使用急诊室数据预测中年和老年肺炎患者的14天再入院:使用生存机器学习方法进行多中心回顾性队列研究
Nguyen Thanh Nhu1, Jiunn-Horng Kang2,3,4,5,6, Tian-Shin Yeh4,7,8,9
1Faculty of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, Viet Nam.
机器学习模型可以使用急诊室数据预测老年人14天的肺炎再入院风险. 随机生存森林模型显示出强大的预测性能,识别了像肌素和年龄这样的关键因素.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 肺部病理学 肺部病理学
背景情况:
- 无计划的肺炎再接收会对患者的健康产生负面影响,增加医疗保健成本.
- 中年和老年患者 (≥45岁) 面临更高的肺炎再入院风险.
- 14天再接收率是医疗保健质量的关键指标.
研究的目的:
- 开发和评估生存机器学习模型,用于预测年龄≥45.5岁的肺炎患者的14天再入院风险.
- 利用急救部门 (ED) 的数据进行早期风险评估.
- 确定重新接收的关键预测特征.
主要方法:
- 一项回顾性,多中心的队列研究,使用来自11,989例肺炎病例 (2014-2021) 的数据.
- 数据被分为培训 (80%),验证 (10%) 和独立测试 (10%) 集.
- 开发和比较了四种生存机器学习模型 (CoxNet,生存树,梯度增强生存分析,随机生存森林).
主要成果:
- 随机生存森林 (RSF) 模型表现出卓越的性能.
- 在独立测试组中,RSF模型实现了0.710的C指数和0.693的AUC.
- 关键预测因素包括肌素,年龄,血红素,查尔森并发症指数和血红蛋白水平,其重要性随时间而变化.
结论:
- 该RSF模型有效预测使用ED数据的肺炎患者的14天再入院风险.
- 该模型使临床医生能够在病房入院或ED出院之前估计风险,促进及时干预.
- 准确的预测支持个性化的医疗保健计划,以防止再入院.
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