一项使用机器学习进行回顾性研究,以开发预测模型以在体内发现尿路感染石头
Yukun Wu1, Qishan Mo2, Yun Xie1
1Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, No. 58, Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, China.
Urolithiasis
|May 31, 2023
概括
在手术前诊断尿路感染结石是具有挑战性的. 一个新的机器学习模型在体内准确地识别这些石头,有助于术后管理和患者预后.
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
- 人工智能的人工智能
背景情况:
- 尿路感染结石的术前诊断很困难,阻碍了有效的术前管理和术后预防策略.
- 准确的石材成分分析通常只能在活体中进行,这限制了实时临床决策.
研究的目的:
- 开发和验证一种机器学习模型,用于在体内进行感染石头的手术前识别.
- 为改善尿路结石患者的风险评估和决策支持提供一个工具.
主要方法:
- 对1168名符合条件的尿路结石患者的临床数据的回顾性分析.
- 开发了五个机器学习模型 (SVM,MLP,DT,RFC,AdaBoost) 使用14个手术前变量.
- 使用接收器运行特征曲线 (AUC) 下面面积的模型的验证,并选择AdaBoost作为最终模型.
主要成果:
- 在AdaBoost模型中,AUC为0.772 (95%CI,0.657-0.887) 的AUC显示强烈的歧视.
- 确定的感染石的主要预测因素是UC阳性和尿液pH值.
- 该模型显示了良好的灵敏度 (0.522) 和高特异性 (0.902).
结论:
- 基于机器学习的预测模型可以有效地在体内识别感染石头,具有良好的预测性能.
- 这种工具可以优化尿路结石的疾病管理,并改善患者的治疗结果.
- 该模型有助于对感染石的风险评估和决策支持.
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