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使用机器学习算法预测分娩后血红蛋白水平.

Sepehr Aghajanian1,2, Kyana Jafarabady1, Mohammad Abbasi1

  • 1Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.

Scientific reports
|June 17, 2024
PubMed
概括

机器学习模型使用实验前临床数据准确预测产后血红蛋白水平. 这有助于预测产后出血 (PPH) 风险,并可及时进行干预,以改善母亲的结果.

关键词:
人工智能的人工智能是人工智能.极端的梯度增强了极端的梯度.机器学习是机器学习.多层感知器多层感知器产后出血 产后出血 产后出血支持矢量机器的支持矢量机器.

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科学领域:

  • 产科和妇科 产科和妇科
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 在分娩前预测产后出血 (PPH) 对及时干预和改善患者结果至关重要.
  • 现有的方法可能无法完全捕捉使用现有临床数据预测PPH风险的复杂性.
  • 机器学习 (ML) 为开发更准确的预测模型提供了一个潜在的途径.

研究的目的:

  • 利用机器学习 (ML) 与分娩前的临床数据和实验室测量来预测不复杂单胎妊娠中的分娩后血红蛋白 (Hb) 水平.
  • 为了确定产后Hb水平的关键预测因素.
  • 开发和验证一种ML模型,用于预测PPH的间接测量.

主要方法:

  • 来自两个学术护理中心的交付数据库的回顾性分析,包括1974年女性.
  • 使用弹性网回归和随机森林算法进行特征选择,以确定重要的交付前预测因素.
  • 培训和评估各种ML算法,包括人工神经网络 (ANN),以预测产后24小时的Hb水平.

主要成果:

  • 产后Hb的关键预测因素包括平价,妊娠年龄,产前血红蛋白,纤维素素水平和产前血小板计数.
  • 人工神经网络 (ANN) 以0.62.2的根平均平方误差 (RMSE) 证明了最高的准确性.
  • 基于ANN模型开发了一个基于网络的计算器:https://predictivecalculators.shinyapps.io/ANN-HB.

结论:

  • 机器学习模型可以准确预测分娩后的血红蛋白水平,作为产后出血 (PPH) 的间接预测因素.
  • 开发的ML模型和基于Web的计算器可以集成到医疗保健系统中,以支持临床决策.
  • 建议使用多样化,基于人口的样本进行进一步验证,以提高模型的通用性.