CPAS: the UK's national machine learning-based hospital capacity planning system for COVID-19.
Alaa, Ahmed M
van der Schaar, Mihaela
Springer Science and Business Media LLC
MetadataShow full item record
Qian, Z., Alaa, A. M., & van der Schaar, M. (2021). CPAS: the UK's national machine learning-based hospital capacity planning system for COVID-19.. Mach Learn, 110 (1), 15-35. https://doi.org/10.1007/s10994-020-05921-4
Funder: University of Cambridge
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.
Article, Automated machine learning, Gaussian processes, Compartmental models, Resource planning, Healthcare, COVID-19
External DOI: https://doi.org/10.1007/s10994-020-05921-4
This record's URL: https://www.repository.cam.ac.uk/handle/1810/316653
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/