开发和验证一个预测模型,用于评估早产婴儿的输出管准备度
Wongeun Song1, Young Hwa Jung2, Jihoon Cho3
1Office of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam, Republic of Korea; Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
International journal of medical informatics
|August 24, 2023
概括
一种名为NExt-Predictor的新模型准确地评估了早产婴儿的输出管准备程度. 这种工具有助于预测成功的输出管,改善早产婴儿的呼吸结果和神经发育健康.
科学领域:
- 新生儿医学 新生儿医学
- 关键的护理关键的护理
- 医疗保健中的机器学习
背景情况:
- 在早产婴儿中,早期输出管提供了显著的短期和长期益处,包括改善呼吸系统健康和神经发育结果.
- 目前的输出管准备性评估严重依赖于医生的自由裁量权,因为缺乏标准化的协议.
- 需要客观,定量预测因素来指导早产婴儿的输出管决策.
研究的目的:
- 确定在早产婴儿中成功输出输出管的强有力的预测因素.
- 开发和验证一个预测模型用于输出管准备.
- 改进新生儿重症监护病房内输出管的临床决策.
主要方法:
- 一项观察性队列研究利用了入住新生儿重症监护病房的早产婴儿 (<32周妊娠年龄) 的数据.
- 开发一个机器学习模型,包括人口统计,生命体征,呼吸器设置和呼吸指数.
- 使用医院数据 (2003-2019) 的内部验证和使用MIMIC-III数据库 (2001-2008) 的外部验证.
主要成果:
- 开发的预测模型 (NExt-Predictor) 在预测输出管成功方面表现强.
- 内部验证显示,接收器操作特征曲线 (AUC) 下的面积为0.805,平均精度为0.917.
- 外部验证得出AUC为0.715,平均精度为0.838,证实了概括性.
结论:
- 该NExt-Predictor模型提供了一种可靠和准确的方法,用于评估早产婴儿的排卵准备.
- 这种工具可以帮助临床医生做出更明智的决定,以确定输出管的时间.
- 改进的输出管策略可能会为脆弱的早产人口带来更好的临床结果.
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