Classification and Comparative Analysis of Prediction Models for Postpartum Depression

doi:10.12114/j.issn.1007-9572.2022.0209

Abstract

Abstract:

Postpartum depression (PPD) is a common maternal mental health issue induced by a combination of biopsychosocial factors, which seriously affects the physical and mental health of mothers and infants. Screening for individual maternal risk factors during pregnancy for predicting PPD will contribute to early identification and treatment of PPD. Many prediction models have been developed and extensively used in the research on the etiology of PPD, and classifying and comparing different types of the models could provide an important reference for selecting a rational method for developing the model, and help to increase the forms of PPD screening and diagnosis.

Key words: Depression, postpartum, Prediction model, Risk factors, Machine learning, Review

摘要：

产后抑郁（PPD）是产妇常见的心理健康问题，是生物-心理-社会多种因素共同作用的结果，严重影响着产妇及婴幼儿的身心健康。个性化识别产妇孕期危险因素并预测PPD的发生风险可为PPD的早期识别和早期干预提供时间和空间。近年来，构建PPD预测模型广泛应用于PPD病因学研究领域，对不同类型的PPD预测模型进行分类与比较能为选择合适的方法构建PPD预测模型提供重要参考，有助于拓展PPD筛查与诊断的形式。

关键词: 抑郁症，产后, 预测模型, 危险因素, 机器学习, 综述

Yongjian WANG,Weijing QI,Yipeng WANG, et al. Classification and Comparative Analysis of Prediction Models for Postpartum Depression[J]. Chinese General Practice, 2022, 25(24): 3036-3042. DOI: 10.12114/j.issn.1007-9572.2022.0209.
王永剑,齐伟静,王翼鹏等. 产后抑郁预测模型的分类与比较[J]. 中国全科医学, 2022, 25(24): 3036-3042. DOI: 10.12114/j.issn.1007-9572.2022.0209.

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