Ye Shangyuan, Zhang Hui, Shi Fuyan, Guo Jing, Wang Suzhen, Zhang Bo
Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA 02115, USA.
Division of Biostatistics, Department of Prevention Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
J Clin Med. 2020 Jan 31;9(2):380. doi: 10.3390/jcm9020380.
The objective of this study was to investigate the use of ensemble methods to improve the prediction of fetal macrosomia and large for gestational age from prenatal ultrasound imaging measurements.
We evaluated and compared the prediction accuracies of nonlinear and quadratic mixed-effects models coupled with 26 different empirical formulas for estimating fetal weights in predicting large fetuses at birth. The data for the investigation were taken from the Successive Small-for-Gestational-Age-Births study. Ensemble methods, a class of machine learning techniques, were used to improve the prediction accuracies by combining the individual models and empirical formulas.
The prediction accuracy of individual statistical models and empirical formulas varied considerably in predicting macrosomia but varied less in predicting large for gestational age. Two ensemble methods, voting and stacking, with model selection, can combine the strengths of individual models and formulas and can improve the prediction accuracy.
Ensemble learning can improve the prediction of fetal macrosomia and large for gestational age and have the potential to assist obstetricians in clinical decisions.
本研究的目的是探讨使用集成方法，通过产前超声成像测量来改善对巨大儿和大于胎龄儿的预测。
我们评估并比较了非线性和二次混合效应模型与26种不同的用于估计胎儿体重的经验公式在预测出生时巨大胎儿方面的预测准确性。调查数据取自连续小于胎龄儿出生研究。集成方法作为一类机器学习技术，被用于通过组合各个模型和经验公式来提高预测准确性。
个体统计模型和经验公式在预测巨大儿时的预测准确性差异很大，但在预测大于胎龄儿时差异较小。两种集成方法，即投票和堆叠，并结合模型选择，可以结合个体模型和公式的优势，提高预测准确性。
集成学习可以改善对巨大儿和大于胎龄儿的预测，并有潜力协助产科医生进行临床决策。
