基于IVF活产结果的临床数据建模及其应用
Liu Liu1, Hua Liang1, Jing Yang2
1Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China.
Reproductive biology and endocrinology : RB&E
|July 8, 2024
概括
机器学习模型准确地预测了体外受精 (IVF) 周期中活产的结果. 该研究确定了关键因素,并开发了一种用于临床决策的高级支持矢量机 (SVM) 模型.
科学领域:
- 生殖医学 生殖医学
- 生物统计学 生物统计学
- 医疗保健中的人工智能
背景情况:
- 试管婴儿 (IVF) 周期面临着低活产率 (LBR) 和复杂的治疗决策的挑战.
- 预测活出生的结果 (LBO) 对于优化患者护理和管理期望至关重要.
研究的目的:
- 开发和验证用于预测试管婴儿患者LBO的机器学习模型.
- 确定影响LBO的关键因素,以帮助临床决策.
主要方法:
- 1405个试管婴儿患者周期的回顾性分析.
- 单变量和多变量分析以确定重要的LBO预测因素.
- 开发人工神经网络 (ANN) 和支持矢量机器 (SVM) 模型用于LBO预测.
主要成果:
- 七个因素与LBO有显著的相关性:年龄,卵巢敏感度指数 (OSI),受控卵巢刺激 (COS) 方案,Gn起始剂量,子宫内膜厚度,HCG天的孕激素 (P) 和胚胎移植策略.
- 无论是ANN和SVM模型都表现出良好的预测性能.
- SVM模型的性能优于ANN模型,并被选择用于临床应用.
结论:
- 一个综合模型包含了重要的IVF因素,可以准确地预测LBO.
- 开发的模型为临床医生提供客观的科学支持,帮助他们定制试管婴儿治疗策略,包括胚胎移植.
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