The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS)—a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital. In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-the-art machine learning algorithms, (4) integrating heterogeneous data sources, and (5) presenting the result with an interactive and transparent interface. CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic—we conclude the paper with a summary of the lessons learned from this experience.

中文翻译：

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CPAS：英国基于机器学习的 COVID-19 医院容量规划系统

2019 年冠状病毒病 (COVID-19) 全球大流行对医疗保健系统构成了威胁，对重症监护资源的需求前所未有。如果没有全国性的集体努力，依靠数据来预测国家、地区、医院和个人层面的医院需求，就无法有效地管理这些需求。为此，我们开发了 COVID-19 容量规划和分析系统 (CPAS)，这是一种基于机器学习的医院资源规划系统，我们已与 NHS Digital 合作在英国的各个医院和跨地区成功部署该系统。在本文中，我们讨论了在全国范围内部署基于机器学习的决策支持系统的主要挑战，并解释 CPAS 如何通过（1）定义适当的学习问题来应对这些挑战，(2) 结合自下而上和自上而下的分析方法，(3) 使用最先进的机器学习算法，(4) 集成异构数据源，以及 (5) 以交互式和透明的界面呈现结果. CPAS 是首批在全国范围内部署在医院中以应对 COVID-19 大流行的基于机器学习的系统之一——我们总结了从这次经验中吸取的经验教训。