确定SGA-LGA的第一季度风险因素使用权重继承投票组合学习
Sau Nguyen Van1,2,3, Jinhui Cui4, Yanling Wang5
1Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Bioengineering (Basel, Switzerland)
|July 27, 2024
概括
一个新的算法使用早期怀孕数据准确地预测胎儿生长条件,如SGA (孕期小) 和LGA (孕期大). 这有助于识别母亲的风险因素,以便及时干预并改善婴儿健康结果.
科学领域:
- 产科和妇科 产科和妇科
- 新生儿健康 新生儿健康
- 医疗保健中的机器学习
背景情况:
- 准确的胎儿生长分类 (对于妊娠年龄来说小 - SGA,对于妊娠年龄来说大 - LGA) 对于新生儿评估至关重要.
- 早期识别子宫内生长限制 (SGA) 和过度胎儿生长 (LGA) 可以优化婴儿和母亲的结果.
- 利用第一季度的数据进行预测,为及时的医疗干预提供了一个窗口.
研究的目的:
- 开发和验证一种新的算法,用于预测SGA,LGA和适当的妊娠年龄 (AGA) 胎儿,使用第一季度的数据.
- 确定与SGA和LGA相关的显著潜伏的孕产妇风险因素.
- 确定特定的生化和母体因素在预测胎儿生长分类中的重要性.
主要方法:
- 分析了2015-2021年7943名孕妇 (424名SGA,928名LGA,6591名AGA) 的数据.
- 开发用于分类的权重继承投票合集学习算法 (WIVELA).
- 应用相关性确定算法来识别关键预测特征.
主要成果:
- 在10倍的交叉验证中,WIVELA算法实现了92.12%的平均准确性,超过了其他五种机器学习算法.
- 确定了主要的孕产妇风险因素,包括第一季度体重变化,怀孕前体重,身高,年龄和产科史.
- 强调了第一季度生物标志物的重要性,如HDL,TG,OGTT,TC,FPG和LDL,反映了SGA/LGA状态.
结论:
- 拟议的WIVELA算法为早期分类胎儿生长条件提供了准确可靠的方法.
- 识别潜伏的孕产妇风险因素和关键生物标志物可以进行主动的临床监测和干预.
- 这种方法支持加强产前保健策略,以管理胎儿生长异常并改善母胎结果.
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