机器学习模型的开发和验证,用于预测辅助生殖技术程序的操作持续时间
Seoyoung Oh1, Hyo Young Kim2, Young Soo Park3
1College of Business Administration, Kookmin University, 77 Jeongneung- ro, Seongbuk-gu, Seoul, Republic of Korea.
BMC medical research methodology
|February 3, 2026
概括
在辅助生殖技术 (ART) 中,预测手术持续时间至关重要. 使用电子医疗记录 (EMR) 数据的可解释线性模型准确预测手术时间,优于简单的启发式.
科学领域:
- 生殖医学 生殖医学
- 医疗信息学 医疗信息学
- 优化外科手术工作流程的优化
背景情况:
- 准确的手术时间预测对于外科手术安排和资源管理至关重要.
- 辅助生殖技术 (ART) 程序是时间敏感的,因此精确的时间确定对于避免工作流程中断至关重要.
- 电子医疗记录 (EMR) 数据为开发ART中的预测模型提供了潜在的来源.
研究的目的:
- 开发和验证可解释的预测模型,用于ART程序中的操作持续时间.
- 利用常规可用的EMR数据来预测外科手术时间.
- 为了比较不同的预测建模范式在ART中的有效性.
主要方法:
- 韩国一家生育诊所的763例ART手术病例的回顾性分析.
- 手术持续时间由EMR开始和结束时间确定;预测因素包括手术类型,患者年龄,预订特征,医生和手术日.
- 使用交叉验证对线性模型,基于树的集合和内核/神经网络方法的评估,与移动平均值启发式对比.
主要成果:
- 规则化的线性模型,特别是脊和贝叶斯脊回归,显示出稳定和可解释的性能.
- 这些线性模型的平均绝对误差约为3.1分钟,比移动平均线的基线改善了7-8%.
- 手术类型,患者年龄和预订类型被确定为最重要的手术持续时间预测因素.
结论:
- 使用EMR数据的可解释线性模型为ART的操作持续时间预测提供了一致的性能改进.
- 这些发现强调了透明和验证的建模策略在高通量临床环境中的实用性.
- 该研究证明了EMR数据在优化时间敏感程序中的日程安排和资源分配方面的价值.
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