组装机器学习模型用于预测高使用率的患者:模型验证和经济影响分析
Joshua Kuan Tan1, Le Quan2, Hao Yi Tan1
1Health Services Research Unit, Singapore General Hospital, Singapore, Singapore.
JMIR medical informatics
|February 20, 2026
概括
机器学习组合模型准确地预测了高医疗保健使用率,识别了未来77%的住院患者和73.9%的未来急诊室用户. 这些模型显示,通过有针对性的干预措施,可以节省大量成本.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 预测分析是一种预测分析.
背景情况:
- 机器学习模型越来越多地用于预测高医疗保健利用率.
- 早期识别有风险的患者使得有针对性的干预措施成为可能.
研究的目的:
- 评估用于医疗保健的多类合并模型的预测性能.
- 在现实场景中评估这些模型的经济影响.
主要方法:
- 四种二进制分类模型 (增强树,MARS,MLP,逻辑回归) 使用堆叠集体方法进行了扩展.
- 多类模型预测了在定义的阶层中停留时间 (LOS) 和急诊室 (ED) 访问的长度.
- 集合模型在2020-2021年的数据上受过训练,并在2021-2022年的数据上使用AUC,准确性和混矩阵指标进行验证.
主要成果:
- 增强型树组合模型表现出最高的性能,在LOS和ED访问中达到0.6877的AUC得分,在ED访问中达到0.7601.
- 模型正确分类了30.3%的住院LOS和39.8%的ED访问,确定了77%的未来住院患者和73.9%的未来ED用户.
- 经济分析预计,通过使用后勤回归基学习者的增强树模型,平均成本减少1.11亿美元.
结论:
- 多类组合模型有效预测多层次的医疗保健使用情况.
- 这些模型有可能节省大量成本,并支持有针对性的干预措施.
- 这些发现可以为人口健康计划的规划和预算制定信息,特别是对于糖尿病等疾病.
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