为患有非创伤性急性腹痛的高风险患者开发和验证风险预测模型:一项前性观察性研究
Rui Li1, Xiao-Hui Wei2, Xiao-Qin Li3
1School of Nursing, Shanghai Jiao Tong University, Shanghai, China.
Frontiers in public health
|December 25, 2025
概括
机器学习模型可以帮助分组护士识别患有非创伤性急性腹痛的高风险患者. 随机森林模型显示出最佳性能,而使用名图的逻辑回归在临床上是适用的.
科学领域:
- 紧急医疗 紧急医疗
- 临床信息学 临床信息学
- 医疗保健中的机器学习
背景情况:
- 非创伤性急性腹痛是一种常见的急诊室呈现.
- 准确和及时的风险分层对于患者安全和资源分配至关重要.
- 现有的分组方法在识别高风险患者方面可能存在局限性.
研究的目的:
- 开发和验证机器学习模型,用于预测患有非创伤性急性腹痛的高风险患者.
- 为了提高这种患者群体的分拣护理的准确性和安全性.
- 确定用于早期风险识别的关键预测因素.
主要方法:
- 从两个三级医院的3090名患者收集前性数据.
- 使用单变量,LASSO和多变量分析对预测因素的选.
- 使用R软件构建和外部验证7个机器学习模型,包括随机森林和物流回归,使用R软件.
主要成果:
- 确定了10个预测因素,包括年龄,病史,MEWS得分和特定的疼痛特征.
- 所有七种模型都显示出良好的预测性能;随机森林模型实现了最高的AUC (0.786).
- 后勤回归模型展示了强大的推断和概括能力,通过名图可视化.
结论:
- 机器学习模型有效预测非创伤性急性腹痛的早期风险.
- 随机森林提供了优越的区分,而用名图的逻辑回归则为分类提供了出色的临床适用性.
- 这些模型可以帮助护士改善患者的识别和管理.
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