机器学习预测模型的开发,用于成年男性割礼手术后的结果
Leonid Shpaner1, Giuseppe Saitta2
1University of California, Los Angeles, Los Angeles, USA. lshpaner@ucla.edu.
BMC urology
|February 10, 2026
概括
机器学习模型可以预测成年男性割礼后的短期并发症. 术内流血和手术技术是关键预测因素,有助于个性化的术前规划,以获得更好的患者结果.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 手术瘤学手术瘤学
- 医疗信息学 医疗信息学
背景情况:
- 男人割礼是一种全球常见的程序,具有已知的好处.
- 结果的变化需要对成年患者进行优化程序规划.
- 解剖学变异和并发症会影响术后的结果.
研究的目的:
- 开发和验证机器学习模型,用于预测成年男性割礼的短期术后并发症.
- 使用监督学习技术识别并发症的关键预测因素.
- 通过个性化风险评估,加强手术前决策.
主要方法:
- 对194个成年男性割礼病例 (2023-2024) 的回顾性分析.
- 评估后勤回归,随机森林和支向量机器模型.
- 利用手术和手术期间的变量,包括手术方式,血液损失和技术,并使用SHAP分析来解释可解释性.
主要成果:
- 支持矢量机 (SVM) 模型显示出优异的性能,AUC ROC为0.907.
- 发现的关键预测因素是手术内流血和手术技术.
- SHAP分析提供了对模型解释性和预测重要性的见解.
结论:
- 可解释的机器学习模型在临床上提供了个性化风险预测的临床实用性.
- 结果可以指导量身定制的手术前决策,特别是在高风险环境中.
- 未来的研究应该专注于外部验证和多样化的群体.
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