病毒性疾病中的智能患者管理:康复性血输血的综合回归模型和多标准决策方法
Thura J Mohammed1, Ahmed S Albahri2, Alhamzah Alnoor3
1School of Computer Sciences, Universiti Sains Malaysia, Minden 11800, Pulau Pinang, Malaysia.
Healthcare (Basel, Switzerland)
|December 30, 2025
概括
一个新的智能框架整合了多标准决策和远程医疗,以便在疫情期间有效分配康复性血. 这种数据驱动的方法确保了重要的治疗资源的透明和公平分配.
科学领域:
- 公共卫生 公共卫生
- 生物医学信息学 生物医学信息学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 病毒性疾病对全球健康构成重大风险,特别是在疫情爆发期间,需要有效地分配资源.
- 康复性等离子体 (CP) 输血显示出希望,但缺乏综合框架来优先考虑患者和供体选择.
- 目前的分配方法是分散的,阻碍了治疗资源的快速和公平分配.
研究的目的:
- 为数据驱动的康复期血分配提出一个融合的智能框架.
- 在远程医疗环境中整合多标准决策 (MCDM) 和基于回归的验证.
- 在公共卫生紧急情况下实现透明和公平的CP分配.
主要方法:
- 使用分析层次流程 (AHP) 来权衡五个关键的临床生物标志物.
- 根据相似度对理想解决方案 (TOPSIS) 进行员工订单偏好,并使用集团决策 (GDM) 进行患者和捐赠者的优先排序.
- 实施了基于回归的模型选择,以实现可靠的优先级,并使用独立数据集进行验证.
主要成果:
- 外部GDM AHP-VIKOR模型实现了高的预测性能 (R2 = 0.971,MSE = 0.0010).
- 内部一致性和相关性分析证实了生物标志物排名的可靠性和稳定性.
- 该框架在患者与捐赠者匹配方面展示了准确性,可解释性和及时性.
结论:
- 开发的框架为康复期血分配提供了准确,可解释和及时的解决方案.
- 这项研究代表了在远程医疗中实现人工智能输血系统的基本步骤.
- 该框架支持透明和公平的CP分配,这对于未来的疫情管理至关重要.
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