预测胎儿出生体重在第三个三个月:回顾性队列研究和组合模型的开发
Jing Gao1, Xu Jie2, Yujun Yao2
1International Peace Maternity and Child Health Hospital, School of Medicine, Hengshan road NO.910, Shanghai, 200030, China, 86-021-64070434.
JMIR pediatrics and parenting
|March 10, 2025
概括
这项研究开发了一种机器学习 (ML) 模型,用于准确预测第三季度的出生体重,其性能优于传统的超声波方法. 人工智能模型通过改善医疗保健资源分配来提高孕产妇和胎儿的结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 孕产妇和胎儿医学 孕产妇和胎儿医学
背景情况:
- 准确的第三个三个月的出生体重预测对于减轻不良妊娠结果至关重要.
- 与传统的超声波技术相比,机器学习 (ML) 显示了提高精度的潜力.
研究的目的:
- 开发一种ML模型,利用临床大数据,精确地预测第三季度的出生体重.
- 通过改善出生体重估计,减少对母亲和胎儿的不良后果.
主要方法:
- 一个回顾性队列研究,对16,655个单子活产儿 (2018-2019) 进行了回顾性研究.
- 在提取的临床和超声波数据上使用5个算法 (Ridge,SVM,Random Forest,XGBoost,MLP) 开发一个集体ML模型.
- 基于准确度,平均平方误差,根平均平方误差和平均绝对误差的模型评估.
主要成果:
- 使用17个变量的"少数特征模型"实现了81%的准确性,超过了传统的超声波方法.
- ML模型在预测低出生体重和宏观胎儿方面表现出卓越的表现.
- 该模型在3331个案例的测试集上得到了验证.
结论:
- 一个创新的AI模型有效地预测胎儿出生体重,优化医疗保健资源利用率.
- 开发的模型通过在大数据时代改善孕产妇和胎儿的结果来推进精准医学.
相关概念视频
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