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一个多目标优化框架,集成ICSL深度学习,用于预测和安排公共卫生紧急情况中的紧急医疗供应需求
Yanzhi Pang1, Shixuan Zhou1, Benxiao Lou1,2
1Guangxi Key Laboratory of International Join for China-ASEAN Comprehensive Transportation, Nanning University, Nanning, 530200, China.
Scientific reports
|December 31, 2025
概括
这项研究引入了一种先进的深度学习算法 (ICSL),用于预测疫情期间的紧急医疗供应需求. 该模型通过考虑紧迫性,时间和成本来优化分配,提高公共卫生紧急情况的效率.
科学领域:
- 运营研究 运营研究
- 公共卫生 公共卫生
- 人工智能的人工智能
背景情况:
- 大型疫情在预测和分配紧急医疗用品方面存在重大挑战.
- 现有的模型往往无法充分考虑疾病动态和公共卫生干预措施.
研究的目的:
- 开发一个端到端的深度学习算法 (ICSL),用于预测对紧急医疗用品的最大需求.
- 构建一个多目标模型,以有效地安排和分配这些供应.
- 为了优化资源分配,考虑到紧迫性,调度时间和成本.
主要方法:
- 提出了一个端到端的ICSL深度学习算法架构.
- 将传染病特征和隔离措施纳入需求预测.
- 开发了一种用于供应调度和分配的多目标粒子群优化算法.
主要成果:
- ICSL模型显示,预测准确性比传统的LSTM增加了29.37%,而比改进的BP神经网络增加了8.63%.
- 调度和分配模型有效优化了交付的及时性,紧迫性和成本效益.
- 使用武汉流行病数据的验证证实了该模型的实际适用性.
结论:
- 拟议的ICSL算法和优化模型为公共卫生危机期间的紧急医疗供应管理提供了强大的解决方案.
- 调查结果提供了有价值的决策见解,以加强在大规模紧急情况下的资源配置策略.
- 这项研究有助于提高未来卫生紧急情况的准备和应对效率.
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