使用随机森林和物流回归模型预测住院患者非计划性输出管的风险预测
Hongyi Mou1, Akmal Ergashev, Bingqi Zhou
1Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China.
Journal of patient safety
|May 27, 2025
概括
与后勤回归相比,一个随机森林模型在预测无计划排泄 (UEX) 事件方面表现出卓越的性能. 这种先进的模型为医院的患者安全提供了更高的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 患者安全 患者安全
- 医疗保健中的机器学习
背景情况:
- 无计划的输出管 (UEX) 是住院患者的关键安全问题.
- 有效预防和早期发现UEX对于高质量的护理服务至关重要.
研究的目的:
- 评估和比较UEX事件的随机森林和物流回归模型的预测能力.
- 确定影响UEX在住院患者环境中的关键因素.
主要方法:
- 追溯分析了775个UEX事件和775个匹配的计划外注事件 (2021年1月至2022年12月).
- 开发和验证随机森林和后勤回归模型,使用7:3的分割.
- 基于精度,灵敏度,特异性,PPV,NPV和AUC的性能比较.
主要成果:
- 对于UEX的关键预测因素包括晚年,男性性别,意识受损以及多次/延长的导管停留时间.
- 随机森林模型确定了导管内置时间,导管数量,年龄,二次固定和导管等级作为重要的预测因素.
- 随机森林模型实现了较高的曲线下的面积 (AUC) 0.812,相比物流回归的0.793.
结论:
- 随机森林模型表现出超越后勤回归对非计划的排泄事件的优异预测性能.
- 后勤回归为理解UEX风险因素提供了有价值的解释性.
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