妊娠晚期胎儿出生体重预测:回顾性队列研究和集成模型的建立。

IF 2.1 Q2 PEDIATRICS
Jing Gao, Xu Jie, Yujun Yao, Jingdong Xue, Lei Chen, Ruiyao Chen, Jiayuan Chen, Weiwei Cheng
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引用次数: 0

摘要

背景:准确的妊娠晚期出生体重预测对于减少不良后果至关重要,机器学习(ML)提供了比传统超声方法更高的精度。目的:本研究旨在建立基于临床大数据的ML模型,准确预测妊娠晚期出生体重,减少孕产妇和胎儿不良结局。方法:于2018年1月1日至2019年12月31日在上海某三甲医院进行回顾性队列研究,纳入16655例无先天性异常(bb0 ~ 28周)的单胎活产婴儿。将初始数据集分为用于算法开发的训练集和以4:1的比例对算法进行划分的测试集。我们从电子病历中提取了产妇和新生儿的分娩结果,以及父母的人口统计数据、产科临床数据和超声胎儿生物测定。使用Ridge、SVM、Random Forest、extreme gradient boosting (XGBoost)和Multi-Layer Perceptron等5种基本ML算法建立预测模型,然后将其平均为集成学习模型。比较模型的精度、均方误差、均方根误差和平均绝对误差。国际和平妇幼保健医院的研究伦理委员会批准了使用患者信息的伦理批准(GKLW2021-20)。结果:训练集和测试集分别包含13324例和3331例。从总共59个变量中,我们选择了17个可用于“少数特征模型”的变量,该模型具有很高的预测能力,准确率达到81%,明显超过超声公式方法。此外,我们的模型在低出生体重和巨大胎儿群体中保持了优越的性能。结论:我们的研究探索了一种创新的人工智能模型,用于预测胎儿出生体重和最大限度地利用医疗资源。在大数据时代,我们的模式改善母婴结局,推动精准医疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fetal Birth Weight Prediction in the Third Trimester: Retrospective Cohort Study and Development of an Ensemble Model.

Background: Accurate third-trimester birth weight prediction is vital for reducing adverse outcomes, and machine learning (ML) offers superior precision over traditional ultrasound methods.

Objective: This study aims to develop an ML model on the basis of clinical big data for accurate prediction of birth weight in the third trimester of pregnancy, which can help reduce adverse maternal and fetal outcomes.

Methods: From January 1, 2018 to December 31, 2019, a retrospective cohort study involving 16,655 singleton live births without congenital anomalies (>28 weeks of gestation) was conducted in a tertiary first-class hospital in Shanghai. The initial set of data was divided into a train set for algorithm development and a test set on which the algorithm was divided in a ratio of 4:1. We extracted maternal and neonatal delivery outcomes, as well as parental demographics, obstetric clinical data, and sonographic fetal biometry, from electronic medical records. A total of 5 basic ML algorithms, including Ridge, SVM, Random Forest, extreme gradient boosting (XGBoost), and Multi-Layer Perceptron, were used to develop the prediction model, which was then averaged into an ensemble learning model. The models were compared using accuracy, mean squared error, root mean squared error, and mean absolute error. International Peace Maternity and Child Health Hospital's Research Ethics Committee granted ethical approval for the usage of patient information (GKLW2021-20).

Results: Train and test sets contained a total of 13,324 and 3331 cases, respectively. From a total of 59 variables, we selected 17 variables that were readily available for the "few feature model," which achieved high predictive power with an accuracy of 81% and significantly exceeded ultrasound formula methods. In addition, our model maintained superior performance for low birth weight and macrosomic fetal populations.

Conclusions: Our research investigated an innovative artificial intelligence model for predicting fetal birth weight and maximizing health care resource use. In the era of big data, our model improves maternal and fetal outcomes and promotes precision medicine.

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来源期刊
JMIR Pediatrics and Parenting
JMIR Pediatrics and Parenting Medicine-Pediatrics, Perinatology and Child Health
CiteScore
5.00
自引率
5.40%
发文量
62
审稿时长
12 weeks
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