用临床测量和随机森林回归来预测老年不育妇女对受控卵巢过度刺激的卵巢反应
Jiajing Wei1, Dongsheng Xiong1, Yanan Zhang1
1Reproductive Medicine Center, Sichuan Provincial Women's and Children's Hospital, The Affiliated Women's and Children's Hospital of Chengdu Medical College, 290 Shayan West Second Street, Wuhou District, Chengdu 610045, Sichuan, China.
概括
机器学习准确地预测了受控卵巢过度刺激的老年不育妇女的卵巢反应. 关键预测因素包括毛囊数量和抗穆勒尔激素水平,有助于治疗规划.
科学领域:
- 生殖医学 生殖医学
- 医疗保健中的人工智能
- 老年妇科老年妇科
背景情况:
- 在中国,老年女性 (≥35岁) 的不孕率不断上升,需要改善辅助生殖技术 (ART) 的结果.
- 试管婴儿/胚胎移植 (IVF/ET) 对年长的不育患者提出了独特的医疗和心理挑战.
- 准确预测卵巢反应对于优化IVF/ET治疗疗效对于这一群体至关重要.
研究的目的:
- 开发和验证一种机器学习模型,用于预测老年不育妇女对受控卵巢高刺激 (COH) 的卵巢反应.
- 为了确定影响COH结果的关键临床预测因素,在这个患者群体.
- 加强对老年不育妇女的IVF/ET的临床评估和结果预测.
主要方法:
- 从689名老年不育患者中回顾收集了12个卵细胞恢复前的临床测量结果.
- 应用随机森林回归模型来预测卵巢对COH的反应.
- 使用十倍交叉验证来评估训练和测试数据集的模型性能.
主要成果:
- 预测模型在训练数据集中表现出强的性能,平均平方误差 (MSE) 从7.56 ± 0.31到13.90 ± 0.37不等.
- 在测试数据集中,观察和预测值之间的高相关系数 (0.86 ± 0.02 到 0.72 ± 0.05) 得到了实现.
- 排卵前卵泡计数 (PFC),腹卵泡计数 (AFC) 和抗穆勒尔激素 (AMH) 被确定为最重要的预测因素.
结论:
- 一种强大的机器学习方法已经成功地建立起来,用于预测老年不育妇女的COH结果.
- 该模型为潜在的治疗结果提供了有价值的见解,有助于患者咨询和管理.
- 识别PFC,AFC和AMH等关键预测因素可以为老年女性个性化生育治疗策略.
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