在5岁时使用围产期数据和机器学习预测低认知能力
Andrea K Bowe1, Gordon Lightbody2,3, Daragh S O'Boyle2
1INFANT Research Centre, University College Cork, Cork, Ireland. abowe@ucc.ie.
Pediatric research
|January 4, 2024
概括
研究人员开发了一种机器学习模型,以预测患有认知发育不良风险的婴儿. 该模型使用了早期生命数据,显示了早期识别和有针对性的查的前景,尽管在人口层面上使用需要进一步改进.
科学领域:
- 发育儿科 发育儿科
- 医疗保健中的机器学习
- 人口健康 人口健康
背景情况:
- 缺乏早期,准确和可扩展的方法来识别患有不良认知结果高风险的婴儿.
- 需要预测工具,以便及时干预和支持处于危险的儿童.
研究的目的:
- 利用机器学习和基于人口的队列数据开发一种可解释的预测模型.
- 为了确定婴儿在童年时具有较低认知结果的高风险,用于早期干预.
主要方法:
- 利用了来自全国代表性的"在爱尔兰成长"队列中的8858名参与者的数据.
- 在9个月时收集母亲,婴儿和社会经济特征;在5岁时测量认知能力.
- 采用数据预处理,合成少数人过量采样,特征选择和随机森林模型,通过十倍交叉验证的网格搜索进行超参数调整.
主要成果:
- 一个随机森林模型有15个易于收集的特征,实现了接受器操作特征曲线 (AUROC) 下的面积为0.77,用于预测5岁低认知能力.
- 该模型确定了72%的婴儿具有低认知能力,其特异性为66%.
结论:
- 开发的模型代表了迈向早期的第一步,认知发展的个人风险分层.
- 为了实现作为人口层面查工具的实施,需要进一步提高性能.
- 突出了机器学习的潜力,用于识别周产期的有风险的婴儿.
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