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针对2019年新冠肺炎疾病的预测性决策支持系统,应对管理和医疗物流规划
Sofiane Atek1, Filippo Bianchini2, Corrado De Vito3
1Department of Aerospace and Mechanical Engineering, Sapienza University of Rome, Rome, Italy.
Digital health
|August 7, 2023
概括
本研究介绍了一种人工智能驱动的决策支持系统,用于管理健康紧急情况. 它准确预测流行病风险,患者流动和医疗供应需求,改善流行病应对和资源管理.
科学领域:
- 流行病学 流行病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 冠状病毒疾病2019 (COVID-19) 凸显的全球生物威胁应对需要先进的工具来评估和预测流行病.
- 决策支持系统 (DSS) 对于加强在流行病爆发期间的健康监测和管理至关重要.
- 物流规划是有效管理卫生紧急情况的关键组成部分.
研究的目的:
- 在2019年冠状病毒疾病地球认知系统 (ECS4COVID19) 项目中展示物流规划的应用实例.
- 展示人工智能 (AI) 算法的价值,用于产生健康紧急情况的预测假设.
- 展示人工智能如何增强流行病情况评估和预测.
主要方法:
- 2019年冠状病毒疾病地球认知系统 (ECS4COVID19) 使用人工智能,社交媒体分析,地理空间分析和卫星成像.
- 机器学习算法用于监控,管理和预测医疗设备的物流需求.
- 关键预测包括区域风险分类,急诊室出勤率和区域医疗供应预测,整合地理空间和时间数据.
主要成果:
- 区域风险颜色代码分类器实现了0.82的宏观平均F1分数和85%的准确性.
- 拉齐奥地区的急诊室出院率预测显示了非常低的平方根平均误差 (<11名患者).
- 拉齐奥和皮埃蒙特地区的药品购买预测导致低根平均平方百分比误差为16%.
结论:
- 准确预测新病例和药物使用情况,可以更有效地管理供应链.
- 在流行病期间,预测对于政府的知情决策,资源分配和政策制定至关重要.
- ECS4COVID19系统为应对卫生紧急情况和改善流行病准备提供了有价值的预测见解.
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