从母亲到婴儿:使用母亲心理健康措施和表式机器学习模型预测婴儿气质
Rawan AlSaad1, Majid Alabdulla2,3, Aliya Tabassum4
1Weill Cornell Medicine-Qatar, Doha, Qatar.
Frontiers in public health
|October 6, 2025
概括
母亲心理健康 (MMH) 和社会人口统计学因素适度预测婴儿负面情绪. 机器学习模型确定了关键预测因素,使得早期识别有风险的婴儿能够及时进行干预.
科学领域:
- 发展心理学 发展心理学
- 围产期心理健康问题
- 医疗保健中的机器学习
背景情况:
- 婴儿的负面情绪,一个关键的气质维度,与以后的困难有关.
- 产后母亲心理健康 (MMH) 可能会影响婴儿的气质,但预测模型尚未得到充分研究.
- 机器学习 (ML) 提供了分析MMH资料和预测婴儿情绪发展的潜力.
研究的目的:
- 预测婴儿在产后第一年的负面情绪.
- 用ML评估母亲抑郁症,焦虑症和出生创伤的预测能力.
- 确定婴儿气质的关键社会人口统计和MMH预测因素.
主要方法:
- 利用了410个母婴双的数据.
- 通过婴儿行为调查问卷修订 (IBQ-R) 评估婴儿气质.
- 使用EPDS,HADS和City BiTS测量母亲的心理健康;采用了六种ML模型 (LightGBM,XGBoost,TabPFN等). 用于预测和预测.
主要成果:
- MMH症状和母婴特征适度预测婴儿负面情绪 (ROC-AUC 0.76与LightGBM).
- 在ROC-AUC,F1得分,灵敏度和特异性方面,LightGBM表现强.
- 关键预测因素包括妊娠年龄,婴儿年龄,孕产妇抑郁症 (EPDS),孕产妇焦虑症 (HADS) 和出生创伤 (City BiTS).
结论:
- 机器学习工具可以帮助早期识别那些面临负面情绪风险的婴儿.
- 将MMH查整合到产后护理中对于早期干预至关重要.
- 预测性见解可以指导个性化干预措施,以支持母亲和婴儿的福祉和发展.
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