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机器学习模型的开发,以预测怀孕期糖尿病风险在怀孕的前半年

Gabriel Cubillos^1,2, Max Monckeberg³, Alejandra Plaza³

¹Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, 8370451, Santiago, Chile.

BMC pregnancy and childbirth

|June 23, 2023

概括

机器学习模型现在可以使用常见变量早期预测妊娠糖尿病 (GDM). 这使得及时的干预能够改善孕产妇和胎儿的健康结果.

科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
产科和妇科产科和妇科

背景情况:

早期预测妊娠糖尿病 (GDM) 对于及时干预至关重要.
GDM可能会对母亲和胎儿造成累积伤害.
为了广泛的临床应用,需要可访问的预测模型.

研究的目的:

开发和优化用于早期GDM预测的机器学习 (ML) 模型.
用广泛可用的变量来开发模型.
促进各种医疗保健机构的早期干预策略.

主要方法:

在1611个怀孕的数据集上训练了12个ML模型.
优化过度参数和使用数据增强来提高预测性能.
使用变量选择方法和AUCROC进行模型评估和概括评估.

主要成果:

识别了多个具有不同灵敏度和特异性的模型.
四个模型使用7-12个变量实现了高灵敏度 (0.82) 和AUCROC (0.81).
使用5个变量的模型显示灵敏度为0.89和AUCROC为0.82.

结论:

早期GDM预测是可行的,使用定期检查和ML.
开发并优化了12个ML模型,以实现高预测性能.
一种新的数据增强方法提高了GDM预测的准确性.

关键词:

数据增强数据增强预测GDM风险预测GDM风险孕期糖尿病 (GDM) 是一种妊娠期糖尿病.机器学习模型的机器学习模型广泛可用的变量.

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