预测不孕症治疗成功的机器学习:对技术的系统文献审查
Shirin Dehghan1, Hamid Moghaddasi1, Reza Rabiei1
1Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Journal of education and health promotion
|April 24, 2025
概括
机器学习 (ML) 模型显示出预测辅助生殖技术 (ART) 成功的前景. 女人的年龄是一个关键因素,支持矢量机器是这些不孕症治疗的流行的技术.
科学领域:
- 生殖医学 生殖医学
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
背景情况:
- 辅助生殖技术 (ART) 对于不孕症治疗至关重要.
- 需要对ART成功的预测模型来指导临床决策.
- 机器学习 (ML) 为开发此类模型提供了先进的工具.
研究的目的:
- 为了确定ART成功的预测模型.
- 确定影响ART结果的关键因素.
- 审查ML技术在ART预测中的应用.
主要方法:
- 在主要数据库 (PubMed,科学网,Scopus,Embase) 进行系统的文献搜索.
- 关于数据集特征,ML技术和性能指标的数据提取.
- 分析了过去5年内发表的27项精选研究.
主要成果:
- 女性年龄是最常用的预测特征.
- 监督学习方法主导了模型开发 (96.3%).
- 支持矢量机 (SVM) 是最常见的ML技术 (44.44%).
- 在ROC曲线下的面积 (AUC) 是主要的性能指标 (74.07%).
结论:
- ML具有显著的潜力,可以改善不育夫妇的结果.
- 可靠的预测建模需要仔细考虑促成因素和适当的ML技术.
- 这些模型可以帮助做出关于不孕症治疗的明智决策.
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