医疗保健运营和COVID-19大流行的黑天事件:一个预测分析
Jinil Persis Devarajan1, Arunmozhi Manimuthu2, V Raja Sreedharan3
1Operations and Supply Chain Management areaNational Institute of Industrial Engineering (NITIE) Mumbai 400087 India.
IEEE transactions on engineering management
|November 13, 2023
概括
由于COVID-19大流行扰乱了医疗保健运营,促使机器学习模型的开发. 这些模型预测了COVID-19的进展,并有助于有效地管理医疗保健资源.
科学领域:
- 医疗保健运营管理 医疗保健运营管理
- 流行病学预测 流行病学预测
- 在公共卫生领域的预测分析.
背景情况:
- COVID-19大流行给全球医疗保健系统带来了前所未有的挑战,耗费了资源,并要求采取适应性的运营策略.
- 虽然最初被认为是白天事件,但大流行对医疗保健运营的破坏性和不确定的影响与黑天事件的特征一致.
- 不稳定的患者入院和需要有效的治疗方案突出了现有的医疗保健操作框架中的关键漏洞.
研究的目的:
- 调查COVID-19疫情对医疗保健运营的影响.
- 开发基于机器学习的预测模型,用于预测COVID-19的进展.
- 利用预测分析来增强医疗保健运营管理.
主要方法:
- 使用时间序列数据分析构建机器学习预测模型.
- 集成了预测分析,以模拟未来的医疗保健运营场景.
- 使用平均绝对百分比误差 (MAPE) 评估模型性能.
主要成果:
- 拟议的模型实现了新的COVID-19病例的预测误差为0.039,活跃病例的预测误差为0.006 (MAPE).
- 模拟分析为未来的康复率,资源管理比率和患者平均循环时间提供了洞察力.
- 这些模型在预测关键的流行病学和操作指标方面表现出了准确性.
结论:
- 机器学习和预测分析提供了强大的工具,用于导航由流行病引起的医疗保健中断.
- 开发的模型可以增强决策,提高弹性,并优化医疗保健运营中的资源配置.
- 通过准确的预测来制定积极的管理策略,对于减轻未来健康危机的影响至关重要.
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