哥伦比亚波哥大早期与晚期预孕:使用传统统计和机器学习的差异化风险因素识别和评估
Ayala-Ramírez Paola1,2, Mennickent Daniela3,4, Farkas Carlos3
1Human Genetics Institute, Faculty of Medicine, Pontificia Universidad Javeriana, Bogotá 110231, Colombia.
Biomedicines
|August 28, 2025
概括
在哥伦比亚妇女中,早发性子宫前 (EOP) 和晚发性子宫前 (LOP) 有不同的危险因素. 机器学习模型确定教育和高血压史是EOP的关键预测因素.
科学领域:
- 产科和妇科
- 产周医学
- 公共卫生
背景情况:
- 孕前 (PE) 是孕产妇和产前死亡的主要原因,特别是在低收入和中等收入国家.
- 早期发病的PE (EOP) 和晚期发病的PE (LOP) 是不同的临床情况,其潜在机制不同.
- 对于拉丁美洲人群中EOP和LOP的差异性风险因素的研究有限.
研究的目的:
- 确定和评估用于预测EOP和LOP的临床风险因素.
- 将传统的统计方法与机器学习 (ML) 对PE亚型预测进行比较.
- 为哥伦比亚波哥大孕妇制定特定环境的产前护理策略.
主要方法:
- 在波哥大对190名患有PE的孕妇 (80名EOP,110名LOP) 进行了横截面观察研究.
- 通过结构化采访和临床记录收集数据,包括风险因素和围产期结果.
- 应用传统统计和11种ML技术进行预测建模和特征重要性分析.
主要成果:
- EOP与母亲的高等教育和高血压史有关;LOP与过敏史有关.
- 在PE亚型预测中,线性差异分析获得了最高回忆率 (0.71).
- 通过ML识别的关键预测因素包括教育水平,周围死亡家族史和高血压史.
结论:
- 在这个哥伦比亚队列中,EOP和LOP具有独特的临床特征.
- 将传统统计与ML整合起来,可以提高PE亚型的早期风险分层.
- 这些发现支持在类似的医疗机构制定量身定制的产前护理策略.
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