure, fetal abdominal circumference, head circumference, and estimated fetal weight were important predictors of SGA.更多还原