机器学习在通过皮肤进行利切除后的全身感染中的诊断性能和相关风险因素的识别
Pengju Li1, Yiming Tang2, Qinsong Zeng1
1Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, PR China.
Heliyon
|May 31, 2024
概括
机器学习可以有效地预测皮肤透神经切除术 (PCNL) 后的全身炎症反应综合征 (SIRS) 和尿. 在PCNL前后血小板计数的变化是这些感染的关键预测因素.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 系统性炎症响应综合征 (SIRS) 和尿道炎症是皮肤透神经切除术 (PCNL) 后的潜在并发症.
- 准确预测这些术后感染对于患者的管理和结果至关重要.
研究的目的:
- 评估机器学习模型的预测性能,以识别患有PCNL后SIRS和泌尿失调风险的患者.
- 探索术前和术后临床因素的实用性,特别是血小板数量的变化,作为预测标记.
主要方法:
- 在2016年1月至2022年7月期间接受PCNL的1067名患者的回顾性分析.
- 应用机器学习技术来开发和选择术后系统性感染的最佳预测模型.
- 解释性分析,以确定重要的风险因素.
主要成果:
- 梯度增强机 (GBM) 模型实现了SIRS的预测功率为0.871和尿路的0.854.
- 确定的显著预测因素包括手术前和手术后的血小板计数变化,血小板缺血和血小板炎都构成风险.
- 在PCNL前后的血小板计数变化超过30*10^9/L (增加或减少) 与SIRS或尿液的风险显著增加有关.
结论:
- 机器学习提供了一种有效的方法来预测PCNL后的SIRS和尿.
- 在PCNL手术前后监测血小板数的变化对于风险分层和早期发现潜在感染至关重要.
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