使用机器学习方法,牛奶成分可以预测牛奶供应不足
Xuehua Jin1,2,3, Ching Tat Lai1,2,3, Sharon L Perrella1,2,3
1School of Molecular Sciences, The University of Western Australia, Crawley, WA 6009, Australia.
Diagnostics (Basel, Switzerland)
|January 25, 2025
概括
机器学习模型通过分析牛奶成分和母乳因素,准确地预测低牛奶供应. 这有助于早期识别和干预母乳养母亲和婴儿营养.
科学领域:
- 生物化学 生物化学
- 数据科学数据科学数据科学
- 儿科 儿科 儿科
背景情况:
- 低乳供应源于遗传,内分泌和清除频率因素影响乳腺功能.
- 这些因素会改变牛奶的成分,影响婴儿的营养.
- 早期识别低牛奶供应对于干预至关重要.
研究的目的:
- 研究低乳供应和正常乳供应的母亲之间的牛奶成分差异.
- 开发预测机器学习 (ML) 模型,用于识别低牛奶供应.
- 将牛奶成分与母亲和婴儿的特征相结合,以提高预测能力.
主要方法:
- 牛奶产量通过测试称重法在24小时内测量.
- 分析了58名低供给 (<600毫升/24小时) 和106名正常供给 (≥600毫升/24小时) 的母亲的牛奶成分.
- ML算法,包括深度学习和梯度提升,用于模型开发.
主要成果:
- 深度学习和梯度增强模型显示出卓越的性能.
- 一个包含14个牛奶成分和其他因素的综合模型实现了87.9%的准确性 (AUC 0.917).
- 一个简化的临床模型保持了78.8%的准确性 (AUC 0.794).
结论:
- 机器学习模型可以高准确度地预测低牛奶供应.
- 将牛奶成分与母婴数据相结合,提供了一种实际的识别方法.
- 早期识别有助于及时进行干预,以支持母乳养和婴儿营养.
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