构建和验证模型来预测生育恐惧:通过机器学习进行横截面人口研究
Zhi-Lin Zhang1, Kang-Jia Chen1, Hui Chen1
1Department of Gynecology and Obstetrics, Maternal and Child Medical Center of Anhui Medical University, The Fifth Affiliated Clinical College of Anhui Medical University, Anhui Women and Children's Medical Center, Hefei, Anhui, People's Republic of China.
International journal of women's health
|February 11, 2025
概括
对分娩的恐惧 (FOC) 影响到孕妇和新生儿. 确定的主要风险因素包括怀孕,不良怀孕史和剖腹产史,为早期干预提供预测模型.
科学领域:
- 产科和妇科 产科和妇科
- 孕产妇和胎儿医学 孕产妇和胎儿医学
- 生殖健康心理学 生殖健康心理学
背景情况:
- 对分娩的恐惧 (FOC) 是影响孕妇的重大心理困扰.
- FOC可能会对母亲和新生儿的结果产生负面影响,需要对其贡献因素进行研究.
- 了解FOC的影响对于开发及时干预至关重要.
研究的目的:
- 确定与FOC发生相关的独立风险因素.
- 使用机器学习技术构建FOC的预测模型.
- 评估开发的FOC模型的预测效率和临床实用性.
主要方法:
- 通过使用问卷数据分析了901名孕妇的队列.
- 风险因素通过单变量和多变量逻辑回归来确定.
- 使用十种机器学习方法构建了FOC的预测模型,并在测试集上进行了验证.
主要成果:
- FOC的独立风险因素包括教育水平,不良怀孕史,先前的剖腹产,计划怀孕,辅助生殖,收入,支付方式,SAS得分和年龄.
- 一个精细的预测模型确定了怀孕,不良妊娠结果,剖腹产史,计划怀孕,支付和SSRS分数作为关键预测因素.
- 基于机器学习的预测模型表现出强的表现.
结论:
- 怀孕,不良怀孕史,先前的剖腹产,计划怀孕,支付方式和SSRS分数是怀孕后期FOC的重要危险因素.
- 建立的风险预测模型为识别有风险的个体提供了高临床参考价值.
- 对FOC的早期识别和干预可以通过这种预测模型得到支持.
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