开发一个决策支持工具,用机器学习来预测医院出院延迟情况
Mahsa Pahlevani1, Enayat Rajabi2, Majid Taghavi3,4
1Department of Industrial Engineering, Dalhousie University, PO Box 15000, Halifax, B3H 4R2, NS, Canada.
BMC health services research
|January 11, 2025
概括
机器学习模型可以预测入院时的替代护理水平 (ALC) 患者,改善医院流动. 关键预测因素包括诊断,年龄和输入代码,使早期识别和资源规划成为可能.
科学领域:
- 医疗保健管理的管理
- 机器学习在医学中的应用
- 医疗信息学 医疗信息学
背景情况:
- 由于需求的增加,医疗保健系统在患者流量管理方面面临挑战.
- 替代护理水平 (ALC) 患者不再需要急性护理,但面临出院延误,导致医院过度拥挤.
- 在入院时对ALC患者的早期预测对于有效的资源规划和改善患者流动至关重要.
研究的目的:
- 为了确定可能在入院时成为替代护理水平 (ALC) 的患者.
- 确定预测ALC状态的关键特征.
- 为在入院时早期识别ALC患者制定指导方针.
主要方法:
- 利用了新斯科舍省卫生局 (2015-2022) 的患者数据,包括人口统计,诊断和临床信息.
- 应用数据预处理技术,如异常值管理,特征工程,缺失值赋值和标准化.
- 采用机器学习分类器,包括随机森林,人工神经网络和极端梯度增强 (XGB),使用类权重,随机过量抽样和SMOTE等技术来处理数据不平衡.
主要成果:
- 使用SMOTE的XGB模型表现出卓越的性能,在ALC患者鉴定中实现了0.95的回忆率和0.97的AUC.
- 即使仅限于入场特征时,XGB模型与SMOTE保持了强大的预测能力 (召回0.91,AUC0.94).
- 确定ALC状态的最重要的预测因素是诊断1,患者年龄和入口代码.
结论:
- 机器学习模型在入院时有效预测替代护理水平 (ALC) 状态,支持实时决策.
- 该研究为早期ALC识别提供了一个框架,将预测分类为概率范围,以指导干预.
- 实施这些预测模型可以显著改善患者流动,缓解医院过度拥挤.
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