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使用机器学习预测婴儿死亡:基于人口的回顾性研究.

Zhihong Zhang1, Qinqin Xiao2, Jiebo Luo3

  • 1School of Nursing, University of Rochester, Rochester, NY, USA; Goergen Institute for Data Science, University of Rochester, Rochester, NY, USA.

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概括
此摘要是机器生成的。

机器学习,特别是XGBoost,有效地使用妊娠年龄和出生体重等关键因素预测婴儿死亡. 一个简化的四因素模型为围产期护理中的风险评估提供了一个实用的方法.

关键词:
在 COVID-19 疫情中,婴儿死亡率 婴儿死亡率机器学习是机器学习.新生儿死亡率 新生儿死亡率预测 预测 预测

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科学领域:

  • 围产期健康 围产期健康
  • 医疗保健中的机器学习
  • 公共卫生信息学 公共卫生信息学

背景情况:

  • 尽管最近的婴儿死亡率有所下降,但美国的婴儿死亡率仍然令人担忧.
  • 减少婴儿死亡率的国家目标尚未实现.
  • 预测建模为了解和缓解婴儿死亡率提供了一种新的方法.

研究的目的:

  • 开发和评估用于预测婴儿死亡的机器学习模型.
  • 确定婴儿死亡率的关键预测因素.
  • 评估在疫情前和疫情期间预测模型的性能.

主要方法:

  • 一项基于人口的回顾性研究,利用2016-2021年美国活产数据.
  • 分析了33个不同的因素,包括分娩设施,产前护理,妊娠史,分娩,分娩和新生儿特征.
  • XGBoost和其他四个机器学习模型的预测准确性进行了比较.

主要成果:

  • XGBoost的表现优于其他模型,新生儿死亡预测曲线下的面积 (AUC) 为0.98.
  • 发现的关键预测因素包括妊娠年龄,出生体重,5分钟APGAR分数和产前检查.
  • 一个简化的四个预测模型实现了与完整模型 (AUC:0.93) 相比的性能 (AUC:0.91) 并超过了现有的风险选工具.

结论:

  • 基于XGBoost的模型为产周护理提供了可靠的预后信息.
  • 简化四因素分类系统是预测婴儿死亡风险的实用工具.
  • 这些模型可以加强围产期教育和辅导工作.