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dc.contributor.authorQian, Zhaozhi
dc.contributor.authorAlaa, Ahmed M.
dc.contributor.authorvan der Schaar, Mihaela
dc.date.accessioned2021-01-25T22:07:13Z
dc.date.available2021-01-25T22:07:13Z
dc.date.issued2020-11-24
dc.date.submitted2020-07-13
dc.identifier.issn0885-6125
dc.identifier.others10994-020-05921-4
dc.identifier.other5921
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/316653
dc.descriptionFunder: University of Cambridge
dc.description.abstractAbstract: 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.
dc.languageen
dc.publisherSpringer US
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectArticle
dc.subjectAutomated machine learning
dc.subjectGaussian processes
dc.subjectCompartmental models
dc.subjectResource planning
dc.subjectHealthcare
dc.subjectCOVID-19
dc.titleCPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19
dc.typeArticle
dc.date.updated2021-01-25T22:07:13Z
prism.endingPage35
prism.issueIdentifier1
prism.publicationNameMachine Learning
prism.startingPage15
prism.volume110
dc.identifier.doi10.17863/CAM.63766
dcterms.dateAccepted2020-09-26
rioxxterms.versionofrecord10.1007/s10994-020-05921-4
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidQian, Zhaozhi [0000-0002-4561-0342]
dc.identifier.eissn1573-0565


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)