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Updated: Jan 9, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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开发和验证一种可解释的数字死亡率预测工具,用于极度早产的婴儿.

T'ng Chang Kwok1,2, Chao Chen3, Jayaprakash Veeravalli3

  • 1Centre for Perinatal Research, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, United Kingdom.

PLOS digital health
|December 10, 2025
PubMed
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此摘要是机器生成的。

一个新的在线工具预测了极度早产婴儿的死亡风险,有助于围产阶段的决策. 这种可解释的模型使用电子健康记录,并显示出比现有方法更高的性能.

科学领域:

  • 新生儿医学 新生儿医学
  • 医疗信息学 医疗信息学
  • 生物统计学 生物统计学

背景情况:

  • 对极度早产婴儿的围产管理带来了重大决策挑战.
  • 死亡率预测工具对于支持新生儿护理的临床决策至关重要.
  • 现有的预测模型可能无法完全满足这种高风险人群的准确性和实用性的需求.

研究的目的:

  • 开发和内部验证一种可解释的在线工具,用于在新生儿出院之前预测极度早产婴儿的死亡率.
  • 为了比较各种机器学习方法在这个群体中的死亡率预测的性能.
  • 与之前发布的模型相比,评估该工具的校准,区分和临床实用性.

主要方法:

  • 利用基于人口的电子病例记录数据,从25,902名婴儿出生在23+0-27+6周的怀孕期间,在185个英格兰和威尔士新生儿病房 (2010-2020) 中.
  • 开发并内部验证了一种使用循序渐进逆向物流回归的死亡率预测工具,该工具从九个机器学习算法中选择.
  • 在一个跨国早产婴儿队列中对该工具的性能进行了外部验证.

主要成果:

  • 开发的工具表现出良好的区分 (AUC 0.746) 和校准,在概率值 (10% - 70%) 上具有优异的净收益.
  • 该工具在校准和实用性方面表现优于之前发布的模型.

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  • 在外部验证队列中证实了可接受的性能,表明了可概括性.

    • 结论:

    • 一个可解释的,在线预测极度早产婴儿死亡率的工具已经开发和验证.
    • 该工具在支持高风险围产阶段决策方面显示出有希望的实用性.
    • 在广泛的临床采用之前,建议进一步评估.