使用机器学习方法预测非常早产婴儿2年认知结果
Andrea K Bowe1, Gordon Lightbody1,2, Anthony Staines3
1INFANT Research Centre, University College Cork, Cork, Ireland.
JAMA network open
|December 26, 2023
概括
使用常规数据的预测模型可以识别具有认知延迟高风险的非常早产婴儿. 这允许早期,有针对性的干预措施,以改善这个脆弱人群的认知结果.
科学领域:
- 新生儿医学 新生儿医学
- 发育儿科 发育儿科
- 医疗保健中的机器学习
背景情况:
- 早期干预对于改善非常早产儿的认知结果至关重要.
- 鉴定最需要干预的婴儿是非常重要的,因为资源有限.
研究的目的:
- 评估一种预测模型,用于在2岁时对非常早产婴儿的认知延迟.
- 该模型利用常规可用的临床和社会人口统计数据.
主要方法:
- 一项使用瑞典新生儿质量登记册 (2015-2022) 的预后研究.
- 机器学习模型被训练来预测认知延迟 (贝利尺度得分<90) 在2年校正年龄.
- 包括出生在妊娠32周以下的婴儿,不包括重大先天性异常.
主要成果:
- 一个具有26个特征的后勤回归模型预测了认知延迟,接收器操作曲线下的面积为0.77.
- 关键预测因素包括非斯堪的纳维亚家庭语言,长时间住院,低出生体重,出院目的地和缺乏母乳养.
- 该模型在识别有认知迟缓的婴儿时实现了高灵敏度 (0.93).
结论:
- 新生儿护理中的预测建模可以促进早期和有针对性的干预.
- 这种方法可以帮助识别患有认知障碍风险最高的早产婴儿.
- 根据预测风险优化干预措施可以改善发育轨迹.
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