需要一个元学习整体框架,以对紧急医疗服务进行可靠和可解释的预测
Tripti Garg1, Durga Toshniwal2, Manoranjan Parida3
1Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India. tgarg@cs.iitr.ac.in.
Scientific reports
|December 9, 2025
概括
本研究介绍了EM-LR,这是一个可解释的组合模型,用于预测印度的紧急医疗服务 (EMS) 需求. EM-LR提高了准确性,减少了预测差异,为公共卫生系统提供了可扩展的解决方案.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 准确的紧急医疗服务 (EMS) 需求预测对于公共卫生系统的资源配置至关重要,特别是在像印度这样的低资源环境中.
- 现有的EMS预测研究主要集中在发达国家的城市地区,使用现实世界的印度救护车数据对地区级预测的探索有限.
- 当前的模型往往会损害准确性或使用复杂的,不可解释的架构的稳定性,阻碍实际部署.
研究的目的:
- 评估一组异质的可解释学习者是否可以优于印度地区级EMS需求预测的现有模型.
- 引入EM-LR (带有线性回归的组合元学习者),这是一个新的元学习框架,旨在提供稳定性和可解释性.
- 用有限的现实世界特征,特别是时间和气象数据来评估EM-LR的有效性.
主要方法:
- 开发了EM-LR,一个元学习框架,通过线性回归元学习器集成多种基本模型 (拉索回归,SVR,MLP,XGB).
- 采用基于SHAP的特征分析和透明的组合权重,以确保模型的可解释性.
- 利用了来自印度北方邦五个地区的每日救护车调度数据和气象信息.
- 与传统和先进的预测模型 (TBLSSVR,AHELM,MHKLDMR) 相比,EM-LR进行了基准测试.
主要成果:
- 与基准模型相比,EM-LR表现出更高的准确性,根平均平方误差 (RMSE) 降低了9.5%.
- 拟议的模型显著减少了40%以上的预测方差,提高了预测的稳定性.
- EM-LR有效地预测每天的EMS呼叫量,仅使用时间和气象输入.
结论:
- EM-LR提供了一个可扩展和可解释的预测解决方案,适合发展公共卫生系统的限制.
- 这项研究强调了异质集团在资源有限的环境中具有强大而准确的EMS需求预测的潜力.
- 调查结果支持数据驱动的应急规划,并可以为印度更公平的医疗保健服务做出贡献.
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