COVID-19期间紧急救护车调度的知识发现--以日本名古屋市为例
Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan
摘要：
准确预测医疗服务需求是一个重要的大数据问题，在自然灾害和流行病等关键时期，这对资源管理至关重要。随着2019冠状病毒病（COVID-19）在全球的蔓延，人们对医疗系统处理医疗服务提供者日常工作突然变化的能力提出了一些担忧。一个重要的问题是大流行期间救护车调度和控制的管理。为了帮助解决这个问题，我们首先分析了日本名古屋市2014年4月至2020年8月的救护车调度数据记录。在大流行期间，数据出现了重大变化，包括日本全国宣布进入紧急状态。在这项研究中，我们提出了一个基于递归神经网络的深度学习框架来估计SoE期间紧急救护车调度（EAD）的数量。数据的融合包括环境因素、移动电话用户的本地化数据和EAD的过去历史，从而为知识发现和更好的资源管理提供了一个通用框架。结果表明，所提出的混合训练数据可以有效地用于在高不确定性，如流行病期间的EAD需求的真实世界的估计。
英文摘要：
Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics.
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