使用机器学习来识别围产期妇女负面情绪的动态进化模式:中国西南地区的纵向研究
Yuan Zhang1,2, Wenlong Li1,2, Jian Zou1,2
1Department of Epidemiology and Health Statistics School of Public Health, Chongqing Medical University Chongqing China.
MedComm
|August 18, 2025
概括
周产期妇女经历各种负面情绪模式,包括焦虑和抑郁. 机器学习模型有效地识别高风险个体,帮助及时进行心理健康干预.
科学领域:
- 围产期心理健康问题
- 精神病学是一个精神病学.
- 医疗保健中的机器学习
背景情况:
- 围产期心理健康显著影响母亲的生活质量.
- 现有的研究往往忽视了孕期妇女的负面情绪和高风险群体识别的多样性轨迹.
研究的目的:
- 分析围产期妇女负面情绪的异质发育轨迹.
- 开发和评估一种机器学习模型,用于识别高风险的个体,这些个体在产前期经历负面情绪.
主要方法:
- 在中国重庆的四家医院招募了860名产周妇女.
- 使用结构化问卷在第一,第二和第三个三个月,以及产后6周的随访.
- 采用增长混合建模来分析负面情绪轨迹和六个机器学习算法来构建一个预测模型,使用SHAP算法验证.
主要成果:
- 确定了四种不同的负面情绪轨迹:低稳定的焦虑,逐渐增加的高焦虑,轻度持续的抑郁和高度进展的抑郁.
- 极端梯度增强模型在预测高风险负面情绪方面表现出卓越的表现,获得24分的得分.
- 机器学习模型有效地识别了与负面情绪轨迹相关的模式和风险.
结论:
- 围产期妇女表现出动态和异质的负面情绪轨迹.
- 基于机器学习的预测模型是识别高风险个体的有价值工具,可以提供有针对性的心理健康支持.
- 这项研究强调了个性化方法对围产期心理健康护理的重要性.
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