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对产后抑郁症风险的以人工智能为导向的预测模型:系统审查

Jie Xia¹, Chen Chen¹, Xiuqin Lu¹

¹School of Nursing and Health Management, Shanghai University of Medicine and Health Sciences, Shanghai, China.

Frontiers in public health

|September 19, 2025

概括

人工智能和机器学习可以利用母亲因素预测产后抑郁症 (PPD) 风险. 需要进一步的研究,以提高模型的通用性和早期干预的跨文化适用性.

科学领域:

计算精神病学是一种计算精神病学.
母亲心理健康母亲心理健康
在医疗保健中的预测建模.

背景情况:

产后抑郁症 (PPD) 影响全球数以百万计的母亲,对母亲和婴儿的福祉构成风险.
早期预测和干预对于减轻PPD不良结果至关重要.
人工智能和机器学习为识别PPD风险提供了新的方法.

研究的目的:

系统地审查使用AI/ML算法进行PPD预测的研究.
从现有研究中评估表现并确定PPD的关键预测因子.
评估AI/ML模型对PPD风险评估的潜力和局限性.

主要方法:

对2024年10月31日之前发表的研究进行系统审查.
包括使用算法进行PPD预测的研究.
使用预测模型偏差风险评估工具 (PROBAST) 进行质量评估.

主要成果:

包括11项研究,随机森林,SVM和物流回归等算法显示出高预测性能 (AUROC>0.9).
确定的主要预测因素包括母亲的年龄,怀孕压力,精神健康史,教育,婚姻状况和睡眠.
模型显示出出色的整体性能,但在概括性和潜在偏差方面存在局限性.

结论:

人工智能/ML模型对早期PPD风险预测和干预支持充满希望.
未来的研究应该专注于优化模型,提高准确性,并确保跨文化适用性.
解决数据质量和算法解释性对于广泛采用至关重要.

关键词:

人工智能的人工智能是人工智能.机器学习是机器学习.产后抑郁症产后抑郁症预测模型是一个预测模型.危险的风险危险的风险

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