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使用多种机器学习技术预测怀孕结果的预测建模,用于体外受精-胚胎移植

Ru Bai¹, Jia-Wei Li², Xia Hong¹

¹Reproductive Centre, The Affiliated Hospital of Inner Mongolia Medical University, No.1 of North Tongdao Road, Huimin District, Hohhot, 010000, Inner Mongolia Autonomous Region, China.

BMC pregnancy and childbirth

|March 20, 2025

概括

这项研究开发了先进的AI模型来预测体外受精 (IVF-ET) 的成功. XGBoost模型准确地预测怀孕,而LightGBM则预测活产,帮助临床决策.

科学领域:

生殖医学生殖医学
医疗保健中的人工智能
生物统计学生物统计学

背景情况:

体外受精和胚胎移植 (IVF-ET) 是一种关键的辅助生殖技术.
在IVF-ET中预测怀孕结果仍然具有挑战性,影响治疗疗效和患者咨询.
需要准确的预测模型来优化IVF-ET程序并提高成功率.

研究的目的:

调查影响IVF-ET怀孕结果的因素.
构建和验证IVF-ET成功的预测模型.
为潜在的临床实施确定高精度模型.

主要方法:

利用了2625名接受IVF-ET (2016-2022) 的女性的临床数据.
开发和比较临床怀孕和活产的预测模型 (如XGBoost,LightGBM).
使用ROC曲线分析和AUC计算来评估模型性能.

主要成果:

在XGBoost模型中,预测怀孕的AUC值为0.999.
在LightGBM模型中,用于预测活产的AUC为0.913.
这两种模型都在预测IVF-ET结果方面表现出很高的准确性.

结论:

XGBoost模型准确地预测了IVF-ET中的怀孕概率 (0.999 AUC).
轻GBM模型有效地预测了IVF-ET (0.913AUC) 中活产的可能性.
这些人工智能驱动的模型为增强IVF-ET临床实践提供了有价值的工具.

关键词:

人工智能的人工智能是人工智能.在体外受精-胚胎移植.预测模型的预测模型.怀孕的结果怀孕的结果

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In Vitro Fertilization

In Vitro Fertilization

In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
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