预测小儿肠道和膀功能障碍对泌尿疗法的不响应:一种机器学习方法
Jackson M Dunning1, Adree Khondker2, Christopher S Cooper1
1Department of Urology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
Journal of pediatric urology
|November 6, 2025
概括
机器学习模型可以预测哪些患有肠膀功能障碍 (BBD) 的儿科患者不太可能仅仅对泌尿疗法做出反应. 早期识别这些不响应者可以及时调整治疗计划,改善患者的治疗结果.
科学领域:
- 儿科泌尿外科 儿科泌尿外科
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 泌尿疗法是小儿肠膀功能障碍 (BBD) 的主要治疗方法.
- 显著的一部分儿科患者对单独泌尿疗法反应有限或没有反应.
- 早期识别非响应者对于优化管理和改善结果至关重要.
研究的目的:
- 开发和评估机器学习模型,用于预测儿科BBD患者对泌尿疗法的不响应.
- 确定儿童泌尿疗法治疗失败的关键预测因素.
- 为了使不响应病例的早期临床干预成为可能.
主要方法:
- 对123名患有BBD的儿科患者 (5-10岁) 的回顾性分析.
- 采用了经过验证的18个问题BBD症状学问卷来收集数据.
- 开发并比较多变量后勤回归和随机森林机器学习模型来预测泌尿疗法不响应,用AUROC评估性能.
主要成果:
- 高龄和缺尿症的存在与更高的尿疗反应可能性有关.
- 初次诊断时的白天失禁与反应的可能性较低有关.
- 随机森林模型与物流回归 (0.67) 相比,实现了0.71的优异AUROC.
结论:
- 机器学习模型有效地预测了儿科BBD中泌尿疗法的不响应.
- 年龄,失尿症和白天失禁是尿道治疗结果的重要预测因素.
- 早期识别不响应者有助于及时实施替代或额外的治疗策略,增强患者护理.
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