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基于机器学习的产前抑郁预测模型的开发和应用.

Chunfei Hu¹, Hongmei Lin², Yupin Xu³

¹School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China; Department of Obstetrics and Gynecology, Shaoxing Maternal and Child Health Hospital, Shaoxing, Zhejiang, China.

Journal of affective disorders

|January 23, 2025

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机器学习模型现在可以预测孕妇产前抑郁症 (AND) 风险. 这允许更早的鉴定和干预,改善母亲和婴儿的结果.

科学领域:

围产期心理健康问题
临床信息学是一种临床信息学.
机器学习在医疗保健中的应用

背景情况:

产前抑郁症 (AND) 对母亲和婴儿的福祉构成重大风险.
目前用于预测AND的临床方法缺乏客观性和普遍适用性.
迫切需要可靠的工具来识别有抑郁症风险的孕妇.

研究的目的:

开发和验证基于机器学习 (ML) 的产前抑郁症 (AND) 预测模型.
利用社会人口统计和与怀孕相关的数据进行准确的和风险评估.
为了能够及早准确地识别患有 AND 风险的孕妇.

主要方法:

利用了来自三家医院的20950名孕妇的数据.
定义并使用爱丁堡产后抑郁量表 (EPDS) 10分或更高的得分.
开发了四个随机森林模型 (基础,基础+一般,基础+产科,完整) 使用34个选择的变量,根据临床相关性进行分类.

主要成果:

性能最好的模型,Base+General,在预测晚期怀孕的测试组中实现了0.710的曲线下面面积 (AUC).
基准模型表现出强的表现,AUC仅比顶级模型低0.022,这表明它对于早期查的实用性.
在测试组中,模型性能在AUC 0.687-0.710之间.

关键词:

产前抑郁症产前抑郁症机器学习机器学习预测模型的预测模型.

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结论:

机器学习模型可以预测各种妊娠阶段的风险和风险.
及早准确地识别有风险的个体有助于及时进行干预.
这些ML模型提供了一种有希望的方法来改善母亲和后代的围产期心理健康结果.