使用机器学习预测术后恶心和吐:一个模型开发和验证研究
Maxim Glebov1, Teddy Lazebnik2,3, Maksim Katsin4
1Department of Anesthesiology, Sheba Medical Center, Derech Sheba 2, Ramat Gan, 52621, Israel. hlebau@gmail.com.
BMC anesthesiology
|March 21, 2025
概括
机器学习模型显著改善了术后恶心和吐 (PONV) 的预测. 与传统方法相比,这些先进的工具提供了更好的患者护理和结果.
科学领域:
- 麻醉学和外科手术期间的医学.
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 手术后恶心和吐 (PONV) 是全身麻醉后的常见并发症,导致患者痛苦.
- 现有的PONV预测得分缺乏令人满意的准确性.
- 需要改善早期和晚期PONV的预后模型.
研究的目的:
- 开发和验证基于机器学习的预后模型,用于预测早期和延迟PONV.
- 为了实现比目前的临床分数更高的预测性能.
- 增强个性化的患者护理和术后期的结果.
主要方法:
- 对35,003名在全身麻醉下接受手术的成年患者进行了回顾性分析.
- 使用k-fold交叉验证开发一个整体机器学习模型.
- 数据分为培训和测试集,保持社会人口统计特征.
主要成果:
- 早期PONV发生在3.82%的患者中,延迟PONV发生在18.80%的患者中.
- 提出的模型在早期的PONV中达到83.6%的准确性,在延迟的PONV中达到74.8%.
- 性能比Koivuranta得分高出13.0% (早期) 和10.4% (延迟),通过特征重要性分析验证.
结论:
- 机器学习模型提供了对PONV的卓越预测.
- 这些模型促进了个性化的术后护理策略.
- 改善PONV预测导致更好的患者结果和满意度.
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