在Endourologic结石手术后预测败血症:一种机器学习方法
Hriday P Bhambhvani1, Adithya Balasubramanian1, Justin Lee2
1Department of Urology, Weill Cornell Medical College, New York, New York, USA.
Journal of endourology
|June 18, 2025
概括
机器学习使用患者数据准确地预测结石手术后的败血症. 随机森林模型识别了血红蛋白A1c (HbA1c) 等关键预测因子,以改善患者的护理.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 传染性疾病 传染性疾病
- 机器学习在医学中的应用
背景情况:
- 在结石手术后尿路感染后的败血症呈现出显著的发病率.
- 有限的研究存在于使用血红蛋白A1c (HbA1c) 预测术后败血症在内分泌学程序.
研究的目的:
- 开发和评估一种机器学习 (ML) 模型,用于预测经过结石手术后的术后败血症.
- 确定包括HbA1c在内的关键临床预测因子,用于术前优化和风险分层.
主要方法:
- 对接受尿路透镜检查,冲击波石流术或皮肤穿透性腎石切除术的患者的回顾性分析.
- 使用人口和临床数据开发五个监督的ML模型 (逻辑回归,随机森林,神经网络,SVM,原始贝叶斯).
- 模型性能使用准确性,AUCROC,校准和Brier评分通过交叉验证和持久测试集进行评估.
主要成果:
- 随机森林模型以91%的准确性和0.88 AUCROC在测试组上表现出卓越的性能.
- 泌尿过敏的主要预测因素包括手术前的血红蛋白,HbA1c,石块大小,手术持续时间和BMI.
- 开发的随机森林模型可以在线访问,以获得潜在的临床应用.
结论:
- 一个随机的森林ML模型有效地预测了结石手术后的败血症风险.
- 这种预测工具可以帮助进行手术前的手术规划,患者优化和术后监测.
- 在广泛临床实施之前,建议进行进一步的验证.
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